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Table of contents :
Cover
GENETICS AND
GENOMICS OF EYE
DISEASE:

Advancing to Precision Medicine
Copyright
Dedication
Contributors
Preface
Acknowledgments
Part I: Introduction to gene mapping
1
Timeline of key discoveries in ophthalmic genetics
References
2
Segregation, linkage, GWAS, and sequencing
Segregation analysis
Segregation: How is a trait inherited?
Simple vs. complex segregation analysis
Advantages of segregation analysis
Disadvantages of segregation analysis
Segregation analyses for ocular traits and diseases
Linkage analysis
What is genetic linkage?
Linkage: What genetic variation contributes to a trait?
Types of linkage analysis
Advantages of linkage analysis
Disadvantages of linkage analysis
Linkage analyses for ocular traits and diseases
Genome-wide association studies
What is a GWAS?
Advantages and disadvantages of GWAS
Examples of GWAS for eye diseases
DNA sequencing
Overview of sequencing technologies: Past to present
Types of sequencing
Advantages and disadvantages of sequencing
References
Part II: Genomics in the eye
3
Whole-exome sequencing and whole-genome sequencing
Development of NGS technologies
Illumina sequencing
Targeted enrichment sequencing
Targeted enrichment techniques
Hybridization-based enrichment
Molecular inversion probes
Targeted amplicon sequencing
Whole-exome sequencing
Commercially available WES kits
Panel vs WES
Whole-genome sequencing technologies
WES vs WGS
Long-read technologies
SMRT sequencing by PacBio
Nanopore sequencing by ONT
Linked-reads sequencing by 10x Genomics
WES/WGS data analysis
Future direction/perspectives
References
4
RNA sequencing and transcriptome analysis
History of transcriptome analysis methods
RNA-Seq
Library preparation
Data analysis-Alignment and count generation
Data analysis-Novel feature detection
Data analysis-Downstream applications
RNA-Seq in the eye
Transcriptome analyses
Models of disease
Disease genes and therapeutic targets
Concluding remarks
References
5
Noncoding genome in eye disease
Introduction to the noncoding genome
MicroRNAs and the eye
lncRNAs and the eye
Noncoding RNAs and eye disease
Age-related macular degeneration
Diabetic retinopathy
Glaucoma
Retinitis pigmentosa
Retinoblastoma
Other diseases of the eye
Concluding remarks
References
Part III: Mendelian disorders and high penetrant mutations
6
Genetic architecture of inherited retinal disease
Spectrum and evaluation of the clinical phenotype of IRD
Inheritance pattern
Mechanistic pathways culminating in photoreceptor degeneration
Ciliary transport and intracellular trafficking
Photoreceptor development
Phototransduction cascade
The visual cycle
Synaptic transmission defects
Spliceosome complex
Interphotoreceptor matrix
Genetic heterogeneity in IRD
Genetic heterogeneity in monogenic IRDs
Allelic heterogeneity in IRD
Incomplete penetrance in RP11
Mutation spectrum of IRD
Regulatory and noncoding variants
Large DNA duplication and deletion in IRD
Splice-site and alternative transcript variants
Conclusion
References
7
Early-onset glaucoma
Clinical features of JOAG
Epidemiology of JOAG
Age of onset
Myopia
Intraocular pressure
Response to therapy
Optic disc morphology
Inheritance pattern
MYOC and JOAG
MYOC-associated glaucoma clinical phenotype (JOAG)
MYOC-associated glaucoma clinical phenotype (POAG)
MYOC pathophysiology
Case report (MYOC-associated JOAG)
OPTN and juvenile-onset open-angle glaucoma
OPTN-associated glaucoma clinical phenotype
Function of OPTN
Association between OPTN and ALS
Effects of OPTN mutations
Transgenic mouse models of OPTN-associated glaucoma
Knock-in mouse model of OPTN-associated glaucoma
Case report (OPTN-associated glaucoma)
TBK1 and JOAG
TBK1-associated glaucoma clinical phenotype
Function of TBK1
Effects of TBK1 duplications
Transgenic mouse model of TBK1-associated glaucoma
Case report (TBK1-associated glaucoma)
Genetic testing and JOAG
Gene-directed therapies
Targeted therapies for MYOC-associated glaucoma
OPTN and TBK1-associated glaucoma direct therapies
Acknowledgments
References
8
Bardet-Biedl syndrome
Bardet-Biedl syndrome
Mode of inheritance
Clinical diagnosis
Pleotropic phenotypes
Retinal degeneration in BBS
Using animal models to study retinal degeneration in BBS
In vitro molecular mechanisms of BBS
Transcriptional variation
Other disorders attributed to BBS genes
Leber congenital amaurosis
Joubert syndrome
Senior-Løken syndrome
Nonsyndromic retinal degeneration
BBS research and advancing biotechnology
Conclusions
References
9
Hereditary predisposition to uveal melanoma
Introduction
Genetic versus environmental basis of UM
Familial uveal melanoma
UM clustering with other cancers
Highly penetrance genes with reported germline mutations in UM
BRCA1-associated protein 1 (BAP1)
Breast cancer 2 (BRCA2)
Mismatch repair genes (MLH1 and MSH6)
MBD4
Birt-Hogg-Dube Syndrome and UM (FLCN)
CDKN2A/ARF and CDK4
Low penetrant genes
HERC2/OCA2
TERT/CLPTM1L
Summary and conclusions
References
Part IV: Complex disorders and low effect-size risk factors
10
Age-related macular degeneration
Introduction
Genomic studies in AMD
Genome-wide association studies
Case-control studies for rare variants
Family-based studies for rare variants
Effect sizes of common versus rare variants
Contribution of common versus rare variants
Functional effect of common versus rare variants
Effect of AMD-associated variants on disease mechanisms
Effect of the common CFH p.Tyr402His variant
Effect of common variants at the ARMS2/HTRA1 locus
Effect of genetic variants on gene expression
Effect of genetic variants on the complement system
Effect of genetic variants on lipoprotein homeostasis
Effect of variants on extracellular matrix remodeling
Effect of variants on neovascularization
Other omics studies in AMD
Effect of genetic variants on treatment response
Dietary supplementation
Anti-VEGF treatment
Complement inhibitors
Conclusions
Acknowledgments
References
11
Genetics of primary open-angle glaucoma
Introduction
Primary open-angle glaucoma
Symptoms and diagnosis
Therapies
POAG genetics
Linkage analyses
MYOC, ASB10, and EFEMP1
Interleukin 20 receptor subunit β
Optineurin and TANK-binding kinase 1
WD repeat domain 36
Neurotrophin 4
Genome-wide association studies
Caveolins 1 and 2
Transmembrane and coiled-coil domain 1
CDKN2B antisense RNA 1
SIX homeobox 6
ATP-binding cassette subfamily A member 1
GDP-mannose 4,6-dehydratase and forkhead box C1
Actin filament-associated protein 1
Thioredoxin reductase 2
Ataxin 2
Endophenotypes
Intraocular pressure
Central cornea thickness
Vertical CDR
RNFL thickness
POAG pathways
Conclusion
References
12
Genetics of diabetic retinopathy
Genetic linkage analysis
Candidate gene studies
Aldose reductase
Endothelial nitric oxide synthase
Receptor for advanced glycation end products
Vascular endothelial growth factor
rs2010963
rs833061
rs699947
rs3025039
Other VEGF polymorphisms
Other candidate genes
Insulin receptor
Angiotensin-converting enzyme
Growth factor receptor-bound protein 2
C-reactive protein
P-selectin
High-mobility-group A1
Solute carrier family 19 member 2/3
Genome-wide association studies
Whole-exome sequencing
Epigenetics
DNA methylation
Histone modifications
MicroRNAs
Mitochondrial DNA
Summary
Acknowledgments
References
13
Genetics of keratoconus
Introduction
Human cornea anatomy
Keratoconus
Clinical signs and diagnosis
Treatment modalities
Genetics of KC
Genome-wide linkage studies in KC
Genome-wide association studies in KC
KC candidate genes identified by Sanger sequencing or targeted genotyping
Conclusion
Conflict of Interest
References
Part V: Genetic testing and genetic risk prediction
14
Genetic testing of various eye disorders
Overview of genetic techniques
Detecting coding variation
Variants leading to aberrant splicing captured by targeted panels
Deep-intronic variants
Variants leading to altered gene expression
Detecting structural variants (CNV and chromosomal aberration)
Genetic modifiers of phenotypic severity of IRDs
Ocular phenotype-A marker of syndromic disease
Conclusions
References
15
Genetic risk scores in complex eye disorders
Introduction
Risk scores and their applications
Ocular traits with well-established risk loci and risk scores
Age-related macular degeneration
Genetics of AMD
Risk scores in AMD
Glaucoma
Genetics of glaucoma
Risk scores in glaucoma
Myopia and refractive error
Genetics of myopia and refractive error
Risk scores in myopia and refractive error
Other complex ocular traits
Age-related cataract and diabetic retinopathy
Fuchs endothelial corneal dystrophy (FECD)
Looking forward: capabilities and limitations of risk scores
Polygenic risk scores
Clinical utility of risk scores
References
Part VI: Gene-based therapy
16
Gene therapy for inherited retinal diseases
Introduction
History
Vectors
Current studies
Nonsyndromic RP
RPGR
RLBP1
PDE6ß
MERTK
Syndromic RP
Usher syndrome
Bardet-Biedl syndrome
Stargardt disease
X-linked retinoschisis
Achromatopsia
Choroideremia
Leber congenital amaurosis
Limitations of gene therapy
Future perspectives
References
17
Gene therapy in animal models
Introduction
Inherited retinal degenerations
Classification
Type of dystrophy
Clinical nomenclature
Basic biology of common IRDs
Animal models of inherited retinal degenerations
Vertebrate models of IRD
Gene therapies of IRDs
Challenges
References
Part VII: Big data and precision medicine
18
Pleiotropy in eye disease and related traits
Introduction
Numerous genes show pleiotropic effects
GWAS SNPs show pleiotropic effects
Genetic risk scores show pleiotropic effects
Implications for genomic medicine
Other aspects of pleiotropy
Conclusions
Acknowledgments
References
Web Resources
19
Advancing to precision medicine through big data and artificial intelligence
Introduction
Precision medicine
Advancing precision medicine with big data
Spearheading precision medicine with AI
Ancient wisdom on medicine
Conclusion
References
Index
Back Cover

Citation preview

GENETICS AND GENOMICS OF EYE DISEASE

GENETICS AND GENOMICS OF EYE DISEASE Advancing to Precision Medicine Edited by

XIAOYI RAYMOND GAO, PHD Department of Ophthalmology and Visual Science, Department of Biomedical Informatics, and Division of Human Genetics, The Ohio State University, Columbus, OH, United States

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2020 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-816222-4 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Andre Gerhard Wolff Acquisition Editor: Peter Linsley Editorial Project Manager: Megan Ashdown Production Project Manager: Swapna Srinivasan Cover Designer: Mark Rogers Typeset by SPi Global, India

Dedication

To all who work so diligently, persistently, and passionately to find the genetic causes and treatments of eye disease.

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Contributors Anneke I. den Hollander Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands

Mohamed H. Abdel-Rahman Department of Ophthalmology and Visual Science, Havener Eye Institute; Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States

Rachayata Dharmat Department of Molecular and Human Genetics; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States

Elizabeth D. Au Department of Ophthalmology, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo, Buffalo, NY, United States

Patty P.A. Dhooge Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands

Milam A. Brantley, Jr. Vanderbilt Eye Institute, Nashville, TN, United States

Michael H. Farkas Department of Ophthalmology, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo; Research Service, Veterans Administration Western New York Healthcare System; Department of Biochemistry, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo, Buffalo, NY, United States

Kinga M. Bujakowska Ocular Genomics Institute, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, United States Colleen M. Cebulla Department of Ophthalmology and Visual Science, Havener Eye Institute; Medical Scientist Training Program, The Ohio State University, Columbus, OH, United States

Kavin Fatehchand Medical Scientist Training Program, The Ohio State University, Columbus, OH, United States

Rui Chen Department of Molecular and Human Genetics; Human Genome Sequencing Center; Department of Structural and Computational Biology & Molecular Biophysics; Department of Biochemistry and Molecular Biology; Program of Developmental Biology, Baylor College of Medicine, Houston, TX, United States

John H. Fingert Department of Ophthalmology and Visual Sciences, Carver College of Medicine; Institute for Vision Research, University of Iowa, Iowa City, IA, United States Xiaoyi Raymond Gao Department of Ophthalmology and Visual Science, Department of Biomedical Informatics, and Division of Human Genetics, The Ohio State University, Columbus, OH, United States

Jessica N. Cooke Bailey Case Western Reserve University, Cleveland, OH, United States Frederick H. Davidorf Department of Ophthalmology and Visual Science, Havener Eye Institute, The Ohio State University, Columbus, OH, United States

Maartje J. Geerlings Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands

Eiko K. de Jong Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands

Jonathan L. Haines Department of Genetics and Genome Sciences; Cleveland Institute for

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Contributors

Computational Biology; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States Carel B. Hoyng Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands Robert P. Igo, Jr. Case Western Reserve University, Cleveland, OH, United States Hacer Isildak Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Naples, FL, United States Tadeusz J. Kaczynski Department of Ophthalmology, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo; Research Service, Veterans Administration Western New York Healthcare System, Buffalo, NY, United States Mariam Lotfy Khaled Department of Cellular Biology and Anatomy, Augusta University, Augusta, GA, United States Yutao Liu Department of Cellular Biology and Anatomy; James and Jean Culver Vision Discovery Institute, Medical College of Georgia; Center for Biotechnology and Genomic Medicine, Augusta University, Augusta, GA, United States Leighanne R. Main Department of Genetics and Genome Sciences; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States Matthew A Miller Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, United States Matthew P. Ohr Department of Ophthalmology and Visual Science, Havener Eye Institute, The Ohio State University, Columbus, OH, United States Robert Pilarski Division of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States

Emily Place Ocular Genomics Institute, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, United States Claudio Punzo Department of Ophthalmology, University of Massachusetts Medical School, Worcester, MA, United States Riccardo Sangermano Ocular Genomics Institute, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, United States Stephen G. Schwartz Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Naples, FL, United States Hilary Scott Ocular Genomics Institute, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, United States Ruifang Sui Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China Dyon Valkenburg Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands Carly J. van der Heide Department of Ophthalmology and Visual Sciences, Carver College of Medicine; Institute for Vision Research; Department of Molecular Physiology and Biophysics, Carver College of Medicine, University of Iowa, Iowa City, IA, United States Naomi Wagner Ocular Genomics Institute, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, United States Andrea R. Waksmunski Department of Genetics and Genome Sciences; Cleveland Institute for Computational Biology; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States Hui Wang Institute of Life Sciences, College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou, China

Contributors

Katie Weihbrecht Department of Pharmacy; Department of Ophthalmology and Visual Science; Department of Pediatrics, University of Iowa, Iowa City, IA, United States

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Hannah Youngblood Department of Cellular Biology and Anatomy, Augusta University, Augusta, GA, United States

Preface include: Introduction to gene mapping, Genomics in the eye, Mendelian disorders and high penetrant mutations, Complex disorders and low effect-size risk factors, Genetic testing and genetic risk prediction, Genebased therapy, and Big data and precision medicine. Since the subject of the GGED is immense, there are topics that could not be covered. While I had to exercise selection for practicality, I sought to cover the major topics and trends in GGED as thoroughly as possible. Editing and writing select portions of this book have been a tremendous experience. The process enriched me with a greater understanding of the GGED and broadened my view. I am grateful for this opportunity to learn, to improve, and to serve. At the same time, I am deeply honored and humbled to have worked with the talented and generous contributors of this book. Thanks to the work of these contributors, I believe this compilation will be useful to a broad range of readers whether read in its entirety or in part. I hope that these pages will inspire and ignite new ideas about the GGED so that we can advance precision medicine in ophthalmology and other fields. While the process of editing this book has come to an end for the time being, I hope that this is the start of a new beginning of working together to fight for reducing the burden of eye disease and blindness, and for advancing toward precision medicine. Together, we can make a difference.

This book began unexpectedly when Elsevier contacted me about editing a book on the essential topic of genetics and genomics of eye disease (GGED). Like my peers, I was consumed with grant applications, research manuscripts, and other responsibilities. However, service comes before self, which is why I chose to be the editor of this book. I hope that through my service, I may contribute to advancing the GGED. As traditional medicine undergoes a paradigm shift to precision medicine, the need for this book has become obvious. The purpose of this book is to provide relevant fundamental scientific knowledge and recent advances of ophthalmic genetics to researchers, clinicians, and trainees as a resource for understanding the GGED. It contains a guide to the latest genomics methods for studying eye disease, including genome-wide association studies, whole exome sequencing, whole genome sequencing, RNA sequencing, and transcriptome analysis. Genomics findings for inherited retinal diseases, early-onset glaucoma, Bardet-Biedl syndrome, uveal melanoma, age-related macular degeneration, primary open-angle glaucoma, diabetic retinopathy, and keratoconus among other diseases are covered in detail. Research and clinical specialists also offer guidance on genetic testing, genetic risk prediction, and gene-based therapy, as well as big data and artificial intelligence, which play increasingly important roles in advancing to precision medicine. This book is organized in seven sections with a total of 19 chapters. The sections

Xiaoyi Raymond Gao

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Acknowledgments I am grateful to all the authors who have generously contributed their expertise, time, and efforts to write the chapters of this book. This project would not have become a reality without them. I also appreciate very much Elsevier for giving me the opportunity to edit this volume and communicate this important topic to our readers and for their support, especially that of Peter Linsley, Megan Ashdown, Nilesh Shah, and Swapna

Srinivasan. I also wish to thank Hannah Moulthrop, Katherine Weibel, and Rosalyn Uhrig for English language review, discussion, and suggestions regarding some of the text. Finally, I am indebted to my family for their support and sacrifices while I spent countless hours writing and editing this book to bring it to fruition.

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Xiaoyi Raymond Gao

C H A P T E R

1 Timeline of key discoveries in ophthalmic genetics Xiaoyi Raymond Gao Department of Ophthalmology and Visual Science, Department of Biomedical Informatics, and Division of Human Genetics, The Ohio State University, Columbus, OH, United States

Study the past if you want to define the future. —Confucius

A timeline gives us a historical overview of the key discoveries that led to where we are today. A timeline also provides perspective for looking ahead at our future direction. Without understanding the past, it is difficult to build on the foundational work of our predecessors to contribute to shaping the future. If one wants to define the future, he or she must study the past. Our predecessors have observed the health and disease patterns of ocular traits as early as approximately 2500 years ago according to historical records. Hippocrates and Aristotle (460–322 BC) observed the familial transmission of ocular traits [1]. The ancient Chinese medical book, Huangdi Neijing (an English translation title: the Yellow Emperor’s Classic of Medicine [2], written approximately between 475 BC and 220 AD [3]), documented the relationship between the eyes and the inner organs [4]. At a molecular level, our past comprises who we are. Genetic materials, such as the DNA that we inherited from our parents, are the building blocks that define us. The genetic information, as simple as A-T-G-C, plays a pivotal role in defining the future, our health, and disease. From Mendel’s plant hybridization experiments [5, 6] to Morgan’s chromosome theory of heredity [7], to Watson and Crick’s double-helix structure of DNA [8], to the sequencing of the human genome by a global team [9, 10], to genome editing using CRISPR (clustered regularly interspaced short palindromic repeats) [11–13], scientists across continents and time have made profound insights into the blueprint of the human body. Developments in genetics and genomics have enabled advances in additional fields as well.

Genetics and Genomics of Eye Disease https://doi.org/10.1016/B978-0-12-816222-4.00001-0

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1. Timeline of key discoveries in ophthalmic genetics

Ophthalmology is one of the fields that has benefited significantly from discoveries in human genetics, while also propelling its advancement. The eye has been at the forefront of human genetic research and its clinical applications. From color blindness, the first human trait mapped to the X chromosome [14], to Zonular pulverulent cataract, the first human disease linked to an autosome [15], to retinoblastoma, the first human cancer gene cloned [16–18], to Leber’s hereditary optic neuropathy the first human disease determined to be caused by a mitochondrial DNA mutation [19], to LUXTURNA (voretigene neparvovec-rzyl), the first gene therapy approved by the FDA (Food and Drug Administration) in the United States [20], there have been numerous ophthalmic hallmarks in the evolution of human genetics and genomics. These cutting-edge discoveries have been achieved using state-of-the-art investigation methods and tools. From linkage to GWAS (genome-wide association study), to nextgeneration sequencing, these advanced methods have been propelling a pipeline of exciting discoveries. CRISPR-guided tools are now gene scissors that enable precise genome editing. Looking ahead, we have also seen the convergence of diverse research methods and resources, such as artificial intelligence and big data being used in ophthalmology. All of these cutting-edge innovations and novel uses of diverse resources are advancing our research and patient care to precision medicine. To help readers understand the past and imagine and craft the future, we present a timeline website for the key discoveries in ophthalmic genetics: http://www.ggedbook. com/timelineeye/ (this website has been tested using the Safari browser). Figs. 1–3 show the screenshots for the time periods: 1800s and earlier, 1900s, and 2000s, respectively. The further back you can look, the further forward you are likely to see. —Churchill

FIG. 1 Key discoveries of the 1800s and earlier.

I. Introduction to gene mapping

1. Timeline of key discoveries in ophthalmic genetics

FIG. 2 Key discoveries of the 1900s.

FIG. 3 Key discoveries of the 2000s.

I. Introduction to gene mapping

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1. Timeline of key discoveries in ophthalmic genetics

References [1] P.J. Waardenburg, Genetics and the human eye, in: P.J. Waardenburg, A. Franceschetti, D. Klein (Eds.), Genetics and Ophthalmology, vol. 1, Blackwell Scientific Publications Ltd, Assen, the Netherlands, 1961. [2] M. Ni, The Yellow Empoeror’s Classic of Medicine, Shambhala Publications, Inc., 1995. [3] The Su Wen of the Huangdi Neijing (Inner Classic of the Yellow Emperor), https://www.wdl.org/en/item/3044/. [4] Q.-P. Wei, A. Rosenfarb, L.-N. Liang, Ophthalmology in Chinese Medicine, People’s Medical Publishing House, 2011. [5] W. Bateson, Experiments in plant hybridization, J. R. Hortic. Soc. 26 (1901) 1–32. € ber Pflanzenhybriden, in: Verhandlungen des naturforschenden Vereines in Br€ [6] J.G. Mendel, Versuche u unn, Bd. IV f€ ur das Jahr, 1865, Abhandlungen, 1866, pp. 3–47. [7] T.H. Morgan, Sex limited inheritance in drosophila, Science 32 (1910) 120–122. [8] J.D. Watson, F.H. Crick, Molecular structure of nucleic acids; a structure for deoxyribose nucleic acid, Nature 171 (1953) 737–738. [9] F.S. Collins, E.D. Green, A.E. Guttmacher, M.S. Guyer, Institute, U. S. N. H. G. R, A vision for the future of genomics research, Nature 422 (2003) 835–847. [10] A.E. Guttmacher, F.S. Collins, Welcome to the genomic era, N. Engl. J. Med. 349 (2003) 996–998. [11] R. Barrangou, C. Fremaux, H. Deveau, M. Richards, P. Boyaval, S. Moineau, D.A. Romero, P. Horvath, CRISPR provides acquired resistance against viruses in prokaryotes, Science 315 (2007) 1709–1712. [12] L. Cong, F.A. Ran, D. Cox, S. Lin, R. Barretto, N. Habib, P.D. Hsu, X. Wu, W. Jiang, L.A. Marraffini, F. Zhang, Multiplex genome engineering using CRISPR/Cas systems, Science 339 (2013) 819–823. [13] M. Jinek, K. Chylinski, I. Fonfara, M. Hauer, J.A. Doudna, E. Charpentier, A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity, Science 337 (2012) 816–821. [14] E.B. Wilson, The sex chromosomes, Arch. Mikrosk. Anat. Enwicklungsmech. 77 (1911) 249–271. [15] J.H. Renwick, S.D. Lawler, Probable linkage between a congenital cataract locus and the Duffy blood group locus, Ann. Hum. Genet. 27 (1963) 67–84. [16] S.H. Friend, R. Bernards, S. Rogelj, R.A. Weinberg, J.M. Rapaport, D.M. Albert, T.P. Dryja, A human DNA segment with properties of the gene that predisposes to retinoblastoma and osteosarcoma, Nature 323 (1986) 643–646. [17] Y.K. Fung, A.L. Murphree, A. T’ang, J. Qian, S.H. Hinrichs, W.F. Benedict, Structural evidence for the authenticity of the human retinoblastoma gene, Science 236 (1987) 1657–1661. [18] W.H. Lee, R. Bookstein, F. Hong, L.J. Young, J.Y. Shew, E.Y. Lee, Human retinoblastoma susceptibility gene: cloning, identification, and sequence, Science 235 (1987) 1394–1399. [19] N.J. Newman, M.T. Lott, D.C. Wallace, The clinical characteristics of pedigrees of Leber’s hereditary optic neuropathy with the 11778 mutation, Am J. Ophthalmol. 111 (1991) 750–762. [20] FDA News Release, https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm589467.htm, 2017. [Online]. Accessed 25 February 2019.

I. Introduction to gene mapping

C H A P T E R

2 Segregation, linkage, GWAS, and sequencing Andrea R. Waksmunskia,b,c, Leighanne R. Maina,b,c, Jonathan L. Hainesa,b,c a

Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, OH, United States bCleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, United States cDepartment of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States

Segregation analysis Segregation: How is a trait inherited? The principles for modern genetics were first characterized by an Austrian monk, Gregor Mendel, who suggested that there are units of heredity that are passed down from parents to offspring [1]. By examining the frequency of traits in the offspring, he was able to describe the Law of Segregation [1, 2], which ultimately defined multiple inheritance patterns including additive, dominant, recessive, autosomal, and sex linked. Before the era of recombinant DNA, the statistical tool of segregation analysis was often used to determine the mode of inheritance of a trait based on these segregation ratios [3]. This was done to determine if a trait has strong enough genetic underpinnings to warrant additional genetic studies, which were generally very laborious and expensive. To perform a segregation analysis, investigators ascertain information regarding individuals’ phenotypes from multiple families. For these analyses, the collection of biospecimens such as DNA is not necessary [4].

Simple vs. complex segregation analysis Segregation analyses are categorized as either simple or complex. Simple (or classical) segregation analyses statistically determine if the ratio of offspring with the trait of interest

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Copyright # 2020 Elsevier Inc. All rights reserved.

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2. Segregation, linkage, GWAS, and sequencing

in a nuclear family deviate from proportions expected for Mendelian inheritance [5]. Complex segregation analyses elucidate the transmission of a trait in families by testing various models of inheritance [5]. Complex segregation analyses estimate population-level parameters such as allele frequencies, penetrance parameters, transmission probabilities (probability of the child’s trait given the parents’ traits), and familial correlations including the correlation between parents, between siblings, and between parent and offspring for qualitative traits [6]. For quantitative traits, similar parameters are also estimated with the following exceptions: penetrance parameters include genotype means and environmental variance [7].

Advantages of segregation analysis Historically, when familial aggregation of a trait was observed, segregation analysis was used to determine its mode of inheritance in families and guide further studies to identify the genetic variation of the trait. Complex segregation analysis allows larger pedigrees to be analyzed and can be used to study either quantitative or qualitative traits [5, 7, 8]. This analysis can model multiple genetic risk factors for a trait as well as environmental factors and phenocopies [5, 9]. Phenocopies resemble the result of genetic variation but are actually the product of different etiology including environmental factors and random chance [9]. The knowledge gained from a complex segregation analysis can help inform model-based linkage analyses to locate the genetic determinants of the trait [4, 5]. This information subsequently increases the power of those analyses because a model misclassification is less likely [4]. With the advancement of genome-wide DNA assays and analyses, it has become a more efficient and cost effective to interrogate the genetic variation of a trait directly allowing both the determination of the level of heritability and the underlying variation simultaneously.

Disadvantages of segregation analysis While segregation analyses have been instrumental in identifying the modes of inheritance for numerous traits and diseases, there are limitations. Unfortunately, the study designs of segregation analyses are restricted to determining trait transmission in families, not the general population. The complexity of segregation analysis is also limited due to the proportional relationship between the amount of data necessary to assess segregation and the number of parameters estimated for the model [5]. These analyses are also most informative for studying Mendelian conditions rather than complex traits, which do not usually follow monogenic patterns of inheritance [10]. Although this analysis provides evidence for the segregation of a phenotype, it cannot directly prove such a phenomenon is true [5]. Additional statistical analyses and functional studies would be required to demonstrate the phenomenon is occurring biologically. Moreover, current genomic approaches do not require data from segregation analyses for input, and the results from these genomewide studies provide more detailed information about inheritance than segregation analyses would. Therefore, in modern genetic epidemiology, segregation analyses are rarely performed in research studies.

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Linkage analysis

9

Segregation analyses for ocular traits and diseases Because there are numerous eye conditions with a strong genetic component, multiple studies utilized segregation analyses to explain inheritance patterns. In 1914, a study aimed to determine if the transmission of myopia was consistent with Mendelian modes of inheritance [11]. Segregation analyses were also used to detect the modes of inheritance for retinitis pigmentosa, which is one of the most common types of inherited retinal degeneration. Studies demonstrated that this disease has multiple patterns of inheritance, including autosomal dominant, autosomal recessive, X-linked, and mitochondrial modes of inheritance [12–14]. A segregation analysis was performed for age-related maculopathy in families from the Beaver Dam Eye Study [15]. Their findings suggested that Mendelian transmission of a major gene could not be rejected and were consistent with a major effect describing about 60% of the observed age-related maculopathy scores in each eye [15]. More recent publications have described segregation analyses for inherited retinal degeneration [16–18], juvenile onset primary open-angle glaucoma (POAG) [19], and familial keratoconus [20]. In these studies, the segregation analysis was often coupled with the next-generation sequencing or other statistical analyses, such as genome-wide association analyses.

Linkage analysis What is genetic linkage? Mendel’s Law of Independent Assortment suggests that different genetic loci are inherited independently of one another [1]. This is true if loci are on different chromosomes (as were Mendel’s original pea traits), or far apart on the same chromosome. However, it was soon observed that some loci tend to be inherited together (essentially being linked to each other), in violation of this law. How frequently two loci are inherited together can be measured statistically and transformed in a relative distance [2, 21, 22]. The key to using a genetic linkage is having at least two measured traits.

Linkage: What genetic variation contributes to a trait? Linkage analysis is a statistical method for discovering the locations of loci underlying a trait of unknown position by testing for co-segregation with genetic polymorphisms of known position in the genome [22]. In contrast to segregation analyses that determine the mode of inheritance for a trait and do not require biospecimens, methods to detect genetic linkage require DNA from families to be genotyped for polymorphisms with known positions [4]. The physical map positions of the genotyped polymorphisms are evaluated to estimate the recombination fraction between them and the hypothesized locus of the trait of interest. Conceptually, the recombination fraction (θ) between two loci is estimated based on the number of recombinants and nonrecombinants that are counted in a pedigree [23]. This can be counted directly in rare cases but is typically statistically estimated [23]. Linkage analyses were initially used to study the genetic etiology of Mendelian conditions but have also been performed for complex traits [24, 25].

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2. Segregation, linkage, GWAS, and sequencing

Types of linkage analysis Multiple types of linkage analyses have been developed to interrogate the genetic basis of qualitative and quantitative traits. These include two-point and multipoint linkage analyses as well as model-free and model-based linkage analyses. Two-point linkage analyses (also called single-point analyses) individually examine the likelihood of linkage between the trait locus and a single genetic polymorphism (SNPs) [26]. Multipoint analyses examine the likelihood of linkage occurring among multiple polymorphisms and the trait locus in a region or on an entire chromosome [26]. Either of these approaches can be utilized in model-free or model-based linkage analyses. By definition, model-free linkage does not require the investigator to define a model (which includes parameters such as mode of inheritance) prior to performing the analysis. This particular type of analysis is more robust when the mode of inheritance is unknown for a trait [4]. It considers if allele sharing among siblings in a family is the result of being identical by descent (IBD), which occurs when the shared alleles were inherited by each child from a common ancestor [4]. For this analysis, the ratios of sib-pairs with 0, 1, and 2 alleles shared IBD at a single locus are estimated and compared to those estimated under the assumption of no genetic linkage [27]. The logarithm(log10) of this likelihood ratio corresponds to the maximum logarithm of the odds (LOD) score. There is significant evidence of linkage if the maximum LOD score (MLS) is >3.0 [22, 27]. For model-based linkage analyses, the researcher defines model parameters, such as trait allele frequency, mode of inheritance with penetrance values, and marker allele frequencies, to be used in the statistical analysis [23]. In linkage analyses, the recombination fraction between the trait locus and the marker of known position is estimated and compared to 0.5 (the expected recombination fraction for unlinked loci) in a likelihood ratio [22]. The log10 of this ratio is considered the “log-odds” or LOD score [22]. In smaller scans of the genome, a LOD score > 3.0, which is equivalent to a pointwise p-value of about 104, is considered significant evidence for genetic linkage [28]. For genome-wide significance, the LOD score should be >3.3, which corresponds to a p-value of 4.9  105 [28].

Advantages of linkage analysis Although linkage analyses were most prevalent in classical genetic epidemiological studies, their utility has reemerged in the genomics era as a result of their advantageous qualities. Linkage analyses are optimal for identifying rare variants that are co-segregating with a trait with high penetrance within families [29]. These rare variants, which could contribute to a significant portion of the trait’s heritability, are often difficult to detect with other analyses because of the reliance on nonfamily, case-control designs for genome-wide association studies (GWAS) [30]. In linkage analyses, the number of polymorphisms required to detect linkage is relatively low compared to methods for elucidating genetic association. Older genome-wide linkage analyses utilize 300–600 microsatellite polymorphisms, which cover map position intervals of 10 centimorgans and 5 centimorgans, respectively [31]. More recent genome-wide linkage studies use 4000–6000 biallelic markers SNPs. Because of the relatively low number of polymorphisms, correction for multiple comparisons is not onerous. The power for these analyses to identify linkage is enhanced when the study cohort is enriched

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Linkage analysis

11

for individuals with the disease allele. This can be accomplished by ascertaining individuals from population isolates, like the Amish, or based on trait characteristics such as illness severity, clinical subtypes, or age of onset [31]. Linkage analyses are also unhindered by allelic heterogeneity because genetic linkage considers the physical proximity between genomic loci rather than alleles [29].

Disadvantages of linkage analysis While linkage analyses have successfully elucidated thousands of susceptibility loci for human traits, they are primarily used for studying families rather than populations. Additionally, linkage analyses are not well suited for detecting genetic variations of small effects when the study’s sample size is small [32]. This becomes especially problematic when studying complex traits for which the genetic variance is typically accounted for by multiple loci of low to modest effect. Consequently, these analyses are most effective for studying traits and disorders that exhibit Mendelian modes of inheritance, which are rare in the general population [31]. Model-based linkage analyses are sensitive to inaccuracies in the defined mode of inheritance, such as overestimating the disease allele frequency [5]. LOD scores in linkage analyses are deleteriously affected by errors in genotyping and/or phenotyping [33]. Locus and clinical heterogeneity can also negatively affect the detection of genetic linkage [33]. The chromosomal region identified by linkage analysis is also relatively large (often 20–50 million base pairs) and requires additional fine-mapping approaches such as linkage disequilibrium mapping or association testing to precisely pinpoint the genetic determinant of the trait [34].

Linkage analyses for ocular traits and diseases Linkage analyses have been vital for mapping the genes responsible for both Mendelian and complex eye traits. The genetic etiology of age-related macular degeneration (AMD) has been extensively studied with linkage studies. A 21-member family with a high incidence of AMD was ascertained for two-point linkage analysis, which identified a 9 centimorgan region on the q arm of chromosome 1 [35]. Investigators mapped nominally significant genomic loci for AMD on chromosomes 5, 9, 12, 15, 16, 18, and 20 using a genome-wide model-free linkage analysis of 34 large families from the Beaver Dam Eye Study [36]. Pooled model-based and model-free linkage analyses for AMD demonstrated the disease relevance of regions on chromosomes 1, 2, 10, and 17 [37]. In 2005, a genome-scan meta-analysis (GSMA) was performed using data from six AMD linkage screens [36–41] to increase the power to detect loci for AMD [42]. This study found significant evidence for linkage to AMD on chromosome 10q26 and nominally significant linkage regions on chromosomes 1q, 2p, 3p, 4q, 12q, and 16q [42]. More recently, a chromosome-specific multipoint linkage analysis was performed in Amish families with AMD [43]. This study included liability classes for carriers of particular genetic variants (Y402H and P503A) in the complement factor H (CFH) gene in the statistical models and determined that these variants were modestly responsible for the significant linkage signal obtained in a separate genome-wide analysis [43]. Additionally, linkage mapping for juvenile open-angle glaucoma, which is a rare Mendelian form of POAG, facilitated the discovery of a locus for the common and complex form of the disease [44]. Evidence for

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2. Segregation, linkage, GWAS, and sequencing

genetic linkage for refractive errors was found on chromosomes 3, 7, and 22 using model-free linkage analysis on 834 sib-pairs in 486 families within the Beaver Dam Eye Study [45]. A genomic locus for congenital cavitary optic disc anomalies on chromosome 14 was also identified by a statistical approach that coupled genome-wide linkage analysis with fine mapping methods [46].

Genome-wide association studies What is a GWAS? As genetic linkage and candidate gene studies could not explain all the heritability of complex diseases, GWAS became feasible with the growing number of individuals genotyped [32]. GWAS generally compare variations in the genome between those affected by the disease (cases) and those who are not (controls) [47]. These studies can also be applied to quantitative traits and endophenotypes for a disease [48]. This methodology is based on the “common disease-common variant” hypothesis, which suggests that common diseases have common underlying influential variants across the population [49]. GWAS have been successfully used to identify associated variants for diseases for over a decade. The first GWAS in 2002 [50] was followed by the first GWAS of common genomic variants in 2005 [51] and the first large-scale, high-coverage GWAS for complex traits in 2007 [52]. Usually, DNA microarrays such as SNP arrays are used to find a significant variation in allele frequencies in cases versus controls. Initial studies focused on only approximately 1000 SNPs, but as a result of rapid technological improvements, larger SNP arrays of 600,000–5,000,000 SNPs are now used and can be customizable [53]. Although examining SNPs is most common, data for GWAS can also be generated from whole genome sequencing (WGS) or whole exome sequencing (WES). These approaches are not currently favorable due to their high price and the limited increase in associated variants found compared to SNP arrays [48].

Advantages and disadvantages of GWAS Although GWAS have uncovered thousands of moderate to low-effect SNPs for hundreds of human traits, the complete genetic architecture of complex traits has remained elusive to solve with GWAS. Most of the associated variants found in GWAS have been common (minor allele frequency > 1%) in the population and have very small overall effects on the penetrance of the trait [48, 54, 55]. Therefore, a large portion of the heritability of many complex traits remains unexplained by known variants. GWAS also mainly focus on the statistical likelihood of trait-associated variation and are unable to implicate the direct biological consequences of the results. Supporting evidence from functional studies must corroborate GWAS results for them to be considered causal for human diseases. Since large sample sizes are also needed to properly perform a GWAS, many consortia have been established to help ascertain and aggregate thousands of cases and controls for a trait of interest [56]. For instance, investigators have collaborated to form the following consortia focused on eye diseases: the International Age-related Macular Degeneration Genomics Consortium (IAMDGC), the International Glaucoma Genetics Consortium, the National Eye

I. Introduction to gene mapping

DNA sequencing

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Institute Glaucoma Human genetics collaBORation Heritable Overall Operational Database (NEIGHBORHOOD) Consortium, and the Consortium on Refractive Error and Myopia (CREAM) [57–63]. Despite these international efforts to generate large cohorts of cases and controls, primarily populations of European decent have been extensively researched with GWAS. This can lead to issues with population stratification and admixture [47]. As additional diverse populations are studied, the number of variants associated with disease is also predicted to grow. Another limitation for GWAS is the correction for multiple testing in the statistical analysis. The Bonferroni adjustment is the most commonly used method and is based on the following equation: αGWAS ¼ αn, α is the family-wide significance level and n represents the number of tests (or SNPs) being used. Point-wise significance in GWAS is considered p-value 20,000 genes in the human genome. Its probe density is high given an overlapping probe design [14]. Agilent SureSelect uses RNA probes instead of DNA probes. The 114–126 bp RNA probes are converted from DNA oligoes that are synthesized on glass slides. Agilent probes reside immediately adjacent to one another across the target exon intervals. Agilent SureSelect Human All Exon V6 covers 60 MB of target regions in 20,000 genes [15]. Illumina provides two different exome enrichment kits, one is the Truseq exome enrichment kit and the other one is the Nextera rapid capture exome kit as shown in Table 2. The probe design of the two Illumina kits leaves small gaps between probes in the target regions. It relies on paired end reads that extend outside the probe sequence to fill the gaps. Instead of a mechanical shear, the Illumina Nextera kit uses transposons to fragment the TABLE 2 Comparison of WES kits. NimbleGen SeqCap EZ Human Exome Probes

Agilent Sureselect Human All Exon V6

Illumina’s Nextera Rapid Capture Exome Kit

Illumina’s Truseq Exome Enrichment Kit

IDT xGen® Exome Research Panel

Probe size

NPa

114–126 bp

95 bp

95 bp

NPa

Probe type

DNA

RNA

DNA

DNA

DNA

Target region

64 Mb

60 Mb

62 Mb

51 Mb

39 Mb

Input DNA

1 μg

100 ng

50 ng

100 ng

500 ng

Adapter addition

Ligation

Ligation

Transposase

Ligation

Ligation

Hybridization hours

72

16

Up to 24

Up to 24

4

a

NP indicates information not provided.

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Whole-genome sequencing technologies

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DNA. It requires less DNA input (50 ng) than other technologies. The Nextera kit exhibits increased coverage of high GC content regions due to sequence bias of transposon fragmentation [14, 15]. Recently, IDT introduced its own exome kit: xGen Exome Research Panel. The unique feature of the IDT exome kit is that all probes are individually synthesized and normalized before pooling to ensure that each probe is represented in the panel at the correct concentration. Its hybridization time is the shortest (4 h) among different technologies and its enrichment is more uniform. After the first successful application of exome sequencing in discovering the causal gene of a rare Mendelian disorder [16], WES is now the most commonly used tool for Mendelian disease gene discovery. More than 1000 genes have been identified between 2010 and 2014 due to the early adoption of WES [17]. WES has also been widely applied to sequence patients with eye diseases since 2011 and has led to the discovery of over 100 new disease-associated genes. In addition, WES has been used in the molecular diagnosis of patients with eye diseases in both research and clinical settings [18–23].

Panel vs WES Depending on the purpose, it is often only necessary to sequence a small set of genes instead of the entire exome. For example, to perform molecular diagnosis for many ocular disorders, it is often sufficient and more cost effective to screen mutations using a gene panel approach where only genes that have been associated with the patient phenotype are enriched and sequenced. Since 2010, targeted disease gene panels were developed by several research groups [24–26]. By focusing on sequencing the coding regions of known disease genes, it is feasible to achieve higher coverage and sensitivity at a relatively lower price compared to WES. However, with the continuously increasing throughput of NGS technology, reduced sequencing cost, and automation of experimental and analysis workflow, it is feasible to sequence the human exome more quickly and affordably. As a result, the gene panel is gradually being replaced by WES.

Whole-genome sequencing technologies Whole-genome sequencing (WGS) allows for sequencing of all 3 billion bases of the human genome including the mitochondria DNA. WGS follows a whole genome shotgun sequencing approach and has a simpler workflow compared to WES by skipping the capture-enrichment step. The DNA sample undergoes library preparation, quantification, and sequencing. Initially, WGS was largely used as a research tool but it has been gradually adopted in clinics as the cost of sequencing rapidly declines [27]. With WGS covering the entire genome, a significant number of variants, approximately 3–4 million, can be identified for each individual [28, 29].

WES vs WGS Hundreds of researchers participated in the Human Genome Project, which was completed in 2003. The project took about 15 years and cost approximately 1 billion dollars. As

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3. Whole-exome sequencing and whole-genome sequencing

sequencing and labor costs continue to decline rapidly, it is possible to sequence an individual’s genome using WGS within a few days for $1000. WGS is the most comprehensive genetic test to date since it provides continuous coverage and identifies sequence variants throughout the genome. Direct comparison between WGS and WES data revealed that WGS is more powerful than WES in detecting exome variants due to greater uniformity of sequence read coverage, less bias in the detection of nonreference alleles, and more reliable CNV detection [30, 31]. It has been estimated that about 85% of the known genetic causes for Mendelian disorders are due to changes in protein-coding regions [32]. Recently, it has been reported that a molecular diagnostic rate of 25% is achieved when WES is applied to a large clinical cohort without a specific clinical diagnosis [33]. Limitations of WES might contribute to the relatively low positive rate. Not all targeted regions in WES can be efficiently captured due to sequence duplication and sequence content (e.g., high G + C content). As a result, 10% of exons may not be covered at sufficient levels for reliable variant identification. Since gene annotation of the human genome is incomplete, the current WES design does not cover the entire coding regions of the genome. Moreover, exome sequencing is limited to detecting certain types of mutations such as large insertions and deletions (indels), chromosomal segment CNVs, and structure variations (SV). Exome capture libraries have a bias toward the plus strand causing significantly less coverage for genes on the minus strand [34]. WES also misses the 16-kb mitochondria genome, which includes 13 protein-coding genes. Finally, it has been demonstrated that DNA variations outside the exons can affect gene activity and protein function and lead to genetic disorders—variations that WES would miss. In contrast, WGS generates more uniform sequence coverage and enables detection of structural variations, copy number changes, and deep intronic and intergenic mutations. In addition, WGS also detects mutations in noncoding genes such as miRNAs and lncRNAs and can uncover mutations in the mitochondrial genome [35]. WGS at high sequencing depths can generate accurate assemblies of the entire mitochondrial genome and detect heteroplasmy [36, 37]. Recently developed PCR-free WGS protocol further reduces the potential GC bias and achieves coverage for all GC-rich first exons and genes recommended by the American College of Medical Genetics (ACMG) [38]. WGS has been used in the molecular diagnosis of rare diseases as proof of principle since 2010 [39–41]. Its usage in a clinical setting was rare until recently. One of the first large-scale WGS projects is the UK 10K project. Based on the success of this project, the United Kingdom’s 100,000 Genomes Project launched with the goal of sequencing 100,000 genomes from the UK National Health Service (NHS) patients who have a rare disease, an infectious disease, or cancer. Researchers in the field of ocular genetics have also applied WGS to their cohorts. Recently, Ellingford et al. performed targeted NGS diagnostic testing in a 562-patient cohort with inherited retinal diseases, among which 46 patients also underwent WGS [42]. Direct comparison of panel data and WGS data revealed that WGS successfully detected large deletions, variants in noncoding regions, complex insertion and deletion events, and additional variants not included in the panel sequencing. In another study, Carss et al. performed WGS on 605 probands with inherited retinal diseases and demonstrated that WGS has advantages in detecting SV, mutations in GC-rich regions, and mutations in regulatory regions [43]. As sequencing technology continues to advance and costs continue to decrease, it is expected that WGS will soon become routine in the diagnostic setting.

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Whole-genome sequencing technologies

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Long-read technologies Although current short-read NGS technologies can effectively generate sequences for the vast majority of the genome, its ability and accuracy of resolving complex regions in the genome is limited. This is a significant shortcoming since about 50% of the human genome is composed of repetitive elements and pseudogenes. Accurate alignment and variant calling in these regions based on short reads is problematic. Several third-generation sequencing platforms have been invented recently to address these issues such as Oxford Nanopore Technologies (ONT) sequencing [44] and single-molecule real-time (SMRT) sequencing by Pacific Biosciences (PacBio) [45]. These platforms can produce significantly longer reads with average read lengths of >10,000 bp and with some read lengths up to 100,000 bp or more, thus having the potential to offer significant improvements over current short-read technologies. Furthermore, long reads enable improved “split-read” analysis so that indels, inversions, translocations and tandem/interspersed regions, and other structural changes can be more readily identified [46, 47]. The third-generation technology has already been used to create detailed maps of structural variations that enable phasing variants across large regions of human chromosomes and the filling in of gaps in the human reference genome [48–50]. SMRT sequencing by PacBio PacBio introduced the first commercial platform using a SMRT sequencing technology in 2010 [45, 51]. Hairpin adaptors are ligated to the double-stranded DNA to form the singlestrand circular DNA template, which is called a SMRTbell. Upon loading to the SMRT cell, each SMRTbell diffuses into a nanostructure called zero-mode waveguide (ZMW), which limits the light detection at a very small space to reduce noise. In each ZMW, a single DNA polymerase binds to the hairpin adaptor of the SMRTbell and starts the replication process by incorporating fluorescently labeled dNTP into the newly synthesized strand. The fluorescent signal is recorded by a charge-coupled device (CCD) camera and each increase of fluorescence signal pulse corresponds to the incorporated fluorescently labeled dNTP and is converted to the template sequence base. PacBio sequencing generates long-read lengths (average >15 kb, some reads >100 kb) with faster sequencing rates than short-read methods. Continuous improvement of this method to achieve higher throughput, lower base error rate, and lower cost per base is essential for further expanding its utility. Nanopore sequencing by ONT Another intriguing third-generation sequencing technology was developed by Oxford Nanopore, which released its nanopore-based single-molecule sequencing technology in 2014. It has been observed that conductivity of the ionic current in a nanopore changes when biological molecules pass through it [52]. Furthermore, the flow of the ion current depends on the shape of the molecule translocating through the pore. The current change is distinct for different nucleotides allowing for identification of the bases [53]. The key advantage of this approach is the minimal sample preparation, long-read length, and inexpensive sequencer [54]. Further improvement of the technology by reducing the base error and the amount of input DNA is needed for expanding its use [55].

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3. Whole-exome sequencing and whole-genome sequencing

Linked-reads sequencing by 10  Genomics Linked-Reads is a virtual long-read technology commercialized by 10 Genomics in 2016. Using a microfluidic device, high molecular weight (HMW) DNA molecules are first sparsely partitioned into microdroplets so that each droplet has zero or one DNA molecule. Inside the droplet, each DNA molecule is fragmented and tagged with a unique barcode. Barcodetagged DNA molecules are released from the droplets and undergo a regular library preparation step and are sequenced using the Illumina platform [56, 57]. Based on the barcode information, sequencing reads can be linked to the originating HMW DNA molecule to generate a virtual long read. This enables the construction of long-range haplotype and structural variant information and allows for de novo diploid assembly of individual genomes.

WES/WGS data analysis Substantial efforts have been put into analyzing the large amount of data generated by WES and WGS. Most analysis procedures include the initial and refined alignment and mapping of sequence reads to the human genome reference, generation of a variant list, and annotation of the variant list. The final interpretation is based on a combination of previous publications, mutation databases, variant in silico prediction, gene function annotation, and clinical phenotypes. Due to the large capacity of the sequencing platform, the samples are often multiplexed by pooling uniquely barcoded libraries to reduce the cost. Upon demultiplexing of the raw sequencing data, Binary Alignment Map (BAM) files and variant call format (VCF) files are generated [58]. To identify potential pathogenic mutations, a set of factors is considered: (1) whether the variants have an effect on gene function since most pathogenic mutations alter gene function; (2) the minor allele frequency (MAF) from population databases such as ExAC, gnomAD, 1000 genomes. Common variants with high allele frequencies should be filtered out using these control population databases; (3) the mode of inheritance of the disease. In autosomal dominant inheritance, mutation in one copy of a disease allele is sufficient to cause the phenotype. While in autosomal recessive inheritance, mutations in both chromosomes are required for an individual to be susceptible to expressing the phenotype; (4) whether the clinical phenotype matches the genotype of the mutant genes needs to be considered, especially when the clinical diagnosis is uncertain; and (5) whether the variant segregates in the family pedigree should be examined. A variety of open-source algorithms and commercial software have been developed specifically for processing WES and WGS data. Examples include: IMPACT, GotCloud, and SeqMule [59–61]. Since large portions of variants have not been reported before, accurate annotation of these novel variants represents one of the biggest challenges in data analysis.

Future direction/perspectives We have witnessed the rapid development of NGS technologies over the past decade. These technologies have been widely utilized by the research and clinical community. Using WES and WGS as diagnostic tools has paved the road for precision medicine. With further

II. Genomics in the eye

References

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improvements in sequencing technologies on the horizon, obtaining the complete genome sequence for each individual will likely become routine in the near future. The challenge will lie in the interpretation rather than the generation of sequencing data.

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[41] J.C. Roach, G. Glusman, A.F. Smit, C.D. Huff, R. Hubley, P.T. Shannon, L. Rowen, K.P. Pant, N. Goodman, M. Bamshad, et al., Analysis of genetic inheritance in a family quartet by whole-genome sequencing, Science 328 (2010) 636–639. [42] J.M. Ellingford, S. Barton, S. Bhaskar, S.G. Williams, P.I. Sergouniotis, J. O’Sullivan, J.A. Lamb, R. Perveen, G. Hall, W.G. Newman, et al., Whole genome sequencing increases molecular diagnostic yield compared with current diagnostic testing for inherited retinal disease, Ophthalmology 123 (2016) 1143–1150. [43] K.J. Carss, G. Arno, M. Erwood, J. Stephens, A. Sanchis-Juan, S. Hull, K. Megy, D. Grozeva, E. Dewhurst, S. Malka, et al., Comprehensive rare variant analysis via whole-genome sequencing to determine the molecular pathology of inherited retinal disease, Am. J. Hum. Genet. 100 (2017) 75–90. [44] Y. Feng, Y. Zhang, C. Ying, D. Wang, C. Du, Nanopore-based fourth-generation DNA sequencing technology, Genomics Proteomics Bioinformatics 13 (2015) 4–16. [45] A. Rhoads, K.F. Au, PacBio sequencing and its applications, Genomics Proteomics Bioinformatics 13 (2015) 278–289. [46] C.S. Pareek, R. Smoczynski, A. Tretyn, Sequencing technologies and genome sequencing, J. Appl. Genet. 52 (2011) 413–435. [47] K. Nakano, A. Shiroma, M. Shimoji, H. Tamotsu, N. Ashimine, S. Ohki, M. Shinzato, M. Minami, T. Nakanishi, K. Teruya, et al., Advantages of genome sequencing by long-read sequencer using SMRT technology in medical area, Hum. Cell 30 (2017) 149–161. [48] M.J. Chaisson, J. Huddleston, M.Y. Dennis, P.H. Sudmant, M. Malig, F. Hormozdiari, F. Antonacci, U. Surti, R. Sandstrom, M. Boitano, et al., Resolving the complexity of the human genome using single-molecule sequencing, Nature 517 (2015) 608–611. [49] V. Kuleshov, D. Xie, R. Chen, D. Pushkarev, Z. Ma, T. Blauwkamp, M. Kertesz, M. Snyder, Whole-genome haplotyping using long reads and statistical methods, Nat. Biotechnol. 32 (2014) 261–266. [50] M. Pendleton, R. Sebra, A.W. Pang, A. Ummat, O. Franzen, T. Rausch, A.M. Stutz, W. Stedman, T. Anantharaman, A. Hastie, et al., Assembly and diploid architecture of an individual human genome via single-molecule technologies, Nat. Methods 12 (2015) 780–786. [51] J. Eid, A. Fehr, J. Gray, K. Luong, J. Lyle, G. Otto, P. Peluso, D. Rank, P. Baybayan, B. Bettman, et al., Real-time DNA sequencing from single polymerase molecules, Science 323 (2009) 133–138. [52] H. Bayley, Nanopore sequencing: from imagination to reality, Clin. Chem. 61 (2015) 25–31. [53] D. Stoddart, A.J. Heron, E. Mikhailova, G. Maglia, H. Bayley, Single-nucleotide discrimination in immobilized DNA oligonucleotides with a biological nanopore, Proc. Natl. Acad. Sci. U.S.A. 106 (2009) 7702–7707. [54] D. Branton, D.W. Deamer, A. Marziali, H. Bayley, S.A. Benner, T. Butler, M. Di Ventra, S. Garaj, A. Hibbs, X. Huang, et al., The potential and challenges of nanopore sequencing, Nat. Biotechnol. 26 (2008) 1146–1153. [55] Y. Wang, Q. Yang, Z. Wang, The evolution of nanopore sequencing, Front. Genet. 5 (2014) 449. [56] G.X. Zheng, B.T. Lau, M. Schnall-Levin, M. Jarosz, J.M. Bell, C.M. Hindson, S. Kyriazopoulou-Panagiotopoulou, D.A. Masquelier, L. Merrill, J.M. Terry, et al., Haplotyping germline and cancer genomes with high-throughput linked-read sequencing, Nat. Biotechnol. 34 (2016) 303–311. [57] S.U. Greer, L.D. Nadauld, B.T. Lau, J. Chen, C. Wood-Bouwens, J.M. Ford, C.J. Kuo, H.P. Ji, Linked read sequencing resolves complex genomic rearrangements in gastric cancer metastases, Genome Med. 9 (2017) 57. [58] A. Magi, M. Benelli, A. Gozzini, F. Girolami, F. Torricelli, M.L. Brandi, Bioinformatics for next generation sequencing data, Genes (Basel) 1 (2010) 294–307. [59] V. Chaitankar, G. Karakulah, R. Ratnapriya, F.O. Giuste, M.J. Brooks, A. Swaroop, Next generation sequencing technology and genomewide data analysis: perspectives for retinal research, Prog. Retin. Eye Res. 55 (2016) 1–31. [60] S. Yohe, B. Thyagarajan, Review of clinical next-generation sequencing, Arch. Pathol. Lab. Med. 141 (2017) 1544–1557. [61] J.D. Hintzsche, W.A. Robinson, A.C. Tan, A survey of computational tools to analyze and interpret whole exome sequencing data, Int. J. Genomics 2016 (2016)7983236.

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C H A P T E R

4 RNA sequencing and transcriptome analysis Elizabeth D. Aua, Michael H. Farkasa,b,c a

Department of Ophthalmology, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo, Buffalo, NY, United States bResearch Service, Veterans Administration Western New York Healthcare System, Buffalo, NY, United States cDepartment of Biochemistry, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo, Buffalo, NY, United States

History of transcriptome analysis methods Whole transcriptome expression analyses have been used in disease research for years, although the methods employed have drastically changed. The earliest incarnations, such as expressed sequence tags (ESTs) and serial analysis of gene expression (SAGE), have been around since the early 1990s. By the late 1990s, microarrays hit the scene and quickly became the most widely used method for transcriptome analysis. Detailed information about these methods have been previously reviewed, and will not be the focus here [1–8]. While these early methods were useful, each had pitfalls which drove the need for a newer, better method for transcriptome analysis, resulting in the advent of RNA sequencing (RNA-Seq). RNA-Seq first became commercially available in 2004, and has since become the gold standard for transcriptome analysis, offering a multitude of advantages over the preceding methodologies, and providing more information than previously thought possible. RNA-Seq studies were first published in 2008 [5, 6, 9] and quickly became popular as the new, best method for whole transcriptome analysis. The basic premise of RNA-Seq is genius in its simplicity: randomly shear cDNA, sequence millions of short (75–150 bp) reads, align each sequence to the genome, and end up with a fully sequenced transcriptome yielding information both about the specific transcripts expressed and their expression levels. RNA-Seq technology offers distinct advantages over previously used methodologies for transcriptome analyses. Most notably, RNA-Seq allows for the sequencing of millions of reads at once,

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unlike ESTs and SAGE, and RNA-Seq does not rely on previously annotated transcripts, and is both quantitative and qualitative, unlike microarrays. Further, RNA-Seq is more cost effective than other methods. It is now possible to perform RNA-Seq with nanogram levels of starting RNA, creating millions of reads, across the entire genome, while maintaining both sensitivity and specificity [3, 10–13]. RNA-Seq gives individual researchers the ability to perform studies that at one time were only possible for large scientific groups or companies.

RNA-Seq RNA-Seq is an extremely powerful tool allowing for the analysis of the entire transcriptome in great detail at the gene, isoform, and even base level. There are additional selection strategies that allow for examination of specific genes, such as targeted RNA-Seq to capture only genes of interest, mRNA-Seq to select for only coding genes, and size selection to allow for micro-RNA analysis. The nature of RNA-Seq enables a researcher to examine the transcriptome, in part or whole, with tremendous accuracy, and allows for the analysis of alternative splicing, isoform-specific expression, low-expressing genes, and even novel genes [14–21].

Library preparation The preparation of sequencing libraries has dramatically improved since the first applications of this technology. The first applications of RNA-Seq required a large amount (1–10 μg) of starting material, and used an inefficient, labor-, and time-intensive protocol [22, 23]. Because the library preparation involved a number of purification steps, there was a significant amount of product lost by the end, resulting in the need for polymerase chain reaction (PCR) amplification in order to have enough product for sequencing. This is problematic because amplification can lead to sequencing bias and PCR artifacts [8, 17]. To address these issues, lower input, higher-throughput methods are constantly being tested, with the goal of minimizing starting material and product loss, minimizing sequencing bias due to PCR, and decreasing protocol time and cost [24–27]. The newest RNA-Seq library preparation methods allow for samples to be indexed with unique barcodes, meaning samples can be multiplexed. Further, the same set of samples can be run across multiple lanes in the same sequencing run, decreasing the potential for variability across samples and lanes. All of this makes RNA-Seq more cost effective, as many samples can be run concurrently.

Data analysis—Alignment and count generation The improvement in RNA-Seq library preparation protocols and accompanying increases in data produced call for accurate and efficient methods to analyze this data. Unfortunately, there is not one universally accepted set of algorithms for data analysis. As we learn more about the nuances of RNA-Seq data, existing algorithms are being improved and new algorithms are constantly becoming available.

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The first step in the analysis of RNA-Seq data is the alignment of reads to a genome or transcriptome. This is an extremely critical step, as all downstream analyses depend on accurate alignment results. It is also a complicated step, as a large portion of reads may require mapping across splice junctions, or may map to multiple isoforms of a transcript. Alignment algorithms are constantly being developed and updated in order to address these challenges. Most aligners have been reviewed, compared, and ranked across a variety of performance features under multiple conditions by several groups, so we will not go into these details [23, 28, 29]. Some of the most widely used RNA-Seq aligners include STAR, TopHat, TopHat2, RUM, GSNAP, MapSplice, SpliceMap, and SoapSplice, among others [15, 23, 30–34]. There are a few key factors to take into consideration when determining the best aligner for a dataset, including: accuracy, detection of splice junctions (annotated and novel), sensitivity (ability to align maximum number of reads), computational resources required, and speed. Unfortunately, there is no ‘best’ algorithm that performs better than all the rest in all categories. This makes choosing which aligner to use a crucial decision in the analysis process. It should also be noted that each aligner has a variety of user-defined parameters that can be altered, and will have an impact on performance. Indeed, a slight change in even one parameter can drastically alter alignment results using the same sequencing data. For this reason, it is vital that alignment is always conducted in the same manner, using the same algorithm, with the same parameter settings, in order to properly analyze data and produce valid results. Once RNA-Seq reads have been successfully aligned to the genome, counts must be generated in order to further analyze gene expression. This is accomplished by submitting a Sequence Alignment/Map (SAM) format or Binary Alignment/Map (BAM) format file containing alignment results and a Gene Transfer Format (GTF) or General Feature Format (GFF) file containing features of interest to an algorithm that generates feature counts. While this seems like a trivial task, there are multiple algorithms from which to choose, and several aspects to the process that require attention. Some of the most popular counting algorithms include Rsubread featureCounts, HTSeq, RNA-Seq by expectation maximization (RSEM), Cufflinks, and SAMtools [35–39]. Regardless of the algorithm used, there are again several parameters which must always be checked to ensure counts are generated correctly. It is always important to correctly identify whether the input reads are single- or paired-end, whether the sequencing library was prepared using a stranded protocol, how the feature identifier is defined in the input GTF/GFF file, and whether to allow reads to be counted as belonging to multiple features. In addition, each algorithm has its own set of parameters that must be studied and adjusted in order to properly generate counts for a given dataset. Without the proper parameter settings, expression count values cannot be properly analyzed in downstream applications. While determining whether data are single- or paired-end, and stranded or unstranded simply require knowledge of the experimental design, deciding whether to count reads across gene or isoform, and whether to allow multi-mapping reads to be counted is more complex. Approximately 80% of annotated genes undergo alternative splicing that yields more than one isoform, and isoform expression may vary based on the experimental condition under examination (i.e., tissue type, cell type, developmental age, disease state, etc.) [40–44]. Generating counts for each isoform allows for a more detailed and accurate analysis of expression in the sample under consideration. Whether multi-mapping reads (reads that align to more than one genomic locus) should be counted is somewhat dependent on whether counts are summed across gene or isoform, and whether allele-specific expression is of interest [45].

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When counts are summed on the transcript level, this is relatively straightforward: one should only keep reads that map to a unique transcript. However, counting by isoform complicates the situation. Because multiple isoforms of the same transcript likely share one or more exons, it is often impossible to know for certain which specific isoform a read is from. One solution to this problem is to allow reads that map to multiple features to be counted, and then examine splice junctions that are unique to one isoform. If no reads span a unique splice junction, the isoform containing that unique splice junction should be considered not present in the data [14]. For those isoforms containing only shared splice junctions, original counts must be kept. While more complicated, generating counts for every isoform yields a great deal more information, and can be used for much more thorough transcriptome analyses.

Data analysis—Novel feature detection Until now, we have only discussed the analysis of annotated transcripts, and this is certainly the most common use of RNA-Seq data. However, it has recently become abundantly clear that there is useful and pertinent information that has yet to be uncovered in the transcriptome, including both novel isoforms of known genes and novel genes [14, 18, 46]. RNA-Seq enables identification of these novel features, due to the random nature of reads. Current protocols allow for deeper sequencing, with longer reads, which aids in identification of novel features that are likely expressed at lower levels [46]. Many aligners provide an analysis of splice junctions, including novel splice junctions. A careful curation of novel splice junctions, and comparison with locations where sequences map to the genome, can uncover novel exons. Other aligners are specifically designed to work without a pre-described genome, and piece together reads so that all features, novel and annotated, are detected at the same time. There are several algorithms designed to aid in the identification of novel features in RNA-Seq data, including StringTie, RNAeXpress, SpliceGrapher, and HMMSplicer [47–50]. However, it is extremely important to verify that any perceived novel features are in fact novel, by comparing results to all known annotation databases. Importantly, because this is highly computational in nature, care must be taken to fully confirm any results.

Data analysis—Downstream applications Once data have been aligned and counts have been generated, there is still one important step before downstream data analysis can occur: normalization. In fact, normalization has been shown to be one of the most critical considerations when it comes to analysis of count data [51]. When deciding on a normalization method, several factors must be taken into consideration, including number of samples, read length, data quality, and intended applications after normalization. As with alignment and count generation, an abundance of normalization methods exist, and they have been extensively reviewed and compared [51–56]. Some of the most popular normalization methods are upper quartile, trimmed mean of M-values (TMM), DESeq normalization, reads per kilobase per million mapped (RPKM), fragments per kilobase per million mapped (FPKM), and RSEM [5, 36, 39, 57–59]. Each method attempts to calculate a new count for each feature examined which accounts for some combination of

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factors that differ either between samples, sequencing runs, or features themselves, and can introduce bias into the raw counts. Such factors include, but are not limited to, total number of reads, number of mapped reads, feature length, and data distribution. RPKM remains the most widely used normalization method, although others are gaining popularity, and some, such as TMM and DESeq, have been shown to offer improvements in certain situations [57, 59]. Beyond examining overall expression levels using RNA-Seq, the most popular use of the data is determining differentially expressed genes among two or more experimental conditions. Differential expression analysis can be used to identify genes that may be tissue- or cell-type specific, or may be involved in the development, disease pathogenesis, or even response to treatment. Because this is the most widely performed analysis, there are an abundance of algorithms that can be used to perform the comparison. Some of the most widely used are DESeq, DESeq2, edgeR, NOIseq, baySeq, and limma/voom [57, 59–62]. These methods have been extensively evaluated in order to analyze their ability to identify differentially expressed genes, maximize true positives and negatives, and minimize false positives and negatives, in an attempt to determine the pipeline providing optimal results [3, 51, 63, 64]. While the majority of these reports focus on the differential expression algorithms themselves and how they treat the data, there is also some discussion regarding the role of prior data treatment on differential expression results [63]. This includes number of samples and alignment and count generation methodologies. While these factors have much less influence, they are still important and can affect results, again emphasizing the importance of choosing the correct data analysis pipeline. Once differential expression results have been determined, those genes showing expression changes can be further analyzed to uncover identifying characteristics. These may include common transcription factor-binding sites, co-expression networks, regulatory networks, functional pathways, gene set enrichment analyses, and more. There are many possibilities for further analysis, and even more tools to use for these studies [65–72]. Because the lists of genes that are differentially expressed are often large, it is sometimes challenging to filter through the results to find truly meaningful information. For this reason, it is vital that any results are confirmed at the bench before making significant conclusions.

RNA-Seq in the eye In the past, RNA has been viewed, for the most part, as a window into what might be happening at the protein level in a given system. However, recently, evidence has begun to emerge showing that RNA has the potential to play a much more crucial role in many aspects of biology [73]. The identification of classes of RNAs, together called noncoding RNA, has added insight and intrigue to what used to be considered simply a stepping stone in the DNA-RNA-protein pathway. Further, noncoding RNAs, such as long noncoding RNAs (lncRNAs), are beginning to be identified as having roles in regulation of gene expression, development, and a variety of diseases [74–76]. Since its inception, high-throughput sequencing has quickened the pace of scientific progress and led to an overwhelming number of discoveries, and advances in the field of vision research are no exception. At the most basic level, RNA-Seq has allowed for more complete

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annotation of the transcriptome, both in mouse and human [18, 77]. Because baseline expression can vary considerably by tissue, it is vital that we fully analyze transcriptome expression for each tissue of interest. This is especially pertinent to studies in the eye, since the ENCODE project does not include any eye tissue [78–80]. Further, many early contributions to the study of retinal disease, particularly regarding the identification of mutations associated with visual dystrophies, were made possible by RNA-Seq [3, 22]. More recently, RNA-Seq has been instrumental in uncovering previously unknown aspects of the transcriptome, and has identified transcriptional activity across 75% of the genome, despite the fact that only 1% is accounted for by coding RNA [81]. It has been shown that noncoding RNAs have, on average, lower expression levels and more tissue-specific expression patterns as compared to coding genes [82, 83]. Further, RNA-Seq studies have identified novel transcribed sequences in the eye, including novel alternative splicing and novel genes [14, 18]. It is likely that future RNASeq studies will reveal even more novel transcriptional activity, identifying elements that have been until now missed due to low expression in the eye, or lack of ubiquitous expression preventing them from being identified in other tissues. RNA-Seq transcriptome analyses are vital for uncovering these genes and understanding their potential role in the eye.

Transcriptome analyses RNA-Seq has been instrumental in characterizing the retinal transcriptome, both in mouse and human [18, 77]. Without a complete and accurately annotated transcriptome, proper research into genes important in disease pathogenesis would be impossible. In order to determine which genes are altered in various disease states, and understand the role of these genes in disease, it is important to identify baseline expression profiles. While various studies have claimed to provide a complete human transcriptome, these have often not used eye tissue as part of the analysis, or have used a combination of multiple tissues [78–80]. Because expression can vary by tissue, it is important to perform baseline transcriptome analyses using individual tissues in order to properly define expression under normal conditions. The increasing depth with which RNA-Seq is being performed enables the identification of lower expressing genes, which have until now been missed. This is particularly true for both noncoding genes, which have been shown to have tissue-specific expression and function, and genes with expression specific to the eye, which will be missed in other tissues [84, 85]. One class of noncoding genes, long intervening noncoding RNAs (lincRNAs), a subclass of lncRNAs, have come to the forefront as playing a role in development and disease pathogenesis. Although lincRNA studies are becoming more common, their inclusion in vision research is still in its infancy. Of the over 20,000 lincRNAs that have been identified to date, just over 1000 have been shown to be expressed in the retinal pigment epithelium (RPE), a cell type important to disease pathogenesis [14, 86]. Importantly, lincRNAs have been shown to play a role in development and a multitude of diseases, including several types of cancer, and Alzheimer’s [87–92]. While they have been shown to be associated with eye disease, their role has yet to be determined [75]. However, given their importance in the pathogenesis of other disease, they have tremendous potential to be important players in the pathogenesis of visual dystrophies.

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The fact that RNA-Seq allows for the identification of novel genes is important, as more of the genome has recently been identified as being transcribed than previously thought [18, 46, 81]. The eye is no exception, as novel genes have been identified in the retina and RPE using RNA-Seq [14, 18]. In particular, lncRNAs are poorly characterized, and RNA-Seq offers the opportunity to determine proper annotation of these typically lower expressing genes [46]. In addition, RNA-Seq studies using eye tissue will enable the identification of genes specific to the eye that have been missed in previous genome studies in other tissues. These genes are especially exciting because they have the potential to play an important role in both normal eye development and in pathogenesis of eye disease. Further, newly identified novel genes offer novel therapeutic targets for treatment. These novel genes will lead to a better understanding of expression in the eye.

Models of disease Due to the difficulty of obtaining human eye tissue, much of vision research has been conducted using mouse models of disease. Importantly, RNA-Seq offers the opportunity to compare transcriptome-wide baseline expression in mouse and human eye, which is vital for mouse models to be properly used in the study of eye disease. Mouse models have been beneficial in the identification of a number of gene mutations associated with glaucoma, retinitis pigmentosa, and Leber congenital amaurosis (LCA), among others [44, 93, 94]. While this has been a useful tool in the past, it is important to note that as studies move to investigating noncoding RNAs and genes specific to the eye, mouse models may be less beneficial. This is due to the fact that lncRNAs have been shown to have not only tissue-specific, but also species-specific expression [82]. For this reason, models must move to human cell lines, and in particular cells that can be differentiated into specific cell types of interest, such as the RPE and retinal organoids.

Disease genes and therapeutic targets Smaller targeted or exome sequencing studies were able to identify mutations in several genes that are associated with visual dystrophies, and RNA-Seq has been used to verify these on a larger scale [93–97]. RNA-Seq allows for the examination of altered expression associated with disease, which is necessary to filter candidate genes, particularly in the case of novel genes, to determine which may be useful as therapeutic targets. For instance, in 2014, Yasuda et al. used axonal injury as a mouse model of glaucoma, and identified 177 genes with altered expression in the retina using RNA-Seq [44]. These genes belong to several pathways of interest, including endoplasmic reticulum stress-related, immune response, and antioxidative defense genes. Further, RNA-Seq has been used to identify additional mutations, which has greatly increased the percentage of eye disease cases that are associated with known mutations [93, 98, 99]. One example of this is in LCA, for which 17 genes have been shown to cause two-thirds of all cases. Falk et al. identified a disease-causing mutation in a novel gene, NMNAT1, using exome and RNA-Seq that explains additional cases of

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LCA [93]. Koenekoop et al. took this a step further, identifying a novel LCA disease pathway associated with the NMNAT1 mutations [97]. However, there are still many cases of disease that cannot be explained by these mutations. RNA-Seq will be instrumental in the identification of novel genes that may be associated with disease. As more disease genes are identified, these also provide additional potential targets for use in gene therapy. This is especially important because many of the disease genes already identified are not suitable for use in current gene therapy protocols. To this end, RNA-Seq will also be the ideal technology to test new therapy techniques, as sequencing allows for an analysis of both expected gene changes due to therapy, and any unexpected changes that occur as a by-product of treatment.

Concluding remarks RNA-Seq is an extremely powerful method for transcriptome analysis, and its full potential is still being realized. In order to glean as much information from sequencing data as possible, careful planning is critical. It is important to comprehensively address both experimental design and computational analysis, and understand that these two aspects of RNA-Seq must work together and be in agreement in order to correctly understand and fully appreciate the wealth of information contained in sequencing results. An RNA-Seq study must start with the proper determination of a number of experimental factors before data analysis, or sequencing itself, even begins, including optimal number of samples, read length, and amount of starting material to generate full coverage of genes of interest. These parameters must be carefully thought out in order to properly analyze lower expressing genes and search for novel features previously missed. Postsequencing computational analysis also requires careful thought and preparation at every stage to properly assess the data and generate meaningful results. RNA-Seq has already played a key role in advancing our understanding of visual dystrophies, including validating mutations associated with disease, identifying new disease mutations, and finding novel transcribed regions that may harbor previously unannotated disease genes. Novel genes also present an opportunity to explore new gene therapy targets that may advance the science of disease treatment. As new studies generate more transcriptome data, especially specific to the eye, our knowledge will increase exponentially.

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C H A P T E R

5 Noncoding genome in eye disease Tadeusz J. Kaczynskia,b, Michael H. Farkasa,b,c a

Department of Ophthalmology, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo, Buffalo, NY, United States bResearch Service, Veterans Administration Western New York Healthcare System, Buffalo, NY, United States cDepartment of Biochemistry, Jacobs School of Medicine and Biomedical Science, State University of New York at Buffalo, Buffalo, NY, United States

Introduction to the noncoding genome Research into the establishment and progression of human eye disease has long focused on protein-coding genes, and while such attention is not unwarranted, it is becoming increasingly clear that a more complete understanding requires a broader investigative scope. With the advent of next-generation sequencing, and the accompanying explosion in our knowledge of the genomic regions contributing to human disease, we have begun to appreciate the influence of the noncoding genome—the vast majority of the genome which has no protein-coding function. Functional noncoding genes are a subset of noncoding genomic loci that are transcribed into noncoding RNAs (ncRNAs) and which enact their functions without being translated into a peptide. Classes of ncRNAs are incredibly diverse and encompass ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), microRNAs (miRNAs), and long noncoding RNAs (lncRNAs), among others. Of the many types of ncRNAs, here we will be addressing miRNAs and lncRNAs. miRNAs, as a group, are rather well understood, while lncRNAs and their functions are not nearly so well characterized. Though we have learned much about these elements in the past few decades, we still have much to discover about how they operate to control various cellular processes and disease states. In this chapter, we can provide only a brief overview of the current understanding of these ncRNAs and their involvement in human eye diseases, but in so doing we hope to cultivate an interest in, and appreciation for, this complex issue.

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MicroRNAs and the eye The biogenesis and regulatory mechanisms of miRNA are well understood in animals (see Ref. [1] for an excellent review of miRNAs in the metazoan lineage). Briefly, the canonical miRNA gene is transcribed by RNA polymerase II to produce a pri-miRNA transcript possessing at least one hairpin secondary structure [1]. The pri-miRNA is then cleaved by the Dicer endonuclease, and the liberated pre-miRNA hairpin is transported to the cytoplasm. Further processing of the pre-miRNA yields a single-stranded miRNA approximately 20 bp in length which is loaded into the Argonaute (AGO) protein, forming a silencing complex. Pairing of the AGO-bound miRNA to sites within messenger RNAs (mRNAs) directs those targets for posttranscriptional repression through RNA decay or translational inhibition pathways. Gene regulation by miRNAs is critical for proper development and mature cellular functioning in animals, and as such there is a broad conservation of miRNAs and their target transcripts across eumetazoan species [2–4]. It is therefore not surprising that the perturbation of miRNA pathways in the mammalian eye disrupts a normal development, a fact highlighted by the gross anatomical and physiological defects observed in mice possessing retinal-specific knockout of Dicer, which is required for miRNA maturation [5–10]. Dissection of the roles of individual miRNAs in the eye has been a protracted endeavor due to the multitudes of miRNAs, their many possible targets, and potentially subtle influence of any given interaction [11]. Yet specific sets of miRNAs have been shown to be involved in eye field specification, developmental timing, Notch signaling, and synaptic connectivity (see Ref. [12] for a thorough review of miRNAs in retinal development). Given the pivotal nodes miRNAs occupy in the retina, the question is not whether miRNA dysregulation contributes to disease states of the human eye but to what extent this is the case.

lncRNAs and the eye Due to their loose classification, it is difficult to describe lncRNAs in absolute terms without running afoul of multiple exceptions. Indeed, lncRNAs [which encompass long intervening noncoding RNAs (lincRNAs), antisense RNAs, and pseudogenes] represent an incredibly diverse group of molecules—one that will likely be partitioned into multiple, more descriptive subcategorizations as our understanding progresses. Yet for the sake of simplicity, it is worthwhile to note the general features possessed by lncRNAs: they are RNA transcripts primarily transcribed by RNA polymerase II, they possess little or no coding potential, and they are often 50 -capped, spliced, and polyadenylated [13]. Although lncRNA functions are still poorly understood, experimental evidence has thus far revealed that they largely operate in the regulation of gene expression via: (1) transcription-dependent activation or repression of genes in cis, (2) mediation of interchromosomal interactions, (3) organization of subcellular structures, (4) formation of R-loops, (5) acting as guides or decoys for transcription factors, (6) scaffolding chromatin modifying complexes, (7) acting as miRNA sponges, (8) mRNA decay regulation, or (9) regulation of protein subcellular localization [14–17]. It is critical to note that because lncRNA research is still in its infancy, there is not a clear

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consensus regarding the veracity of some of the mechanisms through which lncRNAs are believed to function. For example, the ability of lncRNAs to operate as physiologically significant miRNA sponges has been hotly debated [18–20], and although evidence has accumulated in support of the ability of lncRNAs to sponge miRNAs, the extent to which this occurs in vivo is unclear because many studies do not perform the necessary experimentation required to definitively characterize a lncRNA [21]. Importantly, the majority of lncRNAs exhibits a poor sequence conservation between species, and while it is possible for their functions to be conserved through a maintained secondary structure, extreme caution should be taken when comparing lncRNAs among disparate species [22]. In addition, many lncRNAs display a highly tissue-specific expression, and this observation, together with the low interspecies conservation, has led to the speculation that lncRNA function may be a key contributor to species-specific features [23]. With this in mind, it is imperative to remember that what holds true for a lncRNA in one species, may not be the case for another—a particularly important consideration for studies of human disease. A variety of lncRNAs have been demonstrated to be important for the proper development and functioning of the healthy eye. Studies in the murine retina have found that Tug1 is necessary for photoreceptor outer segment development [24], Rncr2 (also known as Miat or Gomafu) and Six3os regulate retinal cell fate specification [25, 26], Vax2os1 is involved in the regulation of cell cycle progression [27], and Rncr4 contributes to the organization of the retinal architecture [28]. Few studies have examined lncRNA function in healthy human retinal cells, yet given their apparent importance in retinal pigment epithelium (RPE) cell differentiation [29, 30] and their implication in retinal diseases [31], it is evident that lncRNAs are required for proper development of the human eye.

Noncoding RNAs and eye disease Our understanding of ncRNAs grows daily, and with this ever-expanding knowledge base, has come an increased interest in discovering how these molecules affect ocular diseases. Here, we will highlight a portion of the research investigating the involvement of miRNAs and lncRNAs in some of the most prominent blinding diseases: age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, retinitis pigmentosa (RP), retinoblastoma (RB), among others. In providing this overview, we hope to convey that miRNAs and lncRNAs are important players in eye disease.

Age-related macular degeneration AMD is a disease which results in the progressive deterioration of central vision, and it is the leading cause of visual impairment in the elderly population [32]. The early and intermediate forms of AMD are usually asymptomatic, and are characterized by an excessive deposition of extracellular debris (termed drusen) under the retina and altered pigmentation. Loss of retinal cells and vision occurs in the advanced stages of AMD, which are termed as either ‘wet’ or ‘dry’ owing to either the presence or absence of abnormal neovascularization beneath the retina.

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AMD pathology has been demonstrated to be influenced by altered miRNA expression (see Ref. [33] for an excellent review of miRNAs in AMD). Multiple studies have probed the AMD miRNA profile, and although some discrepancies exist between the data sets, important commonalities emerge in their comparison. Among the miRNAs most likely to be major players in AMD are miR-9, miR-34a, miR-125b, mir-146a, and miR-155; these miRNAs have been found to be upregulated in the ocular tissues and/or vitreous of AMD patients, and they target transcripts controlling the major pathological characteristics of AMD—inflammation and neovascularization [34–36]. Another study points to a possible mode of dysregulation, identifying AMD-associated variants in three miRNA genes affecting their expression levels, as well as variants in 31 coding genes possibly affecting miRNA-mRNA interactions [37]. Various miRNAs have also been suggested as useful biomarkers for AMD as serum-miRNA levels are variably altered in patients affected with different forms of the disease [38–42]. Various pathological aspects of AMD are thought to possess underpinnings in lncRNA dysregulation. Utilizing microarray and qRT-PCR assays, one study found Vax2os1 and Vax2os2 to be upregulated in a mouse model for ocular neovascularization and in the aqueous humor of patients with AMD, supporting a possibility where these two lncRNAs are involved in the angiogenesis associated with the disease [43]. Another lncRNA, RP11-234O6.2, was demonstrated to be downregulated in AMD patient RPE/choroid, and further analysis provided evidence that decreased expression of this lncRNA may contribute to AMD via diminished cell viability [44]. Additionally, because inflammation may contribute to the RPE cell dysfunction associated with AMD, another study examined how a mixture of cytokines affected the expression of lncRNAs in human RPE-derived ARPE-19 cells. The researchers were able to identify a set of lncRNAs possessing altered expression upon cytokine exposure, and they hypothesized that increased levels of BANCR (which is involved in epithelialmesenchymal transition) may link the inflammatory response with RPE cell dysfunction of AMD [45]. Furthermore, RPE cell dedifferentiation is also thought to contribute to the pathology of AMD, and the downregulation of the lncRNA ZNF503-AS1 in the RPE/choroid of AMD patients has been implicated in this process [30].

Diabetic retinopathy DR is a major complication of diabetes mellitus that involves vascular abnormalities which result in a vision loss [46]. Clinically, DR is divided into two stages. During the early, nonproliferative stage, patients can be asymptomatic but will display retinal pathologies that include microaneurysms and hemorrhages. The later, proliferative stage is characterized by retinal neovascularization, and patients may experience a severe vision loss caused by vitreous hemorrhage or retinal detachment. The most prominent of the pathological features of the DR retina are hyperglycemia, microvascular complications, inflammation, and neurodegeneration. miRNAs have been linked to DR through their involvement in the pathways leading to microvascular complications and cell death indicative of the disease. A study profiling the miRNAs expressed in the retina of a rat model of DR uncovered the upregulation of miR146a/b, miR-155, miR-132, and miR-21 [47], which have been demonstrated to be involved

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in the processes driving vitreous hemorrhaging and neovascularization that lead to disease pathology [48, 49]. Another study using a mouse genetic model of diabetes suggested that the dysregulation of Oxr1 by miR-200b contributes to the oxidative stress and retinal degeneration seen in DR [50]. Other experiments, conducted in both human and rodent cells, have shown that miR-200b [51] and miR-15ab [52] are downregulated in the diabetic state, contributing to DR pathogenesis through increased levels of the pro-angiogenic vascular endothelial growth factor (VEGF). Additionally, increased levels of miR-195 under diabetic conditions have been demonstrated to contribute to DR pathology through a diminished expression of SIRT1—an important regulator of the cell cycle, survival, and metabolism [53]. It is also interesting to note that early-stage (nonproliferative) DR has been linked to the miRNA, let-7, as its overexpression in mouse produced a phenotype mimicking symptoms of the disease, which suggests that different miRNAs may be involved depending on how far the disease has progressed [54]. Within the last few years, an increasing number of lncRNAs have been implicated in the disease processes of DR. A microarray analysis utilizing a diabetic mouse model provided some of the first indications that DR pathology may have foundations in lncRNA dysregulation—uncovering over 300 lncRNAs with altered expression levels in the diabetic retina compared to controls and highlighting the upregulation of the lncRNA, Malat1, as a good candidate for further analysis [55]. Additional experiments, conducted using knockdown strategies, have demonstrated that MALAT1 dysregulation is involved in diabetesassociated vascular defects and that MALAT1 influences the expression of genes involved in inflammation [56, 57]. Other studies have probed the connections between lncRNAs and DR pathology in both human and mouse cells, finding that under high glucose concentrations; ANRIL (also known as CDKN2B-AS1) upregulation leads to an increased expression of the pro-angiogenic VEGF, MEG3 downregulation increases angiogenesis through the disruption of the PI3K-Akt signaling axis, RNCR3 upregulation affects KLF2 transcript concentration leading to vascular dysfunction, and BDNF-AS upregulation may be involved in RPE cell apoptosis through BDNF regulation [58–61].

Glaucoma Glaucoma is an ocular disease characterized by the degenerative damage to the optic nerve which ultimately results in blindness. While the precise inciting mechanism behind the neurodegeneration of glaucoma is not yet known, it is recognized that elevated intraocular pressure (IOP) is a major risk factor for the development of the disease [62]. Increased oxidative stress is also thought to play a critical role in glaucoma pathogenesis [63]. Due to the importance of IOP in the pathology of glaucoma, there is a great interest in examining the aqueous humor of the eye for miRNAs whose dysregulation might contribute to this aspect of the disease. Interestingly, miRNA-containing extracellular vesicles appear to be used as means of communication between disparate cells over long distances (e.g., through the aqueous humor), and they are beginning to be appreciated for their diagnostic and therapeutic value in disease [64]. Indeed, in response to oxidative stress, the trabecular meshwork cells in the anterior eye have been demonstrated to release a set of miRNAs (including miR-21 and miR-450) into the aqueous humor, and these miRNAs may act as intercellular signals in

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glaucoma, affecting apoptotic pathways in retinal cells in the posterior eye [65]. Additionally, recent studies have profiled miRNAs found in the aqueous humor of the glaucomatous eye— uncovering sets of miRNAs that may be useful as potential biomarkers and may contribute to disease by altering the structure of the extracellular matrix thus influencing IOP [66, 67]. Further investigations have also revealed genetic variations affecting the expression and activity of several miRNAs (including miR-182 and miR-4707) found in the aqueous humor of glaucoma patients [68, 69]. The connection between lncRNA genotype and disease phenotype is well documented in the case of glaucoma. Multiple genome wide association studies (GWAS) in human populations of varying ethnicities have identified an association between genotypic variants at chromosomal region 9p21.3 and a susceptibility to optic nerve degeneration in glaucoma [70–73]. These variants were found to lie within the ANRIL gene locus, and although the effects of the variants remain unclear, ANRIL itself appears to confer a neuroprotective effect, as a knockout of the corresponding locus in mouse led to an increase in retinal ganglion cell (RGC) death in response to elevated IOP [74]. Interestingly, polymorphisms in the ANRIL locus have also been reported to be associated with other disparate diseases (including cardiovascular disease and cancer), and it has thus been suggested that ANRIL operates as a signaling node incorporating cell-type information with environmental stimuli [75]. Solidifying the link between glaucoma and lncRNAs, human genetic variants affecting the expression of another lncRNA, LOXL1-AS1, have been demonstrated to be strongly associated with exfoliation syndrome, which is a disorder that confers a greatly increased risk for the development of glaucoma [76].

Retinitis pigmentosa RP refers to a set of inherited retinal dystrophies wherein patients gradually lose their vision due to the progressive loss of rod and cone photoreceptor cells [77]. Most individual cases of RP are monogenic. Yet the disease is highly heterogenic, and for the most part, mutations in any one gene make up only a small proportion of the total cases of RP. Mutations causing RP have been found in genes involved in phototransduction, vitamin A metabolism, and RNA splicing, among other biochemical pathways. RP is yet another disease whose development and progression has been tied to altered expression of miRNAs. Utilizing a set of microarray and quantitative real-time PCR experiments, researchers identified a signature of miRNAs (miR-96, -182, -183, -1, and -142) differentially expressed in mouse models that mimic the pathology of the disease [78, 79]. Subsequent profiling endeavors have expanded the list of dysregulated miRNAs in mouse models of RP, and they have also begun to tease out the possible miRNA-mRNA interactions that may underlie the disease [80]. Interestingly, a set of miRNAs, known to target RP causative genes, exhibited altered expression in oxidatively stressed human RPE cells, uncovering an interwoven relationship between miRNAs, oxidative stress, and the development of RP [81]. Regarding any involvement of lncRNAs in the pathology of RP, while little is known, indications of a connection are beginning to arise in the literature. It has been documented that an excessive exposure to bright light can accelerate RP progression, and this process may

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incorporate lncRNA signaling [82]. The lncRNA, Meg3, has been demonstrated to be both proapoptotic and upregulated in the mouse retina after prolonged light exposure, and while similar studies will be necessary in human cells, it is tempting to hypothesize that MEG3-linked apoptosis might be involved in the retinal degeneration seen in light-induced RP progression [83]. Furthermore, given that certain lncRNAs, such as NEAT1 and MALAT1, are appreciated as key players in the RNA splicing process, it is possible that lncRNA dysregulation could contribute to the disruption of the splicing machinery known to be a hallmark characteristic of RNA splicing factor-associated RP [84, 85].

Retinoblastoma RB is an intraocular malignancy that originates from the retina. It is found primarily in young children, and is fatal if left untreated. Mutations in the RB1 tumor suppressor gene have been found to account for the most cases of RB, although mutations in the MYCN oncogene are now known to initiate a small percentage of RB cases [86]. The disease is genetically recessive and can be grouped into a heritable form (wherein one mutation in one allele is inherited and the other occurs de novo) and a nonheritable form (wherein mutations in both alleles occur de novo), which make up approximately 35% and 65% of cases, respectively [87]. Differential miRNA expression is believed to affect the cellular pathways leading to the development of RB. Among the first miRNAs to be studied in relation to this cancer, let-7 has been demonstrated to be downregulated in RB tumors—a particularly notable discovery as let-7 acts as tumor suppressor by regulating expression of known oncogenes [88, 89]. Similarly, dysregulation of another tumor suppressor miRNA, miR-34a, may be involved in RB progression as it displays altered expression in RB cell lines compared to control cells [90, 91]. Beyond this, additional studies have examined and identified multitudes of miRNAs that have a diagnostic and therapeutic potential for patients with RB [92, 93] (see Ref. [94] for a more comprehensive review). Only very recently have lncRNAs been investigated in relation to their possible contribution to the development of RB. The upregulation of HOTAIR in human RB tissue has been linked to aberrant Notch signaling [95], which is known to be a hallmark feature of malignant RB tumors [96]. PANDAR and LINC00152, both of which display an increased expression in RB cells and tissues, have been implicated in the progression of RB through the deregulation of apoptotic pathways [97, 98]. Additionally, BDNF-AS downregulation in RB tissues was predictive of poor survival among RB patients, and BDNF-AS overexpression inhibited proliferation and migration of RB cells [99]. Similarly, BANCR and MEG3 also demonstrate dysregulation in RB cells, and their aberrant expressions are correlated with poor prognoses [100, 101].

Other diseases of the eye Although there are many other ocular diseases aside from those mentioned above, the depth of ncRNA research with respect to these other diseases is comparatively shallow. Even so, evidence is mounting that miRNAs and lncRNAs are important players in many diseases of the eye.

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Given the fundamental role played by miRNAs in the cell, the prevalence of miRNA dysregulation in ocular diseases is not surprising. As discussed above, neovascularization plays a role in DR and AMD, but the process is also involved in the pathology of other eye diseases, such as retinopathy of prematurity, retinal vein occlusions, and ocular histoplasmosis [102]. While some miRNAs have been implicated in disease-specific neovascular pathways, many others appear to operate more generally; for example, miR-126, miR-181a, miR-351, and other miRNAs have been demonstrated to be regulators of angiogenic growth factors and are potential therapeutic targets in multiple retinal vascular diseases [103–106]. miRNA dysregulation has also been associated with proliferation and migration of RPE cells—processes that contribute to the progression of rhegmatogenous retinal detachment to the more severe proliferative vitreoretinopathy (PVR) [107–110]. Alterations in miRNA expression and activity are also implicated as causative factors in myopia, affecting key developmental pathways necessary for the proper formation of the eye and its refractivity [111, 112]. Despite being relative newcomers to the scientific scene, lncRNAs are beginning to be appreciated as pathological contributors to some less prevalent ocular diseases. One study, using a mouse model of corneal neovascularization, found 154 lncRNAs that were differentially expressed between vascularized and normal corneas [113]. Among this list of lncRNAs, two were confirmed to also be differentially expressed in the vascularized corneas of human patients, though their precise relevance to the disease has not yet been explored. Another study utilized a microarray analysis to reveal the dysregulation of 78 lncRNAs, including MALAT1, in the epiretinal tissues of patients with PVR [114]. Further investigation into the role of MALAT1 in PVR pathogenesis, demonstrated that MALAT1 is involved in the TGFβ1-induced epithelial-mesenchymal transition that is a key feature of the disease [115]. Additionally, the lncRNA, ROR, was found to be upregulated in human ocular melanoma tumor cells, compared to normal cells, and this increased expression has been implicated in processes leading to tumor growth and metastasis [116].

Concluding remarks Though it is all too evident that miRNAs and lncRNAs are important nodes in ocular diseases, much work remains to be done. A primary consideration should be in expanding our understanding of the mechanisms by which lncRNAs enact their functions. Further experimentation will also be important in uncovering the extent to which, and the molecular pathways through which, the dysregulation of any given miRNA or lncRNA contributes to a particular disorder. Furthermore, miRNAs and lncRNAs hold great promise for the diagnosis and treatment of ocular diseases, and this potential must continue to be explored.

References [1] D.P. Bartel, Metazoan microRNAs, Cell 173 (2018) 20–51. [2] E. Berezikov, Evolution of microRNA diversity and regulation in animals, Nat. Rev. Genet. 12 (2011) 846–860. [3] R.C. Friedman, K.K. Farh, C.B. Burge, D.P. Bartel, Most mammalian mRNAs are conserved targets of microRNAs, Genome Res. 19 (2009) 92–105.

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C H A P T E R

6 Genetic architecture of inherited retinal disease Rachayata Dharmata,b, Ruifang Suic, Rui Chena,b,d,e,f a

Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, United States bHuman Genome Sequencing Center, Baylor College of Medicine, Houston, TX, United States cDepartment of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China dDepartment of Structural and Computational Biology & Molecular Biophysics, Baylor College of Medicine, Houston, TX, United States eDepartment of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, United States fProgram of Developmental Biology, Baylor College of Medicine, Houston, TX, United States

Photoreceptors (PR) cells are the primary sensors of light stimulus in the retina that convert the visual signal into electrical impulses. Due to the indispensable function of PR in visual perception, any genetic defects in the formation or maintenance of these cells result in progressive and irreversible loss of vision. Although debilitating, the resulting visual defects do not impact the survival of the affected population and consequently persists across generations in the form of inherited retinal diseases (IRDs). IRDs encompass a large group of clinically and genetically heterogeneous diseases that collectively have an estimated incidence of 1:2000 and are the leading cause of legal blindness between 15 and 45 years of age, causing a profound impact on patients and society [1–3]. Therapeutic options are currently limited to slowing the progression of retinal degeneration, however novel therapeutic approaches including stem cell therapy, optogenetics, and retinal prostheses and more recently gene therapy have potential to restore vision to a remarkable degree. These advances in therapeutic options over the past 2 decades have stemmed from an efficient clinical diagnosis of the broad spectrum of IRD, extensive dissection of underlying genetic etiology and the mechanistic underpinnings of PR cell death. IRDs together comprise of 25 genetic visual disorders and >30 different syndromic disorders with a component of retinal dystrophy (see https://sph.uth.edu/retnet/home.htm). This complexity in IRD stems from allelic heterogeneity (different alleles in the same gene

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cause the same or distinct disease presentations) and genetic heterogeneity (different genes cause the same disease presentations) observed in IRDs. The first few genetic associations with IRD were made in the early 1980s when traditional southern blotting and linkage techniques associated genetic locus with Gyrate atrophy and retinitis pigmentosa (RP) [4–7]. Over the past 3 decades, relentless research efforts utilizing next-generation sequencing (NGS) and targeted genetic testing panels have resulted in the discovery of >260 genes and loci (>5000 mutations) (https://sph.uth.edu/retnet/home.htm) [8] associated with IRDs. Thus, we now have a more comprehensive understanding of the genetic architecture of IRDs in comparison to any other complex genetic human trait.

Spectrum and evaluation of the clinical phenotype of IRD Clinically, the IRDs can be classified based on the primary retinal cell type affected during the disease pathogenesis and the onset and progression of the disease. If the rods are the primary cell type affected, it is classified as rod-cone dystrophy (e.g., RP, MIM-268000), vs conerod dystrophy (CRD, MIM-120970) or Stargardt disease (STGD, MIM-248200), in which cone degeneration precedes rod degeneration. In achromatopsia or color blindness, one or multiple types of cone cells are dysfunctional vs in congenital stationary night blindness (CSNB, MIM-310500) the transmission of visual signal from rods to downstream neurons (bipolar cells) is affected. PR are also functionally dependent on the retinal pigment epithelium (RPE) and the choroid that nourishes them, thus dysfunction in any of these components can result in secondary PR degeneration. IRDs can be stationary, as observed in most cases with CSNB, blue cone monochromacy (BCM, MIM-303700) and achromatopsia (MIM216900), or progressive, such as in cases of RP, macular dystrophy (MD), CRD, and STGD, which have a broad spectrum of age of onset. The most severe form of IRD is Leber congenital amaurosis (LCA, MIM-204000), with onset as early as infancy. In LCA, the primary dysfunction can be observed in both cones and rods of the neural retina as well as the RPE cells based on the underlying genetic etiology. These disorders are distinguished using an array of imaging, electrophysiological, and psychophysical techniques which, by virtue of the location, anatomy, physiology, and transparency, of the eye are largely noninvasive (Table 1). Examination of the ocular fundus (visualizing the retina through a dilated pupil) by color photographs and fundus autofluorescence, is used to observe topographic changes in the fundus, map lipofuscin changes in the RPE and monitor degeneration of the retinal layers. PR loss during IRD causes a reduction in the thickness of retinal layers which is measured by optical coherence tomography (OCT). Advanced techniques that correct for optical aberrations (adaptive optics) now allow imaging at the level of individual PR (Fig. 1). Electrophysiological examinations [including electroretinography (ERG) and electrooculography] which record the electrical responses of the retina to light stimulus, permit quantification of retinal and RPE function, respectively, and help ascertain the primary cell type affected. Since, genetic factors underlying IRDs may, in certain cases, affect other organs in parallel, systemic evaluation and additional nonophthalmologic assessments (e.g., neurological examination, metabolic report, audiometric tests, kidney function, and ultrasound) may be necessary for defining prognosis, monitoring for comorbidities, and other syndromic/systemic presentations.

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TABLE 1

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Clinical features involved in the assessment of key IRDs including STGD, RP, and CRD. STGD

CRD

RP

Onset symptoms

Decreased vision

Decreased vision

Night blindness

Fundus appearance

Macular atrophy with/ without yellow flecks

Normal or atrophy of the RPE and choriocapillaris

Bone spicules pigments, narrowing of arterioles, waxy optic disc pallor

OCT

Disappearance of ellipsoid and interdigitation zone in macular

Disappearance of ellipsoid and interdigitation zone

Disappearance of ellipsoid and interdigitation zone in peripheral retina

VF

Central scotoma

Central scotoma

Concentric contraction

ERG

Reduced cone response with/ without reduced rod response

Reduced cone response earlier than rod response

Reduced rod response earlier than cone response

Inheritance pattern Although complex inheritance has been reported, such as digenic, monogenic Mendelian inheritance is the primary mode for IRD cases. All inheritance patterns for single gene disorders have been observed for IRD, including autosomal recessive (two copies of pathogenic alleles result in a disease phenotype), autosomal dominant (single pathogenic alleles result in a disease phenotype), and X-linked (single pathogenic allele on X-chromosome can result in the disease phenotype in the hemizygous male). In addition, a defect in mitochondrial genes (pathogenic mitochondrial allele results in disease phenotype) can also cause IRD. Of the 268 genes that have been associated to IRD so far, 185 are inherited in a recessive pattern, 63 in a dominant pattern, 13 are in an X-linked pattern, and 7 in mitochondrial pattern (Table 2) (https://sph.uth.edu/retnet/home.htm). Interestingly, some genes, such as CRX and BEST1, can be inherited in both recessive and dominant patterns depending on the nature of the allele.

Mechanistic pathways culminating in photoreceptor degeneration The genetic and phenotypic heterogeneity observed in retinal diseases stems from the underlying vulnerabilities in the unique physiology and biochemistry of PR. Broadly, the genes associated with PR degeneration affect almost all aspects of cellular structure and function, affecting pathways ranging from PR-specific (such as phototransduction cascade, visual cycle, or synaptic transmission) to more general pathways (such as protein folding, splicing or lipid metabolism) (Table 2) [9,10]. Mutation in these genes, although conferring different vulnerabilities, culminates in PR cell death primarily via caspasedependent and independent pathways of apoptosis. Below we discuss major functional pathways affected in IRD, the vulnerabilities that they confer, and the underlying genes (Fig. 2).

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FIG. 1

6. Genetic architecture of inherited retinal disease

Clinical features observed in patients with IRD: (A–C) Phenotype of a STGD patient displaying (A) atrophic maculopathy with yellow retinal flecks in fundus image; (B) low autofluorescence of the fovea with high autofluorescence spots and diffuse hyperfluorescence around the fovea; (C) loss of ellipsoid and interdigitation zone of the fovea in OCT. (D–F) phenotype of a CRD patient displaying (D) normal fundus; (E) slightly reduced autofluorescence of the macula; (F) loss of ellipsoid and interdigitation zone and thinning of the outer layer of retina especially in the macula in OCT. (G–J) phenotype of a CRD patient displaying (G/H) bone spicules pigments and depigmentation of RPE in fundus image; (I/J) loss of ellipsoid and interdigitation zone in the macular region with fovea preservation in OCT. (K–L) phenotype of an Usher patient with (K) fundus image displaying the depigmentation of RPE with salt and pepper pigments in the mid-peripheral retina; (L) audiological test reveals sensorineural deafness.

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TABLE 2

Genes associated with IRD and their proposed functional classes.

No

Recessive

1

Ciliary maintenance and trafficking AHI1, ARL6, BBS1, BBS2, BBS4, BBS5, BBS7, BBS10, BBS12, C8ORF37, CEP290, DFNB31, FAM161A, LCA5, MAK, MKKS, MKS1 MYO7A, OFD1, PTHB1, RPGRIP1, SPATA7, TMEM216, TTC8, TULP1, USH1C, USH1G, USH2A, USH3A, NPHP4, NEK2, SDCCAG8, IFT172, C2orf71, FAM161A, WDPCP, NPHP1, TMEM237, LZTFL1, IQCB1, NPHP3, CEP19, RAB28, CC2D2A, WDR19, PLK4, POC5, BBS9, CSPP1, INVS, CEP78, INPP5E, CEP164, POC1B, IFT81, TTLL5, IFT140, ZNF423, CLUAP1, RPGRIP1L, ARL2BP, KIZ, C21orf2, IFT27

2

3

Dominant

X-linked

RP1, RP1L1, ARL3

RP2, RPGR, OFD1

Phototransduction CNGA1, CNGB1, CNGB3, GRK1, KCNV2, PDE6A, PDE6C, PDE6G, CNGA3, PDE6H, RGS9, OPN1LW, OPN1MW

GNAT1, GUCA1A, GUCA1B, GUCY2D, PDE6B, RHO, SAG, KCNJ13, OPN1SW

Visual cycle ABCA4, CA4, CABP4, LRAT, RBP3, RDH5, RLBP1, RBP4, RDH11

ELOVL4, RDH12, RGR, RPE65, SEMA4A

4

Transcription factors and photoreceptor development EYS, SAMD11, ZNF513, NEUROD1, HMX1, OTX2, NRL, NR2E3, CRX, NR2F1, TUBGCP4 PRDM13, ZNF408, TEAD1

5

Synapse/neurotransmitter CACNA2D4, CLRN1, GPR179, GRM6, LRIT3, SLC24A1, TRPM1

6

7

8

9

10

11

Splicing EXOSC2, DHX38, CWC27, CNNM4

Protein homeostasis and ER DHDDS, TRIM32, EMC1, DHDDS, ATF6, NBAS, TRNT1 ,WFS2, CCT2 ,REEP6 Metabolism PCDH15, PHYH, NMNAT1, POMGNT1, PCYT1A, MTTP, CYP4V2, MVK Photoreceptor disc formation C2ORF71, MERTK, DRAM2

RIMS1, UNC119

CACNA1F

ATXN7, PRPF3, PRPF4, PRPF31, PRPF6, PRPF8, RP9, SNRNP200, TOPORS AIPL1, IMPDH1, KLHL7, MAPKAPK3, WFS1, RCBTB1 ADIPOR1, HK1

FSCN2, PITPNM3, PROM1, PRPH2, ROM1

Extracellular/interphotoreceptor matrix ADAM9, CDH23, CDHR1, CHST6, FBLN5, FBLN6, GPR98, IMPG2, NYX, RS1, IRBP3, CDH3, LAMA1

CRB1, HMCN1, TIMP3, IMPG1, COL11A1, EFEMP1, VCAN

NYX

Mitochondrial pathways SLC25A46, TMEM126A, C12orf65, IDH3B

AFG3L2, MFN2

TIMM8A Continued

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TABLE 2 Genes associated with IRD and their proposed functional classes.—cont’d No

Recessive

Dominant

12

Peroxisomes and lysosomes PEX1, PEX7, PEX2, PHYH, ACBD5, HGSNAT, GNPTG

13

ROS phagocytosis PLA2G5, C2ORF71, MERTK

14

Other pathways/unknown mechanisms FLVCR1, ALMS1, CERKL, USH2B, SLC7A14, GPR125, DTHD1, TLR3, MCDR3, ADGRV1, HARS, COL9A1, RTN4IP1, USH1K, TUB, ASRGL1, ASRGL4, GNB3, USH1H, CIB2, ADAMTS18, RAX2, ARHGEF18

X-linked

SPP2, TREX1, CTNNA1, TSPAN12, CAPN5, BEST1, C1QTNF5, ITM2B, JAG1

Data is from http://www.sph.uth.tmc.edu/retnet/.

FIG. 2 IRD genes and their underlying pathways: the known or putative localization of protein products of IRD genes and their associated pathways within the photoreceptor or RPE cell. *Listed here are representative genes involved in retinal ciliopathy, for an exhaustive list see Table 2.

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Ciliary transport and intracellular trafficking The PR are a highly polarized cell that harbors a distinct structural compartment in the distal half of the cell called the outer segment (OS). The OS comprises a layer of 1000 stacked membranous discs housed on an elaborate microtubule-based cilium called the photoreceptor sensory cilium (PSC) [11]. The PSC comprises the OS axoneme, the connecting cilium (CC), the basal body, and the periciliary ridge complex. Nearly 10% of the OS disc is shed daily and phagocytosed by RPE cells while nascent discs are constantly added at the base of the OS [12,13]. Since the OS is devoid of any protein translation machinery, extensive protein transport takes place from the protein synthesizing inner segment (IS) to the photoreceptive OS via the CC. Consequently, mutations that impact ciliary structure and function directly affects the formation and maintenance of OS thereby causing rapid PR degeneration. Indeed, ciliary genes constitute the largest functional class including 70 IRD associated genes (Table 2). Genes that are functionally unique to PR cilium, cause nonsyndromic retinal ciliopathies (retinal diseases in which the cilia is the primary site of defect) including SPATA7, RPGRIP1, RP1, and RPGR [14–18]. On the other hand, mutations in genes that are essential for the formation/maintenance of the primary cilium can impact other tissues, thus resulting in syndromic ciliopathies, such as CEP290, NPHP4, NPHP1, and SDCCAG8 [19–22]. It is worth noting that the disease severity can vary significantly for patients carrying mutations in genes that are associated with syndromic ciliopathies. For example, hypomorphic alleles in genes involved in the intraflagellar transport (IFT) (e.g., IFT140, IFT81, and IFT172), which function in two complexes (complex A and complex B) to bind and transport ciliary cargo, result in syndromic/nonsyndromic RP depending on the severity of the trafficking defect [23–25]. A secondary transport complex termed the BBSome complex (Bardet-Biedl syndrome complex consisting of BBS1, -2, -4, -5, -7, -8, -9, and -18) serves as an adaptor between the ciliary transport cargo and the IFT complex [26]. Mutations in BBSome subunits often results in a syndromic Bardet-Biedl syndrome (MIM-209900) with a component of IRD [27]. Exceptions are observed with BBS1, BBS2, BBS9, and BBS8, where mutations have been identified in patients with nonsyndromic RP [28–31]. Mutations in CEP290, which supports the structure of the CC (or transition zone in primary cilium), can cause disorders ranging from severe syndromic Senior-Loken syndrome (MIM-266900) to nonsyndromic LCA. RPGR, which in complex with SPATA7 and RPGRIP1 functions in the maintenance of distal CC is associated with 70% of the X-linked-RP (XL-RP) cases [14]. Together the ciliary genes function in the maintenance of OS structure and function, thus their loss can impact the PR function causing a range of phenotypic manifestations from LCA to CRD and RP.

Photoreceptor development PR cell specification and differentiation are controlled by a combination of transcription factors, such as CRX (cone-rod homeobox), NRL (neural retina leucine zipper), and NR2E3 (nuclear receptor subfamily 2, group E, member 3). It is not surprising that mutations in the gene coding of these transcription factors can affect PR development and homeostasis, leading to PR dysfunction and/or death (N ¼ 14, Table 2). Patients harboring mutations in these genes manifest neonatal or early-onset retinal dystrophy. For example, CRX

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mutations cause both early-onset LCA and progressive CRD while mutations in NRL and NR2E3 cause RP with a varying age of onset [32–35]. Although NRL has a rod-specific expression, heterozygous NRL mutations severely affect both rods and cones in affected individuals and function by affecting its phosphorylation [36]. Mutation in NR2E3 causes a progressive form of RD with a characteristic enhanced S-cone syndrome (ESCS, MIM268100) [37]. These defects together stem from vulnerabilities in the regulation of gene expression in PR.

Phototransduction cascade The cascade of biochemical reactions that occurs in the OS in response to light is called the phototransduction pathway (Fig. 3). It begins when light isomerizes the 11-cis-retinal chromophore of rhodopsin to its all-trans isomer that induces a conformational change in Rhodopsin leading to its activation (RHO*). RHO* activates the transducing (Gα*) and its downstream PDE6. Activated PDE6 hydrolyses intracellular cGMP thereby closing the cGMP-gated Na + channels resulting in hyperpolarization and a decline in intracellular calcium. Any aberration in this pathway results in vision loss which in most cases is congenital but varies in progression (see Refs. [38,39] for more details). So far, mutations in 22 genes that are associated with the phototransduction pathway have been identified in IRD patients (Table 2). The most prominent gene in this pathway is RHO that encodes Rhodopsin, mutations in which account for  20%–30% of autosomal dominant RP (adRP) [1,40,41]. Mechanisms underlying rhodopsin mediated adRP range from improper RHO folding (T17M and P23H) to impaired transport of the RHO to the OS (L328, and T342). These defects result in OS disc formation defects and ER stress due to the accumulation of OPSIN protein in the inner segment [42]. PDE6A and FIG. 3 Major molecular components of the vertebrate phototransduction pathway. Proteins underlying the activation phase of the phototransduction cascade in photoreceptor cells are displayed here. The genes functioning in cone cells are illustrated in red and those in rods are illustrated in black.

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PDE6B, key enzymes that maintain the cytoplasmic cGMP concentration in the PR cell, have been associated with autosomal recessive RP (arRP) or in some cases CSNB [43–45]. Similar functions of photoreception in cone cells (Fig. 3) are affected by mutations in five-known autosomal recessive achromatopsia genes, GNAT2, CNGA3, CNGB3, ATF6, PDE6C, and PDE6H, where CNGB3 mutations account for 50%–90% of cases in Northern European descent [46–51].

The visual cycle Photoactivation of rhodopsin and cone opsins causes isomerization of 11-cis-retinal to alltrans-retinal, which is recycled via the visual/retinoid cycle pathway (Fig. 4). Fourteen RD genes are known to affect the visual cycle spanning both PR and RPE cells, such as ABCA4, RDH12, LRAT, and RPE65 (Table 2). ABCA4 (PR-specific ATP-binding cassette transporter) is localized at the rims of the OS discs and functions in the clearance of all-trans-retinal from the discs membranes after rhodopsin activation. Loss of ABCA4 consequently causes an impaired transport of retinoids resulting in the accumulation of toxic agents, which causes the death of RPE and PR cells as observed in patients with STGD [52]. All-trans-retinol is shuttled from PR OS to RPE cells by the interphotoreceptor retinoid-binding protein (IRBP) where it is first converted to its retinyl esters by lecithin retinol acyl transferase (LRAT), before being

FIG. 4 Highlighted here are the key players in the visual cycle—a pathway of enzymatic reactions occurring in the photoreceptor RPE cell that recycle the retinoids that are used during light detection in photoreceptor cells.

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6. Genetic architecture of inherited retinal disease

isomerized to 11-cis-retinol by RPE65 (Fig. 4) [53–56]. Mutations in LRAT and RPE65, thus result in visual cycle defects, causing early-onset LCA or juvenile RP [53,57].

Synaptic transmission defects The PR cells relay the visual signals to the next stage of integration and processing at the outer plexiform layer via synaptic terminals called cone pedicles or rod spherules. Here, changes in membrane potentials are relayed to the bipolar and horizontal cells by the release of glutamate neurotransmitter caused by the influx of calcium via the calcium channels. Ten genes thus far have been implicated in defective synaptic transmission. For example, CACNA1F encodes a subunit of the voltage-gated calcium channels, and thus mutations in this gene cause X-linked CSNB [58,59]. Another Ca2 + channel that causes CSNB is TRPM1, which functions in the light-evoked response of ON bipolar cells [60,61]. These vulnerabilities result in a loss of visual signal without PR degeneration. In some cases, however, synaptic dysfunction can lead to progressive degeneration, for example, in the case of RIMS1 (regulating synaptic membrane exocytosis 1), a synaptic vesicle-associated protein that is involved in exocytosis of the synaptic vesicle. Mutations in RIMS1 cause autosomal dominant CRD due to defects in the neurotransmitter release process [62,63].

Spliceosome complex Pre-mRNA splicing is an essential RNA processing step in which a ribonuclear protein complex called spliceosome cleaves out intronic regions from the nascent transcript to form a mature mRNA. In addition, this process contributes to the overall complexity of the cellular transcriptome by generating transcript isoforms. The retina is more sensitive to dysfunction of the spliceosomal complex due to a combination of high rate of transcription and extensive alternative splicing [64,65]. Indeed, IRD mutations affecting 13 different trans-acting spliceosomal factors have been observed to date. These include six small nuclear ribonucleoprotein particle (snRNP)-specific proteins, two non-snRNP splicing and accessory proteins (PRPF3,-4, -6,-31, CWC27, SNRNP200) [41,66–71]. Most of these genes exhibit dosage sensitivity and dominant mode of inheritance with the exception of CWC27 and DHX38. While PRPF3 and PRPF4 mutations disrupt their associations with the snRNP complex, mutations in PRPF31 destabilize its RNA and SNRNP200 mutations decrease the helicase activity thereby disrupting the splicing machinery by different mechanisms [72,73].

Interphotoreceptor matrix The Interphotoreceptor matrix (IPM) is an organized intercellular structure surrounding the PR cells which extends through the PR layers and subretinal space. Extrapolated from the function of the extracellular matrix, the IPM is hypothesized to be essential for regulating retinoid transport, participating in cytoskeletal organization in surrounding PR cells, and regulation of metabolites and oxygen content for availability to the cell [74]. Genes such

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as IMPG1 and IMPG2, which encode for SPACR (sialoprotein associated with cones and rods), and SPACRCAN (sialoproteoglycan associated with cones and rods) play an important role for stabilizing the IPM scaffold [75,76]. Consequently, mutations in these genes cause a presentation of MD. Another IPM protein IRBP (encoded by RBP3) plays a dual role of transporting 11-cis-retinal through the IPM during the visual cycle while functioning as a fatty acid carrier to maintain the composition of IPM (Fig. 4) [55,77]. Mutations in RBP3 are thus associated with an early-onset presentation of RP. To date, 21 IPM proteins have been identified that cause IRDs when disrupted, including IRBP, IMPG1, IMPG2, and CRB1 (Table 2). Whether these defects stem from vulnerabilities of the IPM or the toxic accumulation of these insoluble proteins in the protein synthesizing PR is currently under investigation.

Genetic heterogeneity in IRD Relentless efforts over the past three decades have resulted in the identification of > 260 IRD genes that accounts for the molecular diagnosis of approximately 70% of cases for Usher syndrome, 75% for LCA, 66% for STGD, and 60% for RP [78], thus highlighting the genotypic multiplicity and phenotypic heterogeneity in IRD. This heterogeneity in the genetic architecture is documented in the following.

Genetic heterogeneity in monogenic IRDs The vast majority of IRD occurs due to mutations in a single gene. However, the number of genes that have been associated with each type of IRD varies tremendously. On one side of the spectrum is retinal diseases that are predominantly caused by mutations in one or few genes, such as ornithine aminotransferase (OAT) for gyrate atrophy (MIM-258870), PRPH2 and BEST1 for Vitelliform MD (MIM-153700), CHM for Choroideremia (MIM-303100), or ABCA4 for STGD [4,52,79–81]. In contrast, RP, the most genetically heterogenous IRD, has been linked with 61 causative genes. The first causative gene of RP, the rhodopsin gene (RHO), was discovered in 1990 in an adRP family [40]. Extensive research efforts over the next few decades led to the identification of 22 genes inherited as autosomal dominant (20%–25%) of which mutations in RHO, RP1, PRPF31, and PRPH2, account for approximately 25%–30%, 5%–10%, 8%–10%, and 5%–10% of all adRP cases, respectively [82–84]. In arRP, 40 disease genes have been identified thus far. Mutations in these genes are rare and most of these genes individually account for only 1% of RP cases except for RPE65, PDE6A, PDE6B, and RP25, which contribute to about 5% of cases [83]. Mutations in OFD1, RP2, and RPGR cause XL-RP, characterized by a severe rod cell degeneration. OFD1 accounts for rare cases of XL-RP while RPGR and RP2 account for approximately 80% and 20% of cases, respectively [85,86]. As a consequence of high genetic heterogeneity of IRD, it is often not possible to pinpoint the underlying mutations based on clinical phenotype alone. Indeed, even for LCA, which is the most severe presentation in IRD and has a relatively similar clinical phenotype, is associated with more than 400 mutations across 23 genes, underscoring the

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importance of including mutation screening in the clinical diagnosis (https://sph.uth.edu/ retnet/home.htm) [87].

Allelic heterogeneity in IRD Multiple levels of allelic heterogeneity have been observed in IRD; first, it is often the case that multiple pathogenic alleles are identified in each IRD associated gene. For example, RHO alone has more than 100 different mutations documented with the most common being p.P23H that accounts for 10% of adRP in the American population [42]. A mutational hotspot also occurs in RHO at C-terminal-end codon 347 (with a CpG dinucleotide) with five diseasecausing sequence variations identified at this locus; Second, different alleles in the same gene can lead to different or overlapping clinical phenotype. For example, over 1000 pathogenic mutations in ABCA4 have been reported with varying severity of IRD presentations, including STGD, CRD, and RP [88]. Comparison of these alleles suggests that the severe loss-offunction allele is more likely to result in STGD while weaker alleles often lead to less severe phenotype such as RP. On the other hand, CEP290 has >100 unique mutations identified (including numerous truncating, 3 missense, and 20 splice-site mutations) yet, no clear genotype-phenotype correlations have been established [89]. This is often observed for many different IRD genes, where it is likely that clinical severity and phenotypic presentation of a patient with mutations in IRD genes results from a combination of varying severity of pathogenic allele and genetic and/or environmental modifying factors. Patients with the same or similar allele can present divergent clinical phenotypes. For example, SPATA7, CWC27, IFT140, RPE65, IMPDH1 mutations cause both severe early-onset LCA and milder phenotype such as RP; GNAT1, RHO, PDE6B, RLBP1 cause RP and CSNB; and RPGR, RPGRIP, and SEMA4A cause CRD and RP which renders extrapolation of phenotype from molecular diagnosis alone challenging (https://sph.uth.edu/retnet/home.htm). Although most of the pathogenic mutations are private, founder mutations that cause IRD have been identified. In geographically or culturally isolated population, genetic drifting can lead to a higher allele frequency of an ancestral allele that is passed down in the population. This phenomenon is termed the founder effect and has been reported for multiple IRD genes. For example, a geographically isolated population of Newfoundland underwent a genetic bottleneck effect in the 17th century due to a destructive typhoon. Today, it has been observed that 4%–10% of the patients in this population suffer from CSNB due to founder alleles in two genes CNGA3 (p.R283Q, p.R427C, and p.L527R) and CNGB3 (p.T383fsX and p.T296YfsX9) [90]. In the culturally isolated population of Ashkenazi Jews, MAK and DHDDS mutations were observed in 25.7% and 8.6% of arRP patients, compared to 2.1% and 0.8%, respectively, for patients with mixed ethnicity [91]. Other genes displaying a founder effect are the TIMP-3 Ser181Cys mutation causing Sorsby fundus dystrophy in patients of British origin [92]; the PRPH2 allele c.828 + 3A > T splice-site mutation causing adRP in the United States [93]; c.375C > G p. (Cys125Trp) in the CERKL gene which accounts for 18% of the RD patients in Finland [94]; and the most common pathogenic mutation identified in 55%–77% of patients with the CEP290-related disease—a deep intronic variant c.2991 + 1655A > G [95]. Identification of such founder effects is valuable since they reduce genetic testing to candidates thereby making it cost effective, simplify molecular diagnosis and genetic counseling and most importantly identify an ideal population for specific gene/allele-specific treatments and trials. III. Mendelian disorders and high penetrant mutations

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Incomplete penetrance in RP11 In addition to allelic heterogeneity, variation in phenotype expressivity is also observed in IRD. One of the most frequently observed cases is for families with mutations in PRPF31. Following autosomal dominant inheritance, asymptomatic carriers are often observed in the affected families [82]. Since 90% of PRPF31 mutations are null, incomplete penetrance is hypothesized to result from variable haploinsufficiency due to the existence of differentially expressed PRPF31 alleles. This is in part due to cis-acting factors in close proximity to PRPF31 including copy-number variations (CNVs) at a nearby MSR1 element and a trans-acting CNTOF (a repressor protein) [96]. These genetic factors make the expression of PRPF31 a polygenic trait, consequently resulting in variable expressivity and nonpenetrance in some carriers. Other IRD gene variants displaying incomplete penetrance are p.S86W variant in RHO, p.R172W variant in PRPH2 and variants in PRPF8 [97–99].

Mutation spectrum of IRD Despite the discovery of >250 genes, 30%–40% of the IRD cases remain unsolved. This missing heritability can arise from (1) genes that remain to be associated with IRDs, or (2) novel genetic mechanisms beyond what is classically expected for a monogenic Mendelian disease, such as regulatory and noncoding RNA (ncRNA) mutation, CNVs, canonical and noncanonical splice-site variants, and mutations in alternative retina-specific exons. In the past 5 years, many such mutations have been identified as discussed in the following.

Regulatory and noncoding variants ncRNA elements play a crucial role in the development and physiology of the retina. ncRNAs such as microRNA (miRNA) and long noncoding RNA (lncRNA) are expressed and function in retina and RPE cells (miR-96, miR-182, miR-183, and miR204) [100–102]. Consequently, variation in these noncoding elements has been recently associated with IRD, where mutations in the seed region of miRNA-204 (n.37C > T) segregate in affected members of adRP and coloboma [103]. In addition, inactivation of DICER1, an RNase III endonuclease that functions in the production of mature miRNA, has been implicated in retinal degeneration highlighting the importance of ncRNA elements in the retina [104,105]. Mutations affecting the promoter are also observed in IRDs. Deletion of LCR (locus control region), the promoter of red cone opsin (OPN1LW) and green cone opsin (OPN1MW) is a common mechanism of BCM, where only blue cones are functional in the patients [106]. Mutation in the promoter sequence of CHM has also been recently identified to cause Choroideremia [107]. These promoter mutations can change or abolish the binding capacity of cis-elements with the trans-acting protein factors thereby affecting gene expression.

Large DNA duplication and deletion in IRD Large DNA fragment CNVs (>1 kb) are recently being appreciated as an important class of mutations that contributes to the missing genetic heritability in IRD [108]. Identification III. Mendelian disorders and high penetrant mutations

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of CNVs requires different identification strategies including multiplex ligation-dependent probe amplification, SNP or tiling arrays, and more recently NGS-based approach [109–111]. To date, 1345 CNVs have been identified in 81 different genes from 300 different studies with the majority of these studies carried out in the past decade [112]. CNVs are often observed in genes spanning large genomic regions. For example, EYS, PCDH15, and USH2A, which are the largest IRD genes, also have the most number of reported CNV cases. Another factor that makes a gene susceptible to CNVs is the presence of repeat elements including Alu repeats, long interspersed nuclear elements (LINE), long terminal repeat elements (LTR), and segmental duplications (SDs). For example, CLN3 and NPHP1 which are enriched for Alu repeats and structural duplications have high total CNV counts (515 and 75, respectively) [112]. A recent study of overlap between NAHR (genomic recombination between paralogous repeat sequences) region in the genome and IRD genes has shown that 35 IRD genes are likely to be affected by NAHR, 9 of which have large indels reportedly affecting some exons of the gene while 13 are reported with CNVs affecting the entire gene (NPHP1, CA4, BBS4, CIB2, CDHR1, RGR, and CNNM4 to name a few). These CNVs are pathogenic for haplodeficiency genes such as OPA1 and PRPF31 [108]. Further investigation of the contribution of CNVs to IRD and the inclusion of CNV assessment to increase the solving rate of IRDs has been proposed.

Splice-site and alternative transcript variants Mis-regulation of splicing is a common feature of many human diseases, including several retinal diseases. Splicing defects stem from either mutation in the trans-regulatory splicing factors (Table 2) or mutations in the cis-regulatory splice site. Splice-site mutations disrupt the canonical GU-AG splice-site sequence and cause exon skipping, intron inclusion, novel exon inclusion, or the usage of cryptic upstream/downstream splice sites. For example a G > T substitution in the canonical splice-codon of intron 16 in the MERTK gene disrupts the donor splice site resulting in loss of exon 16. This leads to a downstream frameshift and premature translation termination which disrupts the kinase domain of the protein thereby causing arRP in the patients [113]. The effect on splicing of variants at positions other than the canonical sites can vary depending on the nature of the substitution which can change the strength of the consensus sequence recognized by the splicing machinery. In LCA, for example, the most frequently mutated allele in CEP290 is c.2991 + 1655A > G, which introduces a 128 bp cryptic exon into the CEP290 mRNA, creating a premature-termination codon immediately downstream of exon 26 (p.Cys998*) [95]. Recently, several deep intronic variants in ABCA4 have been reported contributing to the underlying genetics of autosomal recessive STGD1 [114,115]. Such variants, for example, c.4539 + 2001G > A and c.4539 + 2028C > T cause insertion of a cryptic exon, resulting in a downstream frameshift in the protein [116]. Other mechanisms include generation of a hypomorphic allele due to an in-frame insertion or deletion caused by a splice variant. For example, a splice-site mutation observed in syndromic ciliopathy gene BBS8 results in the deletion of the PR-specific alternatively spliced exon 2a (in-frame) [31]. This splice-site allele resulted in RP without syndromic phenotypes. Tissue-specific alternative splicing is an important mechanism for providing and regulating protein diversity in a tissue. Alternative splicing can result from alternative 5’ or 3’ splice

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References

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site usage, exon skipping, or intron retention etc., which results in vastly different protein products transcribed from the same gene. The most well known alternatively spliced gene in the retina is retinitis pigmentosa GTPase regulator (RPGR), a CC-specific protein in PR. RPGR is well documented to undergo an alternative splicing, producing two main transcripts: RPGR(Ex1–19) which is expressed across all cells, and a retina-specific RPGRORF15 transcript, in which a unique C-terminal exon called ORF15 is included [15,117]. It has been estimated that mutations in the ORF15 account for 80% of the alleles in the RPGR/RP3 gene. These mutations occur due to the presence of a highly repetitive domain in the ORF15 which serves as a mutational hotspot [118]. These splice-site and alternative exon mutations highlight a mechanism whereby universally expressed genes result in tissue-specific phenotype in this case of IRDs.

Conclusion In the past couple of decades, remarkable progress in the discovery of the genetic and molecular underpinnings of IRD has facilitated a thorough understanding of the genetic architecture of this heterogeneous disease. This has resulted in an improved molecular diagnosis for IRD patients which informs genetic counseling and aids in re-diagnosis in case of underlying syndromic genes. Improved molecular diagnosis coupled with the better prospects of therapy including the recently approved (RPE65) and ongoing (ABCA4, CHM, MYO7A, etc.) trials for gene therapy has initiated a new era in IRD diagnosis, and therapy [119]. Significant gaps exist, however, firstly, in discovering the genetic factors underlying unresolved IRD cases and secondly, in uncovering the pathophysiology of numerous gene classes impacted in IRD. Given the recent success of gene-based therapeutic options in IRDs, addressing these gaps has become an endeavor of the highest priority.

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III. Mendelian disorders and high penetrant mutations

C H A P T E R

7 Early-onset glaucoma Carly J. van der Heidea,b,c, Matthew A Millera, John H. Fingerta,b a

Department of Ophthalmology and Visual Sciences, Carver College of Medicine, University of Iowa, Iowa City, IA, United States bInstitute for Vision Research, University of Iowa, Iowa City, IA, United States cDepartment of Molecular Physiology and Biophysics, Carver College of Medicine, University of Iowa, Iowa City, IA, United States

Glaucoma is a disease of the optic nerve that results in progressive loss of vision. It is the leading cause of irreversible vision loss and may affect up to 80 million people by 2020 [1]. The core features of glaucoma are (1) characteristic optic nerve damage (cupping) and (2) corresponding topographic patterns of visual field loss. High intraocular pressure (IOP) is a risk factor for development of glaucoma; however, glaucoma can occur at any IOP. Glaucoma that occurs with lower IOP (i.e., 21 mmHg) has been termed normal tension glaucoma (NTG). IOP is a key factor in glaucoma and is determined by the rates of the aqueous humor flowing in and out of the eye. Angle-closure glaucoma occurs when aqueous outflow through the iridocorneal angle is visibly obstructed by apposition of the cornea and iris. Conversely, primary open-angle glaucoma (POAG) occurs with no visible obstruction for aqueous humor to drain from the eye. The majority of open-angle glaucoma patients have adult-onset disease, which has an onset after 40 years of age. However, glaucoma may also occur earlier in life, though at a lower frequency [2]. Glaucoma that occurs before 3 years of age has been termed congenital glaucoma. The genetic bases of congenital and adult-onset glaucoma have been recently reviewed elsewhere [3, 4]. This chapter focuses on early-onset glaucoma that occurs between 3 and 40 years of age, which may also be called juvenile-onset open angle glaucoma (JOAG) [5, 6, 6a].

Clinical features of JOAG Epidemiology of JOAG JOAG is a subset of POAG with early onset and consequently there are numerous clinical similarities between JOAG and adult-onset POAG. Both categories of glaucoma occur with Genetics and Genomics of Eye Disease https://doi.org/10.1016/B978-0-12-816222-4.00007-1

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unobstructed (open) drainage pathways and the core features of glaucoma (optic disc cupping and corresponding visual field defects). JOAG is rare and has a prevalence that has been estimated to be approximately 1/50,000–1/300,000 [6].

Age of onset JOAG by definition occurs after the age range for congenital glaucoma (0–3 years of age) and before the age range for POAG (40 years of age). However, some have also designated glaucoma with onset between 10 and 35 years of age as JOAG [5].

Myopia Many patients with JOAG also have myopia. In a study of 42 incident cases of JOAG, 54% of patients were myopic [7]. Similarly, a study of large autosomal dominant JOAG pedigrees detected myopia in up to 87% of patients with glaucoma [8]. Additionally, high myopia (more than 6 diopters of myopia) has been reported in 38.5% of JOAG patients [5].

Intraocular pressure The IOP exhibited by patients may be 21 mmHg (consistent with NTG), moderately elevated (>21 mmHg), or markedly high (≫ 21 mmHg). However, markedly high IOPs are often present in JOAG. Pressures may reach over 50 mmHg in many cases [7, 9, 10].

Response to therapy Topical aqueous suppressants (beta-blockers, alpha-agonists, and carbonic anhydrase inhibitors) and topical medicines that promote aqueous outflow (prostaglandin analogues and cholinergics) may be effective in lowering IOP in JOAG patients. The efficacy of newer glaucoma agents, that is, netarsudil (Rhopressa) has not been studied in JOAG patients. While topical glaucoma medications may lower IOP in JOAG patients, many patients have markedly high intraocular pressures (i.e., >50 mmHg) and require surgery for adequate pressure control [6, 8, 9].

Optic disc morphology The features of the optic discs of patients with JOAG have been compared to patients with glaucoma onset after 40 years of age (POAG). In a study of 37 JOAG patients and 382 POAG patients, Jonas and coworkers showed that on average, JOAG patients had deeper optic cups with steeper cup margins than POAG patients [11]. In another analysis, Gupta and coworkers similarly determined that the optic cups of patients with JOAG are deeper than optic cups observed in other types of glaucoma [12].

III. Mendelian disorders and high penetrant mutations

MYOC and JOAG

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Inheritance pattern Patients with JOAG often have a positive family history of glaucoma, with reports ranging from 24% to 43% [7, 13]. Moreover, many pedigrees have been described in which JOAG is transmitted from generation to generation in a pattern consistent with autosomal dominant inheritance. The combination of early onset of disease and autosomal dominant inheritance pattern frequently results in large numbers of family members with glaucoma in a single pedigree. Moreover, autosomal dominant inheritance (a Mendelian pattern) suggests that a single gene might be primarily responsible for the glaucoma that runs in a particular JOAG pedigree. Numerous large JOAG pedigrees with autosomal dominant inheritance have been described, many of which have been shown to have glaucoma due to mutations in either the myocilin (MYOC), optineurin (OPTN), or the TANK-binding kinase 1 (TBK1) genes as described in subsequent sections.

MYOC and JOAG Mutations in MYOC are the most common molecularly defined cause of both adult-onset POAG and JOAG. Research that led to the discovery of MYOC’s role in glaucoma pathogenesis began with investigations of large pedigrees with JOAG. Pedigree-based studies of several, large autosomal dominant JOAG families mapped the first glaucoma-causing gene to the chromosome 1q locus (GLC1A) [14–16]. Subsequent investigations of these “linked” pedigrees identified several disease-causing mutations in the MYOC gene which is located within the GLC1A locus. One set of MYOC mutations is responsible for 4%–60% of JOAG cases, while a different set of MYOC mutations has been detected in 3%–5% of patients with adult-onset POAG in cohorts around the world [10, 17–21].

MYOC-associated glaucoma clinical phenotype (JOAG) Some of the large JOAG pedigrees used to locate and identify MYOC as a glaucomacausing gene were found to carry GLY364VAL, THR377MET, or TYR437HIS mutations [10, 21]. Other commonly detected MYOC mutations in JOAG patients and pedigrees include PRO370LEU, ILE477ASN, and ASN480LYS [18, 20, 22, 23]. Many of these mutations have been detected in sufficiently large number of patients to establish mutation-specific clinical features. The mean age at onset and mean maximum IOP for several MYOC mutations associated with JOAG are shown in Table 1 (data collected from www.myocilin.com). One mutation, PRO370LEU, is remarkable for being associated with glaucoma that has an especially early age at diagnosis, with a mean of 10–13 years of age [18, 20]. Another MYOC mutation, ILE477ASN [10, 22], was discovered in a JOAG pedigree of immense size that was first described by WH Stokes in 1940 [24]. Patients in the Stokes pedigree that were included in genetic studies had an average age at diagnosis of 18–21 years and a mean maximum IOP of 38–40 mmHg [10, 22]. Several very large French JOAG pedigrees that included 71 family members with glaucoma were found to carry a ASN480LYS mutation that originated from a common ancestor. Patients with an ASN480LYS mutation had an early age at diagnosis, with 75% of mutation carriers being diagnosed with glaucoma by 32 years of age [23]. Finally,

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TABLE 1 MYOC mutations associated with JOAG. Amino acid change

Nucleotide change

Mean age at diagnosis (years)

Mean maximum IOP (mmHg)

Number of individuals described

GLY364VAL

1091G > T

34a

36a

22

THR377MET

1130C > T

21.4  5

16.8  3.7

TYR437HIS

1309 T > C

a

19.9

a

43.7

35

PRO370LEU

1109C > T

13.3  2.4

30.9  3

137

ILE477ASN

1430 T > A

20.7  1.3

39.6  1.9

67

ASN480LYS

1440C > A

31.6  4.1

7.4  2.9

157

ASN480LYS

1440C > G

25.4  3.1

27.4  11.2

6

145

a

Standard deviation is not available. The mean age at diagnosis and mean maximum IOP is listed for individuals having different MYOC mutations. Data was obtained from www.myocilin.com.

patients with a TYR437HIS mutation were found to have an average age at diagnosis of 20 years and a mean maximum IOP of 44 mmHg [10]. Medical management of JOAG associated with MYOC mutations may be challenging. The clinical phenotype of family members from several branches of the Stokes’ JOAG pedigree has been reported. These JOAG patients with the ILE477ASN MYOC mutation were noted to have only temporary success at controlling IOP using topical medications. Surgery at an early age was usually required [9, 25]. More than half of the patients with glaucoma due to a THR377MET mutation required filtration surgery [26]. Similar reports have suggested that patients with other JOAG-associated MYOC mutations also typically require surgery for adequate IOP control [10, 20, 25]. However, it is important to note that some failures of medical management in these JOAG patients may be due in part to the limited options for topical medical treatment at the time. Many of these reports occurred before the advent of topical prostaglandin analogs and other powerful topical therapies.

MYOC-associated glaucoma clinical phenotype (POAG) One MYOC mutation, GLN368STOP has been routinely detected in typical POAG patients with an age of onset over 40 years of age [10, 21]. As many as 1.6% of POAG cases may be due to the GLN368STOP mutation [17], which has been detected in patient populations from Northern Europe, Canada, the United States of America, and Australia [17, 18, 27]. Consequently, glaucoma caused by the GLN368STOP mutation is the most common, molecularly defined cause of POAG. The mean age at diagnosis for GLN368STOP-associated POAG is 52–59 years and the mean maximum IOP is 28–30 mmHg [10, 27]. POAG caused by the GLN368STOP mutation has autosomal dominant inheritance. However, due to the later age at diagnosis, pedigrees with this mutation tend to have fewer living family members with glaucoma spread across fewer generations than have been observed in pedigrees with glaucoma caused by other MYOC mutations. Patients with POAG caused by the GLN368STOP mutation typically have

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a clinical course that is indistinguishable from that of other POAG patients with no known mutations. A case-control study by Graul et al. compared 18 patients with GLN368STOPassociated POAG (cases) and 36 patients with POAG and no known mutations (controls). The cases and controls had remarkably similar clinical features. No difference in age at diagnosis; maximum IOP; number of glaucoma medications; or frequency of glaucoma surgeries was detected between POAG patients with a GLN368STOP mutation and POAG patients with no known mutations [28].

MYOC pathophysiology Although the normal function of MYOC remains unknown, much has been discovered about how mutations in this gene may lead to the development of glaucoma. MYOC encodes a protein that is secreted by trabecular meshwork cells into the aqueous humor [29–33]. Secretion of MYOC from the trabecular meshwork and the protein’s concentration in aqueous humor is reduced in patients with glaucoma-associated MYOC mutations [32]. This leads to an intracellular accumulation of mutant MYOC protein in trabecular meshwork cells, which may be toxic to the cells and compromise their function, damage the outflow pathway, and ultimately lead to elevated IOP and glaucoma [34]. It has been hypothesized that the molecular basis for decreased secretion of the protein is that MYOC mutations cause production of abnormal, misfolded proteins that accumulate in the endoplasmic reticulum of trabecular meshwork cells [35]. Transgenic mice have been engineered to have a glaucoma-causing MYOC mutation in their genome. These mice not only recapitulate clinical features of human glaucoma by developing high intraocular pressure and optic nerve damage, but the mice also mimic molecular features of MYOC-associated glaucoma. Secretion of MYOC protein is reduced and mutant MYOC protein is retained within trabecular meshwork cells of these mice [35]. Finally, analysis of a donor eye from a patient with JOAG caused by a TYR437HIS MYOC mutation showed increased accumulation of intracellular MYOC protein in trabecular meshwork cells. The intracellular MYOC protein was further localized within the ER [36]. These studies of human donor tissue provide more evidence that abnormal retention of MYOC protein in trabecular meshwork cells has a central role in the pathophysiology of MYOC-related glaucoma. Intracellular aggregates of MYOC protein are a target for new potential glaucoma therapies. 4-phenylbutyrate is a chemical chaperone that was investigated for its ability to help mutant MYOC protein fold properly, restore secretion, and prevent glaucoma caused by a MYOC mutation. Both oral and topical administration of 4-phenylbutyrate eliminated intracellular accumulation of MYOC protein, lowered IOP, and prevented optic nerve damage in transgenic mice [35, 37]. While 4-phenylbutyrate has not yet been tested in human glaucoma patients, this drug has the potential to provide a molecularly-targeted intervention for some patients with MYOC-associated glaucoma, who may be resistant to currently available medical therapy.

Case report (MYOC-associated JOAG) G.F. was diagnosed with JOAG at age 20. At presentation G.F.’s visual acuity was 20/20 OD and 20/20 OS with spectacle correction of 2.75 + 0.75  165 OD and 4.75 + 2.00  60 OS. His IOP was 36 mmHg OD and 38 mmHg OS and gonioscopy revealed wide open angles.

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FIG. 1 Glaucoma associated with a GLY367ARG MYOC mutation. At 20 years of age, a patient with severely elevated intraocular pressure and asymmetric optic discs was diagnosed with JOAG. Genetic testing later revealed a GLY367ARG mutation in the MYOC gene. Disc photos taken at age 20 showed moderate optic disc cupping (A—right optic disc and B—left optic disc) with some superior thinning of the neural rim in the left optic disc (B). Goldmann visual field testing revealed full visual fields in the right eye (C) and an inferior nasal defect in the left visual field (D).

At the time of his diagnosis, G.F. had asymmetric optic discs with a larger left optic cup (Fig. 1B) than right optic cup (Fig. 1A). Goldmann perimetry revealed an essentially intact right visual field (Fig. 1C) and an infero-nasal depression in his left field (Fig. 1D). G.F.’s mother and sister also had severe high-tension glaucoma. Medical therapy including topical timolol and dipivefrin was initially effective at lowering G.F’s IOP. However, after one year his IOP increased to 42 mmHg OD and 40 mmHg OS and did not respond to additional therapies including oral methazolamide and laser trabeculoplasty. G.F. was treated surgically with trabeculectomy in both eyes with subsequent good pressure control.

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Genetic testing identified a glaucoma-causing MYOC mutation, GLY367ARG, which has been previously associated with severe JOAG. The GLY367ARG mutation has been detected in JOAG patients from Northern Europe, India, and Japan and is associated with an average age at diagnosis of 25–39 years, maximum IOP of 30–53 mmHg, and the need for (like G.F.) surgery to achieve adequate IOP control [38–43].

OPTN and juvenile-onset open-angle glaucoma In contrast to MYOC mutations that typically cause glaucoma with high IOP, mutations in the OPTN gene are a leading Mendelian (single-gene) cause of NTG [44], a subtype of POAG that has been defined to occur with maximum IOP  21 mmHg. OPTN mutations are most common in familial cases of NTG with autosomal dominant inheritance, which usually manifest by the age of 40 [45, 46]. The most studied OPTN mutation, GLU50LYS, has been detected in 1%–2% of cases of NTG in several large case-control studies [44, 46–48]. The GLU50LYS mutation is responsible for an even larger proportion of NTG cases that have a strong family history of glaucoma. This mutation has been detected in 3.5%–13.5% of NTG pedigrees with autosomal dominant inheritance [44, 45, 48]. The GLU50LYS mutation has been frequently identified in several Caucasian patient populations, but has only rarely been observed in studies of Asian (Japanese) patients [48–54]. While the GLU50LYS mutation in OPTN has been firmly established as a cause of NTG, other mutations in this gene have only been rarely observed and have less certain roles in the pathogenesis of glaucoma. Some of these rare, potentially pathogenic OPTN mutations include HIS26ASP, c.691_692insAG, and ARG545GLN [44, 51]. At present these mutations are of unknown clinical significance pending future larger population studies and/or investigations of their functional effects. Common DNA sequence variations have also been detected in the OPTN gene. One missense mutation, MET98LYS, is frequently observed in both NTG patients and in normal subjects. There are conflicting reports of whether the MET98LYS mutation is statistically more common in NTG patients than in controls [44, 46–48]. In some (but not all) studies of Japanese cohorts, the MET98LYS mutation has been detected at a statistically higher frequency in NTG patients than in controls [48, 50–53, 55] suggesting that this mutation might contribute risk for developing glaucoma in patients of Japanese ancestry. Cohorts of POAG patients (i.e., with maximum IOP > 21 mmHg) have also been tested for OPTN mutations and no statistical associations with POAG have been detected. A few instances of OPTN mutations have been identified in some POAG patients, however, the clinical significance of these rare OPTN variants (i.e., HIS486ARG and LEU41LEU) remains unknown [48, 56–58].

OPTN-associated glaucoma clinical phenotype The GLU50LYS mutation was originally associated with NTG in a large pedigree of 15 affected individuals diagnosed between 23 and 65 years of age (mean of 44 years). The majority of these NTG patients had a maximum IOP  21 mmHg and all developed visual field loss

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[44, 59]. A subsequent case-control study compared 11 cases of NTG patients with a GLU50LYS mutation (nine in two families and two sporadic cases) and 87 patients without this mutation. NTG patients that carried the GLU50LYS mutation had a younger age of onset (mean of 40.8 years) and presented with more advanced optic nerve damage than those without the mutation. NTG patients with OPTN GLU50LYS were also more likely to require filtration surgery (72.7%) than patients with no mutation (25.3%) [45]. These data suggest that clinical features of glaucoma may be more severe in patients with NTG due to an OPTN GLU50LYS mutation.

Function of OPTN OPTN is a 67 kDa cytosolic protein that is a major participant in many biological processes important for cell homeostasis, such as vesicle trafficking, structural maintenance of the Golgi complex, regulation of the NF-ΚB transcription factor, antibacterial and antiviral signaling, and removal of aggregated and damaged proteins and organelles via autophagy [60, 61]. OPTN is expressed in many tissues, including heart, brain, liver, kidney, and skeletal muscle [62]. In the eye, OPTN is found in the cornea, lens, sclera, choroid, and retina, with high expression in retinal ganglion cells [63, 64]. Based on its protein sequence, OPTN has numerous putative functional domains including an ubiquitin-binding domain, an LC3-interacting region, coiled-coil domains, a NEMO-like domain, and a zinc-finger domain. Moreover, many OPTN-binding partners have been described (Rab8, myosin VI, huntingtin, metabotropic glutamate receptor, transferrin receptor, transcription factor IIIA, serine/threonine kinase receptor-interacting protein 1, LC3, and TBK1) [65]. Recent studies, however, have highlighted OPTN as an autophagy receptor and have stressed the importance of OPTN-binding domains for LC3 and for ubiquitin [66]. LC3 and TBK1 are two key proteins involved in autophagy, and they both interact with OPTN to facilitate autophagic clearance of intracellular pathogens and damaged cellular components. LC3 is an autophagy protein localized to autophagosomal membranes and binds to autophagy receptors to deliver selective cargo to autophagosomes. OPTN acts as an autophagy receptor through its LC3-interacting motif and ubiquitin-binding domain to link ubiquitin-tagged targets with LC3 protein in forming autophagosomes [66]. OPTN’s role as an autophagy receptor has another link with glaucoma pathogenesis. OPTN-mediated autophagy is promoted by TBK1, which phosphorylates OPTN at serine177 to increase its binding affinity to LC3 [66]. Moreover, the OPTN GLU50LYS mutation enhances the binding affinity between TBK1 and OPTN [67, 68]. Like OPTN, the TBK1 gene has been identified as a Mendelian cause of familial NTG [69, 70] (discussed in the following section) and has also been associated with amyotrophic lateral sclerosis (ALS) [71]. Current studies are exploring whether neurodegeneration caused by OPTN and TBK1 is mediated by the functions of these genes in autophagy.

Association between OPTN and ALS While one set of OPTN mutations (i.e., GLU50LYS) is associated with glaucoma, a different set of OPTN mutations has been associated with ALS, a rare degenerative disease of motor neurons in the spinal cord [72]. The mutations that confer risk for ALS appear to be III. Mendelian disorders and high penetrant mutations

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loss-of-function mutations that interfere with the function of OPTN’s ubiquitin-binding domain or its LC3-binding domain. Similar observations have been made for mutations in TBK1, the other Mendelian gene for NTG (described in the following section). One set of TBK1 mutations (gene duplications or triplications) have been associated with glaucoma, while loss-offunction mutations have been associated with ALS [71, 73]. The association of OPTN and TBK1 with neurodegeneration in glaucoma and ALS as well as a growing body of evidence from in vitro and in vivo experiments suggest that these genes may have neuroprotective effects that are compromised by disease-causing mutations.

Effects of OPTN mutations Many in vitro studies have found that expression of glaucoma-associated OPTN mutations in retinal cells increases cell death [74–77] and dramatically alters key homeostatic pathways including NF-ΚB signaling [78], endocytic trafficking [77, 79–82], and autophagy [76, 77, 83]. There is some evidence that the OPTN GLU50LYS and MET98LYS mutations promote retinal cell death through aberrant functioning of OPTN in autophagy. Expression of either OPTN GLU50LYS or MET98LYS in retinal cells causes increased cell death that is dependent on LC3 binding to OPTN [76, 77]. OPTN GLU50LYS and MET98LYS mutant retinal cells also have higher levels of LC3 protein, indicating a greater abundance of autophagosomes [76, 83–85]. Moreover, Sirohi et al. showed that autophagosome formation and cell death induced by OPTN MET98LYS are dependent on its phosphorylation at serine-177 by TBK1 [85]. Similar results have been reported from in vivo experiments. Retinal ganglion cells of transgenic mice with a GLU50LYS mutation (discussed in the following section) exhibited elevated LC3 expression and an increased number of autophagosome-like structures visible with transmission electron microscopy [83, 84]. OPTN has several specific roles in autophagy that could be influenced by disease-causing mutations to contribute to glaucoma pathogenesis. OPTN functions in recruitment of polyubiquitinated cargoes to autophagosomes, ubiquitin-independent clearance of protein aggregates, and induction of mitophagy, a selective type of autophagy whereby autophagy receptors are recruited to damaged mitochondria that have been targeted for degradation [86]. Recently OPTN was identified as one of the primary autophagy receptors that induce mitophagy [87–89]. In vitro and in vivo experiments by Shim et al. showed that the NTGassociated OPTN GLU50LYS mutation may stimulate mitochondrial fission and increase mitophagy in retinal ganglion cells [84]. Conversely, the ALS-associated OPTN GLU478GLY mutation appears to attenuate mitophagy by impairing OPTN binding to damaged mitochondria [87]. Thus, aberrant mitophagy is a potential pathological mechanism in common between two OPTN-associated neurodegenerative diseases, NTG and ALS. Moreover, OPTN-mediated clearance of damaged mitochondria is dependent on its phosphorylation by TBK1 (discussed in subsequent sections), another gene implicated in both NTG and ALS. Further study is needed to explore how OPTN and TBK1 may alter mitophagy and lead to neurodegeneration.

Transgenic mouse models of OPTN-associated glaucoma Multiple strains of transgenic mice have been generated to study the effects OPTN mutations on retinal ganglion cells in vivo. The first reported transgenic mouse model was created III. Mendelian disorders and high penetrant mutations

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by overexpressing the mouse Optn gene with the GLU50LYS mutation under the CAG promoter [90]. Heterozygous Optn GLU50LYS transgenic mice have normal intraocular pressure (15  1 mmHg) but exhibit a 25% loss of retinal ganglion cells at 16 months of age compared to control mice overexpressing wild-type Optn. However, GLU50LYS transgenic mice also have a 28% decrease in total retinal thickness caused by markedly increased apoptosis of non-RGCs throughout all retinal layers, a retinal phenotype that is not a feature of glaucoma. This panretinal degeneration might be due to toxicity from global overexpression of Optn GLU50LYS protein in the Optn GLU50LYS transgenic mice. Other analyses of these transgenic mice have suggested the presence of aberrant autophagy in their retinal ganglion cells, perhaps due to the GLU50LYS mutation. For example, Shen et al. detected increased Optn and LC3 immunoreactivity in retinal ganglion cells of Optn GLU50LYS transgenic mice and increased autophagosome-like structures in retinal ganglion cells [83]. Mitochondrial abnormalities have also been observed in these mice, suggesting that mitophagy may be influenced by the Optn GLU50LYS mutation. Transmission electron microscopy of retinal ganglion cell axons of aged transgenic mice with the Optn GLU50LYS mutation shows an increased total number of mitochondria that are significantly smaller with a fragmented appearance and greater cristae density compared with wild-type controls [84]. These data from Optn GLU50LYS transgenic mice support the idea that autophagy, and perhaps mitophagy in particular, may mediate retinal ganglion cell death caused by the GLU50LYS mutation. Tseng and coworkers designed transgenic mice using a bacterial artificial chromosome containing the human OPTN gene (and its native promoter) with the GLU50LYS mutation (BAC-hOPTNE50K) [91]. BAC-hOPTNE50K transgenic mice express human OPTN protein with the GLU50LYS mutation close to physiological levels in addition to the endogenous mouse Optn protein. Heterozygous BAC-hOPTNE50K mice have a progressive, age-related glaucoma phenotype. Transgenic mice exhibit a 40% decrease in retinal ganglion cell density and almost twice the number of degenerated optic nerve axons compared to nontransgenic littermates at 18 months of age. In contrast to the Optn GLU50LYS transgenic strain, only retinal ganglion cell loss was detected in BAC-hOPTNE50K mice. BAC-hOPTNE50K transgenic mice also had contrast sensitivity deficits in their visual function. The measurable vision deficits and age-dependent loss of retinal ganglion cells make the BAC-hOPTNE50K transgenic mice a valuable tool for studies of OPTN-associated glaucoma.

Knock-in mouse model of OPTN-associated glaucoma A knock-in Optn GLU50LYS mouse strain was recently generated using CRISPR-Cas9 genome editing [54]. Similar to BAC-hOPTNE50K transgenic mice, the expression of mutant protein in knock-in Optn GLU50LYS mice is controlled by the endogenous promoter that is more likely to produce physiologic expression levels and patterns. However, the Optn GLU50LYS knock-in mice express mouse Optn GLU50LYS mutant protein regulated by the native mouse Optn promoter, while the BAC-hOPTNE50K transgenic mice express human OPTN mutant protein. Optn GLU50LYS knock-in mice have a 20% reduction in RGC fiber layer thickness at the optic nerve head compared to wild-type mice with no change in intraocular pressure. Thinning occurs exclusively in the RGC fiber layer and not throughout the entire retina.

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Thus, like BAC-hOPTNE50K transgenic mice, GLU50LYS knock-in mice have a retinal phenotype that is similar to human glaucoma. Experiments using these mice to test the utility of potential therapeutics are currently in progress. RGC fiber layer thinning in homozygous Optn GLU50LYS knock-in mice can be rescued with oral administration of amlexanox [54], a potent TBK1 inhibitor [92] that is already approved for clinical use. These data indicate that TBK1 may be an important mediator of OPTN GLU50LYS glaucoma and a potential pharmacologic target for therapy.

Case report (OPTN-associated glaucoma) At age 45, B.R. had no visual complaints but had her first eye exam in 15 years due to her strong family history of glaucoma. At this exam, her visual acuity was 20/20 OD and 20/20 OS without spectacles. Her IOP was 15 mmHg OD and 16 mmHg OS. Ophthalmoscopy showed large cup-to-disc ratios (0.8 OD and 0.85 OS) with thin neural rims in the inferior aspect of both optic discs. Visual field testing with a Humphrey’s perimeter revealed glaucomatous visual field defects, a superior nasal step OD (Fig. 2C) and arcuate/superior paracentral defects OS (Fig. 2D). Subsequently, B.R. was managed with topical medications (latanoprost, timolol, and dorzolamide) and selective laser trabeculoplasty. On a follow-up examination at age 53, B.R. had VA of 20/20 OD and 20/15 OS and IOP of 15.5 mmHg OD and 12 mmHg OS. Optic nerve photos were obtained (Fig. 2A and B) and some progression of visual field loss was observed (Fig. 2E and F) when compared with her baseline testing (Fig. 2C and D). B.R. underwent genetic testing which identified a GLU50LYS mutation in the OPTN gene.

TBK1 and JOAG TBK1 is the third and most recent gene identified to be a Mendelian cause of POAG. TBK1 was discovered to be a cause of glaucoma via linkage analysis of a large NTG pedigree. Members of this pedigree with NTG were all found to have a duplicated segment of chromosome 12q14 that spans the TBK1 gene [69]. Patients with this copy-number variation (CNV) mutation have an extra copy of the TBK1 gene in their genome. Subsequent analyses of multiple cohorts of unrelated NTG patients from around the world found that TBK1 CNVs account for 0.4%–1.3% of NTG cases in Caucasian, Japanese, and Indian populations [69, 70, 93–95]. No TBK1 CNVs have ever been detected in control subjects or in POAG patients with IOP > 21 mmHg [69, 94, 96]. Furthermore, TBK1 CNVs have not been found in cohorts of patients with other forms of glaucoma including JOAG (n ¼ 30), pigmentary glaucoma (n ¼ 209) or steroid-induced glaucoma (n ¼ 79) [97]. One patient out of 225 with exfoliation glaucoma was found to have a TBK1 duplication and is currently the only example of a TBK1 CNV associated with a diagnosis other than NTG [97].

TBK1-associated glaucoma clinical phenotype The association between TBK1 CNVs and familial NTG was discovered by genetic analyses of two families with autosomal dominant inheritance of glaucoma with early onset of disease

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FIG. 2 Glaucoma associated with a GLU50LYS OPTN mutation. A 45-year-old-asymptomatic patient was first recognized to have NTG at 45 years of age. Genetic testing later detected a GLU50LYS mutation in the OPTN gene. Disc photos (A—right optic disc and B—left optic disc) taken at age 53 demonstrate large optic cups OS (B) greater than OD (A). Humphrey visual field testing (SITA 24–2) obtained at 45 years revealed glaucomatous defects OS > OD (C and D) that had worsened by 53 years of age (E and F).

[69]. The first pedigree was a large African-American family with 12 members, ten of whom were diagnosed with NTG. Detailed ophthalmic records from six of the affected members showed early disease onset (mean age of diagnosis 36  8.2 years) and normal IOP (mean maximum IOP 18.2  4.1 mmHg, right eye; 16.7  3.6 mmHg, left eye). Profound optic nerve

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cupping at first examination (mean cup-to-disc ratio 0.95  0.083, right eye; 0.93  0.10, left eye) and visual field defects were observed in six affected family members. The second pedigree was a Caucasian family who presented with clinical features similar to the AfricanAmerican pedigree and was composed of eight affected members with early-onset NTG also inherited in an autosomal dominant pattern [69, 98]. The affected individuals in this pedigree had a mean age of diagnosis of 29  6.7 years, mean maximum IOPs of 19.0  4.3 mmHg (right eye) and 18.8  3.8 (left eye), and mean cup-to-disc ratios of 0.85  0.071. The glaucoma phenotype in both of these families with TBK1 duplications was characterized by severe, early-onset optic nerve damage with vision loss and normal IOP.

Function of TBK1 TBK1 is a serine-threonine kinase with key regulatory roles in immunological signaling and autophagy [99]. TBK1 is ubiquitously expressed [100] and essential for life; homozygous loss of the gene is embryonic lethal [101]. TBK1 stimulates the production of many essential immune molecules through its ability to phosphorylate and activate NF-ΚB [102], IRF3, and IRF7 [103], major transcription factors involved in inflammation and host defense. In addition to promoting innate immunity by modulating transcription, TBK1 activates autophagy proteins OPTN [66] and p62 [104] to recruit autophagosome machinery for sequestering and degrading intracellular pathogens (xenophagy). TBK1 kinase activity also promotes the clearance of intracellular wastes such as protein aggregates [105] and damaged organelles (i.e., mitochondria/mitophagy) [88, 106]. TBK1 phosphorylates serine-177 of OPTN which activates it as an autophagy receptor [88, 106]. Activated OPTN promotes engulfment of mitochondria marked for degradation by ubiquitin tags by enhancing OPTN binding to both (1) ubiquitin chains on damaged mitochondria [107] and to (2) LC3 protein on a forming autophagosome. In this way activated OPTN brings targets (i.e., damaged mitochondria) into an autophagosome [66]. TBK1 and OPTN have key roles in mitophagy. TBK1 and OPTN are recruited to damaged mitochondria [108] where mitochondrial depolarization stimulates the phosphorylation and subsequent activation of TBK1 [106], which may subsequently stimulate autophagy and degradation of mitochondria. Experiments by Moore and Holzbaur show that damaged mitochondria accumulate when TBK1 is depleted or chemically inhibited [89]. In this study, mitophagy was also impaired by ALS-associated mutations in OPTN and TBK1. Current studies are beginning to address how NTG-associated TBK1 duplications may alter autophagy or mitophagy to cause retinal ganglion cell death.

Effects of TBK1 duplications TBK1 gene duplications have been shown to cause increased expression of TBK1 protein and may cause dysregulated autophagy. The level of TBK1 RNA in skin fibroblasts collected from patients with a TBK1 duplication (n ¼ 6) was found to be 1.6-fold higher than that of controls [69]. In the retina, TBK1 protein is localized to retinal ganglion cells and nerve fibers, the primary tissues affected by glaucoma [69, 109]. To test the hypothesis that TBK1 duplications cause dysregulated autophagy in neurons, Tucker et al. analyzed the levels of lipidated

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LC3 (a component of autophagosomal membranes) in retinal ganglion cell-like neurons differentiated from skin-derived-induced pluripotent stem cells (iPSCs) generated from NTG patients with TBK1 duplications and also from normal control subjects [110]. Western blot analysis showed that lipidated LC3 levels are higher in skin fibroblasts that carry a TBK1 gene duplication compared to cells with no duplication, and are elevated to an even greater degree in iPSC-derived retinal ganglion cell-like neurons. These data suggest that TBK1 duplications confer an increase in the abundance of autophagosomes and support the hypothesis that TBK1 duplications contribute to retinal ganglion cell loss by dysregulating autophagy. Future work aims to test this hypothesis in vivo and to further evaluate autophagy (and mitophagy) as a potential mechanism of optic nerve degeneration in TBK1-associated NTG.

Transgenic mouse model of TBK1-associated glaucoma To study the phenotypic effects of TBK1 duplications in vivo, transgenic mice were engineered to have one wild-type copy of human TBK1 and its native promoter added to their genomes [111]. These transgenic mice (called Tg-TBK1 mice) have a total of three copies of this gene (one human TBK1 transgene and two native mouse Tbk1 genes). Intraocular pressure of Tg-TBK1 mice was measured at a range of ages (5 weeks to 18 months) and was no different than that of wild-type littermates. Immunohistochemistry performed on retinas of Tg-TBK1 mice shows that an extra copy of the TBK1 gene causes increased TBK1 protein expression in retinal ganglion cells. Tg-TBK1 mice were assessed for signs of glaucoma (loss of retinal ganglion cells) as they aged to 18 months. Tg-TBK1 mice had a 9% loss of retinal ganglion cells at 3 months of age and a 13% loss by 18 months when compared to wild-type littermates. Moreover, there is a dose response between the retinal ganglion cell loss and the number of TBK1 transgenes. Mice with two copies of the TBK1 transgene (produced by an intercross of Tg-TBK1 mice) had even more retinal ganglion cell loss. These data provide compelling evidence that TBK1 duplications are pathogenic to retinal ganglion cells in both humans and mice with normal intraocular pressure. Moreover, Tg-TBK1 mice may be a powerful tool for future studies of the pathogenesis of NTG and may also provide a means of testing new potential therapies.

Case report (TBK1-associated glaucoma) At age 33 D.G. had no visual complaints but was diagnosed with NTG during an eye exam. At the time of his first examination, his visual acuity was 20/20 OD and 20/20 OS with spectacle correction of 2.00 + 1.75  86 OD and  2.00 + 1.75  86 OS and his IOP was 16 mmHg OD and 16 mmHg OS. Ophthalmoscopy revealed large cup-to-disc ratios OU (Fig. 3A and B). Automated perimetry revealed severe visual field loss OU (Fig. 3C and D). D.G. has a strong family history of NTG and is a member of a pedigree that has been previously described [98]. Genetic testing ultimately revealed a triplication of the TBK1 gene.

Genetic testing and JOAG Genetic testing may provide valuable information to JOAG patients and their physicians that has the potential to improve clinical outcomes. However, it is critical for physicians to

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FIG. 3 Glaucoma associated with a TBK1 gene mutation (gene triplication). At 33 years of age, D.G. had visual acuity of 20/20 OD and 20/20 OS and was incidentally noted to have signs of severe glaucoma including large optic cups (A—right optic disc and B—left optic disc). Genetic testing later detected an abnormal triplication of the TBK1 gene. His intraocular pressure was 16 mmHg OU. Automated perimetry was performed with an octopus visual field analyzer, which identified severe constriction of his right (C) and left (D) visual fields.

carefully select patients for whom genetic testing is appropriate. Guidelines for genetic testing in ophthalmology have been provided by the American Academy of Ophthalmology, which suggest that testing only be pursued if the results will impact treatment or modify disease surveillance [112]. Based on these principles, routine genetic testing for adult-onset POAG is not currently recommended due to a lack of clinical utility. Most cases of adult-onset POAG

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have a complex genetic basis that involves the interaction of dozens and dozens of genetic and environmental factors that are either not well understood or not yet discovered. In contrast, a large proportion of JOAG cases are caused primarily by defects in one of a few individual genes (i.e., MYOC, OPTN, or TBK1). Moreover, due to the early age at onset and the autosomal dominant pattern of inheritance, numerous multi-generation pedigrees have been reported in which many members have JOAG. These pedigrees have a high likelihood of identifying a disease-causing mutation and, therefore, genetic testing tends to be more useful by enhancing early diagnosis, providing prognostic information, and by guiding therapeutic decision making. For example, screening patients with adult-onset POAG for MYOC mutations will yield a positive result in only 3%–4% of patients. Conversely, relatives of those known to have MYOC-associated glaucoma have up to a 50% risk of carrying a MYOC mutation and therefore screening these individuals is recommended. It is also appropriate to screen JOAG or POAG patients that have early onset of disease, markedly high IOP, and/or a strong family history of disease (e.g., dominant inheritance), as they have a higher likelihood of having MYOC-associated glaucoma. Similar recommendations can be made for testing for OPTN mutations. In general, OPTN testing should be reserved for NTG patients that have family members with known OPTN-associated glaucoma or for NTG patients with early onset of disease and strong family history [113]. Identifying patients with high-risk disease-causing mutations allows physicians to recommend closer surveillance and initiate treatment earlier in the course of the disease. Appropriately screening the relatives of these patients helps determine whether they also warrant closer monitoring. In addition, when this screening is negative, family members can be reassured their risk of developing glaucoma is likely no higher than the general population, avoiding the loss of time, money, and potential anxiety associated with frequent surveillance. Genetic testing should be performed by reputable laboratories that are Clinical Labs Information Act (CLIA) certified and ordered by experienced physicians. The availability of genetic counseling is also advisable to ensure that test results are appropriately explained to patients and their families. The National Center for Biotechnology Information (NCBI) maintains an online list of laboratories that provide testing, the Genetic Testing Registry https://www. ncbi.nlm.nih.gov/gtr/, which is a resource for identifying genetic tests offered at both CLIA-certified diagnostic laboratories and research laboratories. Another useful online reference is GeneReviews, https://www.ncbi.nlm.nih.gov/books/NBK1116/, which provides information about specific genetic conditions and genetic tests. Genetic testing is widely available for MYOC and OPTN mutations. Testing for TBK1 mutations is not currently available for clinical use.

Gene-directed therapies Genetic studies of glaucoma have provided important insights into the pathogenesis and have identified specific biological pathways that are altered by disease. These discoveries are the first steps toward a new generation of therapies that are specifically targeted to disease at its most basic, molecular level. Moreover, glaucoma is highly heterogeneous such that patients with the same clinical diagnosis (i.e., NTG) may often have disease caused by different

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molecular causes (i.e., a TBK1 mutation, an OPTN mutation, or an unknown cause). Genetic studies also hold the promise for making more precise diagnoses for individual patients, which would further facilitate personalized therapies that are tailored to the specific cause of each patient’s disease.

Targeted therapies for MYOC-associated glaucoma A key feature of the pathogenesis of MYOC-associated glaucoma is that mutations prevent normal folding of MYOC protein inside trabecular meshwork cells and prevent its secretion (as described in the prior section of this chapter). Based on these observations, new therapeutic strategies for MYOC-associated glaucoma have been developed to: (1) prevent misfolding of mutant MYOC protein; (2) to prevent intracellular accumulation of potentially toxic MYOC protein; and (3) to restore MYOC secretion from trabecular meshwork cells. To achieve these goals, a class of drugs known as chemical chaperones was investigated for their ability to promote proper folding of mutant proteins. Promising studies of one chemical chaperone drug, 4-phenylbutyrate, have demonstrated therapeutic efficacy in a murine model of MYOCassociated glaucoma [35, 37]. Although 4-phenylbutyrate is an FDA-approved drug, additional animal and human clinical trials are needed to investigate optimal doses, routes of administration (oral or topical), and efficacy for MYOC-associated glaucoma. Genome editing has also been explored as a potential therapy for MYOC-associated glaucoma. This approach would employ molecular machinery originally discovered in bacterial cells to alter a patient’s genome and remove or inactivate a glaucoma-causing mutation. The CRISPR/Cas9 genome editing system has been successfully used to inactivate mutant gene sequences and to correct missense mutations in both in vitro (cell culture) and in vivo (animal models) of human eye disease [114]. Recently, CRISPR/Cas9 genome editing was successfully employed in human cell culture, human organ culture, and transgenic mouse studies of MYOC-associated glaucoma. Genome editing with CRISPR/Cas9 effectively reduced expression of mutant MYOC gene sequences, lowered IOP, and prevented development of glaucoma in young transgenic MYOC mice [115]. In addition to preventing onset of ocular hypertension and glaucoma, genome editing may also have utility in preventing further progression of glaucoma and vision loss that has already occurred. The same genome editing of MYOC used in young mice also lowered IOP in middle-aged mice (9 months of age) [115]. Further studies to establish the longevity of this treatment effect and to confirm the absence of off-target, potentially harmful genome editing are needed, however, these initial CRISPR/ Cas9 genome editing results suggest great potential for glaucoma therapeutics.

OPTN and TBK1-associated glaucoma direct therapies Several recent discoveries about OPTN and TBK1 have led to a new direction for research into glaucoma therapeutics: (1) OPTN has been identified as an autophagy receptor [66]. (2) TBK1 is a kinase that stimulates autophagy by phosphorylating OPTN [66]. (3) TBK1 gene duplications and triplications cause NTG, possibly by increasing activation of OPTN and autophagy [69, 110]. Together these observations have suggested that some OPTN and TBK1 mutations cause excess autophagy that may be toxic to retinal ganglion cells and lead

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to glaucoma. This hypothesis suggests that drugs that block TBK1 phosphorylation and activation of OPTN may have therapeutic utility for either OPTN or TBK1-associated glaucoma. Minegishi and coworkers investigated the effects of a TBK1 kinase inhibitor, amlexanox, on the retinal ganglion cell loss in transgenic OPTN mice that develop glaucoma due to a GLU50LYS mutation. In these animals, amlexanox reduced autophagy and had utility in treating OPTN-associated glaucoma [54]. Other molecules that stimulate or inhibit autophagy might also be useful in developing a new set of mutation-directed therapies for OPTN and TBK1-associated glaucoma. Further investigations are needed; however, these discoveries provide compelling evidence that personalized therapies for a subset of glaucoma is possible in the not too distant future.

Acknowledgments This research was supported in part by NIH R01EY023512, NIH P30 EY025580, and by funds from the Marlene and Leonard Hadley and Martin Carver Chair in Glaucoma (JHF).

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C H A P T E R

8 Bardet-Biedl syndrome Katie Weihbrecht Department of Pharmacy, University of Iowa, Iowa City, IA, United States Department of Ophthalmology and Visual Science, University of Iowa, Iowa City, IA, United States Department of Pediatrics, University of Iowa, Iowa City, IA, United States

Bardet-Biedl syndrome Ciliopathies are a family of diseases classified by defects to an organelle called the cilium. Bardet-Biedl syndrome (BBS), despite being a relatively rare disorder, is one of the most well studied [1]. BBS is an autosomal recessive, genetically heterogeneous, pleiotropic disorder [2–4], with 21 genes currently linked as causative mutations leading to BBS [5–23]. These 21 genes can be classified into three subclasses based on their association with a complex known as the BBSome: BBSome associated (BBS1–5, 7–9, 14, 17, 18), BBSome chaperonin (BBS6, 10, 12), or non-BBSome associated (BBS11, 13, 15, 16, 19–21). The BBSome is a complex made up of eight of the known BBS proteins (BBS1, BBS2, BBS4, BBS5, BBS7, BBS8, BBS9, and BBS18), and is involved in intracellular trafficking and protein trafficking within the cilium [5, 7–9, 11, 12, 21, 24–26]. The responsibility of proper trafficking of the BBSome to and within the cilium falls on BBS3, BBS14, and BBS17 [19, 27–30]. BBSome assembly requires a chaperonin complex which includes BBS6, BBS10, and BBS12 [6, 14, 21, 31]. With their roles in the cilia and with the BBSome currently not well characterized, the remaining BBS proteins are the nonBBSome-associated proteins (BBS11, BBS13, BBS15–16, and BBS19–21) [13, 15–17, 20–23, 32]. The photoreceptor connecting cilium (CC) and outer segment (OS) shares similarities to the primary cilium and in addition to their role in the primary cilia, BBS proteins are thought to play a role in protein trafficking in the photoreceptor CC and OS [33, 34].

Mode of inheritance Through the use of segregation analysis of large pedigrees and consanguineous families, the autosomal recessive nature of BBS has been clearly established [35]. The prevalence of BBS is population dependent, with the highest rate of incidence seen in the Faroe Islands (1:3700)

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[36] and the lowest frequency in Switzerland and Tunisia (1:160,000); additional prevalences have been noted in other populations, such as 1:18,000 in Newfoundland and 1:36,000 in the mixed Arab populations in Kuwait [37–40]. Homozygous or compound heterozygous mutations in five BBS genes [BBS1, BBS10, BBS2, BBS9, and MKKS (BBS6)] have been reported to cause the majority of BBS cases [40, 41]. Early on, it was proposed that BBS may display a “triallelic” inheritance pattern caused by three pathogenic alleles at two different loci [42]. This hypothesis is based on BBS patients identified with three putative mutant alleles in two different BBS genes (BBS2 and BBS6) and unaffected individuals carrying two BBS2 mutations. The same group reported that BBS1 has a complex inheritance pattern [43], as the group identified asymptomatic individuals with homozygous mutations for BBS1. In addition, this group reported a higher prevalence of the M390R allele in a control cohort compared to the number of affected M390R individuals, suggesting an oligogenic model of BBS for this allele [43]. However, as the number of sequenced genomes/exomes has increased, the exome aggregation consortium data show that the minor allele frequency for the M390R allele is known to be three times lower than previously reported, thus does not seem to follow an oligogenic model of inheritance. Before the increased availability of genomic and exomic information, additional studies by other groups also did not support a triallelic inheritance pattern, with the vast majority of BBS patients reported to date displaying classic Mendelian autosomal recessive inheritance [44–46]. In addition to patient information, multiple BBS mouse models support an autosomal recessive mode of inheritance [33, 34, 47–53]. Clarifying the mode of inheritance of BBS is important for the purpose of counseling parents. An oligogenic inheritance pattern has < 25% recurrence risk of an autosomal recessive disorder. Presenting parents with accurate information is vital in establishing their understanding of the disorder their child has inherited, as well as the risk of affliction should they choose to have another child [35]. It has also been proposed that a third mutation in a different BBS gene can result in a more severe phenotype in patients with homozygous or compound heterozygous mutations in a BBS gene [54]. Data from animal models along with the presentation of other ciliopathies or nonsyndromic retinal degeneration from mutations to BBS genes provide support for the presence of modifier genes. For example, the loss of one copy of BBS4 results in the BBS14 (CEP290) homozygous mutant mouse displaying increased obesity and an accelerated rate of retinal degeneration [55]. The addition of a third mutation in BBS4 resulted in one of two afflicted homozygous BBS7 family members displaying a more severe phenotype [56]. In theory, genetic modifiers may act on homozygous BBS gene mutations to affect the phenotypic presentation of BBS. It is important to note that many of the genes reported to cause nonsyndromic retinal degeneration code for ciliary proteins and hence, they potentially interact with each other [57]. In practice, the phenotypic variability observed in patients with the same genotype and within families is likely to reflect a complex interplay between multiple genetic factors and environmental influences. In the future, access to data from the “Omics” (genomics, transcriptomics, proteomics, epigenomics, and metabolomics) may very well allow us to identify phenotypic modifiers and further elucidate the cause of specific variability in phenotype. Our current understanding barely scratches the surface of the interplay of genes, transcription, protein expression, and metabolism leading to phenotypic presentation [1, 58, 59].

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Clinical diagnosis A clinical diagnosis of BBS requires one of two sets of criteria to be met. Either a patient displays four primary features or a combination of three primary and two secondary features of BBS [41]. Primary features include retinal degeneration, truncal obesity, cognitive impairment, postaxial polydactyly, hypogonadism, and renal anomalies. Secondary features include additional eye abnormalities such as strabismus, cataracts, astigmatism, speech delay, developmental delay, behavioral abnormalities, orodental abnormalities, diabetes mellitus, cardiovascular anomalies, hepatic involvement, craniofacial dysmorphisms, brachydactyly/syndactyly, ataxia/poor conditioning, mild hypertonia, Hirschsprung disease, and anosmia [41]. Unfortunately, the age of diagnosis tends to be variable. It is mostly driven by the age of onset of symptomatic rod-cone dystrophy [1]. For some individuals, this manifests as early as infancy. However, it is more usually seen between 5 and 10 years of age, beginning with night blindness [3]. Isolated polydactyly at birth or obesity seen from infancy does not regularly prompt referral. An affected older sibling generally leads to an earlier diagnosis, with antenatal diagnosis extremely rare except in the cases of known familial history. It has been suggested that children presenting with renal abnormalities or renal failure may be diagnosed earlier than those without. Unfortunately, there is insufficient data to confirm this thought. One confounding factor is that a small subset of individuals will present with isolated rod-cone dystrophy, showing a notable absence of any other BBS-related features. The lack of other primary or secondary BBS phenotypes tends to lead to a much later diagnosis, with most not being diagnosed until adulthood. It is only more recently that these individuals are being picked up, as the introduction of panel-based genetic testing and major diagnostic studies such as the Deciphering Developmental Disorders (exome) study [60] and the UK 100,000 Genomes Project [61]. Rod-cone dystrophies can have many causes, which resulted in BBS mutations previously being overlooked as the causative genes as it wasn’t understood that BBS genes could cause this feature in isolation [1].

Pleotropic phenotypes In addition to retinal degeneration, which will be discussed more extensively later in this chapter, patients suffer from a variety of phenotypes that affect their everyday life. It is interesting to note that while many BBS patients suffer from hypogonadism and genital anomalies [62], patients of both sexes have successfully reproduced [3, 38]. Additionally, polydactyly is present in almost 95% of BBS patients, primarily with a postaxial presentation (69% of BBS cases) [63]. As previously stated, isolated incidents of polydactyly at birth do not generally result in referral for BBS diagnosis [1]. Obesity is the most prevalent phenotype, occurring in 72%–92% of patients beginning in early childhood and worsening with age [64], and is noted by the BBS society as the most disturbing of the phenotypes [65]. When compared to non-BBS subjects with comparable body mass index (BMI), BBS subjects actually display higher adiposity [65, 66], lower lean mass [67], and significantly higher levels of abdominal visceral fat than BMI-matched control subjects [66]. Morbid obesity, classified as a BMI over 40 kg/m [2], is elevated in BBS patients

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compared to the control population [68]. Obesity is frequently observed in individuals heterozygous for a BBS mutation, which raises the question of whether or not a heterozygous status for certain BBS genes may predispose individuals to obesity [69]. Variants in BBS2, 4, and 6 have been shown to increase susceptibility to obesity in non-BBS patients [70]. It is currently unclear what mechanisms are affected by variation in BBS genes leading to increased adiposity. Some proposed mechanisms include these variations working in conjunction with other known genes. For example, obesity is attenuated in BBS knockout mice following administration of the melanocortin receptor agonist, melanotan II, suggesting a possible role for MC4R (melanocortin 4 receptor) [71]. Other involved regions may include FTO (fat mass and obesity-associated gene) [72] or unidentified loci that influence fat mass. It has also been proposed that the dominant negative mode of action of these BBS gene variants may explain their effects on adiposity [73]. What is ultimately clear is that the role of BBS genes/proteins is still poorly understood and will require further research to provide insight into how this phenotype occurs. Renal disease in BBS is estimated to be seen in between 53% and 82% of patients [74–76]. Renal failure may not present until late childhood and onward but is a major cause of morbidity and mortality in this disorder [75]. Classically, BBS is associated with polycystic kidney disease, a typical feature of most ciliopathies that have renal manifestations [18, 73, 74, 76–80]. Unfortunately, around 8% of patients will go on to develop end-stage renal disease that requires dialysis or transplantation [74]. The majority of these patients do so in early childhood (before the age of 5), with most cases showing rapid deterioration frequently requiring dialysis within the first year of life [74]. It is interesting to note that many patients with structural renal abnormalities do not go on to develop functional renal disease [81]. Of all the primary phenotypes, cognitive impairment is the most variable phenotype: severe intellectual disability is seen in only 29% of BBS patients, while 38% display moderately reduced intelligence, and 29% display average intelligence. A subset of patients (4%) even display above average intelligence considered to have an intellectual performance in the higher range of abilities within the normal population [63].

Retinal degeneration in BBS While the previously described phenotypes are all cardinal features of BBS, retinal degeneration is the most highly penetrant feature of BBS. A fundus abnormality characteristic of BBS is atypical pigmentary retinal dystrophy [retinitis pigmentosa (RP)] [41, 82, 83]. In addition, electrophysiological tests usually find BBS patients to have a rod-cone dystrophy with early macular involvement [40, 83–86]. It is this diagnosis that regularly prompts investigation for BBS and is seen in more than 90% of individuals with BBS [64]. BBS patients typically develop symptoms of RP in the first decade of life, often reaching legal blindness between the second and third decades of life [87]. Usually noted around age 8.5, night blindness (nyctalopia) is the most common initial visual symptom [3, 84]. RP also causes decreased peripheral vision, which presents as a constricted visual field [84, 85]. During this progressive loss of the rods, the patient also loses some cone photoreceptors, which causes a diminution of color discrimination and loss of visual acuity [86].

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Retinal degeneration in BBS

Using animal models to study retinal degeneration in BBS Use of animal models allows us to gain a better understanding of the pathology of both a disease as a whole as well as a specific phenotype of interest. With mouse models available in all but two of the 21 BBS genes (Bbs9 and Bbs20), the cause of retinal degeneration has been a focus in many of these models, with much focus on the role of BBS proteins in the development and maintenance of the mammalian retina [15, 33, 34, 47–50, 52, 53, 88–100]. In addition to these mouse models, the rapid development of the eye in the zebrafish has also been utilized to investigate how loss of BBS genes affects eye development and maintenance, as well as the effect on vision [89]. BBS can be caused by mutations in three groups of genes: BBSome-associated, BBSome chaperonin, and non-BBSome genes. Mutations in BBSome-associated and BBSome chaperonin genes are more common in humans, which resulted in earlier identification of these genes compared to most of the non-BBSome genes [2, 5, 7–9, 11, 12, 24, 27]. The identification of causative gene mutations in small pedigrees and isolated cases has been made possible by the recent advent of next-generation sequencing methods [17, 21]. Although mouse models are available for all but two of the BBS genes, the retinal phenotype is most well studied in the BBSome (models available in seven of the eight genes and a zebrafish model available for the remaining BBSome gene) [33, 49, 53, 88, 89, 92], BBSome chaperonin complex genes (three of three mouse models available) [52, 90, 91], or BBSome-interacting genes (three of three) [34, 47, 95, 96, 99] (Table 1). Mouse models of the non-BBSome or TABLE 1 Summary of BBS genes, HGNC names, references in which they were classified as causative genes of BBS, protein product classification, model organisms in which they have been studied, in vitro work that has been performed, and additional disorders in which the gene has been identified as the causative mutation BBS gene

HGNC

Patient identificationa

Protein classification

Model organism(s)a

BBS1

BBS1

12118255

BBSome component

Mouse (15322545, 18032602, 23160237)

In vitroa

Additional diagnosesa

22500027

NSRP (23143442)

Zebrafish (24069149) BBS2

BBS2

11285252

BBSome component

Mouse (15539463)

22500027

NSRP (25541840)

BBS3

ARL6

15214642

BBSome Associated

Mouse (22139371)

22139371

NSRP (19956407)

Zebrafish (21282186) BBS4

BBS4

11381270

BBSome component

Mouse (16794820, 15173597)

22500027, 24681783

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TABLE 1 Summary of BBS genes, HGNC names, references in which they were classified as causative genes of BBS, protein product classification, model organisms in which they have been studied, in vitro work that has been performed, and additional disorders in which the gene has been identified as the causative mutation—cont’d BBS gene

HGNC

Patient identificationa

Protein classification

Model organism(s)a

BBS5

BBS5

15137946

BBSome component

Mouse (25849460)

In vitroa

Additional diagnosesa

22500027

Zebrafish (16399798) BBS6

MKKS

10973238

BBSome Chaperonin

Mouse (15772095)

20080638

BBS7

BBS7

12567324

BBSome component

Mouse (23572516)

22500027

Zebrafish (24938409) BBS8

TTC8

14520415

BBSome component

Mouse (21646512)

22500027

NSRP (20451172)

Zebrafish (20643117) BBS9

PTHB1

16380913

BBSome component

Zebrafish (22479622)

22500027

BBS10

BBS10

20805367

BBSome Chaperonin

Mouse (26273430)

20080638

Zebrafish (16582908) BBS11

TRIM32

16606853

Non-BBSome

Mouse (19155210, 21775502)

28498859

Zebrafish (16606853) BBS12

BBS12

17160889

BBSome Chaperonin

Mouse (22958920)

20080638

Zebrafish (17160889) BBS13

MKS1

18327255

Non-BBSome

Mouse (19776033, 21045211)

JBTS (24886560), MKS (16415886)

Zebrafish (18327255) BBS14

CEP290

18327255

BBSome Associated

Mouse: (16632484, 21623382)

25552655

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Retinal degeneration in BBS

TABLE 1 Summary of BBS genes, HGNC names, references in which they were classified as causative genes of BBS, protein product classification, model organisms in which they have been studied, in vitro work that has been performed, and additional disorders in which the gene has been identified as the causative mutation—cont’d BBS gene

HGNC

Patient identificationa

Protein classification

Model organism(s)a

In vitroa

Additional diagnosesa

Zebrafish (18327255) BBS15

WDPCP

20671153

Non-BBSome

Mouse (24302887)

20671153

Zebrafish (24302887) BBS16

SDCCAG8

20835237

Non-BBSome

Mouse (24722439, 24302887, 29444170)

20835237, 24722439

SLS (20835237)

Zebrafish (20835237) BBS17

LZTFL1

22510444

BBSome Associated

Mouse (27312011, 26216965)

22510444, 26216965

BBS18

BBIP1

24026985

BBSome component

Mouse (24316073)

24316073

BBS19

IFT27

24488770

Non-BBSome

Mouse (25446516)

25446516

BBS20

IFT74

27486776

Non-BBSome

Zebrafish (27486776)

BBS21

C8ORF37

27008867

Non-BBSome

Mouse [32]

NSRP (22177090)

Zebrafish (27008867) a

Ref # are PMID. NSRP, nonsyndromic retinitis pigmentosa; JBTS, Joubert syndrome; MKS, Meckel-Gruber syndrome; LCA, Leber congenital amaurosis; SLS, Senior-Løken syndrome.

BBSome-interacting genes Bbs11, Bbs13, Bbs15, Bbs16, Bbs19, and Bbs21 are available [15, 50, 97, 98, 100–102], with a zebrafish model in bbs20 available [23]. Unfortunately, retinal phenotypes in the Bbs19 mouse and bbs20 zebrafish are unreported and knockout mice for Bbs11 and Bbs13 lack retinal BBS phenotypic features [15, 97]. In addition, the Bbs15 mouse model shows a relatively severe retinal phenotype (anophthalmia), which is inconsistent with patient phenotypes [100]. Rhodopsin mislocalization defects were one of the earliest discoveries surrounding retinal degeneration in BBS and remained a trend as new BBS models were generated, suggesting at least one common mechanism of disease. Normally, rhodopsin is localized to the OS of rods,

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where it performs the first step of phototransduction. However, BBS mutant mice show abnormal rhodopsin localization to the inner segment (IS) and cell bodies of the outer nuclear layer (ONL) [33, 49, 50, 92], which diminishes its presence in the OS [52]. In addition to rhodopsin mislocalization, apoptosis-driven photoreceptor death has also been noted in BBS mouse models [49, 103]. Along with the rhodopsin mislocalization, disorganization of the OS [33, 34] is seen along with progressive loss of OS, IS, and ONL. However, the inner retinal layers are preserved [48, 52]. A bb9 zebrafish knockdown model also shows a complete loss of the OS [89]. Early work in the available mouse models focused on changes to photoreceptor structure or to the localization of specific proteins such as rhodopsin, transducing, and arrestin. More recently, work in the Bbs17 mouse looked at the OS proteome in mutant mice to identify global changes in protein localization. While eight proteins showed decreased OS localization, 138 proteins showed an abnormal enrichment in the OS. In the wild type (WT) mouse, many of the enriched proteins aren’t even present in the OS. Concomitant with the accumulation of non-OS proteins in the OS of mutant Bbs17 mice was the loss of these proteins in the cell body (IS, ONL, and synaptic terminal), where many are necessary for maintaining normal retinal function [34]. Based on their findings, the authors suggested two things: one, BBS proteins play either a direct or indirect role in normal trafficking of OS proteins and two, the photoreceptor cell death seen in BBS mutants may be due to the combination of insufficient protein functions in the cell body and aberrant accumulation of proteins in the OS [35]. It has been shown recently that early disorganization of photoreceptor OS occurs in multiple models of BBS [104, 105]. Changes to the OS discs are observed as early as P10 in mutant mice, with discs appearing elongated and misoriented. Thus, while overt retinal degeneration is not yet observed, mice do show reduced retinal function due to the developmental defect of OS disc formation [105].

In vitro molecular mechanisms of BBS Analogous to the transition zone in the primary cilium, photoreceptors have a CC [106]. As the name suggests, the CC bridges the IS (contains the metabolic and trafficking components of photoreceptors) with the OS (contains the phototransduction apparatus). The CC and the primary cilium have a similar structure, an axoneme that is comprised of nine microtubule doublets. Like trafficking through the primary cilium, proteins and membrane components are trafficked through the CC by a process that requires BBS proteins known as intraflagellar transport (IFT) [107, 108]. While the mechanisms behind the trafficking of the leptin receptor (LR) [71] and the insulin receptor (IR) [109] are not well understood, it is known that the BBSome likely plays a role in LR and IR trafficking as well as plays a role in transport within the cell. Along with its role in cellular trafficking, it is linked to ciliary receptor localization, as mutations to many BBS genes result in an array of ciliary receptors showing mislocalization: smoothened receptor [28], neuropeptide Y-family receptors [110], melanin-concentrating hormone receptor 1 [111], somatostatin receptor type 3 [111], OSM-9 [112], and ODR-10 [112]. It’s interesting to note that the majority of these receptor trafficking defects involve G-protein-coupled receptors. This would affect both cyclic adenosine monophosphate (cAMP) and phosphatidylinositol

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signaling [113]. While further work is necessary to better understand the retinal degeneration phenotype, based on the knowledge of receptor mislocalization in the primary cilium, the shared structure of the CC and the transition zone of the primary cilium would suggest that the degeneration phenotype is likely due to changes in ciliary receptor trafficking and not intercellular trafficking [35].

Transcriptional variation With the role of BBS proteins in retinal development, it’s not surprising to know that some BBS genes have retina-specific isoforms: BBS3, BBS5, and BBS8 [96, 114, 115]. BBS3 has a retina-specific isoform that is one exon longer than canonical BBS3 and is, therefore, called BBSL. Loss of the bbs3l in zebrafish causes vision loss that cannot be rescued by the canonical isoform of Bbs3. It is interesting to note that while loss of the Bbs3l isoform in the mouse causes disorganization of the IS, severe retinal dysmorphology is not observed. Green cone opsin mislocalization can be rescued with Bbs3l, which does still suggest an important role for Bbs3l specific to eye morphology and physiology. Neither BBS5L nor the BBS8 isoform has been well studied at a functional level. However, while it is not found in the CC, BBS5L is known to localize to the axonemal structure of the photoreceptor and is found at the boundary of the IS and OS, in the IS and OS, the inner plexiform layer, and ganglion cell layer [114]. Based on the current patient information and known mutations, no specific pathology can currently be ascribed to BBS5L [114]. In patients with nonsyndromic RP, a mutation that results in the skipping of BBS8 retina-specific isoform has been identified, supporting the role of this isoform in retinal pathology [115]. While our knowledge of the retina continues to grow constantly, the retina is a unique structure and the presence of these splice variants confirms there is still much to be learned about the retina and its function, along with the role of BBS genes in eye development and function [35]. To date, approximately one quarter of retinal genes have been found to express alternate transcripts [116]. Further work may identify additional BBS genes with retinalspecific splice variants, which may account for additional cases of nonsyndromic RP or may act as modifiers of the eye phenotype of BBS patients [35]. In addition to pursuing the identification of additional splice variants of BBS genes, further investigation of the role of known isoforms BBS8 and BBS5L is essential in fully understanding the role of BBS genes and their isoforms in retinal disease.

Other disorders attributed to BBS genes While other chapters provide detailed information pertaining to additional retinal disease, retinal degeneration is a common feature of many ciliopathies. It would be remiss to not point out that in addition to BBS, mutations in genes that usually cause BBS have currently been identified in three additional ciliopathies, all of which present with retinal degeneration, as well as in nonsyndromic retinal degeneration (Table 1). Thus, it is important to discuss the similarities and differences between these disorders.

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Leber congenital amaurosis Leber congenital amaurosis (LCA) is the most severe form of retinal degeneration caused by mutations to a BBS gene. Currently, there have been at least 17 genes implicated in the development of LCA, including CEP290 (BBS14) [117]. Presenting shortly after birth, LCA is characterized by severe dysfunction of both photoreceptor types, keratoconus, reduced pupillary response, nystagmus, and hyperopia [118–120]. Franceschetti’s oculo-digital sign (excessive eye poking and rubbing) is commonly seen in LCA patients [121], contributing to the development of keratoconus in these patients [118, 121].

Joubert syndrome Joubert syndrome (JBTS) is a rare disorder that affects many organ systems including the eye. Currently, there are at least 34 genes associated with JBTS including MKS1 (BBS13) and CEP290 (BBS14) [122]. Onset of retinal degeneration is variable, sometimes as severe as neonatal onset and congenital blindness and sometimes as mild as a stable loss of vision and is seen to only occur in approximately 30% of patients [123].

Senior-Løken syndrome Senior-Løken syndrome (SLS), first reported in 1961, is a rare autosomal recessive disease characterized by having both nephronophthisis and LCA [124, 125]. Presently, at least nine genes are associated with SLS including CEP290 (BBS14) and SDCCAG8 (BBS16) [17, 126–130]. Classification of SLS is dependent on the manifestation of renal failure, either infantile, juvenile, or adolescent. RP and the subsequent blindness that follows may occur as early as birth or it can manifest as a progressive loss of vision during childhood [126, 131, 132]. By 10 years of age, almost all SLS patients have been diagnosed with eye alterations. Patients with juvenile renal failure, which is linked to mutations in NPHP5 and CEP290, have a greater propensity for RP [126, 133]. While RP is milder in patients with mutations in other nephronophthisis (NPHP) genes, disease symptoms often present during the first 2 years of life [134, 135].

Nonsyndromic retinal degeneration Syndromic and nonsyndromic BBS-associated retinal degenerations show very similar clinical findings; early central vision loss coupled with a progressive loss of peripheral vision. This can ultimately result in complete blindness in some patients. Thus far, nonsyndromic RP has been reported from mutations in five different BBS genes. First, while the BBS1 M390R mutation is the most common cause of clinically diagnosed BBS, two patients with homozygous M390R mutations have been diagnosed only with nonsyndromic RP, not BBS [136]. Second, a set of four missense mutations in BBS2 has been identified that cosegrated with nonsyndromic RP in five independent families [137]. Next, a homozygous mutation in ARL6 (BBS3) and a novel splice site mutation in TTC8 (BBS8) have been identified in patients with nonsyndromic RP [115, 138]. Last and most recently, homozygous splice and homozygous missense mutations in C8orf37 (BBS21) have been reported as associated with nonsyndromic autosomal-recessive cone-rod dystrophy (CRD) and RP [139]. However, it’s

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important to note that the low prevalence of BBS plus gene- and mutation-level heterogeneity makes the inference of mutation specificity for either a nonsyndromic retinal degeneration diagnosis or a BBS challenging.

BBS research and advancing biotechnology While mutations in 21 genes have been identified as causative of BBS, only around 80% of patients diagnosed in the clinic have a causative gene identified [41]. This leaves roughly 20% of patients with a clinical diagnosis of BBS but no identified cause, even in our world of rapidly improving genome sequencing and analysis. As described earlier, other ciliopathies share phenotypes with BBS. Thus, it is possible that a patient may have a clinical diagnosis of BBS but instead has a different ciliopathy with closely shared phenotypes. It is vital that we continue to run whole exome or genome sequencing on these patients to either identify potential new BBS genes or mutations in other known ciliopathy genes, allowing the doctor to provide a new, more accurate diagnosis for their patient. Identifying interactors of BBSomeassociated and non-BBSome-associated proteins may provide a subset of genes that requires closer investigation in BBS-affected patients lacking a genetic diagnosis. With this, to accurately distinguish between variants that cause BBS, other ciliopathies, and nonsyndromic retinal degeneration, more work is necessary for researchers to understand many different BBS proteins and their functions. Some new approaches to answer these questions include transcriptomics (taking a broad look at how transcription varies in clinically diagnosed patients), proteomics (looking for protein expression differences in patients), animal studies, and cellular studies that utilize the relatively new technology of patient-derived pluripotent stem cells [35]. Combining these approaches will allow us to further study some of the more recently identified BBS genes, such as BBS19-20, as well as to provide a comprehensive analysis of newly identified genes, looking at their role in the retina as well as other commonly affected organs like the kidney. To further study the role of protein accumulation in the photoreceptor cell on cell death, the availability of numerous BBS mouse models will allow for further investigation into things such as the difference in protein accumulation between BBSome-associated proteins/ chaperonins or non-BBSome-associated proteins. Currently, the lack of interaction between the BBSome-associated and BBSome-unassociated proteins leaves the question of whether these unassociated genes cause BBS or some other ciliopathy with closely shared phenotypes. Further work on protein mislocalization may also help identify novel BBS or retinal ciliopathy genes. Identification of similar protein accumulation in animal models would provide support for a shared mechanism between the BBSome-associated and unassociated BBS genes. It would also be interesting to compare the transcript products in the eyes of these animal models to look for splice variants that may play a role in retinal degeneration. Development of mouse models, especially patient-mutation specific mouse models, is both time and resource consuming. Recent scientific advancements help scientists look at a different system to try and get the same or relatively close to the answers provided by using animal models [35]. To assess the pathogenicity of new patient mutations, it will be necessary to utilize all of the up and coming technology. Some interesting new technologies that are being

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utilized to study BBS include in silico protein modeling [140] and in vitro assessment of patient mutations using CRISPR/Cas9 in induced pluripotent stem cells (iPSCs) [141], allowing the researcher to study the effect of a specific patient mutation in a cell type of interest in relation to the disease, along with slightly older technology such as gene therapy. It will be necessary to look closely at disease progression, as a timeline of functional delivery of different treatments will be important in properly treating a patient. To treat retinal degeneration, patients in the early stages of the disease may successfully respond to gene therapy to treat [142], whereas patients with advanced retinal degeneration will likely require treatment with photoreceptor precursor cells from autologous patient derived iPSCs, with the timing and effectiveness of these therapies currently being studied [143]. While all of this work focuses on clarifying known mutations and working toward treatment, 20% of patients do still remain without a diagnosis. Our understanding of nonexonic DNA is still expanding and it is possible that causal mutations may lie in regulatory or nonexonic regions, along with the possibility of unidentified alternatively spliced isoforms of known BBS genes. Sequencing and genomic analysis along with molecular characterization will be required to identify these variants, as our current skills sets lack the ability to detect mutations like this.

Conclusions In summary, BBS is an interesting and complex disease. Despite its rarity in the general population, it is one of the most well-studied ciliopathies. That said, there’s still an extensive amount of work to be done to fully understand the genes that lead to this disorder. In addition to gaining a better understanding of the known genes, 20% of patients remain without an identified genetic mutation for their clinical BBS diagnosis. It is important that work continues to push forward in identifying new causative genes or identifying mutations that suggest an incorrect clinical diagnosis, with the actual diagnosis being one of the many phenotypically similar ciliopathies. In vitro and in vivo work has greatly expanded our knowledge on this disorder. However, up and coming technology will allow us to study known BBS genes and gene mutations in a totally different light, along with allowing us to work on identifying new causative mutations or reclassifying the patient’s clinical diagnosis based on the laboratory findings. Ultimately, the goal is to gain a better understanding of this disorder to develop successful therapies for affected patients, whether that is early gene therapy intervention or later cell replacement therapy. With better understanding comes more successful treatments for the patients.

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Otto, Group, GPNS, Identification of 99 novel mutations in a worldwide cohort of 1,056 patients with a nephronophthisis-related ciliopathy, Hum. Genet. 132 (8) (2013) 865–884. A.A. Bizet, A. Becker-Heck, R. Ryan, K. Weber, E. Filhol, P. Krug, J. Halbritter, M. Delous, M.C. Lasbennes, B. Linghu, E.J. Oakeley, M. Zarhrate, P. Nitschke, M. Garfa-Traore, F. Serluca, F. Yang, T. Bouwmeester, L. Pinson, E. Cassuto, P. Dubot, N.A. Elshakhs, J.A. Sahel, R. Salomon, I.A. Drummond, M.C. Gubler, C. Antignac, S. Chibout, J.D. Szustakowski, F. Hildebrandt, E. Lorentzen, A.W. Sailer, A. Benmerah, P. Saint-Mezard, S. Saunier, Mutations in TRAF3IP1/IFT54 reveal a new role for IFT proteins in microtubule stabilization, Nat. Commun. 6 (2015) 8666. A.S. Dekaban, Familial occurrence of congenital retinal blindness and developmental renal lesions, J. Genet. Hum. 17 (3) (1969) 289–296. R.N. Schimke, Hereditary renal-retinal dysplasia, Ann. Intern. Med. 70 (4) (1969) 735–744. I. Perrault, N. Delphin, S. Hanein, S. Gerber, J.L. Dufier, O. Roche, S. Defoort-Dhellemmes, H. Dollfus, E. Fazzi, A. Munnich, J. Kaplan, J.M. Rozet, Spectrum of NPHP6/CEP290 mutations in Leber congenital amaurosis and delineation of the associated phenotype, Hum. Mutat. 28 (4) (2007) 416. M. Medhioub, D. Cherif, F. Benessy, F. Silbermann, M.C. Gubler, D. Le Paslier, D. Cohen, J. Weissenbach, J. Beckmann, C. Antignac, Refined mapping of a gene (NPH1) causing familial juvenile nephronophthisis and evidence for genetic heterogeneity, Genomics 22 (2) (1994) 296–301. D.A. Braun, F. Hildebrandt, Ciliopathies, Cold Spring Harb. Perspect. Biol. 9 (3) (2017). A. Estrada-Cuzcano, R.K. Koenekoop, A. Senechal, E.B. De Baere, T. de Ravel, S. Banfi, S. Kohl, C. Ayuso, D. Sharon, C.B. Hoyng, C.P. Hamel, B.P. Leroy, C. Ziviello, I. Lopez, A. Bazinet, B. Wissinger, I. Sliesoraityte, A. Avila-Fernandez, K.W. Littink, E.M. Vingolo, S. Signorini, E. Banin, L. MizrahiMeissonnier, E. Zrenner, U. Kellner, R.W. Collin, A.I. den Hollander, F.P. Cremers, B.J. Klevering, BBS1 mutations in a wide spectrum of phenotypes ranging from nonsyndromic retinitis pigmentosa to Bardet-Biedl syndrome, Arch. Ophthalmol. 130 (11) (2012) 1425–1432. E. Shevach, M. Ali, L. Mizrahi-Meissonnier, M. McKibbin, M. El-Asrag, C.M. Watson, C.F. Inglehearn, T. BenYosef, A. Blumenfeld, C. Jalas, E. Banin, D. Sharon, Association between missense mutations in the BBS2 gene and nonsyndromic retinitis pigmentosa, JAMA Ophthalmol. 133 (3) (2015) 312–318. M.A. Aldahmesh, L.A. Safieh, H. Alkuraya, A. Al-Rajhi, H. Shamseldin, M. Hashem, F. Alzahrani, A.O. Khan, F. Alqahtani, Z. Rahbeeni, M. Alowain, H. Khalak, S. Al-Hazzaa, B.F. Meyer, F.S. Alkuraya, Molecular characterization of retinitis pigmentosa in Saudi Arabia, Mol. Vis. 15 (2009) 2464–2469. A. Estrada-Cuzcano, K. Neveling, S. Kohl, E. Banin, Y. Rotenstreich, D. Sharon, T.C. Falik-Zaccai, S. Hipp, R. Roepman, B. Wissinger, S.J. Letteboer, D.A. Mans, E.A. Blokland, M.P. 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Drack, Subretinal Gene Therapy of Mice With Bardet-Biedl Syndrome Type 1Subretinal Gene Therapy of Mice With BBS1, Invest. Ophthalmol. Vis. Sci. 54 (9) (2013) 6118–6132. B.A. Tucker, R.F. Mullins, E.M. Stone, Stem cells for investigation and treatment of inherited retinal disease, Hum. Mol. Genet. 23 (R1) (2014) R9–R16.

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C H A P T E R

9 Hereditary predisposition to uveal melanoma Mohamed H. Abdel-Rahmana,b, Robert Pilarskib, Kavin Fatehchandc, Frederick H. Davidorfa, Colleen M. Cebullaa,c a

Department of Ophthalmology and Visual Science, Havener Eye Institute, The Ohio State University, Columbus, OH, United States bDivision of Human Genetics, Department of Internal Medicine, The Ohio State University, Columbus, OH, United States cMedical Scientist Training Program, The Ohio State University, Columbus, OH, United States

Introduction Uveal melanoma (UM) including iris, ciliary body, and choroidal melanoma, is the most common primary intraocular malignancy in adults. UM arises from melanocytes within the uveal tract. It is relatively rare with an incidence of 4.3–6 per million in most Western countries with 2000–3000 new cases diagnosed each year in the United States. Known risk factors for the risk of UM development include white race, blue/green iris eye color, older age, and some environmental risk, in particular arc welding. UM represent 3%–5% of all melanomas but it is biologically and genetically different from cutaneous melanoma (CM). Nearly 50% of UM patients will develop metastatic disease which has no effective treatment [1]. Based on the rarity of familial UM (about 1%), characterized by more than one individual with UM in the family, the contribution of genetics to the etiology of UM has been considered minimal in the traditional ophthalmology literature. However, multiple lines of evidence suggest that genetics contribute much more to the etiology of UM tumor predisposition. In the following chapter, we summarize the evidence of a genetic contribution to UM predisposition and discuss current genes suspected to be associated with the disease.

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Genetic versus environmental basis of UM The environmental basis of CM has been well established. Sunlight ultraviolet (UV) radiation has been shown to be an important risk factor for CM arising from sun exposed skin with increase in disease prevalence in geographical regions closer to the equator [2, 3]. In contrast, the environmental association of UV with UM has been debated. The difference in the incidence of the disease between different geographical locations has been inconsistent. Some of the studies suggesting association of UM with sun exposure depended on factors such as eye and skin color and presence of nevi as evidence of increased sunlight exposure which, as will be discussed later, can be readily explained by genetics. One of the stronger pieces of evidence against association of sun is the finding in several studies that the incidence of UM is higher in indoor compared to outdoor workers [4–9]. In addition, UM tumors do not exhibit a typical UV-mutation signature [10]. However, there are some factors supporting minimal contribution of UV light to UM. First, ocular tissue has been demonstrated to be susceptible to UV-light damage and the radiation energy from UV light is able to induce freeradical formation in the retina which may play a role in carcinogenesis [11–13]. Second, in some studies, UM originates more frequently in UV-light susceptible regions of the eye, specifically the macula and inferior iris compared to other regions [12, 14]. Singh et al. demonstrated that these tumor initiation sites seemed to be correlated with intraocular light distribution, with the density of initiation sites decreasing with increasing distance from the macula [15]. However, others such as Schwartz et al. [16] demonstrate no association between UV light and sites of tumor initiation. They and others argue that there is virtually no sunlight UV-A or UV-B that is transmitted through both the lens and cornea rendering any intraocular carcinogenic effects minute. Although sunlight UV radiation seems to have confounding results, UV radiation exposure from arc welding has been demonstrated to have a correlation with UM [14]. In summary, in contrast to arc welding there is very minimal if any contribution of sunlight source of UV to UM tumorigenesis suggesting that other etiological factors in particular genetics could play an important role in the pathogenesis of the disease.

Familial uveal melanoma Familial UM (FUM), defined as two or more UM in the same blood line, is rare and represents 0.6% of all UM [17]. A total of 121 families with 283 subjects have been reported [18], Table 1. The UM in all of these 121 FUM families was unilateral with one case having two ipsilateral tumors. In the majority (78.5%) of these families only two members were affected. In 20 families (16.5%), three members had UM while four or more UM in the same bloodline were reported in only six families (5%). UM patients were first or second degree relatives in 93 (77%) and in the remaining 22 (23%) they were more distant relatives. The sex distribution was almost even. Patients with FUM were four times as likely to have a second primary malignant neoplasm as the general population [17]. The cancer phenotypes in FUM families are diverse and include multiple different cancers in addition to UM. Canning and Hungerford (15) reviewing 14 kindreds with FUM suggested that UM in familial cases occur at an earlier average age (42 years old) compared to sporadic cases (52 years old). However,

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UM clustering with other cancers

TABLE 1

Summary of clinical features of UM in 121 families with multiple cases of UM.

Age (reported in 241/283 patients)

Median 54 years (range 15–85)

Sex (reported in 280/283 patients)

Males:138; Females:142

Families with 2 UM 3 UM >4 UM

95 (78.5%) 20 (16.5%) 6 (5%)

Degree of relation between UM patients in the familya 1st degree 2nd degree 1st and other 2nd and other 3rd and other 4th degree

52 (43.3%) 25 (20.8%) 13 (10.8%) 3 (2.5%) 21 (17.5%) 6 (5.0%)

Genes tested (mutant/total) BAP1 CDKN2A CDK4 BRCA2

22/57 0/21 0/11 0/2

a

In one family the relation was not reported.

when we expanded the assessment to the 283 UM patients from 121 FUM the median age is 54 years (ranging from 15 to 85 years) but still younger than 62 years which is the median age of UM patients in SEERS database [19]. Genetic testing was carried out on a subset of the families. None of the tested FUM patients was positive for a mutation in the melanoma genes CDKN2A and CDK4 but different pathogenic mutations in BAP1 were identified in multiple unrelated subjects.

UM clustering with other cancers Compared to the general public, UM patients have an overall increased risk for second primary cancers [20, 21]. The largest study assessing second cancers in UM patients was carried out by Scelo et al. using data from 13 cancer registries, including 10,396 cases in which the UM occurred first and 404 cases in which it was second. Standardized incidence ratios (SIRs) of 32 types of cancer were calculated [22]. They also calculated SIRs of second UM after other primaries. They identified significantly increased risks of CM (SIR ¼ 2.38, 95% CI 1.77–3.14), multiple myeloma (SIR ¼ 2.00, 1.29–2.95), liver (SIR ¼ 3.89, 2.66–5.49), kidney (SIR ¼ 1.70, 1.22–2.31), pancreas (SIR ¼ 1.58, 1.16–2.11), prostate (SIR ¼ 1.31, 1.11–1.54), and stomach (SIR ¼ 1.33, 1.03–1.68) cancers. Risks of CM were highly variable between registries and were mainly increased in females, in younger patients, in the first years following diagnosis, and for patients diagnosed after 1980. The risk of UM was significantly increased only after prostate cancer (SIR ¼ 1.41, 1.08–1.82) [22].

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Other smaller studies showed similar findings. Hemminki and Jiang [21] analyzed data from the Swedish Family-Cancer Database and found an increased rate of breast cancer in first-degree relatives of UM patients. The Collaborative Ocular Melanoma Study Group (COMS), reporting on a cohort of 2320 UM patients with no prior second cancer history at enrollment, found the rate of subsequent second primary cancers to be 7.7% (95% CI: 6.6%–9.0%) at 5 years and 14.9% (95% CI: 12.9%–17.1%) at 10 years post UM diagnosis [23]. No relationship between the development of second primary cancers and type of UM treatment was identified. Bergman et al. [20] analyzed data from the Swedish Cancer Registry on second cancer rates in 2995 patients with UM compared to expected rates in the Swedish population. They found an increased risk for subsequent cancers with a SIR of 1.13 (95% CI: 1.02–1.26). The higher incidence of second primary cancers in UM patients could reflect the inclusion of a subset of high-risk patients in these cohorts as well as an overall increased risk of cancer for all UM patients. In addition to the increased risk of second primary cancers in UM patients, a subset have family histories suggestive of hereditary predisposition to cancer. In an unselected series of 121 patients with UM seen in a university-based tertiary referral program one patient reported a family history of UM and ten additional patients reported personal and/or family histories consistent with predisposition to a known hereditary cancer syndrome, including six with possible hereditary breast cancer, two with hereditary colon and two with hereditary melanomas [24]. Twenty three patients (19%) had a personal history of a second cancer after exclusion of nonmelanoma skin and cervical cancers. The frequency of CM was significantly higher in UM patients than the general population (RR: 2.97, 95% CI: 1.00–6.94). Patients with a family history suggestive of a high-risk predisposition to a known cancer syndrome had a significantly higher risk of having a second cancer than the remaining UM patients (P ¼ .02). The results of that study suggests that the frequency of UM patients with high risk for a hereditary cancer predisposition is much higher than earlier estimates and that it could be as high as 11.6% [24]. As we will discuss later, germline mutations in known high-penetrance cancer genes such as BAP1 could explain familial cancer clustering in some, but not all, UM patients. Also alterations in low-penetrance cancer genes could explain the overall increased prevalence of second primary cancers in UM patients.

Highly penetrance genes with reported germline mutations in UM Germline mutations in several high-penetrance cancer genes have been reported in patients with UM, Table 2. To date BAP1, Breast cancer 2 (BRCA2), MBD4, and FLCN are the only genes with germline mutations reported in more than one UM patient. Only single case reports are available for the other genes such as BRCA1 [25], MLH1 [26], CDKN2A [27], and MSH6 [28]. Secondary analysis in UM patients of the germline alterations in the Cancer Genome Atlas (TCGA) project found deleterious mutations in the BAP1, FANCA, FANCM, HRAS, NBN, POT1, and PTCH1 genes several of which encode components of the DNA damage repair pathway [29]. In the following we evaluate the evidence of gene-disease association of the reported high-penetrance cancer genes in UM patients.

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Highly penetrance genes with reported germline mutations in UM

TABLE 2

Summary of genes with reported germline mutations in uveal melanoma patients.

Gene

Location

# of cases

OMIM

BAP1

3p21.1

>80

614327

BRCA2

13q13.1

6

MBD4

3q21.3

FLCN

Carrier frequencya

141

Syndrome

Cancers

1:20217 (2/40434)

BAP1-TPDS

UM, CM, RCC, MMe

612555

1:295 (159/47021)

Breast Ovarian Cancer Susceptibility 2

Breast Ca, Ovarian Ca, Prostate, Pancreatic

4

603574

1:803 (61/49005)



UM responding to immunotherapy

17p11.2

2

135150

1:390 (125/48822)

Birt-Hogg-Dube

Follicular hamartomas, kidney tumors, and spontaneous pneumothorax

BRCA1

17q21.31

1

604370

1:394 (123/48510)

Breast Ovarian Cancer Susceptibility 1

Breast Ca, Ovarian Ca, Prostate, Pancreatic

MLH1

2p21p16

1

120435

1:4276 (12/51307)

Lynch Syndrome

Colon Ca, Endometrial Ca

MSH6

2p16.3

1

614350

1:888 (49/43501)

Lynch Syndrome

Colon Ca, Endometrial Ca

CDKN2A

9p21.3

1

606719

1:1982 (21/41616)

Melanoma, Pancreatic

CM, Pancreatic

a

Carrier frequency: Frequency of loss of function null variants in noncancer non-Finish European controls in GnomAD database divided by the average number of sequenced subjects for the gene.

BRCA1-associated protein 1 (BAP1) A comprehensive assessment of BRCA1 associated protein 1 (BAP1)-Tumor Predisposition Syndrome (TPDS) has been recently reported on 181 BAP1 germline variant-positive families worldwide carrying 140 unique BAP1 germline variants. Germline pathogenic mutations in BAP1 have been reported in at least 80 UM patients. While the frequency of germline BAP1 mutations is 1% in unselected UM [30, 31], it increases to 3%–4% in UM patients with strong personal and family histories of cancer [31–34], and to 17.8% in familial UM [18]. Biallelic inactivation of BAP1 has been reported in several UM patients with germline BAP1 mutations. BAP1 is expressed in uveal melanocytes and it is an important tumor suppressor in the pathogenesis of UM. Based on the Clinical Genome Resource (ClinGen) guidelines there is definitive evidence of association of BAP1 with UM [35]. BAP1 is a deubiquitinating enzyme located on chromosome 3p21.1. Germline BAP1 mutations are associated with a hereditary cancer syndrome involving malignant mesothelioma, CM, renal cell carcinoma, and UM (BAP1 tumor predisposition syndrome; BAP1-TPDS). BAP1 was identified in 1998 as a bona fide tumor suppressor [36]. BAP1 is a nuclear deubiquitinating hydrolase with at least five known functions: (i) protein deubiquitination [36], (ii) cell cycle regulation and cell growth [37], (iii) DNA damage repair [38, 39], (iv) chromatin remodeling and regulation of gene expression [40], and (v) regulation of apoptosis [41].

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The BAP1 ubiquitin C-terminal hydrolase (UCH) selectively interacts with 20 proteins [42]. The deubiquitinase function of BAP1 is crucial for its tumor suppressor role [43]. BAP1 is also important for double-strand break repair by homologous recombination (HR) and it is a phosphorylation target for the DDR kinase ATM. BAP1 is required for efficient assembly of the HR factors BRCA1 and RAD51 at ionizing radiation (IR)-induced foci [38, 39]. Both BAP1 catalytic activity and its phosphorylation are critical for promoting DNA repair and cellular recovery from DNA damage [38, 39]. In addition to its nuclear functions BAP1 localizes at the endoplasmic reticulum (ER) where it binds, deubiquitinates, and stabilizes Inositol 1,4,5trisphosphate receptor type 3 (ITPR3) [41]. This in turn modulates calcium (Ca2+) release from the ER into the cytosol and mitochondria and promotes apoptosis. Significant reduction in apoptosis was observed in fibroblasts from patients with heterozygous germline mutations in BAP1 and in cells with BAP1 knock down by siRNA. It has been suggested that decreased apoptosis associated with BAP1 mutations leads to the accumulation of DNA damage, resulting in cellular transformation [41]. It is important to recognize the BAP1-TPDS in the context of UM because BAP1 germline mutations tend to be associated with more aggressive cancers with an increased propensity for metastasis (71% vs 18%). In fact, the mean survival of UM patients with BAP1 mutations was 4.74 years compared to 9.97 in patients without a BAP1 mutation [31]. Additionally, patients with BAP1 mutations tend to develop tumors at an earlier age (51 vs 62 years) and have more ciliary body involvement (75% vs 21.6%) [31]. Physicians who are involved in the care of UM patients need to be made aware of the BAP1-TPDS in order to make decisions on genetic testing and counseling. UM patients with either early-onset UM (diagnosed below age 30), a personal history of one other primary tumor, a history of another UM case in their family, or with a family history of at least 2 other primary tumors in first- or second-degree relatives should be tested for germline BAP1 mutation status [44]. For unaffected individuals with a deleterious BAP1 mutation, yearly dilated eye exams and ophthalmic imaging by an ocular oncologist need to begin at age 11 [44]. In addition, follow up with other medical specialists is recommended for the early diagnosis of other cancers associated with BAP1-TPDS such as CM, renal cell carcinoma, and mesothelioma. Table 3 summarizes the current suggested management guidelines [44, 45]. Ophthalmologists need to understand how to screen for BAP1-TPDS in order to earlier detect and treat patients with UM in order to facilitate the best prognosis [44, 45].

Breast cancer 2 (BRCA2) Breast cancer 2 (BRCA2) is associated with hereditary predisposition to breast, ovarian, pancreatic, and prostate cancers [46]. Other cancers including UM have been observed in patients with germline BRCA2 mutation and UM has been suggested as potentially one of the rare phenotypes of the disease [46–48]. Given the high frequency of breast cancer in UM patients and families, mutations in BRCA2 have been investigated in UM [21]. It should be noted however that several of these studies had limitations including using less-sensitive mutational screening techniques and/or limitation of the assessment to a single mutation. Also they likely overcalled the pathogenesis of variants identified, meaning that a number of the reported “mutations” are not likely to be deleterious. In the following, we review the

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TABLE 3

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Proposed management recommendations for families with germline BAP1 mutation.

Risk

Age of earliest reported case

Occurrence in nonproband truncating variant carriers

Uveal Melanoma

16

29/183 (15.9%)

• Yearly dilated eye exams and ophthalmic imaging by ocular oncologista beginning age 11b • If UM is diagnosed, consider a “highrisk” patient monitoring protocol for systemic metastatic surveillance (e.g., liver directed imaging every 3–6 months, pulmonary imaging every 6–12 months)

Mesothelioma

34

31/183 (16.9%)

• None exist • Yearly physical examinations recommended

Cutaneous Melanoma, Basal Cell Carcinoma and BAP1inactivated melanocytic nevus

25

22/183 (12%)

• Yearly dermatology full body skin exam beginning age 20b • Self-skin exam following ABCDE characteristics of melanoma • Use of sun protection • If atypical Spitz tumors are detected, BRAF mutation testing and immunostaining for BAP1 should be performed

Renal Cell Carcinoma

36

9/183 (4.9%)

• None exist • Protocol for VHL syndrome renal screening could be considered (yearly abdominal ultrasound exam, MRI every 2 years)

a b

Screening recommendations

https://www.eyecancer.com/find-a-doctor. 5 years earlier than earliest reported case.

available reports on BRCA2 mutations in UM using the American College of Medical Genetics and Genomics and the Association for Molecular Pathology established guidelines for variants classification [49]. Sinilnikova et al. studied 62 UM patients of which 35 had a family history of breast cancer, ovarian cancer, UM, or other cancers using heteroduplex analysis and protein truncation test. The authors identified seven variants in BRCA2 and suggested that three of them are deleterious. However, based on the current guidelines for variant classification only one of these variants, 5179delC-ter1668, should be considered pathogenic while the others should be classified as either variants of uncertain significance (two), benign or likely benign variants (four). The patient with pathogenic variant had both UM and breast cancer with family history of a

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mother with pancreatic cancer. There were no BRCA2 mutations in five families with at least three cases of early-onset breast or ovarian cancer and seven families with FUM. The authors suggested that another gene is involved in the development of both breast cancer and UM with a higher predilection for UM when compared to BRCA2 [48]. Scott et al. studied BRCA2 variants in 67 UM and four conjunctival melanoma patients who either were diagnosed at age 50 years or younger, had bilateral disease, or reported a family history of UM. They used protein truncation test and denaturing high-performance liquid chromatography for mutational screening. Six variants were identified including two coding and four noncoding. One of the coding variants was a missense variant of uncertain significance while the other was a truncating mutation K3326X classified in ClinVar, a public archive of reports of the relationships among human variations and phenotypes, as a benign variant [50]. In another study by Iscovich et al., a cohort of Ashkenazi Jewish patients with UM born in Israel were assessed for the BRCA2 6172 del T variant, which is a founder mutation in the Ashkenazi Jewish population. Out of 143 individuals with UM, 4 were found to have the mutation but the prevalence was not statistically significant between UM patients and the general population in Israel [51]. In another cohort of 234 UM patients, one deleterious mutation in BRCA2 and a deleterious mutation in BRCA1 were identified [25]. Finally, Hearle et al., studying 385 UM patients identified no mutation in BRCA2 [52]. In total, out of the 748 UM patients assessed for mutations in BRCA2 only two cases of clearly deleterious mutations were identified. The frequency (1:374) is similar to the BRCA2 carrier frequency of 1:250–1:350 in the general US population, Table 2. In addition, no statistical difference in the prevalence of the BRCA2 founder mutation was observed between Ashkenazi Jewish UM patients from Israel and the general Israeli population. Unfortunately, biallelic inactivation of BRCA2 in the UM tumor was not assessed in any of the reported cases. Collectively these findings suggest no or very limited evidence of association between BRCA2 deleterious mutation and UM.

Mismatch repair genes (MLH1 and MSH6) Lynch syndrome, also known as hereditary nonpolyposis colorectal cancer (HNPCC), is an autosomal dominant hereditary cancer syndrome conventionally associated with cancers of the colon and endometrium as well as ovaries, small bowel, urothelium, biliary tract, and stomach [53]. Lynch syndrome is caused by germline mutations in the MLH1, MSH2, MSH6, and PMS2 genes [53]. Lobo et al. described a case of early-onset UM and a family history of Lynch syndrome who demonstrated a germline MLH1 mutation, high-microsatellite instability (MSI-High) in the UM tumor, and a loss of MLH1/PMS2 expression in the tumor suggesting a potential role of MLH1 in UM development [26]. In addition, a missense variant of uncertain significance, p.R18C (but predicted as deleterious by multiple computational tools) has been identified in 1/80 UM patients sequenced through the TCGA [29]. And finally, a germline mutation in MSH6 was reported in one UM case in the literature, but no details were provided for that patient [28]. In summary, based on the rarity of MLH1

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truncating mutations in the general population (1:4276) and the evidence of biallelic inactivation of MLH1 in the tumor from one UM patient there is limited evidence of association of MLH1 with predisposition to UM.

MBD4 MBD4 codes for a glycosylase involved in the detection and repair of deamination of methyl-cytosines. Its inactivation in tumors leads to a hypermutator phenotype. Germline mutation in MBD4 in UM was first reported by Rodrigues 2018 in a patient with exceptional response to immune-checkpoint therapy. Deleterious germline mutations of MBD4 were found in one additional UM patient from TCGA [54]. Deleterious mutations in two additional cases was later reported by another group [55] including another patient with exceptional response to immune-based therapy. Deleterious mutations in MBD4 are extremely rare (1:803) in the general population and identifying four unrelated cases of provides limited evidence of association of the gene with predisposition to UM.

Birt-Hogg-Dube Syndrome and UM (FLCN) Birt-Hogg-Dube syndrome (BHDS) is a rare autosomal dominant disorder characterized by numerous cutaneous fibrofolliculomas and other cutaneous lesions (trichodiscomas and acrochordons), pulmonary cysts with pneumothorax, and multifocal renal tumors. BHDS is caused by germline mutations in the FLCN gene. Two cases of UM have been reported in BHDS patients [56, 57]. In one, three flat choroidal nevi near the UM were noted [56] while in the other patient sectoral choroidal melanocytosis was observed [57]. Genetic testing was carried out on one of the two cases and showed a truncating mutation (c.1285delC) in the FLCN gene [56]. Tumor biallelic inactivation of FLCN was not assessed in either report.

CDKN2A/ARF and CDK4 CDKN2A and ARF are two distinct tumor suppressor proteins encoded by alternative transcripts of the same gene CDKN2A. CDKN2A (p16INK4A) is involved in cellular G1-phase arrest by pRB inactivation [58] whereas ARF (p14ARF) promotes TP53 degradation by interacting with MDM2 [59]. Germline mutations of CDKN2A have been involved in 20% of families with hereditary predisposition to CM [60]. Kannengiesser et al. described a germline mutation of CDKN2A in a family with both UM and CM. Here, immunohistochemical staining of CDKN2A in the malignant choroidal melanoma cells demonstrated a loss of immunoreactivity suggesting a complete loss of CDKN2A function [27]. Abdel-Rahman et al. demonstrated that in seven studies of familial UM with CDKN2A testing, only 1/76 (1.3%) patients had a germline mutation in CDKN2A [61]. Out of >500 unselected UM cases tested for germline mutation in CDKN2A only one case showed pathogenic mutation suggesting that CDKN2A/ARF plays very minimal if any role in predisposition to UM.

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Low penetrant genes HERC2/OCA2 Although CM and UM have distinct somatic genetic characteristics, they frequently occur in the same families suggesting a common germline variation underlying both cancer types. We evaluated 28 single nucleotide polymorphisms (SNPs) known to be risk factors for CM in a series of 272 UM cases, and showed a significant association of UM risk with SNPs in the pigmentation genes HERC2/OCA2 [62]. The brown-eye color allele of rs12913832 was protective allele for UM (OR ¼ 0.53, 95% CI 0.42–0.67, P-value ¼ 8.4708) [62], which is consistent with the association of blue eye and fair skin with UM [63]. There is some evidence for a biological mechanism underlying this potential association. In melanocytes lacking functional OCA2, tyrosinase (TYR) doesn’t traffic properly and is retained in the ER-Golgi compartments [64, 65]. This leads to defective eumelanin (black/brown pigment) synthesis, but normal pheomelanin synthesis [66] with melanin precursor accumulation in particular DHICA. Melanin precursors have been identified in the serum and urine of melanoma patients [67, 68]. DHICA has been shown to cause single-strand breaks in plasmid DNA following UV exposure as well as in the absence of UV [69]. Reactive oxygen species and H2O2 are also formed during eumelanin synthesis which can lead to DNA damage; however, eumelanin itself has antioxidant properties [70, 71]. In summary, there is limited to moderate evidence of association of HERC2/OCA2 variant with increased predisposition to UM.

TERT/CLPTM1L A genome-wide association study (GWAS) comparing 535 UM patients and 585 control individuals revealed a new candidate locus at chromosome 5p15.33 in the TERT/CLPTM1L region (rs421284 with OR ¼ 1.7, CI 1.43–2.05, P-value ¼ 5.009) [62, 72]. This risk variant was positively associated with a higher expression of CLPTM1L, which was shown to contribute to RAS-dependent transformation and tumorigenesis [73]. TERT/CLPTM1L loci is a complex pan-cancer risk region, in which the positive or negative regulation of TERT, coding for the enzymatic subunit of the enzyme telomerase, is thought to be the major player in cancer risk [74]. However, TERT was found to be marginally expressed in UMs, without correlation between risk genotypes and its expression [72].

Summary and conclusions - Arc welding is a risk factor, while solar UV radiation plays a very limited role in the etiology of UM. - Several observations over time support a strong genetic contribution to UM etiology. - Although familial UM (FUM) is rare (80% of genetic risk for UM is still unknown. - Brown-eye color allele rs12913832 is a protective allele for UM (OR ¼ 0.53, 95% CI 0.42–0.67, p-value ¼ 8.4708) [62]. - TERT/CLPTM1L variant rs421284 is associated with UM (OR ¼ 1.7, CI 1.43–2.05, p-value ¼ 5.009).

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Ito, 5-S-cysteinyldopa as diagnostic tumor marker for uveal malignant melanoma, Jpn. J. Ophthalmol. 45 (2001) 538–542. [68] L.L. Peterson, W.R. Woodward, W.S. Fletcher, M. Palmquist, M.A. Tucker, A. Ilias, Plasma 5-S-cysteinyldopa differentiates patients with primary and metastatic melanoma from patients with dysplastic nevus syndrome and normal subjects, J. Am. Acad. Dermatol. 19 (1988) 509–515. [69] M.C. Pellosi, A.A. Suzukawa, A.C. Scalfo, P. Di Mascio, C.P. Martins Pereira, N.C. De Souza Pinto, D. De Luna Martins, G.R. Martinez, Effects of the melanin precursor 5,6-dihydroxy-indole-2-carboxylic acid (Dhica) on DNA damage and repair in the presence of reactive oxygen species, Arch. Biochem. Biophys. 557 (2014) 55–64. [70] L. Denat, A.L. Kadekaro, L. Marrot, S.A. Leachman, Z.A. Abdel-Malek, Melanocytes as instigators and victims of oxidative stress, J. Invest. Dermatol. 134 (2014) 1512–1518. [71] X. Song, N. Mosby, J. Yang, A. Xu, Z. Abdel-Malek, A.L. 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III. Mendelian Disorders and high penetrant mutations

C H A P T E R

10 Age-related macular degeneration Eiko K. de Jong, Maartje J. Geerlings, Anneke I. den Hollander Department of Ophthalmology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, The Netherlands

Introduction Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss among elderly individuals, accounting for 8.7% of blindness worldwide. The disease is most prevalent in populations of European ancestry with approximately 1%–3% of the total population suffering from an advanced form of AMD [1–3]. Globally, the total number of patients with any type of AMD is expected to increase to 288 million affected individuals in 2040 [3]. Patients experience blurring of their central visual field or images may appear as distorted (metamorphopsia). Over time, the central field may become obscured and central vision loss (scotoma) is experienced as the disease progresses [4]. The early clinical signs of AMD are the appearance of yellowish deposits, called drusen, between the retinal pigment epithelium (RPE) and Bruch’s membrane (BM). Drusen contain a high variety of components, including proteins, lipids, cholesterol, and cellular fragments of the RPE [5, 6]. In early stages of the disease drusen number is limited and does not affect visual function. As the number of drusen increases, or pigmentary changes are apparent in the retina due to the degeneration of RPE cells, the disease progresses from early to intermediate AMD [7, 8] (Fig. 1). Eventually the disease can progress to late AMD, which can be distinguished in two forms: neovascular AMD, known as wet AMD, or geographic atrophy, known as advanced dry AMD. The neovascular form is characterized by infiltration of abnormal blood vessels from the choroidal vasculature into the retina. These newly formed vessels are fragile and break easily, leaking blood in the retina and leading to sudden vision loss (Fig. 1). The second form, geographic atrophy, is the result of gradual degeneration of RPE and photoreceptors cells as well as the constriction of choroidal blood vessels [7, 8] (Fig. 1). Although neovascularization occurs in only 15%–20% of cases, it is responsible for the vast majority of vision loss caused by AMD. Intraocular injections of drugs targeting vascular

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10. Age-related macular degeneration

FIG. 1 Graphical representation of the retinal anatomy in normal and various AMD stages. (A) Normal retinal architecture focused at the macula comprised of various cell layers from anterior (top) to posterior (bottom). (B) As the disease progresses Bruch’s membrane (BM) thickens and drusen form between the BM and the retinal pigment epithelium (RPE), attracting immune cells like macrophages and microglia cells. Eventually, the disease can progress in one of two late forms: wet (C) and dry (D) AMD. Wet AMD is characterized by the invasion of abnormal and leaky blood vessels and an accumulation of macrophages. In dry AMD general degeneration of the RPE and photoreceptors is observed.

endothelial growth factor (VEGF), one of the central molecules in neovascularization, have proven to be very successful in neovascular AMD [9]. However, no treatment is available for patients who have early, intermediate or atrophic AMD. Furthermore, there are currently no effective means of preventing progression of early to advanced stages [10, 11]. IV. Complex disorders and low effect-size risk factors

157

Genomic studies in AMD

AMD is a multifactorial disease caused by the combined effect of genetic variants and environmental and lifestyle factors. A variety of nongenetic factors has been identified, of which aging, ethnicity, dietary habits, and cigarette smoke are consistently associated with AMD [1, 3, 12]. Dietary intake of antioxidants and zinc reduce the risk of developing AMD in elderly individuals [13, 14]. High doses of oral antioxidants (vitamin C, vitamin E, and carotenoids lutein and zeaxanthin), in addition to zinc, reduce AMD progression. It was shown that these supplements were able to reduce AMD progression by approximately 25% over 5 years [11, 13–15]. Evidence for a strong genetic component in AMD was established using twin and family studies. Twin studies observed a high concordance of AMD between monozygotic pairs, even double compared to dizygotic pairs, and estimated the heritability of AMD to be as high as 46%–71% [16–18]. Family studies noted a higher prevalence of AMD characteristics and an earlier onset of disease symptoms among relatives of patients compared to control families [19–21].

Genomic studies in AMD Genome-wide association studies Genome-wide association studies (GWASes) are hypothesis-free approaches that use genetic variation across the genomes of thousands of individuals to search for genetic influences on traits. The success of GWASes is based on the common-disease common-variant hypothesis that postulates that common diseases, such as AMD, can largely be explained by common variants found in >1%–5% of the population [22, 23]. Using an array-based platform [a genome-wide single-nucleotide polymorphism (SNP) microarray], on which several hundred thousands of single nucleotide variants can be captured, common variants with small to large effect sizes can be detected. In AMD, GWASes and subsequent meta-analyses have been exceptionally successful [24–38] (Table 1). The initial GWASes performed in AMD included only 96 cases; despite the small sample size, genome-wide significant associations were reported at the CFH and ARMS2/HTRA1 loci, respectively [24, 25]. This is due to the relatively TABLE 1

Genome-wide association studies performed in AMD using genome-wide SNP microarrays.

Study

Population

Variants tested

Cases

Controls

Genome-wide significant loci

Klein et al. (2005) [24]

USA

116,204

96

50

CFH

Dewan et al. (2006) [25]

Asia

97,824

96

130

ARMS2/HTRA1

Neale et al. (2010) [26]

USA

632,932

979

1709

CFH, ARMS2/HTRA1, after replication: LIPC, TIMP3/SYN3

Chen et al. (2010) [27]

USA

324,067

2157

1150

CFH, ARMS2/HTRA1, C2/CFB, C3, suggestive: TIMP3/SYN3, CETP, LIPC Continued

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TABLE 1 Genome-wide association studies performed in AMD using genome-wide SNP microarrays.—cont’d Study

Population

Kopplin et al. (2010) [28]

USA

Ryu et al. (2010) [29]

Variants tested

Cases

Controls

Genome-wide significant loci

186,807 (casecontrol cohort), 361,556 (families)

377 and 293 subjects of 34 families

161

CFH, ARMS2/HTRA1, C2/CFB, C3, SKIVL2, suggestive: MYRIP

USA

103,895

395

198

CFH, ARMS2/HTRA1

Yu et al. (2011) [30]

USA

6,036,699

2594

4134

CFH, ARMS2/HTRA1, C2/CFB, C3, CFI, after replication: TIMP3/ SYN3, LIPC, CETP, FRK/ COL10A1, VEGFA

Arakawa et al. (2011) [31]

Japan

457,489

827

3323

CFH, ARMS2/HTRA1, after replication: TNFRSF10ALOC389641, REST-C4orf14POLR2B-IGFBP7

Cipriani et al. (2012) [32]

UK

2,272,849

893

2199

CFH, ARMS2/HTRA1, C2/CFB, after replication: TNXB, NOTCH4

Sobrin et al. (2012) [33]

USA

6,036,699

2594 and 209 sibling pairs

4134

CFH, ARMS2/HTRA1, C2/CFB, C3 (GA and CNV), PECI (GA only) CFI (CNV only)

Holliday et al. (2013) [34]

USA, Europe, Australia, Asia

2,500,000

4089 (early AMD)

20,453

CFH, ARMS2/HTRA1, suggestive: APOE, GLI3, GLI2, TYR

Scheetz et al. (2013) [35]

USA

500,000

400

400 (glaucoma cases)

CFH, ARMS2/HTRA1, several suggestive loci

Naj et al. (2013) [36]

USA

668,238

1207

686

CFH, ARMS2/HTRA1, C2/CFB, suggestive: 17q22/COL1A1

Fritsche et al. (2013) [37]

worldwide

2,442,884

7650

51,844

CFH, ARMS2/HTRA1, C2/CFB, C3, TIMP3/SYN3, APOE, CETP, VEGFA, TNFRSF10A, LIPC, CFI, COL10A1, new loci: COL8A1/ FILIP1, IER3/DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9, B3GALTL

Ruamviboonsuk et al. (2017) [38]

Thailand

NA

377

1074

CFH, ARMS2/HTRA1, suggestive: LINCO1317

IV. Complex disorders and low effect-size risk factors

Genomic studies in AMD

159

large effect sizes of common variants at these two loci, CFH p.Tyr402His and ARMS2/HTRA1 p.Ala69Ser, with an odds ratio (OR) between 2.5 and 3 in these and subsequent GWASes. However, the majority of variants identified in subsequent GWASes have modest to small effect sizes, with an OR between 0.7 and 1.5. The largest meta-analysis of GWAS data to date involved 7650 cases and 51,844 control individuals, and identified common variants at 19 loci to be associated with AMD [37]. These common variants collectively have been estimated to account for 15%–65% of the genomic heritability of AMD [37].

Case-control studies for rare variants The common-disease rare-variant hypothesis proposes that rare variants, or specifically multiple risk alleles each of which is individually rare, may explain the heritability [39, 40]. Rare variants, found in 10), although not all variants individually reached genome-wide significance (reviewed in Geerlings et al. [43]). In addition, a burden of rare genotyped variants was found for the CFH, CFI, SLC16A8, and TIMP3 genes, meaning that rare variants in these genes were collectively observed more frequently in AMD cases than in controls [44]. Two smaller studies using exome arrays identified low-frequency variants in the CETP (p.Asp442Gly), C6orf223 (p.Ala231Ala), and SLC44A4 genes (p.Asp47Val) [45], and protective low-frequency variants in the PELI2 (p.Ala307Val) and CFH (p.Asn1050Tyr) genes [46]. Extremely rare or novel rare variants will not be detected using exome arrays, but require sequencing of genes to detect all genetic variation. Sequencing studies in AMD case-control cohorts have focused on targeted gene sets [47–51], or involved a genome-wide approach using whole exome sequencing (WES) [52–56] or whole genome sequencing (WGS) [57, 58] (Table 2). Associations of single rare variants with AMD were detected in the CFH (p.Arg1210Cys), CFI (p.Gly119Arg), C3 (p.Lys155Gln), C9 (p.Pro167Ser), C2 (p.Pro73Leu, p.Arg269His), CFB (p.Arg74His), and FGD6 (p.Lys329Arg) genes [47–51, 53, 57]. In addition, a burden of rare or low-frequency variants was detected in the CFI, COL8A1, and CETP genes, while a lower occurrence of low-frequency variants was detected in the C2 and CFB genes in AMD cases compared to controls [49, 51, 55].

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TABLE 2 Case-control association studies for rare variants in AMD. Study

Population

Method

Cases

Controls

Associated genes/variants

Raychaudhuri et al. (2011) [47]

USA

Sequencing of CFH introns, exons and promoter

33

27

CFH variant p.Arg1210Cys

Van de Ven et al. (2013) [48]

Netherlands, Germany

Sequencing of CFI exons

84

192

CFI variant p.Gly119Arg

Helgason et al. (2013) [57]

Iceland

WGS followed by imputation

1143

51,435

C3 variant p.Lys155Gln

Seddon et al. (2013) [49]

USA

Sequencing of 681 genes in AMD loci

1676

745

Rare variant burden in CFI gene; C3 variant p. Lys155Gln, C9 variant p. Pro167Ser

Zhan et al. (2013) [50]

USA

Sequencing of 57 genes in 10 AMD loci

2335

789

CFH variant p.Arg1210Cys, C3 variant p.Lys155Gln

Cheng et al. (2015) [45]

Asia

Exome chip, including 4,471,719 SNPs and 120,027 coding variants

2119

5691

Low-frequency CETP variant p.Asp442Gly, lowfrequency C6orf223 variant p.Ala231Ala, lowfrequency SLC44A4 variant p.Asp47Val, common FGD6 variant p.Gln257Arg

Huang et al. (2015) [52]

China

WES

216

1553

Common UBE3D variant p.Val379Met

Fritsche et al. (2016) [44]

worldwide

Exome chip, 12,023,830 variants including 163,714 directly typed protein-altering variants

16,144

17,832

Common variants at 16 new loci: COL4A3, PRLR/SPEF2, PILRB/PILRA, KMT2E/ SRPK2, TRPM3, MIR6130/ RORB, ABCA1, ARHGAP21, RDH5/CD63, ACAD10, CTRB2/CTRB1, TMEM97/VTN, NPLOC4/ TSPAN10, CNN2, MMP9, C20orf85; seven rare variants: CFH variants p.Arg1210Cys, rs148553336, rs191281603, rs35292876, CFI variant p.Gly119Arg, C9 variant p.Pro167Ser, C3 variant p.Lys155Gln; rare variant burden in CFH, CFI, TIMP3, and SLC16A8 genes

Huang et al. (2016) [53]

China

WES

194 (PCV), 155 (CNV)

1253

Rare FGD6 variant p.Lys329Arg

IV. Complex disorders and low effect-size risk factors

161

Genomic studies in AMD

TABLE 2

Case-control association studies for rare variants in AMD.—cont’d

Study

Population

Method

Cases

Controls

Associated genes/variants

Momozawa et al. (2016) [51]

Japan

Sequencing of 34 genes in AMD loci

2886

9337

Common SKIVL2 variant p.Met214Leu; suggestive: low-frequency C2 variants p.Pro73Leu and p. Arg269His and CFB variant p.Arg74His; enrichment of low-frequency CETP variants in cases; lower frequency of low-frequency C2 and CFB variants in cases

Sardell et al. (2016) [54]

USA

WES

39 (low genetic risk score)

36 (high genetic risk score)

No genome-wide significant associations

Yu et al. (2016)

USA

Exome chip, including 72,503 rare nonsynonymous variants

4332

25,268

Protective low-frequency variants: PELI2 p.Ala307Val and CFH p.Asn1050Tyr; protective common variant in CTRB1

Pietraszkiewicz et al. (2018) [58]

USA

WGS

1689

1518

Enrichment of rare loss-offunction variants in the complement pathway

Corominas et al. (2018) [55]

Netherlands

WES

1125

1361

Rare variant burden in COL8A1

Wen et al. (2018) [56]

China

WES

20 (PCV), 21 (CNV)

20

IGFN1 variant (PCV only)

Family-based studies for rare variants Family studies have shown a higher prevalence of AMD characteristics among relatives of AMD patients than in control families [19–21, 59]. The genetic risk of families with multiple affected individuals can in part be explained by a clustering of common risk variants in these families [60]. In families, for which common variants cannot explain the high burden of disease, the heritability is thought to lie in rare variants with large effect sizes [60]. These rare variants can be difficult to detect in case-control studies due to their rare frequency, appearance in only one or a few families, or occurrence in specific populations [46, 61, 62]. Recently, a number of studies using WES have successfully identified novel rare genetic variants by analyzing multiple affected individuals of large AMD families [46, 61–67] (Table 3). The majority of these rare genetic variants were detected in the CFH and CFI genes [46, 61, 62, 65–67]; other genes include FBN2 and HMCN1 [62, 63]. In some families these variants (FBN2 p.Glu1144Lys; CFH p.Asp90Glu, p.Arg53Cys, p.Cys192Phe, c.790+1G>A, p.Arg175Pro, p. Arg127His; CFI p.Val412Met; HMCN1 p.Pro1388Hisfs*14) completely segregated with the

IV. Complex disorders and low effect-size risk factors

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10. Age-related macular degeneration

TABLE 3 Family-based studies for rare variants identified in AMD using WES. Study

Family structure and segregation

Gene

Variant(s)

Detection method

Additional genotyping

Ratnapriya et al. (2014) [63]

Two generations; five affected and two unaffected; complete segregation

Fibrillin 2 (FBN2)

c.3430G > A; p. Glu1144Lys

WES, Sanger sequencing, allelespecific genotyping

Sequencing of exon 24–34 in 196 individuals identified 4 additional rare variants in FBN2. Common variant c.2893G > A; p. Val965Ile in FBN2 showed a suggestive association (OR 1.10; p ¼ 3.79  105) in a case-control study of 11,511 individuals.

Hoffman et al. (2014) [61]

One generation; four affected and two unaffected; incomplete segregation

CFH

c.1507C > G; p. Pro503Ala

WES, custom array

Genotyped 973 Amish individuals, including 95 selfreported AMD cases and 2371 non-Amish individuals. An additional 15 CFH p. Pro503Ala carriers were identified in the Amish cohort (8 affected and 5 unaffected). No carriers were identified in the nonAmish.

Yu et al. (2014) [46]

Two families: (A) one generation of five affected and one unaffected. (B) two generations; eleven affected and one unaffected. Complete segregation in both families

CFH

(A) c.269A > G; p. Asp90Glu (B) c.157C > T; p. Arg53Cys

WES, allelespecific genotyping, custom array

Screening of 2421 individuals revealed four CFH p. Asp90Glu carriers affected by AMD, but CFH p.Arg53Cys was not found.

Pras et al. (2015) [62]

Three two generation families; (A) two affected and four unaffected; (B) and (C) five affected and two unaffected; complete segregation

CFI in family A and B.

CFI c.1234G > A; p.Val412Met

WES, Sanger sequencing, restriction digestion assay

For CFI, WES data of 146 individuals revealed two CFI carriers. Restriction digest assay revealed 10 carriers in 200 unrelated population controls

Hemicentin (HMCN1) in family C.

HMCN1 c.4162delC; p.Pro1388Hisfs*14

IV. Complex disorders and low effect-size risk factors

163

Genomic studies in AMD

TABLE 3 Study

Family-based studies for rare variants identified in AMD using WES.—cont’d Family structure and segregation

Gene

Variant(s)

Detection method

Additional genotyping (Jewish Tunisian). For HMCN1, the variant was not identified in the WES cohort and was unknown in public databases.

Duvvari et al. (2016) [64]

Five families; at least two affected individuals; incomplete segregation

Screening 289 candidate genes.

A total of nine variants were detected in four families. In two families no variants remained.

WES

In addition to the 14 family members, 12 sporadic cases were screened for the candidate gene list using WES.

Saksens et al. (2016) [65]

22 families; at least three affected individuals; incomplete segregation

Screening for specific rare variants

CFI c.355G > A p. Gly119Arg; C9 c.499C > T; p. Pro167Ser, and C3 c.463A > C; p. Lys155Gln

WES, Sanger sequencing, allelespecific genotyping

In addition to the 174 family members, 2975 individuals were screened for four rare genetic variants

Wagner et al. (2016) [66]

Four families: (A) one generation with five affected and two unaffected. (B) and (C) two affected siblings and (D) two generations with three affected and one unaffected; complete segregation

CFH

(A) c. c.575G > T; p. Cys192Phe (B) c.790 + 1G > A (C) c.524G > C; p. Arg175Pro (D) c.380G > A; p. Arg127His

WES

Geerlings et al. (2017) [67]

22 families; at least three affected individuals; incomplete segregation

Screening candidate genes CFH, CFI, C3, and C9.

CFH c.578C > T; p. Ser193Leu, CFH c.524G > A; p. Arg175Gln, CFI c.1657C > T; p. Pro553Ser, CFI c.392 T > G; p. Leu131Arg, C9 c. 352C > T; p. Arg118Trp, and C3 c.481C > T; p. Arg161Trp

WES, Sanger sequencing, allelespecific genotyping

In addition to the 174 family members, 3198 individuals were screened for specific rare variants in CFH, CFI, C3, and C9.

Abbreviations: BM ¼ Bruch’s Membrane; DAA ¼ decay-accelerating activity; WES ¼ whole-exome sequencing.

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10. Age-related macular degeneration

disease [46, 62, 63, 66], while incomplete segregation was observed in other families [61, 64, 65, 67]. Incomplete segregation reflects the multifactorial nature of the disease, where the occurrence of AMD in families is determined by a combination of common variants, rare variants, and environmental factors.

Effect sizes of common versus rare variants Genetic studies have identified both common and rare variants in AMD. The common variants generally have relatively small effect sizes, while several rare variants have been reported with large effect sizes. This is reflected by an inverse relationship between the OR and the frequency of the genetic variant in the population [68] (Fig. 2). Variants such as CFH rs570618 (in high linkage disequilibrium with CFH p.Tyr402His), ARMS2 rs3750846 (in high linkage disequilibrium with ARMS2 p.Ala69Ser), and protective CFH variant rs10922109, are unique examples of common variants with relatively large effect sizes (OR > 2, or in the case of protective variants OR < 0.5). These variants plot in the lower right corner of Fig. 2 and are effectively identified by GWAS. Rare variants with small effect sizes plot in the left lower corner of Fig. 2. These types of variants are difficult to detect and require large sample sizes to reach genome-wide significance. Rare variants with a large effect size, like CFH p.Arg1210Cys and CFH p.Arg53Cys (upper left corner of Fig. 2), behave like highly penetrant Mendelian mutations, and are often

FIG. 2 Frequency and effect size of AMD-associated variants. Odds ratio (OR; y-axis) and minor allele frequency (MAF; x-axis) with MAF based on control individuals without AMD. The filled diamonds represent 52 independently associated rare and common variants found in 34 loci as published by Fritsche et al. [44]. The outlined diamonds represent 23 rare variants in CFH, CFI, C3, and C9 that reached P < 0.05 as reviewed in Geerlings et al. [43]. Variants CFH p.Arg1210Cys, CFI p.Gly119Arg, C9 p.Pro167Ser, and C3 p.Lys155Gln (in bold) are shown both as filled and outlined diamonds. Protective ORs are transformed (1/OR) and depicted in orange.

IV. Complex disorders and low effect-size risk factors

Effect of AMD-associated variants on disease mechanisms

165

found to cluster in populations or families. A challenge when studying rare variants, however, is that the effect size can only be estimated if the variant is identified in a sufficient number of cases and controls to calculate the OR. For rare variants identified in only a small number of cases or families, the effect size cannot accurately be calculated, making it difficult to provide an accurate risk assessment to carriers of such rare variants.

Contribution of common versus rare variants Common variants are estimated to be the major contributor to disease risk in complex disorders. Variants involved in complex diseases are skewed toward MAF >20%, rather than low-frequency (MAF T, CAST c.-40 + 7414T > C, DOCK9 c.2262A > C, IL1RN c.214 + 242C > T, and SLC4A11 c.2558 + 149_2558 + 203del54 [60, 72, 87, 88]. Each one of these variants, except the variant in MIR184, has been suggested to be associated with KC. MIR184 c.57C > T has been functionally confirmed to be a mutation using an in vitro TABLE 1

Linkage loci identified in KC-affected families.

Linkage region(s)

Population

Gene(s) and variants

References

14q24.3

White English, Indian, Iranian, and Pakistani

N/A

[70]

5q32-q33

Southern Italian

N/A

[71]

2q13-q14.3 and 20p13-p12.2

Ecuadorian

IL1RN c.214+ 242C>T SLC4A11c.2558+ 149_2558+203del54

[72]

5q14.3-q21.1

Caucasian

CAST c.-40+ 7414T>C

[73, 74]

16q22.3-q23.1

Northern Finnish

N/A

[75]

15q22.32-24.2

Northern Irish

MIR184 c.57C>T

[60, 76, 77]

13q32

Ecuadorian

DOCK9 c.2262A>C, DOCK9 c.720 +43A>G, IPO5 c.2377-132A >C, STK24 c.1053+ 29G>C

[78–80]

17p13

Pakistani

N/A

[81]

9q34

White/Hispanic

N/A

[82]

1p36.23-p36.21 and 8q13.1-q21.11

Caucasian

N/A

[83]

3p14-q13

Italian

N/A

[84]

2p24

Caucasian, Arabian, and Caribbean African

N/A

[85]

N/A, not available.

IV. Complex disorders and low effect-size risk factors

224

13. KC Genetics

model. Thus, in vitro and/or in vivo models are necessary to include after identifying the potential pathogenic variants in KC to verify the role of the identified variants in KC pathogenesis. However, various limitations may hinder the discovery of KC pathogenic mutations using linkage analysis including (1) limited resolution of linkage analysis (a large-size linkage locus containing numerous putative genes which require further filtering), (2) limited statistical power as the statistical evaluation depends on the evidence in favor of the co-segregation of marker loci with a trait, (3) family collection difficulty as linkage analysis requires a multigeneration family with many affected and unaffected individuals [66, 69], and (4) large genetic heterogeneity in KC pathogenesis with incomplete penetrance.

Genome-wide association studies in KC Genome-wide association studies (GWAS) is a method performed by genotyping hundreds of thousands of single nucleotide polymorphisms (SNPs) genome wide for statistical associations between a marker and a trait in unrelated large case-control populations. A P-value of less than 5  108 is often used as a significance threshold in GWAS to minimize the false discovery rate. Thus, a significant alteration of the allele frequency with a SNP, between cases and controls, is considered to be associated with the disease [69, 89]. GWAS have identified multiple KC-associated SNPs in different population groups from China, Australia, Europe, and the United States (Table 2). HGF upstream SNPs were reported to be associated with KC in four of five cohorts [92]. Moreover, the same study reported a significant elevation of serum HGF concentration with each T allele of the SNP rs3735520 [92]. Basal epithelial cells of keratoconic corneas showed moderate-to-strong immunoreactivity for HGF and its receptor (c-met) [101]. HGF encodes for hepatocyte growth factor, a corneal expressing protein, and has been reported to show upregulation in response to corneal injury. Thus, HGF elevation could be associated with KC pathogenesis, but more work is TABLE 2 SNPs associated with keratoconus identified through genome-wide association studies. Gene (SNP ID)

Functional category

Population

Reference

LOX (rs10519694, rs2956540), (rs2956540)

Intronic

Caucasian

[90, 91]

HGF (rs3735520, rs17501108/rs1014091)

Upstream

Caucasian (Australia and US)

[91, 92]

RAB3GAP1 (rs4954218)

Upstream

Caucasian

[93, 94]

FOXO1 (rs2721051)

Intergenic

South Australian and US

[95]

FNDC3B (rs4894535),

Intronic

South Australian and US

[95]

RXRA-COL5A1 (rs1536482)

Intergenic

South Australian and US

[95]

RXRA-COL5A1 (rs3118515)

Intronic

Latinos

[96]

MPDZ-NF1B (rs1324183)

Intergenic

Australian, US, and Chinese

[95, 97, 98]

COL5A1 (rs7044529)

Intronic

South Australian and US

[95]

COL5A1 (rs1536482/rs7044529)

Intergenic/Intronic

Caucasian

[99]

BANP/ZNF469 region (rs9938149)

Intergenic

South Australian and US

[95, 97]

WNT10A (rs121908120)

Missense

Australian

[100]

IV. Complex disorders and low effect-size risk factors

Genetics of KC

225

needed to identify the mechanism through which HGF expression is enhanced during KC manifestation. Another SNP (rs4954218) in the RAB3GAP1 gene (RAB3GTPase-activating protein) has been identified by two independent studies in Caucasian populations [93, 94]. SNP rs4954218 has been hypothesized to have a regulatory role with two genes [RAB3GAP1 (6.4 kb away) and YSK4 (21.2 kb away)]. However, further studies are necessary to confirm this regulatory role [93]. Interestingly, RAB3GAP1 mutations have been reported in patients with Warburg Micro syndrome [102–105]. It is a rare autosomal recessive syndrome characterized with severe mental retardation, microcephaly, microphthalmia, microcornia, and congenital cataracts [102–105]. Central corneal thickness (CCT) is a clinical phenotype related to KC development. Thinner CCT is considered as a risk factor for developing KC. CCT is a normally distributed quantitative trait with an estimated heritability of up to 95% [106]. GWAS identified numerous CCT-associated genes including RXRA-COL5A1, COL8A2, FAM53B, FOXO1, and ZNF469, which were investigated in several large KC cohorts [107–111]. Rare congenital connective tissue disorders such as brittle cornea syndrome and osteogenesis imperfecta are usually accompanied by extremely thin corneas [112, 113]. SNPs in collagen coding genes have been reported to be associated with CCT, which helps researchers analyze the association between CCT and KC in large cohorts [95, 108]. Lu et al. identified six SNPs, which later were replicated by others in different populations [96–99, 114]. Recently, Iglesias et al. identified 36 CCTassociated SNPs, of which 28 SNPs (77%) were positively associated with KC, which adds evidence to the impact of CCT in KC risk [108]. Pathway enrichment analysis for the CCT and KC-associated loci (including ADAMTS2, ADAMTS8, COL6A2, COL12A1, FBN1, LOXL2, LUM/DCN/KERA, THSB2, TGFB2, and LTBP1) indicates that collagen, ECM, cornea thickness, and TGFB signaling pathways potentially have a significant role in KC pathogenesis [95, 108]. Moreover, the neighboring genes for the most significant SNPs have been found to be highly expressed in the corneas of both humans and mice, including COL8A2, COL5A1, FNDC3B, FAM46A, LPAR1, SMAD3, and ZNF469 [95]. Iglesias et al. have used the Ingenuity Pathway Analysis (IPA) for the KC-associated genes and have reported many genes involved in functions in connective tissues, the visual system, and skeletal muscle disorder. Additionally, knockout mice models for KC-associated genes have shown corneal-related phenotypes including corneal opacity (LUM) [115–117], corneal endothelium absence (TGFB2) [118], and also thin corneal stroma (FBN1, KERA, LUM, and TGFB2), which is a feature for KC corneas [115–120]. One significant SNP (rs7308752) is located near three different genes DCN, KERA, and LUM. Despite being different genes and each encoding a different proteoglycan protein, all are highly expressed and have a significant role in corneal integrity and transparency [121–126]. Many KC-associated SNPs identified through GWAS are implicated in similar pathways including collagen fibrils and ECM regulatory pathway. Thus, even though the unrelated KC cases may have a different genetic etiology, shared signaling pathways may have a major role in controlling the disease progression. Further research is necessary to study these pathways to identify potential therapeutic targets.

KC candidate genes identified by Sanger sequencing or targeted genotyping Multiple KC studies have used direct screening of specific genes in search of the mutation(s) that may implicate the disease pathogenesis. VSX1 is one of these genes that IV. Complex disorders and low effect-size risk factors

226

13. KC Genetics

TABLE 3 Direct screening for KC candidate genes. Gene

Mutation(s)

Population

Reference

VSX1

R166W, L159M, G160D

Caucasian

[127]

P247R, D144E L17P, G160D

Italian

[128]

R166W, H244R

Iranian

[129]

N151S, G160V

Korean

[130]

Q175H

Indian

[131, 132]

COL4A3

D326Y (only significantly distinctive in KC patients)

Slovenian

[133]

COL4A4

M1237V, F1644F (only significantly distinctive in KC patients)

COL4A4

M1327V

Iranian

[134]

SOD1

7-base deletion in intron 2

Not mentioned

[135]

Greek

[136]

CAST (rs4434401)

Intronic

Caucasian

[88]

IL1B (rs1143627 and rs16944)

Promoter

Korean, Japanese, and Chinese

[137–139]

has been widely studied in KC-affected individuals with different ethnicities (Table 3). Several VSX1 mutations have been reported in KC and posterior polymorphous corneal dystrophy including D144E, G160D, P247R, and L17P [127, 128]. Moreover, P247R and D144E have been reported to follow familial segregation in Italian individuals by Bisceglia et al. [128]. However, others have not identified a pathogenic VSX1 mutation in their studies [80, 136, 140–147]. Udar et al. identified a deletion of seven bases in the second intron of SOD1, which co-segregates with KC in one family [135, 148]. SOD1 is a major cytoplasmic antioxidant enzyme that catalyzes the conversion of superoxide radicals to molecular oxygen and hydrogen peroxide [149]. Moschos et al. reported a similar finding in a Greek population [136]. However, Stabuc-Silih et al. failed to identify any pathogenic mutations in SOD1 [144]. Interestingly, the activity of extracellular SOD1 in the central cornea of KC was found to be reduced by 50% compared with that in normal control corneas [150]. Thus, higher corneal oxidative stress could be the potential result of the identified SOD1 intronic deletion in KC patients [136, 150]. Irregularities and breaks in the Bowman’s layer were featured histopathological changes in keratoconic corneas [7, 151]. Bowman’s layer is a collagenous layer containing collagen type IV [151]. These histopathological changes motivated Stabuc-Silih and his colleagues to screen COL4A3 and COLA4 for any pathogenic mutations in 104 unrelated KC patients versus 157 healthy individuals [133]. COL4A3 and COL4A4 encode for two of six α-chains that form heterotrimeric type IV collagen molecules [152]. Although they couldn’t find any KCspecific mutations, they identified three coding variants, D326Y in COL4A3, M1237V, and

IV. Complex disorders and low effect-size risk factors

Conflict of Interest

227

F1644F in COL4A4 to be associated with KC risk (Fisher’s exact test, P < .05) [133]. Later, an Iranian group replicated the association of variant M1237V in COL4A4 gene with KC [134]. Results from both candidate gene studies and GWAS support the involvement of the deregulated collagen fibril and ECM regulation in KC, which also reflects the corneal histopathological changes observed in KC. The combination between linkage and association studies is a valuable method to identify candidate mutations in the specific linkage region. A research group led by Dr. Rabinowitz, who identified a chromosome 5 linkage region 5q14.3-q21.1 in a Caucasian family, genotyped tightly spaced SNPs in this region using both family and case-control KC cohorts. They successfully identified a SNP in the CAST (rs4434401) intronic region to be significantly associated with KC in both familial and case-control datasets [88]. CAST encodes for calpastatin, a natural endogenous highly specific inhibitor of calpains, which plays a role in corneal epithelial cell turnover and wound healing in rabbits [153]. LOX intronic SNPs (rs10519694 and rs2956540) were also identified in both family-based and case-control studies [90]. LOX encodes for lysyl oxidase which is a copper-dependent amine oxidase responsible for the formation of lysine-derived crosslinks in ECM proteins [154]. A number of SNPs have been replicated in independent studies including LOX (rs2956540) and HGF (rs3735520) [90–92], RAB3GAP1 (rs4954218) [93, 94]. Variants in IL1B promoter region may cause overexpression of IL1B which results in KC-associated keratocytes apoptosis. Two SNPs in IL1B (rs1143627 and rs16944) have also been reported to be associated with KC in Korean, Japanese, and Chinese populations [137–139]. IL1B encodes for interleukin 1 beta, a member of the IL1 superfamily, which plays an important role in inflammatory response, cell growth, and tissue repair [155]. However, a study on Turkish individuals did not identify any significant association between IL1B SNP (rs16944) and KC [156].

Conclusion In conclusion, KC is a complex multifactorial disorder. It appears in both genders and all ethnicities. Treatment of KC patients varies from spectacles to corneal transplantation according to the progression stage of the disease. Multiple pieces of evidence indicate that genetic factors play an important role in KC pathogenesis. Linkage analyses have been widely used to identify the causative mutation(s) in KC families. Identifying novel mutations in KC families and patients remains challenging. GWAS have successfully identified many genetic variants associated with KC risk, many of which are associated with CCT. It is necessary to characterize the potential role of these KC-associated genetic variants in normal and KC-affected corneas. The recent development in human genome/exome sequencing and new large-scale KC cohorts will lead to the novel discovery of additional KC-related genetic factors in the near future.

Conflict of Interest None for all authors.

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13. KC Genetics

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[128] L. Bisceglia, M. Ciaschetti, P. De Bonis, P.A.P. Campo, C. Pizzicoli, C. Scala, M. Grifa, P. Ciavarella, N.D. Noci, F. Vaira, C. Macaluso, L. Zelante, VSX1 mutational analysis in a series of Italian patients affected by keratoconus: detection of a novel mutation, Invest. Ophthalmol. Vis. Sci. 46 (2005) 39–45. [129] S. Saee-Rad, H. Hashemi, M. Miraftab, M.R. Noori-Daloii, M.H. Chaleshtori, R. Raoofian, F. Jafari, W. Greene, G. Fakhraie, F. Rezvan, M. Heidari, Mutation analysis of VSX1 and SOD1 in Iranian patients with keratoconus, Mol. Vis. 17 (2011) 3128–3136. [130] J.W. Mok, S.J. Baek, C.K. Joo, VSX1 gene variants are associated with keratoconus in unrelated Korean patients, J. Hum. Genet. 53 (2008) 842–849. [131] P. Paliwal, A. Singh, R. Tandon, J.S. Titiyal, A. Sharma, A novel VSX1 mutation identified in an individual with keratoconus in India, Mol. Vis. 15 (2009) 2475–2479. [132] P. Paliwal, R. Tandon, D. Dube, P. Kaur, A. Sharma, Familial segregation of a VSX1 mutation adds a new dimension to its role in the causation of keratoconus, Mol. Vis. 17 (2011) 481–485. [133] M. Sˇtabuc-Sˇilih, M. Ravnik-Glavac, D. Glavac, M. Hawlina, M. Strazˇisˇar, Polymorphisms in COL4A3 and COL4A4 genes associated with keratoconus, Mol. Vis. 15 (2009) 2848–2860. [134] R. Saravani, F. Hasanian-Langroudi, M.H. Validad, D. Yari, G. Bahari, M. Faramarzi, M. Khateri, S. Bahadoram, Evaluation of possible relationship between COL4A4 gene polymorphisms and risk of keratoconus, Cornea 34 (2015) 318–322. [135] N. Udar, S.R. Atilano, D.J. Brown, B. Holguin, K. Small, A.B. Nesburn, M.C. Kenney, SOD1: a candidate gene for keratoconus, Invest. Ophthalmol. Vis. Sci. 47 (2006) 3345–3351. [136] M.M. Moschos, N. Kokolakis, M. Gazouli, I.P. Chatziralli, D. Droutsas, N.P. Anagnou, I.D. Ladas, Polymorphism analysis of VSX1 and SOD1 genes in Greek patients with keratoconus, Ophthalmic Genet. 36 (2015) 213–217. [137] S.H. Kim, J.W. Mok, H.S. Kim, C.K. Joo, Association of -31T >C and -511 C> T polymorphisms in the interleukin 1 beta (IL1B) promoter in Korean keratoconus patients, Mol. Vis. 14 (2008) 2109–2116. [138] T. Mikami, A. Meguro, T. Teshigawara, M. Takeuchi, R. Uemoto, T. Kawagoe, E. Nomura, Y. Asukata, M. Ishioka, M. Iwasaki, K. Fukagawa, K. Konomi, J. Shimazaki, T. Nishida, N. Mizuki, Interleukin 1 beta promoter polymorphism is associated with keratoconus in a Japanese population, Mol. Vis. 19 (2013) 845–851. [139] Y. Wang, W. Wei, C. Zhang, X. Zhang, M. Liu, X. Zhu, K. Xu, Association of interleukin-1 gene single nucleotide polymorphisms with keratoconus in Chinese Han population, Curr. Eye Res. 41 (2016) 630–635. [140] A.J. Aldave, V.S. Yellore, A.K. Salem, G.L. Yoo, S.A. Rayner, H. Yang, G.Y. Tang, Y. Piconell, Y.S. Rabinowitz, No VSX1 gene mutations associated with keratoconus, Invest. Ophthalmol. Vis. Sci. 47 (2006) 2820–2822. [141] D.P. Dash, S. George, D. O’prey, D. Burns, S. Nabili, U. Donnelly, A.E. Hughes, G. Silvestri, J. Jackson, D. Frazer, E. Heon, C.E. Willoughby, Mutational screening of VSX1 in keratoconus patients from the European population, Eye (Lond.) 24 (2010) 1085–1092. [142] F.A. Dehkordi, A. Rashki, N. Bagheri, M.H. Chaleshtori, E. Memarzadeh, A. Salehi, H. Ghatreh, F. Zandi, N. Yazdanpanahi, M.A. Tabatabaiefar, M.H. Chaleshtori, Study of VSX1 mutations in patients with keratoconus in southwest Iran using PCR-single-strand conformation polymorphism/heteroduplex analysis and sequencing method, Acta Cytol. 57 (2013) 646–651. [143] P. Liskova, N.D. Ebenezer, P.G. Hysi, R. Gwilliam, M.F. El-Ashry, L.C. Moodaley, S. Hau, M. Twa, S.J. Tuft, S.S. Bhatacharya, Molecular analysis of the VSX1 gene in familial keratoconus, Mol. Vis. 13 (2007) 1887–1891. [144] M. Stabuc-Silih, M. Strazisar, M. Hawlina, D. Glavac, Absence of pathogenic mutations in VSX1 and SOD1 genes in patients with keratoconus, Cornea 29 (2010) 172–176. [145] Y.G. Tang, Y. Picornell, X. Su, X. Li, H. Yang, Y.S. Rabinowitz, Three VSX1 gene mutations, L159M, R166W, and H244R, are not associated with keratoconus, Cornea 27 (2008) 189–192. [146] M. Tanwar, M. Kumar, B. Nayak, D. Pathak, N. Sharma, J.S. Titiyal, R. Dada, VSX1 gene analysis in keratoconus, Mol. Vis. 16 (2010) 2395–2401. [147] A. Verma, M. Das, M. Srinivasan, N.V. Prajna, P. Sundaresan, Investigation of VSX1 sequence variants in South Indian patients with sporadic cases of keratoconus, BMC Res. Notes 6 (2013) 103. [148] N. Udar, S.R. Atilano, K. Small, A.B. Nesburn, M.C. Kenney, SOD1 haplotypes in familial keratoconus, Cornea 28 (2009) 902–907. [149] I. Fridovich, Superoxide radical and superoxide dismutases, Annu. Rev. Biochem. 64 (1995) 97–112. [150] A. Behndig, K. Karlsson, B.O. Johansson, T. Brannstrom, S.L. Marklund, Superoxide dismutase isoenzymes in the normal and diseased human cornea, Invest. Ophthalmol. Vis. Sci. 42 (2001) 2293–2296.

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C H A P T E R

14 Genetic testing of various eye disorders Riccardo Sangermanoa, Hilary Scotta, Naomi Wagner, Emily Place, Kinga M. Bujakowska Ocular Genomics Institute, Massachusetts Eye and Ear Infirmary, Department of Ophthalmology, Harvard Medical School, Boston, MA, United States

About 12.5 million Americans suffer from one of the common vision impairments such as age-related macular degeneration, diabetic retinopathy, or glaucoma, all of which may result in severe vision impairment or blindness in the later stages of life [1]. Therapeutic treatments for these diseases are currently limited, partially due to a poor understanding of their causes, which are a combination of environmental and genetic risk factors [1–4]. Further 24.4 million people in the United States have cataracts, which also have a genetic etiology but for which surgical treatment has proven very effective [1, 5, 6]. The prevalence of common ocular diseases is estimated to double in the United States by 2050, due to the aging population [1], therefore it is of great importance to determine the genetic causes in order to develop adequate treatments. In addition to common diseases, an estimated 1 in 250–400 individuals is affected by a hereditary ocular disorder, which include syndromic and non-syndromic forms of retinal degeneration, hereditary glaucoma, corneal dystrophies, and eye movement disorders among othersa [7]. The presence of both complex and Mendelian forms of ocular diseases requires tailored genetic testing approaches to identify the underlying causes. The Online Mendelian a

These authors contributed equally to this work.

a

The cumulative prevalence of hereditary eye disorders was estimated from data provided by authors of the specific chapters in Gene Reviews [7]. The following disorders were included: Alstr€ om syndrome, aniridia, anophthalmia, ataxia with oculomotor apraxia, optic atrophy, chondrodysplasia punctata, coloboma, congenital disorder of glycosylation, congenital fibrosis of the extraocular muscles, disorders of intracellular cobalamin metabolism (methylmalonic aciduria and homocystinuria), Bardet-Biedl syndrome, Duane syndrome, hereditary ataxia, idiopathic infantile nystagmus, incontinentia pigmenti, Joubert syndrome, Leber congenital amaurosis, Leber hereditary optic neuropathy, Leigh syndrome, Lowe syndrome, Marfan syndrome, microphthalmia, NARP (neuropathy, ataxia, and retinitis pigmentosa), non-syndromic retinitis pigmentosa, Noonan syndrome, ocular and oculocutaneous albinism, C3 glomerulopathy, primary congenital glaucoma, Treacher-Collins syndrome (eyelid coloboma), Usher syndrome, Von Hippel-Lindau syndrome, Weill-Marchesani syndrome, Wolfram syndrome, and Zellweger spectrum disorder.

Genetics and Genomics of Eye Disease https://doi.org/10.1016/B978-0-12-816222-4.00014-9

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Copyright # 2020 Elsevier Inc. All rights reserved.

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Inheritance in Man (OMIM) database includes almost 1200 genes (23% of the total) that are associated with an ocular phenotype (https://www.omim.org, last accessed November 2018). Multiple classes of pathogenic variants have been associated with ocular disorders, which include coding and non-coding changes, copy number variation (CNV), and chromosomal aberrations. The detection of different mutation types can be achieved by employing specific sequencing strategies, while follow-up experimentation is required to fully assess their pathogenicity. This chapter describes the different aspects that need to be considered for the adequate design of the genetic diagnostic tests and data analysis. Most of the examples are taken from the genetically heterogeneous group of diseases called inherited retinal degenerations (IRDs).

Overview of genetic techniques Early efforts to find genetic causality of disease were primarily carried out through linkage analysis in large pedigrees, which relied on common variation of the genomic sequence such as restriction fragment length polymorphism (RFLPs) [8], variable number of tandem repeats (VNTRs) [9], and single nucleotide polymorphisms (SNPs) [10, 11]. The precision of analyzing DNA at levels of a single nucleotide came with a technique developed by Sanger and Coulson, where DNA polymerization was coupled with chain-terminating dideoxynucleotides, resulting in DNA fragments of variable length that could be resolved by polyacrylamide gel electrophoresis or capillary electrophoresis [12]. In parallel, Maxam and Gilbert developed a non-enzymatic sequencing method which relied on selectively fragmenting DNA molecules at specific bases [13]. The major limitation of the above methods was their low throughput and high cost per sequence base, which led to the development of costeffective techniques, such as denaturing gradient gel electrophoresis (DGGE) [14] and single-strand conformational polymorphism (SSCP) [15], which were used for the detection of known pathogenic variants. The first high-throughput method for the genetic diagnostic testing came at the turn of the century with arrayed primer extension (APEX), which relied on hybridization of probes immediately upstream of the investigated variants, followed by a polymerase-based extension with a fluorescently labeled nucleotide [16, 17]. In the early 2000s, next-generation sequencing (NGS) technology revolutionized genomic research by providing high throughput and low cost per base. Sequencing of the first human genome in 2001 with an automated Sanger method cost $100,000,000 and took a year, whereas in 2018 it cost $1000 and took 1–2 days [18–20]. There are a few NGS techniques, which differ by the chemistry used, sequence-read length, variant calling accuracy, and throughput. What unites them however is the parallel sequencing of millions of molecules and large volumes of data, the analysis of which require sophisticated bioinformatic algorithms [18, 21, 22]. The NGS can be applied to selected regions of interest, for example, exons of specific genes or all exons in the genome (i.e., whole exome sequencing—WES) and to the entire genomic sequence of an individual (i.e., whole genome sequencing—WGS). Each of these strategies can be used in the genetic diagnostic setting; however, to reduce experimental cost and analytical burden, a multitier approach is commonly used, with WGS only performed when no genetic causes are found after WES analysis (Table 1).

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Detecting coding variation

TABLE 1

241

Next-generation sequencing methods and applications Targeted exome sequencing (TES)

Whole exome sequencing (WES)

Whole genome sequencing (WGS)

Features

Probe-based enrichment of known disease genes leading to greater coverage and higher sensitivity of variant calls

Probe-based capture similar to TES, but all coding regions are targeted with some overlap into introns and UTRs

Methods vary for the enrichment and sequencing of whole genes including exonic and intronic regions by probe-based capture or CRISP/Cas9

Platform/chemistry

Illumina/sequencing by synthesis

Illumina/sequencing by synthesis

Illumina/sequencing by synthesis PacBio/SMRTsequencing 10  genomics/GEM technology

Read length

150–250 bp Shorter reads have limitations in mappability in regions with high repetitiveness

150–250 bp Shorter reads have limitations in mappability in regions with high repetitiveness

Standard read lengths of 150–250 bp with Illumina. PacBio can produce long reads up to 20 kb (with higher error rate). Linked reads with 10  genomics produces synthetic long reads using microfluidic gel emulsion technology to “link” a single DNA molecule by unique sequence tags for long read mappability

Applications

Coding variants including SNVs and CNV can be detected within known disease genes

Coding variants including SNVs and CNV can be detected within known and new disease genes. However, large structural variants are difficult to solve

Coding and non-coding variants including SNVs, SV, CNV as well as haplotype phasing using long or linked reads

Detecting coding variation Before the implementation of the NGS techniques in routine diagnostics, mutation screening was performed by Sanger sequencing, or later APEX, which limited the number of genes that would be screened for pathogenic variants relying on the clinical phenotype of the eye disorders. This is especially constraining in the case of IRDs, in which over 260 genes have been implicated so far [23]. Indeed, patients were often only screened for mutations in genes associated with a narrowly defined phenotype [24–32]. For example, patients diagnosed with Leber congenital amaurosis (LCA) would only be screened for LCA genes, despite the growing evidence of a considerable phenotypic and genetic overlap with other IRDs such as retinitis pigmentosa or cone-rod dystrophies [33, 34]. The development of the NGS technologies enabled selective exon capture and sequencing of an increasing number of genes in every patient. Such NGS panel sequencing was widely applied in IRDs,

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FIG. 1 Genetic heterogeneity of IRDs. A Venn diagram of the most common forms of inherited retinal disorders with overlapping regions in which the same genes are responsible for different disorders. Usher and Bardet-Biedl syndromes were selected to represent syndromic forms of retinal disease. BBS, Bardet-Biedl syndrome; CD/CRD, cone or cone-rod dystrophy; CSNB, congenital stationary night blindness; LCA, Leber congenital amaurosis; MD, macular degeneration; RP, retinitis pigmentosa; USH, Usher syndrome (RetNet, the Retinal Information Network, accessed December 2018).

leading to a better characterization of the genetic heterogeneity of IRDs and further emphasizing the large genetic overlap between the different clinical subtypes (Fig. 1), including the syndromic and non-syndromic forms [35–45]. These studies thus have demonstrated that the most accurate genetic diagnosis of IRD patients are based on unbiased assessment of variants in all IRD genes, instead of focusing on genes associated with the initial clinical diagnosis. With the decreasing sequencing costs, WES became increasingly applied as a genetic diagnostic test [46–48]. Clinical WES has the advantage of capturing those genes that might have been only recently associated with disease, thus enabling the analysis of a complete gene set associated with the disease at the time of the test or in the future, when additional genes have been discovered. WES also enables new disease gene discovery in cases where no V. Genetic testing and genetic risk prediction

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mutations in known genes were found. However, the main disadvantages of this technique are its higher data storage demand and the presence of incidental genetic findings, that is, known pathogenic or expected pathogenic variants in a select number of genes unrelated to the primary reason for testing. Although the identification of secondary genetic findings may complicate genetic counseling, the American College of Medical Genetics and Genomics (ACMG) recommends that laboratories performing clinical exome or genome sequencing should always report medically actionable secondary findings [49]. The potential for results to reveal additional health information or risks outside of the indication for testing adds a challenge to the pretest informed consent process, especially for pediatric patients [50, 51], therefore the updated ACMG recommendations suggest that patients should have the option to opt out of receiving information about secondary findings. In addition, the interpretation of and management recommendations for secondary findings can be challenging in the absence of clinical symptoms or family history since guidelines were not created in the context of general screening of asymptomatic populations [52]. Concerns about a potential increase in patient anxiety related to clinical WES/WGS have also been raised [53]. Based on the abovementioned issues, panel sequencing in genetic diagnostics of eye disorders is at present more commonly used than WES or WGS. In addition, targeted panel sequencing has better coverage that increases sensitivity in variant calling while being more cost-effective and easier to customize, for instance, including additional target regions such as introns carrying known pathogenic variants [54–57]. The analysis of the large number of variants derived from NGS sequencing as well as ascertainment of pathogenicity to the sequence changes require a prior knowledge about the disease pathophysiology, its incidence, possible inheritance modes, the genes and pathways associated with the disease, and the nature of different classes of pathogenic variants. The complexity of NGS data analysis led to the formulation of guidelines for the interpretation of sequence variants issued by the ACMG and the Association for Molecular Pathology [58]. The recent emergence of successful, specific gene therapy treatments (led by Luxturna for patients with mutations in the RPE65 gene [59–65]) has made finding the genetic causes of rare diseases clinically significant. However, despite substantial progress in genetic technologies, and over 260 known IRD genes identified [23], current strategies can genetically solve only about 55–60% of IRD cases [39, 40, 44, 46, 47, 66–68]. The remaining missing diagnoses are in part due to novel, yet to be discovered IRD genes. However, mutations in new diseaseassociated genes are extremely rare, affecting only a handful of IRD patients [69–87], suggesting that the missing genetic causality largely lies in the known IRD genes. A considerable proportion of these elusive mutations are due to structural variations, such as CNVs or deep-intronic variants that affect splicing [54–57, 88–95], which are normally not captured by standard targeted NGS pipelines. In addition, proper interpretation of most nucleotide variants returned after targeted NGS may be challenging because it requires extensive functional follow-up [e.g., to investigate if variants in the untranslated regions (UTRs) alter transcription or mRNA stability]. These changes are categorized as variants of unknown significance (VUS) or ignored altogether in the clinical diagnostic reports. Another class of variants that is often overlooked during the interpretation of the causality of rare recessive diseases is the missense changes with a mild hypomorphic effect that may be more common in the population than expected by the incidence of the disease. Such variants may prove deleterious when coinherited in trans with a loss-of-function or highly hypomorphic allele and together lead to a milder or late-onset disease phenotype [96]. These more frequent coding variants associated V. Genetic testing and genetic risk prediction

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FIG. 2 Overview of single nucleotide variants (SNVs) and their outcomes. The panel on the top left depicts coding variants and their possible consequences. (A) Nonsense mutations arise when the sequence is altered to produce a premature stop codon (PSC) leading to the destruction of the transcript by nonsense-mediated mRNA decay (NMD). (B) Frameshift variants include small indels that shift the open reading frame producing PSC leading to NMD. (C) Missense variants in the coding region can be pathogenic resulting in altered protein structure leading to a reduction in the activity of the protein, or misfolding that triggers ER-Golgi retention and ultimately unfolded protein response (UPR). (D) UTR variants alter expression levels either by disrupting regulatory regions within 50 UTR or by decreasing the stability of the transcript in the case of 30 UTR variants. The panel on the right depicts examples of splice altering mutations. (E) Mutations to the canonical splice sites can lead to exon skipping. The consequences vary with in-frame exon skipping resulting in a truncated protein, and out of the frame exon skipping that leads to PSC and NMD. (F) Changes to intronic sequences flanking splice sites can result in the splicing machinery mistakenly incorporating part of the intron into the final transcript leading to PSC and NMD or altering the final structure of the protein. (G) Deep intronic variants may create cryptic splice sites that lead to pseudo-exons if there is a canonical splice site nearby. Additionally, these deep intronic mutations can strengthen splice site enhancers resulting in the inclusion of a pseudo-exon. Normal exons are in blue with gray showing the elimination of the exon in the resulting transcript, introns are in green in color, normal splicing is shown with solid black line, aberrant splicing is shown with dotted black line. PSC, premature stop codon; UPR, unfolded protein response; NMD, nonsense-mediated mRNA decay.

with disease have been found in several IRD genes such as ABCA4 [96, 97], BBS1 [98], BBS10 [98], and NMNAT1 [99–102]. Some variants are more frequent (AF > 0.1%) than expected in certain populations, despite leading to frameshift and nonsense-mediated decay of the transcript or protein truncation (Fig. 2) and are considered pathogenic, such as p.Cys91Leufs*5 in BBS10 [98] or p.Thr383Ilefs*13 in CNGB3 [103] (see http://gnomad.broadinstitute.org). V. Genetic testing and genetic risk prediction

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Finding and characterizing novel classes of mutations in the known IRD genes will bring the biggest impact toward finalizing genetic diagnosis for many IRD patients, some of whom may be eligible for the emerging genetic therapies.

Variants leading to aberrant splicing captured by targeted panels Non-canonical splice site (NCSS) variants are located close to canonical splice positions (+1, +2, 1, 2) of an exon that can lead to exon skipping or intron retention altering the transcript (Fig. 2). The NCSS variants are usually detected by Sanger sequencing or NGS, as intronic NCSS variants are mostly situated between positions +3 and +6 downstream of exons or 14 and 3 upstream of exons [104]. For long time these variants have been overlooked, classified as VUSs, and their effect on splicing largely not assayed. This may partly be due to the lower nucleotide conservation of non-canonical positions compared to canonical positions, for which it was already known that any nucleotide changes at these sites would have fully impaired splicing, regardless of the surrounding genomic sequence. Some NCSS variants may be frequently present in affected cases, such as ABCA4 c.5461-10T> C, the third most frequent ABCA4 variant in STGD1 patients [105], yet only recent functional studies were able to later reclassify the variant as fully penetrant null-allele, leading to double skipping of exon 39 and 39–40 [106]. Today, the increased awareness toward the putative pathogenic role of rare NCSS variants and the cost-effectiveness of splice assays has paved the way for larger functional studies. For example, the splice spectrum of all reported NCSS variants in ABCA4, the most frequently mutated IRD gene, was recently investigated by employing midigenebased splice assays. Results were striking, as 44/47 variants resulted in splice defect, mainly exon skipping, truncation, elongation, or intron retention [107]. Synonymous variants, which usually occur in the third base of a codon, can be misinterpreted as they do not alter the protein sequence. However, recent work has shown that over 50 human diseases are associated with synonymous variants creating or strengthening cryptic splice sites or binding sites for transcription factors [108]. To date, causality of synonymous variants in IRD genes has been experimentally proven only for few variants [54, 109, 110].

Deep-intronic variants Less than 2% of the human genome encodes for proteins (i.e., coding sequence), while the remaining 98% is “non-coding” and made by introns, UTRs, and intergenic regions [20]. Therefore, NGS panels and WES cover only a small fraction of the human genetic variation, which partially explains a high percentage of patients currently unsolved [46, 68, 111]. The capture of the remaining genetic regions requires WGS, which is still rarely performed on the clinical basis due to high cost and important challenges of data analysis and interpretation [112]. Nevertheless, clinical genomes may eventually become standard of care, as our understanding of the non-coding variation increases. A significant portion of non-coding sequence in our genes has been shown to regulate correct pre-mRNA (messenger RNA) splicing by allowing spliceosome machinery to recognize

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exon-intron boundaries with nucleotide precision [113]. However, splicing regulation can be altered by variants distant to the canonical splice sites through the inclusion of intronic sequences, called pseudo-exons, in the pre-mRNA [114]. Often the inclusion results from a deep-intronic variant creating/strengthening a cryptic splice site in cis with a pre-existing cognate splice site within the same intron (Fig. 2). Other mechanisms are the creation of splicing enhancer motifs able to trigger the inclusion of an intronic sequence flanked by two pre-existing splice sites [115] or the inclusion of a pseudo-exon carried by transposable elements in our genome, in particular, short interspersed nuclear elements such as Alu repeats [116, 117]. To date, more than 200 deep-intronic variants in 82 different genes resulting in pseudoexon formation have been reported [114], the list includes IRD-associated genes such as ABCA4 [54], CEP290 [55], CHM [118], COL2A1 [119], COL11A1 [119, 120], OAT [116], OFD1 [57], OPA1 [121], PROM1 [122], PRPF31 [56], RB1 [123], and USH2A [95, 124]. Pseudo-exons constitute a suitable target for splice-modulating therapy. In fact, the relative distance of the pseudo-exon from flanking normal exons allows antisense oligonucleotides to block this sequence from pre-mRNA inclusion, ultimately restoring correct splicing [115, 125–127].

Variants leading to altered gene expression Variants in the promoter and UTRs may influence the expression of a gene by altering transcription factor binding (promoters/50 UTRs) or by affecting mRNA stability (30 UTRs). UTR variants are most often included in the WES capture kits, but not always in the NGS panel testing. Even though several examples of such mutations in Mendelian disorders have been reported [128–131], they remain largely understudied in inherited ocular diseases. Functional consequences of promoter and UTR variants require cell-based luciferase reporter assays, preferable in relevant cell lines [129, 132–135]. Ubiquitously expressed genes can sometimes be studied by measuring the level of gene expression in white blood cells using quantitative PCR [136]. Variability in gene expression is believed to contribute to the common ocular disease, such as age-related macular degeneration, diabetic retinopathy, or glaucoma, where environmental and genetic risk factors determine the development of the disease. The majority of genetic risk loci remain unknown, and for the known variants, the underlying causal mechanisms are not well understood. However, the fact that most of the pathogenic variants lie in non-coding regions suggests that transcriptional regulation plays a key role in the pathogenesis of common eye diseases [2, 3]. Unfortunately, transcriptional regulation in ocular tissues is not well understood and large-scale transcriptomic and genomic analyses will need to be undertaken as was done for other tissues in the Genotype Tissue-Expression (GTEx) project [137, 138]. The GTEx project enabled the identification of genetic variants associated with differences in gene expression (expression quantitative trait loci or eQTLs) in 50 normal human tissues, which has yielded multiple insights into the causal mechanisms of a range of common diseases [137–140].

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Detecting structural variants (CNV and chromosomal aberration) Structural variation can be defined as a larger than 1 kb change of genomic DNA, which is either unbalanced (deletions and duplications) or balanced (translocations and inversions) (Fig. 3) [141]. Large structural aberrations can be detected with older cytogenic techniques, such as karyotyping for aneuploidy or partial aneuploidy and higher resolution methods involving banding of metaphase chromosomes, offering maximal resolution of 3–5 Mb [142]. Smaller structural variants involving 50–100 kb can be detected by fluorescence in situ hybridization, which utilizes fluorescently labeled probes that bind to specific regions of the genome, which were preselected for the particular patient [142–144]. Comparative genomic hybridization (CGH) is a genome-wide approach, which allows for the detection of CNVs by differentially labeled test and reference genomic DNAs, which are co-hybridized and relative fluorescence provides information about deleted or duplicated regions [145].

FIG. 3 Chromosomal rearrangements leading to structural variants. Arrow heads represent genes or gene segments along a specific chromosome with white and black belonging to non-homologous chromosomes, red arrow heads indicate genetic regions being altered in each case. Red arrows indicate the direction of alteration. Imbalanced rearrangements are a result of a change of the overall number of genes/gene segments within the region affected: (A) tandem duplication, (B) deletion, (C) translocation to a non-homologous chromosome resulting in a deletion from the original chromosome and an insertion into the new chromosome. Balanced rearrangements will not result in copy number change of the genetic region affected: (D) inversion of a genetic segment.

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The CGH has been replaced by array CGH, which provides a greater resolution (50 kb rather than 2–3 Mb) [146], and this technique is still widely used in the ocular disease [37, 88, 89, 147–150]. An alternative to array CGH are the SNP arrays, which although designed for SNP genotyping can also be used for the CNV detection [151, 152]. Even though structural variants are not easily detectable from targeted NGS data, there is a large interest in developing tools that would enable CNV detection from the targeted panels. Several algorithms for CNV detection such as ExomeDepth and germline CNV-genome analysis toolkit have been developed and widely used over the years [22, 153], however, due to their high false positive rate, experimental validation by genomic quantitative PCR or other techniques is recommended [154]. Another limitation is that the detection of other structural variants such as inversions and translocations cannot be detected by the targeted exome approaches, unless sequence breakpoints lie in the coding regions. Here, however, WGS offers an advantage as sequence break points in the non-coding regions are detectable with adequate algorithms [155]. Structural variants comprise a significant part of missing heritability in ocular disease. A comprehensive analysis of the contribution of CNVs in IRDs reported 330 unique CNVs in 81 different IRD genes [156]. As expected, the strongest correlation was found between the gene size and number of CNVs, as large genes like EYS, PCDH15, and USH2A harbored more CNVs compared to smaller genes. This however is not always true as no CNVs have been reported to date in one of the largest IRD genes, ABCA4 [156]. Apart from the size, genes containing in their sequences long interspersed nuclear elements (LINEs) and long terminal repeats (LTRs) were also shown to be prone to CNVs [156].

Genetic modifiers of phenotypic severity of IRDs Another aspect of the IRDs is that mutations in the same gene can lead to variable phenotypes [57, 157–169], which often cannot be explained by the primary pathogenic variants. Genetic modifiers can either be located in cis with the primary causal variant thus forming a complex allele (i.e., cis modifiers) or be located on a different gene, the product of which interacts directly or indirectly with the protein encoded by the main disease gene (i.e., trans modifiers). Several genetic modifiers have already been proposed [163, 167, 169–183], where extreme examples are cases of digenic inheritance of syndromic and non-syndromic IRDs [176, 177, 184] and the “rescuing” wild-type allele effect in PRPF31-related dominant retinitis pigmentosa [185–187]. Likely, additional variants with small or medium effects on gene expression have phenotype modifying effects. The identification of such modifiers may offer a therapeutic alternative to gene-augmentation therapies, which may not be adequate for autosomal dominant diseases and genes that are too large for the currently used vectors [60, 63, 64, 188, 189].

Ocular phenotype—A marker of syndromic disease Ocular phenotypes may be early presentations of syndromic disease, and therefore broad genetic screening enables early diagnosis and disease management. For example, retinal degeneration is often the first symptom of juvenile neuronal ceroid lipofuscinosis (also known

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as Batten disease), which is caused by mutations in CLN3 [190]. Similarly, corneal opacities may be the presenting sign for tyrosinemia type II due to pathogenic variants in TAT [191], mucolipidosis type IV due to mutations in MCOLN1 [192], or nephropathic cystinosis due to pathogenic variants in CTNS [193]. Corneal opacities can also be a marker of affected males or carrier females of X-linked Fabry disease, due to mutations in GLA [194]. Optic atrophy is an early sign of Canavan disease [ASPA] [195], Krabbe disease [GALC] [196], or neuronal ceroid lipofuscinosis-1 [PPT1] [197]. Congenital or infantile cataracts can be a sign of Cockayne syndrome [ERCC8] [198], Lowe syndrome [OCRL] [199], and galactosemia [GALT] [200]. These are just some examples of ocular phenotypes that can mark a more severe syndromic disease, which could be detected by well-composed genetic diagnostic panels.

Conclusions In summary, a great number of genes harboring different types of pathogenic variants can lead to ocular disease. Their detection requires an in-depth understanding of the contemporary genetic techniques, comprehensive analysis of sequences or other genomic data, and a good knowledge of the studied medical condition. Since considerable genetic and phenotypic overlap has been observed in different ocular diseases, for example, syndromic and non-syndromic forms of IRDs, a broader use of genetic panels is recommended. The genetic heterogeneity of ocular disorders also calls for a revised system of disease definitions that includes the genetic etiology in the naming, for example, RHO-associated rod-cone dystrophy. Such naming will improve our understanding of these disorders and clarify their description for patients and clinicians. It will also facilitate the identification of patients who may benefit from gene-based therapies for these disorders [59–64].

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C H A P T E R

15 Genetic risk scores in complex eye disorders Robert P. Igo, Jr., Jessica N. Cooke Bailey Case Western Reserve University, Cleveland, OH, United States

Introduction In the age of precision medicine, translational researchers are tasked with identifying measurable risk factors for disease, while physicians are tasked with contextualizing those risk factors for use in clinical settings. One such risk factor, recently come into prominence, is genetic variation. Advances in genotyping technology have made available vast quantities of available genetic data for genomic studies of disease, but the practical applications of such studies, in the form of genetic screening and prediction tests, have been slow. Common ocular diseases, which have a prominent genetic component [1], are often mentioned as candidates for genetic testing to determine personal risk and treatment options. However, except for rare, familial forms, these conditions have complex genetic architecture, with potentially hundreds of contributing genetic loci, and in most cases, individual risk factors have only modest effects. This chapter aims to review advances and limitations in predicting outcomes for ocular diseases using genomic data.

Risk scores and their applications A genetic risk score (GRS) estimates disease susceptibility or severity based on the information from one or more associated genetic variants. It may predict overall genetic risk (i.e., the probability of becoming affected on account of genetic factors), predict progression from one stage of disease to another, or may classify individuals into high- and low-risk categories for which different treatments are recommended [2, 3]. At the population level, a GRS can evaluate the overall contribution of genetic factors to an outcome of interest.

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A GRS is most commonly calculated as a weighted sum of the number of “risk alleles” at each marker—the allele associated with greater susceptibility or, in the case of a quantitative trait, greater phenotype value—multiplied by the effect size (or regression coefficient) of the risk allele [4]. Alternatively, a simple unweighted count of risk alleles across all associated markers, sometimes called an “allele score,” may be computed [3]. The GRS may also incorporate environmental predictors such as age and smoking status, or interaction effects between genes and the environment. The predictive power of a GRS for a binary (affected/unaffected) trait may be evaluated by measuring sensitivity or specificity, or positive and negative predictive value, depending on the application. However, these measures of predictive accuracy require a threshold for the GRS above which the test predicts that an individual is or will become affected. Sensitivity and specificity—the power to detect truly affected and unaffected individuals, respectively—may be summarized across all possible thresholds by means of the receiveroperator characteristic (ROC) curve [5, 6]. An area under the ROC curve (AUC) of 0.5 is expected for a purely uninformative test, whereas AUC ¼ 1 indicates a test with perfect predictive power. Tests with clinical utility to identify high-risk individuals generally have an AUC of at least 0.75–0.8 [5]. The AUC of a gene-based test depends on both the risk or variability captured by the measured genetic variation and on overall trait heritability, as discussed below. The concordance index (C-index) is a generalization of the AUC applicable to time-to-event (survival) data. Other measures of predictive accuracy, such as the population attributable risk (PAR), are also sometimes used [4]. For quantitative phenotypes, a natural evaluative measure of GRS is the multiple R2 measure from linear regression, indicating the proportion of outcome variability explained by the predictors (for example, Ref. [7]).

Ocular traits with well-established risk loci and risk scores Age-related macular degeneration Genetics of AMD Age-related macular degeneration (AMD) is the progressive degeneration of the central retina (macula), causing central vision loss (typically) in individuals over 55 years of age. AMD is the leading cause of blindness in the developed world. The genetic component of AMD is better defined than that of most complex diseases. Over half of the disease heritability is accounted for by two major loci: CFH and ARMS2/HTRA1 [8]. Numerous other loci have been identified that contribute to AMD, including ADAMTS9-AS2, COL8A1, CFI, C9, C2-CFB-SKIV2L, VEGFA, TNFRSF10A, TGFBR1, B3GALTL, RAD51B, LIPC, CETP, C3, APOE, SYN3-TIMP3, and SLC16A8 [9]. Additional loci recently reported include COL4A3, PRLR-SPEF2, PILRB-PILRA, KMT2E-SRPK2, TRPM3, MIR6130-RORB, ABCA1, ARHGAP21, RDH5-CD63, ACAD10, CTRB2-CTRB1, TMEM97-VTN, NPLOC4-TSPAN10, CNN2, MMP9, and C20orf85 [10]. Risk scores in AMD Several GRS have been published attempting to predict risk for developing AMD or for progressing from early to later stages based upon varying levels of clinical, lifestyle, and

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genetic data (for reviews, see Refs. [11, 12]). In a high-performing, nongenetic predictive model of advanced AMD, Chiu et al. [13] reported internal and external C-indices of 0.88 and 0.91, respectively, based upon eight baseline predictors including age, sex, education level, race, smoking status, the presence of pigment abnormality, soft drusen, and maximum drusen size. In genetics-only models, the predictive ability of eight single-nucleotide polymorphisms (SNPs) for AMD [10, 14] and the choroidal neovascularization (CNV) AMD subtype [10, 15], as measured by AUC, ranged from 0.80 to 0.82. Models combining clinical, lifestyle, and genetic data had AUCs of 0.907–0.915 for prediction of 10-year progression to advanced AMD [16, 17]. In the most recent International Age-Related Macular Degeneration Genomics Consortium publication reporting 16 novel loci, Fritsche et al. [10] examined 16,144 patients and 17,832 controls and calculated a weighted 52-SNP GRS combining novel and established loci. In this model, individuals in the highest decile of genetic risk had a 44-fold increased risk of developing advanced AMD compared with those in the lowest decile. GRS has proved useful in AMD gene discovery [12]. Sardell et al. [18] calculated a weighted GRS based on 19 risk variants [9] and sequenced cases and controls with the lowest and highest scores, respectively, in an effort to detect novel risk and protective variants. Focusing clinical trials recruitment on individuals at the highest genetic risk for developing advanced AMD could improve power and reduce sample size requirements [12]. Despite the well-established genetic profile of more than half of the genetic component of AMD [10], a lack of replication in populations not of Western European descent complicates the potential future application of GRS. Even in AMD, where two loci each account for a significant portion of disease heritability in Europeans, index variants for these two loci, CFH and ARMS2/HTRA1, failed to replicate in African Americans, Mexican Americans, and Singaporeans [19]. The A69S variant (rs10490924) in ARMS2 that is common among European-descent individuals and associated with increased AMD risk in non-Hispanic whites and Mexican Americans was, in contrast, protective in non-Hispanic black individuals [20]. Similarly, the CFH Y402H risk variant (rs1061170) common among Caucasians is present in only 5% of Chinese and Japanese individuals [21]. Even among populations of European descent, known loci do not necessarily replicate: in Amish, 19 known AMD risk loci [9] accounted for a lower proportion of AMD risk than in non-Amish Caucasians [22]. The implications of this complexity support the incorporation of ethnic variation in future risk prediction models [12], which would be more feasible with larger studies in more diverse population samples.

Glaucoma Genetics of glaucoma Glaucoma describes a collection of disorders resulting in optic nerve degeneration that are among the leading causes of irreversible blindness worldwide. Age of onset varies by subtype and ranges from birth (congenital) to after age 40 (adult-onset). Glaucoma has a substantial heritable component [23] and several genetic loci contributing to disease have been identified through the application of various statistical genetic techniques [23]. Common forms of glaucoma include primary open-angle glaucoma (POAG), exfoliation glaucoma (XFG), and angleclosure glaucoma (ACG).

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Genetic loci associated with adult-onset POAG that have consistently replicated across multiple studies include 8q22 [24], ABCA1 [25], AFAP1 [25], ATXN2 [26], CAV1 [27], CDKN2B-AS1 [28], FNDC3B [29], FOXC1 [26], GAS7 [26, 29], GMDS [25], MYOC [30], OPTN [31], PMM2 [32], SIX1/SIX6 [24], TGFBR3 [33], TMCO1 [28], TXNRD2 [26], and WDR46 [34]. An additional 14 loci have been recently reported [35, 36] and await confirmation in independent studies. Despite large and well-powered genome-wide association study (GWAS) (e.g., Ref. [26]), known POAG loci account for only a small fraction of the full genetic component of this disease [23, 29]. Further complicating the search for genetic susceptibility factors for POAG is the use of “endophenotypes,” measures related to the disease but not identical to it: optic cup area, intraocular pressure (IOP), and vertical cup-to-disc ratio (VCDR) (reviewed in Ref. [37]). Loci associated with these endophenotypes often compose GRS used to predict POAG case-control status [38–43]. Exfoliation syndrome (XFS) is a major risk factor for XFG, a secondary open-angle glaucoma. Consistently replicating genes associated with XFS and, therefore, XFG includes LOXL1 [44] and CACNA1A [45, 46]. SNPs in LOXL1 are reported to have odds ratios (ORs) of 20 (reviewed in Ref. [47]); however, the risk and protective alleles for two highly associated coding variants are switched in certain populations (reviewed in Refs. [47, 48]). A recent multiethnic GWAS identified seven new loci in or near AGPAT1, POMP, RBMS3, SEMA6A, and TMEM136 [45], but GRS has not been developed for XFG. Primary angle-closure glaucoma (PACG) also has a known genetic component. Nine genes have been implicated in PACG, which are distinct from POAG risk loci [49–51]. To date, there are no known common, consistently replicating genetic loci for pigmentary glaucoma, a secondary form of open-angle glaucoma, despite evidence suggesting that there is a genetic component to this and the closely related pigment dispersion syndrome [52]. Risk scores in glaucoma Prior to the first published GWAS, an early POAG “family score” to predict glaucoma risk was based upon disease status, number of affected relatives, age, sex, and degree of relatedness [53]; one unit increase in this family score correlated with a 1.59-fold increase in odds for POAG. Interestingly, adding IOP to this model did not improve the OR, supporting the role of IOP-independent genetic components in glaucoma. Ramdas et al. [42] developed GRS for OAG based on SNPs associated with VCDR and IOP in a GWAS of 5304 samples. Even in their small subset of 171 OAG cases, scores associated with VCDR were also associated significantly with OAG. In a multiethnic meta-analysis reported in 2014, the International Glaucoma Genetics Consortium (IGGC) reported results from evaluating 21,094 individuals of European ancestry and 6784 individuals of Asian ancestry [43]. This study identified 10 new loci associated with variation in VCDR; in a separate analysis of five case-control studies, these 10 SNPs together with eight previously known VCDR-associated SNPs were used to generate a weighted GRS to predict POAG affectation status; scores were divided into quintiles, and Caucasians in the highest quintile had a 2.5-fold increased risk of POAG compared with those in the lowest quintile. Despite lack of replication of known loci, Hoffman et al. [54] detected significant association between a weighted GRS based on established loci and OAG in African Americans in a

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sample composed of 658 prevalent cases and 6067 controls. In an analysis to evaluate the potential differing genetic architecture of POAG subtypes, Mabuchi et al. reported a weighted GRS based on nine IOP and VCDR-related genetic variants evaluated in a Japanese sample composed of 255 with high-tension glaucoma (HTG), 261 with normal tension glaucoma (NTG), and 246 controls [39]. HTG GRS was significantly higher vs controls and cases with GRS 12 had 2.54 times higher risk of HTG vs all cases. Nannini et al. [41] performed weighted and unweighted GRS analyses based on SNPs associated with VCDR in Latinos; in a model including age, sex, and SNPs significant at the nominal P < 0.005 level in their GWAS, they obtained an AUC of 0.809 for POAG in a sample of 4018 including 229 with POAG [41]. In a southern European Mediterranean population of 391 POAG cases and 383 controls, Zanon-Moreno et al. evaluated an allele score based on four SNPs in known POAG-associated loci (TMCO1, CAV1/CAV2, CDKN2B-AS1, and CDKN2A), in addition to including age and sex [55]. Subjects in the top third of GRS distribution were at 2.92-fold increased risk for POAG compared to those in the bottom third (P < 0.001). The most recently reported GRS relevant to glaucoma are reported based on the UK Biobank [38, 40]. In the UK Biobank and the US-based NEIGHBORHOOD study, Khawaja et al. [38] reported a meta-analysis of over 139,000 individuals of European descent, identifying 112 genomic loci associated with IOP 68 of which were novel. In a regression-based glaucoma-prediction model developed based on these SNPs, the AUC was 0.764 in NEIGHBORHOOD HTG subset and 0.708 in the smaller NTG subset; the AUC was 0.74 in independent glaucoma cases from the UK Biobank. Also relying on the availability of and power afforded by the UK Biobank data, MacGregor et al. [40] recently reported an analysis identifying 101 IOP-associated SNPs, 85 of which were novel, based on the UK Biobank and previously published IGGC for a total sample of over 134,000 including 11,000 glaucoma cases. In an allele score weighted based on the effect in VCDR and IOP analyses, individuals in the top decile had an OR of 5.6 relative to those in the bottom decile.

Myopia and refractive error Genetics of myopia and refractive error Myopia, although usually not disabling, is the most prevalent ocular disease, affecting a quarter of the population in the developed countries [56], often developing in childhood, and its prevalence is increasing [57]. Myopia has a prominent genetic component. Estimates of heritability vary widely, from 0.25 to 0.94, depending on the measure used [58–60], but cluster between 0.7 and 0.9 [61]. It also has well-known and common environmental risk factors, especially educational attainment and time spent reading and performing other near work [62, 63]. Genetic studies of myopia have generally either focused on case/control status for high myopia (typically, 6 diopters of correction) or on quantitative measures of corrective error, including spherical equivalent refraction and axial length [64]. Early genetic studies focused on the binary high myopia phenotype, and did not produce replicable risk loci (reviewed in Refs. [61, 64]). In 2013, two large studies of European-ancestry cohorts, from the Consortium for Refractive Error and Myopia (CREAM) [65] and from the 23andMe

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company database [66], described several dozen genetic loci affecting refractive error. The CREAM Consortium [65] conducted a meta-analysis of 27 European and five Asian-ancestry samples and identified 24 novel loci associated with spherical equivalent refractive error, as well as two loci, at GJD2 and RASGRF1, reported in two previous studies of refractive error in Europeans [67, 68]. Using 23andMe database and a survival analysis model, Kiefer et al. [66] discovered 22 loci associated with self-reported myopia age of onset at genomewide significance, including the GJD2 and RASGRF1 genes. Remarkably, 86 markers in 11 loci were genomewide significant in both studies [69], including markers in or near PRSS56/CHRNG, BMP3, LAMA2, SFRP1/ZMAT4, TOX/CA8, BICC1, RDH5, PCCA/ZIC2, GOLGA8B/GJD2, RASGRF1, and MYO1D/TMEM98 [65, 66]. These findings, from predominantly European cohorts, were partially replicated in a Japanese sample, with individual markers at eight loci, GJD2, RASGRF1, BICC1, KCNQ5, CD55, CYP26A1, LRRC4C, and B4GALNT2, achieving studywide significance [70]. Several, more recent GWAS have built on these results with progressively expanding samples. A genomewide meta-analysis of nine European-descent populations for myopia and hyperopia as binary traits confirmed loci at TOX for myopia, and at TOX and GJD2 for hyperopia, at genomewide significance, and an additional 10 loci at significance qualifying for replication [71]. A GWAS of myopia-related traits in a Japanese cohort, with replication in Chinese and Caucasian samples, confirmed the GJD2 locus for myopia, and also identified WNT7B as a risk locus for axial length and corneal curvature [72]. The CREAM, in 2016 [73], conducted a meta-analysis, including a gene  educational attainment interaction effect, of 25 European and 9 Asian cohorts, including many from the previous CREAM study [65]. This study uncovered nine novel risk loci associated with refractive error: FAM150B/ACP1, LINC00340, FBN1, DIS3L-MAP2K1, ARID2-SNAT1, SLC14A2, AREG, GABRR1, and PDE10A, in addition to replicating 17 known loci [73]. A recent meta-analysis, the largest genetic study to date on myopia, incorporated over 250,000 individuals in a GWAS and replication for refractive error [74]. This analysis, which included most previous samples, identified 161 independent association signals, including the 37 reported in the CREAM and 23andMe 2013 papers. The heritability explained by all common genetic variation was 0.172–0.214 in the CREAM and 23andMe subsets of the European sample, but merely 0.053 in the Asian subset. Risk scores in myopia and refractive error As with other complex traits, genetic testing for myopia is in its infancy [59]. GRS for myopia may best be served to recommend interventions such as increased time outdoors, although this intervention has not had a significant effect on refraction in the general population [59]. In the 2013 CREAM study, Verhoeven et al. [65] constructed a GRS from 26 significantly associated variants, which yielded an AUC of 0.67 for predicting myopia vs hyperopia in an independent European cohort (the Rotterdam Study [75]). The odds of myopia vs hyperopia differed by >20-fold between the lowest and highest GRS risk categories. However, the GRS accounted for only 3.4% of the variation in refractive error in the Rotterdam study. The CREAM applied the 26-marker GRS, both weighted and unweighted, to examine the relationship between education and genetic predisposition to myopia [76] in the Rotterdam Study cohort. Education and genetic risk synergistically increased the overall risk of myopia vs emmetropia; the odds of myopia were about 50-fold greater for high-risk individuals with higher education than for low-risk individuals with primary education only [76]. In the V. Genetic testing and genetic risk prediction

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concurrent 23andMe report, Kiefer et al. [66] summed risk alleles across 22 top variants to create a “propensity score” that explained 2.9% of the total variability in age of onset. Individuals in the top 10% of scores were twice as likely to develop myopia by age 10 or by age 25 as an individual in the bottom 10% [66]. Still, the variability explained by these GRS is far below the estimates of total heritability. Two studies employed a GRS constructed from 39 index variants derived from the 2013 CREAM and 23andMe studies to examine variations in genetic risk for myopia-related traits with age. Tideman et al. [77] found that the strength of association between an unweighted 39-SNP GRS and two measures of myopia, axial length and corneal radius, increased threefold between children aged 75% accuracy, where accuracy was assessed by correct assignment of case/control pairs based on the case having a greater probability of being affected. Association of rs613872 with large effect at TCF4 has been confirmed repeatedly in European-ancestry samples [88–92] (for a meta-analysis, see Ref. [93]). Allele frequencies of this SNP vary widely across populations; the risk allele, G, is rare in African, East Asian, and Native American Human Genome Diversity samples, concordant with reduced prevalence of FECD in those populations [90]. The TCF4 locus was strongly associated with FECD in a Chinese sample, even though the rs613872 risk allele was absent [94]. A GRS derived from a linear regression model incorporating genotype of two TCF4 markers, rs1348047 and rs17089887, and including sex, yielded an AUC of 0.71 for FECD case/control status in the Chinese cohort [94]. A likely candidate for the causal factor at TCF4 is expansion of a trinucleotide repeat within intron 3 of the gene [95]. The presence of >50 copies of the CTG trinucleotide in a Caucasian sample predicted FECD affectation status with sensitivity of 0.79 and specificity of 0.96, a more accurate predictor of FECD than rs613872 [95]. The expanded repeat predicted FECD less well in an Indian cohort, with sensitivity of 0.34 and specificity of 0.96, whereas the rs613872 variant was not significantly associated with FECD in this sample [96]. In the largest FECD GWAS to date, Afshari et al. [97] discovered three additional loci associated with FECD at KANK4, LAMC1, and LINC009970/ATP1B1. A GRS constructed from the top variants at these loci and at TCF4 had strong predictive value (AUC ¼ 0.782), mostly due to the TCF4 marker rs784257, which by itself explained 21.9% of the FECD risk in the discovery sample (AUC ¼ 0.750). The remaining three index variants together accounted for 2.6% of the risk [97]. Estimates of AUC and explained risk may be inflated in this study because they were assessed on the discovery sample rather than an independent one.

Looking forward: capabilities and limitations of risk scores AMD has been considered as one of the success stories of the GWAS paradigm: more than half of the heritable risk for AMD in European populations has been explained by genetic variants associated with the trait at a genomewide level of significance [10], and as described above, GRS for AMD has achieved AUCs of >0.8. However, difficulty of identifying additional AMD risk variants has increased dramatically as most risk loci of substantial effect have already been discovered, and almost half the heritable variation remains to be explained. Moreover, a test with AUC ¼ 0.8 falls short of a truly diagnostic test, which should have an AUC of around 0.99 [5]. Consequently, it is of keen interest to improve predictive power for GRS.

Polygenic risk scores The genetic component of a complex trait like AMD or POAG is determined by many loci across the genome, most with small enough effect that no study with a reasonable sample size reveals them at genomewide significance. The PRS extends the GRS to thousands of markers, attempting to account for all the heritable variation regardless of statistical significance.

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Because the costs of GWAS panel genotyping and even whole-genome sequencing have fallen to the point that these technologies are becoming available for clinical diagnostics, the PRS has become popular [3, 98–100]. Generally, a PRS includes variants with a nominal association p value below a specified, relatively low threshold [98, 99] that balances total information with dilution of the genetic signal with large numbers of weakly associated variants [101]. Normally, markers included in a risk score are assumed to be independent (i.e., not in linkage disequilibrium), and many methods for computing PRS screen the available genetic variants to obtain an effectively independent subset [98–100], although a method that accounts for correlations among associated markers has been developed [102]. The technique of PRS was pioneered in Schizophrenia research [99], and though it has been employed to predict risk and to select treatment options for several common traits (reviewed in Ref. [3]), no true PRS has been published thus far for any of the ocular diseases discussed here, save the very recent reports on refractive error [74] and VCDR [41]. The theoretically achievable predictive power from any test of genetic risk, including a PRS, depends on the heritability explained by measured variants, the risk ratio for siblings of affected individuals, and the overall trait prevalence [6]. Taking 11.8 for the prevalence and 2.2 for sibling relative risk ratio, an AUC of 0.92 is theoretically obtainable for a PRS for AMD. However, even with modern GWAS panels, a PRS will fail to capture all of the heritable variation for complex ocular diseases—a problem termed the “missing heritability” [103]. The technique of genotype imputation may capture some of the variation not accounted for by variants in the marker panel, but is limited to markers common enough to occur in the study sample. Sources of heritability not captured in GWAS include effects of rare genetic variants not captured in the PRS, and effects not usually tested in single-marker association analysis: gene-by-gene interactions (epistasis; Ref. [104]), genomic structural variation, geneby-environment interactions [105] and heritable epigenetic effects such as methylation of cytosine residues of DNA [106]. A further complication is heterogeneity between populations and study samples: a PRS determined from a given study may not be perfectly relevant to another sample even from the same population, if differently ascertained for the trait of interest. We have seen that across ethnic groups, major risk loci for AMD and POAG are not consistent [19]. Moreover, GRS determined for a given ocular trait is often applied to similar measures or conditions, as in the case of POAG and IOP, and refractive error and myopia case/control status.

Clinical utility of risk scores While PRSs are not yet ready for widespread use, they have been shown to be clinically relevant on a subset of patients at the extremes of the population risk distributions. Recent studies showed that they may provide clinically useful information on this subset of patients [2, 3, 107]. A recent study of predictive power of PRS for five common diseases identified individuals at very high risk (OR > 3), who may benefit from preventative measures [2]. Similar success is in principle achievable for ocular diseases, whose heritabilities generally compare well with those of other common traits [1]. In fact, when the results of the IAMDGC study were extrapolated to a general population, individuals within the top decile for a 52-marker GRS had a 22.7% risk for AMD, and the top percentile had a risk of almost 50%, more than

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ninefold greater than the assumed population prevalence [10]. Moreover, identifying affected individuals at low risk for disease [18], and unaffected individuals at high risk, may facilitate discovery of novel genetic variants conferring risk and protection, respectively. In summary, the greatest promise for clinical utility of GRS in common ocular traits, in the near term, is likely to continue to be restricted to identifying individuals at the extremes of genetic risk, but within those extremes may provide valuable information toward diagnosis and treatment. As other types of gene-related data become widely available, such as gene expression and epigenetic markers, GRS may eventually be incorporated into more informative measures of overall physiological risk [3].

References [1] P.G. Sanfilippo, A.W. Hewitt, C.J. Hammond, D.A. Mackey, The heritability of ocular traits, Surv. Ophthalmol. 55 (2010) 561–583. [2] A.V. Khera, M. Chaffin, K.G. Aragam, M.E. Haas, C. Roselli, S.H. Choi, P. Natarajan, E.S. Lander, S.A. Lubitz, P.T. Ellinor, S. Kathiresan, Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations, Nat, Genet. 50 , (2018) 1219–1224. [3] A. Torkamani, N.E. Wineinger, E.J. Topol, The personal and clinical utility of polygenic risk scores, Nat. Rev. Genet. 19 (2018) 581–590. [4] J.N. Cooke Bailey, R.P. Igo Jr., Genetic risk scores, Curr. Protoc. Hum. Genet. 91 (2016) 1.29.1–1.29.9. [5] A.C.J.W. Janssens, R. Moonesignhe, Q. Yang, E.W. Steyerberg, C.M. van Duijn, M.J. Khoury, The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases, Genet. Med. 9 (2007) 528–535. [6] N.R. Wray, J. Yang, M.E. Goddard, P.M. Visscher, The genetic interpretation of area under the ROC curve in genomic profiling, PLoS Genet. 6 (2010) e1000864. [7] J.J. Lee, R. Wedow, A. Okbay, E. Kong, O. Maghzian, M. Zacher, T.A. Nguyen-Viet, P. Bowers, J. Sidorenko, R.I. Linner, M.A. Fontana, T. Kundu, C. Lee, H. Li, R. Li, R. Royer, P.N. Timshel, R.K. Walters, E.A. Willoughby, L. Yengo, et al., Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals, Nat. Genet. 50 (2018) 1112–1121. [8] M.M. DeAngelis, L.A. Owen, M.A. Morrison, D.J. Morgan, M. Li, A. Shakoor, A. Vitale, S.K. Iyengar, D. Stambolian, I.K. Kim, L.A. Farrer, Genetics of age-related macular degeneration (AMD), Hum. Mol. Genet. 26 (2017) R45–R50. [9] L.G. Fritsche, W. Chen, M. Schu, B.L. Yaspan, G. Thorleifsson, D.J. Zack, S. Arakawa, V. Cipriani, S. Ripke, R.P. Igo Jr., G.H.S. Buitendijk, X. Sim, D.E. Weeks, R.H. Guymer, J.E. Merriam, P.J. Francis, G. Hannum, A. Agarwal, A.M. Armbrecht, I. Audo, T. Aung, G.R. Barile, M. Benchaboune, A.C. Bird, P.N. Bishop, K.E. Branham, M. Brooks, A.J. Brucker, W.H. Cade, M.S. Cain, P.A. Campochiaro, C.-C. Chan, C.-Y. Cheng, E.Y. Chew, K.A. Chin, I. Chowers, D.G. Clayton, R. Cojocaru, Y.P. Conley, B.K. Cornes, M.J. Daly, B. Dhillon, A.O. Edwards, E. Evangelou, J. Fagerness, H.A. Ferreyra, et al., Seven new loci associated with age-related macular degeneration, Nat. Genet. 45 (2013) 433–439. [10] L.G. Fritsche, W. Igl, J.N. Cooke Bailey, F. Grassmann, S. Sengupta, J.L. Bragg-Gresham, K.P. Burdon, S. Hebbring, C. Wen, M. Gorski, I.K. Kim, D. Cho, D. Zack, E. Souied, H.P.N. Scholl, E. Bala, K.E. Lee, D.J. Hunter, R.J. Sardell, P. Mitchell, J.E. Merriam, V. Cipriani, J.D. Hoffman, T. Schick, Y.T.E. Lechanteur, R.H. Guymer, M.P. Johnson, Y. Jiang, C.M. Stanton, G.H.S. Buitendijk, X. Zhan, A.M. Kwong, A. Boleda, M. Brooks, L. Gieser, R. Ratnapriya, K.E. Branham, J.R. Foerster, J.R. Heckenlively, M.I. Othman, B.J. Vote, H.H. Liang, E. Souzeau, I.L. MacAllister, T. Isaacs, J. Hall, S. Lake, D.A. Mackey, I.J. Constable, J.E. Craig, T.E. Kitchner, Z. Yang, Z. Su, H. Luo, D. Chen, H. Ouyang, K. Flagg, D. Lin, G. Mao, H. Ferreyra, K. Stark, C.N. von Strachwitz, A. Wolf, C. Brandl, G. Rudolph, M. Olden, M.A. Morrison, D.J. Morgan, M. Schu, J. Ahn, G. Silvestri, E.E. Tsironi, K.H. Park, L.A. Farrer, A. Orlin, A. Brucker, M. Li, C.A. Curcio, S. Mohand-Saı¨d, J.-A. Sahel, I. Audo, M. Benchaboune, A.J. Cree, C.A. Rennie, S.V. Goverdhan, M. Grunin, S. Hagbi-Levi, P. Compochiaro, N. Katsanis, F.G. Holz, F. Blond, H. Blanche, J.-F. Deleuze, R.P. Igo Jr.,

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C H A P T E R

16 Gene therapy for inherited retinal diseases Patty P.A. Dhooge, Dyon Valkenburg, Carel B. Hoyng Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands

Introduction According to the US Food and Drug Administration, human gene therapy is the administration of genetic material to modify or manipulate the expression of a gene product or to alter the biological properties of living cells for therapeutic use [1]. Gene therapy aims to treat the fundamental cause of a disease by restoring individual cell function and thereby halting or slowing down the disease process [2]. While several approaches to achieve this goal have been developed, including inactivating disease-causing genes with a dominant-negative effect, introducing a modified gene into the cell and the application of gene-editing platforms like CRISPR/Cas9 [3], gene therapy traditionally entailed the intracellular introduction of a healthy gene copy [1]. This method is otherwise known as gene augmentation therapy (GAT) and will be the focus of this chapter. While applicable in various forms of genetic disease, GAT works best with recessive monogenic disorders that cause a loss of function of the encoded protein. By inserting a healthy copy of the gene, the loss of function will be corrected [4]. The eye, and specifically the retina, has played a leading role in the clinical translation of gene transfer therapies for a number of reasons. First, the eye is immune-privileged. Its unique microenvironment and the blood-retinal barrier diminish a maximal immune response both locally and systemically [5]. Second, the retina is easily accessible by way of surgical intervention and can be monitored extensively using noninvasive imaging techniques. Third, as retinal cells do not proliferate after birth, a single treatment can lead to life-long improvement [6]. Lastly, there are many animal models available, especially for inherited retinal diseases (IRDs). In comparison with retinal cells in culture, animal models provide information on retinal function and the overall impact of the intervention, like the effect on immune response.

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IRDs are a clinically heterogeneous group of retinopathies. Over the last decades, more than 260 causative genes for IRDs were identified [7–9]. Mutations in these genes lead to disruption of various retinal cellular mechanisms, including dysfunction of the retinoid cycle and metabolic pathways, structural (ciliary) abnormalities, and improper protein trafficking, resulting in degeneration of the photoreceptors and outer retina, retinal pigment epithelium (RPE), and/or choroid and ultimately leading to (severe) visual impairment. The most common IRDs include: retinitis pigmentosa (RP), Stargardt disease (STGD1), X-linked retinoschisis, achromatopsia (ACHM), choroideremia, and Leber congenital amaurosis (LCA). Over the past two decades, a lot of progress has been made with regard to ocular gene therapy. This led to the first approval by the US Food and Drug Administration for a one-time gene therapy product indicated for the treatment of patients with LCA type 2 [10]. This chapter describes the current knowledge about gene therapy for IRDs and provides a summary of the current clinical trials. An overview of these trials can be found in Table 1.

History The history of retinal gene therapy goes back to the 1990s when adenovirus-based vectors were tested in different mouse models of RP to see if they could penetrate the RPE and the photoreceptors [11]. The first successful retinal gene transfer rescued mice with RP caused by a defect in the β subunit of the cGMP phosphodiesterase gene (βPDE) by using an adenovirusmediated delivery of a wild-type βPDE cDNA to the photoreceptors [12]. This adenovirusmediated gene transfer technique was further developed in canine models. Simultaneously, alternative viral vectors based on a lentivirus or adeno-associated virus (AAV) were developed [13]. In 2001, successful subretinal gene delivery was described in the Swedish Briard dog; a canine model that naturally develops LCA [14]. This led to the first human subretinal gene therapy clinical trials that established safety, and in a measure efficacy, in children with RPE65-associated LCA (LCA2) [15]. In December 2017, the gene therapy reagent used to treat patients with LCA2 was the first to receive approved drug status by the United States Food and Drug Administration [10]. There has been a major development in gene therapy in the recent years. Particularly through improved knowledge of genetic basis and development of alternative vectors. At this moment, ocular diseases account for 1.3% of all gene therapy clinical trials [16]. After more than 15 years since its first success in LCA2, gene therapy has now expanded to various other IRDs and multiple clinical trials are currently in progress.

Vectors Efficient and successful transfection of neuroretinal cells can be achieved in various ways. Currently, the most frequently used method in clinical trials is by employing recombinant, replication-incompetent, adeno-associated viruses (rAAVs). Viral load is replaced by a

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TABLE 1 Current clinical trials regarding gene therapy for inherited retinal diseases registered at https://clinicaltrials.gov/ Type/Phase

Vector/administration route

Date

Sponsor

NCT

Nonsyndromic retinitis pigmentosa

Dose escalation / Phase I–II

AAV (AAV-RPGR)/ Subretinal injection

March 16, 2017 – February 2019

Nightstar Therapeutics

NCT03116113

Dose escalation / Phase I–II

AAV (rAAV2tYF-GRK1-RPGR)/ Subretinal injection

January 2018 – January 2024

Applied Genetic Technologies Corp.

NCT03316560

Dose escalation / Phase I–II

AAV (AAV2/5-hRKp.RPGR)/ Subretinal injection

July 14, 2017 – November 2020

MeiraGTx

NCT03252847

Proof of concept/ Phase I–II

AAV (CPK850)/ Subretinal injection

August 2018 – May 5, 2025

Novartis Pharmaceuticals

NCT03374657

Dose escalation / Phase I–II

AAV (AAV2/5-hPDE6B)/ Subretinal injection

November 6, 2017 – June 2022

Horama S.A.

NCT03328130

Dose escalation / Phase I–II

AAV (rAAV2-VMD2hMERTK)/ Subretinal injection

August 2011 – August 2023

Fowzan Alkuraya

NCT01482195

Syndromic retinitis pigmentosa

Dose escalation / Phase I–II

Lentivirus (UshStat, SAR421869)/ Subretinal injection

March 20, 2012 – February 24, 2020

Sanofi

NCT01505062

(Usher syndrome)

Long-term followup / Phase I–II

Lentivirus (UshStat, SAR421869)/ Subretinal injection

September 12, 2013 – February 24, 2035

Sanofi

NCT02065011

Stargardt disease

Dose escalation / Phase I–II

Lentivirus (StarGen, SAR422459)/ Subretinal injection

June 10, 2011 – November 27, 2019

Sanofi

NCT01367444

Long-term followup / Phase I–II

Lentivirus (StarGen™, SAR422459)/ Subretinal injection

December 14, 2012 – November 27, 2034

Sanofi

NCT01736592

Vectors

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Disease

Continued

281

Type/Phase

Vector/administration route

Date

Sponsor

NCT

X-linked retinoschisis

Dose escalation / Phase I–II

AAV (AAV8-scRS/IRBPhRS)/ Intravitreal injection

December 2014 – July 2021

National Eye Institute (NEI)

NCT02317887

Dose escalation / Phase I–II

AAV (rAAV2tYF-CB-hRS1)/ Intravitreal injection

May 2015 – October 2022

Applied Genetic Technologies Corp.

NCT02416622

Dose escalation / Phase I–II

AAV (AAV2/8-hCARp. hCNGB3)/ Subretinal injection

January 12, 2016 – February 2019

MeiraGTx

NCT03001310

Long-term followup / Phase I–II

AAV (AAV2/8-hCARp. hCNGB3)/ Subretinal injection

June 27, 2017 – August 7, 2023

MeiraGTx

NCT03278873

Dose escalation / Phase I–II

AAV (AGTC-402)/ Subretinal injection

May 1, 2017 – June 2023

Applied Genetic Technologies Corp.

NCT02935517

Dose escalation / Phase I–II

AAV (rAAV2tYF-PR1.7hCNGB3)/ Subretinal injection

February 2016 – December 2022

Applied Genetic Technologies Corp.

NCT02599922

Dose escalation / Phase I–II

AAV (rAAV.hCNGA3)/ Subretinal injection

November 2015 – November 2017

STZ eyetrial

NCT02610582

Safety and Efficacy / Phase III

AAV (AAV2-REP1)/ Subretinal injection

December 11, 2017 – March 31, 2020

Nightstar Therapeutics

NCT03496012

Safety (Bilateral) / Phase II

AAV (AAV2-REP1)/ Subretinal injection

November 6, 2017 – March 31, 2020

Nightstar Therapeutics

NCT03507686

Long-term followup / Phase I–III

AAV (AAV2-REP1)/ Subretinal injection

June 4, 2018 – April 2024

Nightstar Therapeutics

NCT03584165

Intervention / Phase II

AAV (rAAV2.REP1)/ Subretinal injection

January 2016 – March 2018

STZ eyetrial

NCT02671539

Intervention / Phase II

AAV (AAV2-REP1)/ Subretinal injection

August 2016 – August 2021

University of Oxford

NCT02407678

Achromatopsia

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Choroideremia

16. Gene therapy for inherited retinal diseases

Disease

282

TABLE 1 Current clinical trials regarding gene therapy for inherited retinal diseases registered at https://clinicaltrials.gov/—cont’d

Leber congenital amaurosis

QLT091001 /Oral

November 2009 – August 2012

QLT Inc.

NCT01014052

Safety and Efficacy / Phase I–II

QLT091001 / Oral

January 2012 – June 2014

QLT Inc.

NCT01521793

Dose escalation / Phase I–II

AAV (AAV2/5-OPTIRPE65)/ Subretinal injection

April 2016 – October 2018

MeiraGTx UK II Ltd.

NCT02781480

Safety and Efficacy / Phase I–II

AAV (AAV2-hRPE65v2)/ Subretinal Injection

November 2010 – November 2026

Spark Therapeutics

NCT01208389

Dose escalation / Phase I–II

AAV (AAV2/5-OPTIRPE65)/ Subretinal injection

November 2016 – April 2023

MeiraGTx UK II Ltd.

NCT02946879

Efficacy / Phase III

AAV (AAV2-hRPE65v2)/ Subretinal injection

July 2015 – July 2029

Spark Therapeutics

NCT00999609

Safety / Phase I

AAV (AAV2-hRPE65v2) / Subretinal injection

September 2007 – July 2024

Spark Therapeutics

NCT00516477

Safety / Phase I

AAV (rAAV2-hRPE65)/ Subretinal injection

February 2009 – January 2017

Hadassah Medical Organization

NCT00821340

Safety / Phase I

AAV (rAAV2-CBSB-hRPE65)/ Subretinal injection

July 2007 – June 2026

University of Pennsylvania

NCT00481546

Safety and Efficacy / Phase I–II

AAV (rAAV2-CB-hRPE65)/ Subretinal injection

June 2009 – September 2017

Applied Genetic Technologies Corp.

NCT00749957

Vectors

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Safety / Phase I

283

284

16. Gene therapy for inherited retinal diseases

complementary DNA plasmid of the healthy gene transcript and the virus’s intrinsic properties facilitate gene transduction. Subsequently, the cell’s function is restored and this potentially rescues the transfected cell [17–19]. While many current trials employ rAAVs, and some have promising results, one major obstacle to their use in various IRDs is the limited effective packaging capacity of approximately 4.7 kb [20]. Several ways to overcome this obstacle have been proposed, either by using a “dual rAAV” method, in which the transgene is split and divided over two separate rAAVs and reconstituted intracellularly [21–24], or by using vectors with a larger cargo capacity. To this extent, nonprimate lentiviral vectors have been proposed for use in conditions with causative genes with a maximum length of approximately 8–10 kb. Subretinal injection of these vectors resulted in consistent and efficient transduction of RPE cells, yet the efficiency of transduction of neuroretinal cells remains variable and does not yet rival rAAVs [25]. A second obstacle with viral vectors is the potential for an immune response. While ocular immune privilege and the blood-retina barrier limit both local and systemic immune reactions, viral vectors retain their potential to cause infection and thereby jeopardize therapeutic safety and efficacy. Nanoparticle delivery systems aim to address this problem by encapsulating the cDNA plasmid in cationic polymers, peptides, or lipids, which have a more favorable safety profile. Cationic lipids have been investigated extensively and allow delivery of DNA into the retinal cells using liposomes [26, 27]. Not only do lipid-based nonviral vectors drastically reduce the chance of an immune response, but also they potentially allow for unconventional routes of administration. Viral vector-based gene therapy is usually administered either intravitreally or subretinally, depending which part of the retina is being targeted. Lipidbased gene therapy, however, allows for topical application of a liposome containing LacZ cDNA plasmids. This resulted in the successful transfection of retinal ganglion cells and local expression of B-galactosidase [28].

Current studies Nonsyndromic RP RP is a heterogeneous genetic disorder. To date, 61 genes have been reported to associate with nonsyndromic RP [8, 29]. In all RP animal models and patients analyzed so far, these mutations lead to apoptosis of photoreceptors [30]. Various types of gene therapy addressing different mutations associated with RP are now being developed in animal models. This section focuses on gene therapy for retinitis pigmentosa GTPase regulator (RPGR), retinaldehydebinding protein 1 (RLBP1), phosphodiesterase subunit beta (PDE6ß), and MERTK as gene therapy for these genes is currently investigated in clinical trials. Below, we describe per gene the current knowledge and, if available, the preliminary results of these clinical trials. RPGR The causative gene in X-linked retinitis pigmentosa (XLRP) is RPGR. The majority of mutations are found in exon open reading frame 15 (ORF15). It encodes a ciliary protein that

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285

regulates trafficking of proteins to the outer segment of photoreceptors [31]. On March 16, 2017, the first patient with XLRP underwent gene therapy in a phase I/II dose-escalation trial using an AAV-encoding RPGR (NCT03116113) [32]. AAV vectors expressing human RPGRORF15 are also used in phase I/II clinical trials for patients with XLRP caused by mutations in RPGR ORF15 (NCT03316560, NCT03252847). RLBP1 The RLBP1 gene encodes the cellular retinaldehyde-binding protein (CRALBP), a key factor in the rod visual cycle [33]. Dysfunction of this protein leads to improper binding of 11-cis-retinol and 11-cis retinal and causes RP [34]. CPK850 is a recombinant AAV8 vector containing the human RLBP1 gene which can be used in patients with RP due to biallelic mutations in the RLBP1 gene. A proof-of-concept study has been set up to explore the maximum tolerated dose of CPK850. Recruitment has not yet started (NCT03374657). Efficacy and safety were already demonstrated in a mouse model of RLBP1 deficiency [35, 36]. PDE6ß Patients with RP harboring mutations in the PDE6ß gene have a defect in PDE6ß expression. This leads to the accumulation of cGMP that results in photoreceptor degeneration [37]. For patients with PDE6ß mutations HORA-PDE6ß has been developed which uses an AAV2 vector containing functional hPDE6ß. The phase I–II dose-ranging trial assessing safety and efficacy was started in December 2017 (NCT03328130). MERTK RP patients with a mutation of the receptor tyrosine kinase gene, MERTK, have a defective phagocytosis pathway in the RPE that leads to retinal degeneration [38]. rAAV2-VMD2hMERTK is an AAV2 vector that was altered to carry the human MERTK gene for the treatment of patients with RP due to mutations in the MERTK gene. Six patients were entered in a phase I open-label, dose-escalation trial and the study is still ongoing (NCT01482195). Based on 2-year follow-up, the ocular and systemic safety profile of rAAV2-VMD2-hMERTK was acceptable [39].

Syndromic RP Usher syndrome Usher syndrome is divided into three different clinical subtypes and mutations involve at least 12 loci. The most prevalent form is Usher 1B, caused by mutations in the MYO7A gene, which encodes the molecular motor protein Myosin VIIa [29, 40]. This protein participates in the transport of rhodopsin from rod inner segments to the outer segments and in the localization of melanosomes and phagosomes in the apical microvilli in the RPE [41, 42]. Gene therapy could be used to introduce cDNA encoding functional Myosin VIIa into retinal cells. Because of the large size of the MYO7A gene (7 kb), lentiviral vectors are currently the first choice [43]. Dual AAV trans-splicing or hybrid vectors have also been used to express MYO7A in mouse models mimicking Usher 1B [23, 44]. However, a dual AAV vector approach was found less effective than a lentiviral approach [45]. Mice studies showed that both

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human immunodeficiency virus (HIV) and equine infectious anemia virus (EIAV) can be used to deliver MYO7A to photoreceptors and RPE cells [46, 47]. SAR421869 is an EIAV-based lentiviral vector with a cytomegalovirus (CMV)-based promoter expressing human MYO7A. Currently, a phase I/II open-label dose-escalation trial is being conducted to evaluate the safety and tolerability of a subretinal injection of SAR421869 (NCT01505062). Four patients have been treated thus far with different doses and no significant adverse events were reported at the ARVO Conference of 2015 [48]. Bardet-Biedl syndrome AAV-based gene therapy for Bardet-Biedl syndrome (BBS) is currently being developed for mutations in the BBS1 and BBS4 genes using various mice models. Knock-in mice with homozygous M390R mutations in BBS1 mimic BBS. Subretinal injection of normal AAVBbs1 in these mice rescued the cilium/basal body complex and the rhodopsin localization and slightly improved ERG [49]. Another mouse model for BBS is the BBS4 null mouse, where the BBS4 gene is deleted. After suppletion of the BBS4 gene with an AAV-BBS4 vector, the rhodopsin mislocalization was rescued and photoreceptor cell death was prevented. However, only small average retinal coverage has been achieved since only a small central part of the retina can be treated by subretinal injection. In RP, this does not yield much benefit [50]. In all mice models, it turned out to be difficult to find a good balance between the amount of gene needed for the treatment and overexpression toxicity that leads to cell death. Excess BBS1 protein leads to toxicity in wild-type mice [49]. To date, no human clinical trials have been announced.

Stargardt disease STGD1 is caused by mutations in the photoreceptor-specific ABCA4 gene which encodes the adenosine triphosphate-binding cassette, subfamily A, member 4 [51, 52]. This leads to impaired removal of all-trans-retinal with its conjugate from photoreceptor outer segment discs. The impaired removal causes lipofuscin accumulation, with secondary photoreceptor degeneration [53]. There is a broad spectrum of ABCA4 variants, with different phenotypes and more than 1000 sequence variations reported to date [52, 54, 55]. Gene therapy for STGD1 is currently being developed, however, the large size of the ABCA4 coding sequence (6.8 kb) exceeds the maximum AAV capacity and thus, the most successful AAV vectors used for other ocular genes cannot be employed to introduce a healthy copy of the gene into photoreceptor cells [20]. Therefore, currently investigated gene therapy options for STGD1 are based on lentiviral, dual AAV, and nanoparticle vectors. Dual AAV trans-splicing and hybrid vectors rescued the ABCA4 / knock-out mouse retinal phenotype and efficiently transduced mouse and pig photoreceptors in preclinical studies, indicating their potential for gene therapy for STGD1 [56, 57]. A reduction in lipofuscin was also noticed in ABCA4-deficient mice treated with DNA nanoparticles formulated with polyethylene glycol-substituted polylysine (CK30PEG) carrying ABCA4, which was the first step toward DNA nanoparticle-mediated gene delivery in the eye [58]. SAR422459 is an EIAV-based lentiviral vector which can express the photoreceptor-specific ABCA4 protein.

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It was the first type of gene therapy for STGD1 to be studied in human clinical trials (NCT01367444 and NCT01736592). Interim results of the clinical trial involving SAR422459 were reported at the ARVO Conference of 2015. In all, 16 patients had then been enrolled and the dose-escalation phase had been completed with follow-up ranging from 9 to 42 months. Neither harmful effects nor biological activity has been observed in these treated patients with advanced STGD1 [59].

X-linked retinoschisis X-linked retinoschisis is caused by mutations in the retinoschisin 1 (RS1) gene. The gene encodes for retinoschisin, a protein that is involved in intercellular adhesion through interactions with αB-crystalline and β2-laminin, forming a multimolecular complex [60]. The gene transcript is approximately 675 bp and, therefore, a good candidate for AAV-based gene therapy [61]. In contrast with most of the previously discussed conditions where the viral therapeutic agent is administered subretinally to ensure transfection of the central posterior pole, RS1 rAAV therapy ideally needs to transfect the entire retina. As such, intravitreal injection is the preferred route of administration. Preclinical studies in Rs1-KO mice showed successful transfection of retinal cells and improvement in all measures of retinal structure and function that were performed, including dark-adapted ERG and OCT. The effect did not improve when the dose was increased higher than 1  108 vector genomes per eye, however, RS1 expression never equaled that of wild-type mice, suggesting structural and functional improvement does not require wildtype expression levels. The improvement was greatest at 6–9 months postinjection [62]. Clinical trials are currently being undertaken to determine safety and efficacy in human subjects (NCT02317887 and NCT02416622).

Achromatopsia To date, six genes causing ACHM have been documented, including ATF6 [63], CNGA3 [64], CNGB3 [65], GNAT2 [66], PDE6C [67], and PDE6H [68]. These genes contribute to photo-transduction in all three types of cones and mutations result in reduced downstream visual signaling. Mutations in the CNGB3 and CNGA3 are most common [64, 65]. These genes encode subunits of the cyclic nucleotide-gated ion channel that are involved in cone cell signal transduction [69]. Since ACHM is limited to cones, photoreceptor-specific promoters are being developed to optimize specificity and to restrict expression. Gene therapy trials have been developed for the two most prevalent ACHM causing genes (CNGA3 and CNGB3). At the ARVO Conference of 2018, the preliminary results of the first clinical gene therapy trial for ACHM based on mutations in CNGA3 were discussed (NCT02610582). There were no unexpected adverse events in nine treated patients and BCVA increased slightly despite foveal detachment [70].

Choroideremia The causative gene in choroideremia is the similarly named choroideremia- or CHM gene, located on the long arm of the X-chromosome at position 21.2 (Xq21.2). The gene encodes for

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16. Gene therapy for inherited retinal diseases

the Rab escort protein-1 (REP1), which is involved in the prenylation of Rab GTPases and vesicle trafficking [71, 72]. This process of covalently binding geranylgeranyl groups to the protein’s C-terminal cysteine(s) through Rab geranylgeranyl transferase (RGGT) is vital for proper protein function. The main function of REP1 is thought to be either to present unprenylated Rabs to the RGGT or to escort prenylated Rabs to their destination membrane [73–76]. Choroideremia is a good candidate for retinal gene therapy using AAV vectors due to its relatively small gene transcript size of 1.9 kb [74], and clinical trials have recently reached phase III in an effort to determine therapeutic efficacy (NCT0384165, NCT02671539, and NCT02407678). Initial findings of two clinical trials revealed a low rate of serious adverse events, with only one recorded localized intraretinal immune response in the first 12 patients who underwent subretinal AAV2.REP1 injection. While best-corrected visual acuity (BCVA) may improve by up to 22 ETRDS letters after treatment, it remained stable in the majority of patients. Retinal atrophy surface area measured on fundus autofluorescence in the treated eyes decreased at the same rate as the untreated eyes [77, 78]. Another study confirmed BCVA remained stable in most treated patients and the atrophy surface area was comparable between treated and untreated eyes. In addition, this study reported a small increase in retinal sensitivity and gaze stability as measured by microperimetry [79]. A phase III trial is currently being undertaken to confirm clinical therapeutic efficacy (NCT03496012).

Leber congenital amaurosis A genetic cause may be found in up to 70%–80% of all LCA cases [80, 81] , and of the 23 genes [8] known to be associated with this disease, CEP290 is most frequently mutated, with up to 30% of cases explained by pathogenic variants in this gene [39, 82, 83]. The considerable size of the gene transcript of approximately 8 kb [84] means it is incompatible with conventional AAV-based gene therapy. Lentiviral vectors have been tested in vitro and while successful transfection of retinal cells was achieved, they showed poor viability, possibly due to CEP290 overexpression [85]. As an alternative, antisense oligonucleotides (AONs) have been developed. AONs are stabilized strands of RNA that can be injected intravitreally and transfect retinal ganglion and photoreceptor cells. These subsequently bind to a mutated splice site and prohibit cryptic pseudoexon insertion during the splicing process. In vitro results look promising, and AONs are currently being tested in a clinical setting (NCT03140969). The second major causative gene for LCA is the retinoid isomerohydrolase RPE65 or RPE65 gene. This protein is responsible for the production of 11-cis-retinal, essential for rod and cone signal transduction, from all-trans-retinyl esters [86–89]. The gene transcript, approximately 3.2 kb in size, is easily compatible with AAV-based gene therapy and has recently been approved by the US Food and Drug Administration and the European Medicines Agency as a registered therapy for LCA type 2. Research into this first-ever registered ocular gene therapy spanned over two decades and the results of the first clinical trials in 2008 confirmed partial recovery of the visual cycle could be attained, albeit with very little to no clinical improvement [17, 19, 90]. Most importantly, these studies were a proof-of-concept for this new therapeutic approach, and further research

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Future perspectives

289

into clinical efficacy was warranted. This led to multiple follow-up studies currently investigating long-term therapeutic efficacy (NCT00481546, NCT01208389, and NCT00999609). The success of preclinical studies and clinical trials for RPE65-LCA encouraged gene therapy research in a wide range of retinal dystrophies.

Limitations of gene therapy Gene therapy is a promising first-ever treatment option for a group of disorders that were previously untreatable. The first clinical trials with gene therapy for IRDs show an acceptable safety profile, with a low risk of a local immune response or serious adverse events. However, several limitations for this novel therapy need to be considered. First, the effectiveness of gene therapy, especially in the long term, remains uncertain as only relatively few patients have been treated and most clinical trials are still at an early stage. As mentioned, the retina of patients with choroideremia may continue to degenerate despite initially successful treatment. Second, in addition to the unknown long-term effectiveness of gene therapy, there is only a narrow window of opportunity to treat IRDs, as gene therapy is dependent on a living host cell and this group of disorders collectively lead to degeneration of the photoreceptor, RPE, and/or choroid cells. If gene therapy is to be useful, the IRDs must be diagnosed and treated in an early stage. For patients in advanced stage disease, where the retinal architecture is irreparably damaged, gene therapy often comes too late. To make gene therapy a success for all IRDs, a number of developments are still needed. Perhaps, most important is the development of optimal vectors for the target disease. At the moment, viral vectors, especially AAV, show a high gene transfer efficiency, but the limited cargo capacity of these AAV’s restrains gene therapy for diseases with variants in genes larger than 4.5 kb. While lentiviral vectors support genes of up to roughly 8–10 kb, their retroviral nature and possible oncogenic potential raises safety concerns. Nonviral vectors potentially offer larger cargo capacity compared to AAV or lentiviral vectors. However, there are concerns toward stability of expression when using nonviral approaches, and the success of gene therapy is largely reliant on the vector’s specificity for certain cell types and its ability to successfully transport the transgene into the target cell. Lastly, the lack of standard, clinically meaningful outcome measures to assess therapeutic efficacy further complicates gene therapy development. This potentially limits the ability of phase I/II clinical trials to properly measure therapeutic efficacy. Identification of clinically significant biomarkers to highlight disease progression and approval of these biomarkers by the US Food and Drug Administration is, therefore, needed. Natural history studies and genotype-phenotype studies have a crucial role in this since they describe the natural course of IRDs from which optimal study parameters and stages for treatment can be derived.

Future perspectives For a long time, there was no treatment to prevent blindness from IRDs. Now that the first gene therapy for the treatment of an IRD (LCA type 2) has been approved by the US Food and

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Drug Administration, gene therapy for other diseases within this group could become available in the foreseeable future. The identification of the disease-causing genes is most important for a patient to be eligible for gene therapy. By focusing on these patient-specific mutations and with advanced directed gene delivery, precision medicine for IRDs can continue to improve. Active areas of research will be the development of viral vectors with more precise cell-type targeting capabilities, optimization of viral vector transduction, the development of appropriate transgene promoters, and the development of additional vectors with larger gene capacities. Alongside the development of gene therapy, further development of gene-editing will continue to take place. This is particularly important for the treatment of dominant disorders where the use of GAT is more difficult. CRISPR/Cas9 can be used to directly correct or change genes [3]. Splice defects can be corrected by AONs that bind with pre-mRNA and alter the splicing process [91]. Small interfering RNA can inhibit expression of a gene [92]. Optogenetic therapy is used to introduce light sensitivity into cells that do not normally detect or respond to light. The first clinical trials using optogenetic therapy are recruiting (NCT02556736 and NCT03326336). It is expected that late-stage retinal dystrophies can be treated with optogenetic therapy, even if photoreceptors are irreparably damaged and regardless of any specific disease-causing mutations [93].

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[86] M. Jin, S. Li, W.N. Moghrabi, H. Sun, G.H. Travis, Rpe65 is the retinoid isomerase in bovine retinal pigment epithelium, Cell 122 (2005) 449–459. [87] G. Moiseyev, Y. Chen, Y. Takahashi, B.X. Wu, J.X. Ma, RPE65 is the isomerohydrolase in the retinoid visual cycle, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 12413–12418. [88] T.M. Redmond, E. Poliakov, S. Yu, J.Y. Tsai, Z. Lu, S. Gentleman, Mutation of key residues of RPE65 abolishes its enzymatic role as isomerohydrolase in the visual cycle, Proc. Natl. Acad. Sci. U. S. A. 102 (2005) 13658–13663. [89] T.M. Redmond, S. Yu, E. Lee, D. Bok, D. Hamasaki, N. Chen, P. Goletz, J.X. Ma, R.K. Crouch, K. Pfeifer, Rpe65 is necessary for production of 11-cis-vitamin A in the retinal visual cycle, Nat. Genet. 20 (1998) 344–351. [90] A.V. Cideciyan, T.S. Aleman, S.L. Boye, S.B. Schwartz, S. Kaushal, A.J. Roman, J.J. Pang, A. Sumaroka, E. A. Windsor, J.M. Wilson, T.R. Flotte, G.A. Fishman, E. Heon, E.M. Stone, B.J. Byrne, S.G. Jacobson, W. W. Hauswirth, Human gene therapy for RPE65 isomerase deficiency activates the retinoid cycle of vision but with slow rod kinetics, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 15112–15117. [91] A. Garanto, S.D. Van Der Velde-Visser, F.P.M. Cremers, R.W.J. Collin, Antisense oligonucleotide-based splice correction of a deep-intronic mutation in CHM underlying choroideremia, Adv. Exp. Med. Biol. 1074 (2018) 83–89. [92] O.F. Khan, P.S. Kowalski, J.C. Doloff, J.K. Tsosie, V. Bakthavatchalu, C.B. Winn, J. Haupt, M. Jamiel, R. Langer, D. G. Anderson, Endothelial siRNA delivery in nonhuman primates using ionizable low-molecular weight polymeric nanoparticles, Sci. Adv. 4 (2018) eaar8409. [93] B.M. Gaub, M.H. Berry, M. Visel, A. Holt, E.Y. Isacoff, J.G. Flannery, Optogenetic retinal gene therapy with the light gated Gpcr vertebrate rhodopsin, Methods Mol. Biol. 1715 (2018) 177–189.

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17 Gene therapy in animal models Claudio Punzo Department of Ophthalmology, University of Massachusetts Medical School, Worcester, MA, United States

Introduction In 2001, Lancelot, a Briard dog whose vision was restored by ocular gene therapy [1], was brought before Congress as a testament for the need of continued funding for gene therapy research. The therapeutic success with the Briard dog, which was treated for Leber congenital amaurosis type 2 (LCA2) due to a deficiency in the retinal-pigmented epithelium protein 65 kilodaltons (RPE65), served two purposes. First, it showed that ocular gene therapy can be scaled-up from a small to a large animal with an eye size comparable to that of humans. Second, because like humans the behavior and movement of a dog relies more heavily on visual cues when compared to that of a mouse, Lancelot’s appearance before Congress also allowed nonscientists to appreciate the benefits of gene therapy for blinding diseases. The success with the LCA2 gene therapy in the dog led to a fast expansion of the ocular gene therapy field. Presently, every class of inherited retinal degenerations (IRDs) has been treated in animals and many clinical trials have been initiated in humans [2, 3]. Interestingly, one of the first viral deliveries to the eye was reported in 1987 by the Cepko laboratory [4]. While those experiments used viral (lentivirus) gene delivery for cell linage analysis rather than for gene therapeutic purposes, they established a proof-of-concept that gene delivery in the retina is possible. The first gene therapy to correct gene expression in photoreceptors of mice was reported in 1996 using the adenovirus as a gene delivery vector [5]. Four years later, mouse photoreceptors were targeted for the first time using the adeno-associated virus (AAV) to restore gene expression [6]. One year later the rescue of the LCA2 pathology in the Briard became public, which, in many ways, was the catalyst that led to the expansion of the field in the 2000s [3]. Over the last three decades since the initial experiments by the Cepko laboratory many different viral vectors have been used to transduce retinal cells [7]. Out of all the vectors tested the AAV has emerged as the vector of choice to transduce the postmitotic retinal cells. The reasons for this are its ability to easily infect postmitotic cells, its low

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immunogenicity [8] and its ability to persist indefinitely as a nuclear episome without integrating into the host genome. Additionally, AAVs come in a wide range of serotypes [9], naturally [10] or engineered [11, 12], which enable targeting many different cells [10]. The biggest drawback of the AAV is its limited packaging capacity of 4.7 kb [13, 14]. Where necessary, this limitation is being circumvented mainly by the use of lentiviral vectors [7, 13].

Inherited retinal degenerations Classification IRDs are generally caused by mutations in genes expressed in photoreceptors. If the mutated gene is not exclusively expressed in the retina, the condition can manifest as syndromic disease also affecting other tissues (e.g., Bardet-Biedl syndrome [15]: BBS2, BBS4, BBS6, CEP290; Joubert syndrome [16]: CEP290; Senior-Loken syndrome [17]: NPHP5, CEP290; Meckel-Gruber syndrome [18]: CEP290; and Usher syndrome [19]: MYO7A; USH2A). To complicate matters, different mutations in the same gene can cause a range of clinical diseases (e.g., CEP290), while the same pathology can be caused by mutations in different genes (e.g., retinitis pigmentosa). Thus, often there is no clear genotype to phenotype correlation making retinal degenerations one of the most heterogenous disease groups in humans. Because IRDs are monogenic they follow a Mendelian inheritance pattern. Different mutations in the same gene can be inherited in a dominant or recessive manner. X-linked mutations manifest in males with a wide range of heterogeneity depending on the nature of the mutation. In females, pathologies are generally milder, if present at all, but can also be quite variable due to the random inactivation of one of the X-chromosomes. Type of dystrophy The clinical pathologies of IRDs progress as rod-cone dystrophy (RCD), cone dystrophy (CD), or cone-rod dystrophy (CRD). The nomenclature denotes which of the two photoreceptor cell types, rods or cones, dies first. The type of dystrophy that develops can be an indicator for the cell type in which the mutant allele is expressed. This is because rod loss always leads to cone loss in retinas where the rods significantly outnumber cones [20]. In contrast, loss of cones has no effect on rod survival in retinas where rods outnumber cones [21]. Consequently, in human and mouse, there cannot be a rod-only dystrophy. Mutations in rod photoreceptor-specific genes always progress as RCDs, while mutations in cone photoreceptor-specific genes manifest as CDs. If the disease progresses as a CRD, the mutated gene has to be expressed in both photoreceptor cell types or in cells that are important for maintaining retinal homeostasis (e.g., RPE cells). Clinical nomenclature Retinitis pigmentosa describes a fundus pathology and not a specific type of dystrophy. The name originated because clinicians diagnosed the fundus pathology as an inflammation of the retina (itis: inflammation) with pigment deposition. The disease progression is generally described as causing initial loss of night vision, due to the loss of rods, followed by complete blindness due to the subsequent loss of cones [22]. Although this is consistent with an

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RCD, the late stage fundus pathology of a CRD is indistinguishable from that of an RCD. However, out of convention retinitis pigmentosa refers nowadays generally to an RCD. Initial loss of night vision should not be confused with stationary night blindness [23], which affects night vision without causing rod loss. Genes that when mutated can cause stationary night blindness are expressed either in rod bipolar cells, which are the interneurons that connect the photoreceptors to the ganglion cells [24], or in rods. In cases where the gene is expressed in rods, rod loss is either nonexistent or so slow that clinically the disease is referred to as stationary night blindness rather than retinitis pigmentosa [23]. Cone dystrophies often manifest as achromatopsia (ACHM), a condition that results in total loss of cone function [25, 26]. This differs from color blindness, where only one of the three cone types is affected due to a mutation in one of the cone-specific opsins. In ACHM, all three cone types are affected causing photophobia and complete lack of color discrimination [25, 26]. Fortunately, most ACHM mutations progress slowly, leaving ample time for therapeutic intervention. Cone-rod dystrophies with a disease onset in the first year of life and complete blindness in childhood are clinically referred to as LCA [27]. However, LCA describes the condition of early childhood blindness and not the origin of cellular dysfunction. For example, LCA2 is caused by a mutation in the RPE protein RPE65 [28]. Another example of an RPE expressed protein that when mutated can cause LCA is lecithin retinol acyltransferase (LRAT) [29, 30]. In contrast, LCA10 is caused by mutations in CEP290, a cilia protein expressed in photoreceptors and other cells that depending on the mutation can also cause many other syndromes [15–18]. Finally, there are a few forms of IRDs in humans that are somewhat atypical from the aforementioned ones. Stargardt disease is a condition that primarily affects the foveal cones. The disease is caused by mutations in the photoreceptor-specific protein ATP-binding cassette, subfamily A, member 4 (ABCA4) [31] or the photoreceptor-enriched protein elongation of very long chain fatty acids-like 4 (ELOVL4) [32]. Mutations in either gene also cause secondary RPE pathologies [33, 34], which are atypical for mutations in photoreceptor-expressed genes. Because of the early macular RPE pathologies, the disease is seen as an inherited early form of macular degeneration. Retinoschisis [35], choroideremia [36], and Leber’s hereditary optic neuropathy (LHON) [37] are other rare forms of IRDs for which at least some genes have been identified. For example, a juvenile form of X-linked retinoschisis is caused by mutations in the RS1 gene (Retinoschisis-1) [38]. RS1 encodes for a cell-surface adhesion molecule expressed in photoreceptors and bipolar cells causing splitting of the retinal layers when mutated in males [35]. Choroideremia, which is also X-linked, is characterized by the degeneration of photoreceptors, the RPE and the choroid [39]. The disease is caused by mutations in the Rab escort protein 1 (REP1) gene [40]. In contrast to all other diseases mentioned thus far LHON causes blindness through atrophy of the optic nerve due to ganglion cell loss [37]. The disease is often caused by mutations in the NADH dehydrogenase subunit 4 complex 1 (ND4) gene, which is encoded in the mitochondrial DNA [41]. Normally, ganglion cell loss is associated with glaucoma [42], which like age-related macular degeneration is a complex disease that develops only later in life.

Basic biology of common IRDs Mutations that cause IRD can be grouped by their biological or cellular functions such as photoreceptor development, ciliary genes, phototransduction, and housekeeping genes [43].

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Most of the genes that fall into the developmental group encode for transcription factors [44] (e.g., CRX: cone-rod homeobox; NRL: neural retina leucine zipper; NR2E3: nuclear receptor subfamily 2, group E, member 3) or hormone receptors (e.g., RORβ: retinoid-related orphan receptor beta; TRβ2: thyroid hormone receptor beta 2) that are required for proper cell fate specification and maturation. Mutations in any of these genes can lead to LCA, CRD, or retinitis pigmentosa [43]. Additionally, some of these genes can also alter the rod-cone ratio (e.g., NR2E3) or the expression of the proper cone opsins (TRβ2) causing enhanced S-cone syndrome (too many blue cones) [45, 46]. Genes encoding for ciliary proteins are often syndromic as the cilium is not unique to photoreceptors. However, because the photoreceptor cilium is larger and more specialized than other cilia, it is more susceptible to functional changes of a protein [47]; thus, not all mutations are syndromic. The nonsyndromic mutations progress as LCA, CRDs, or retinitis pigmentosa. By far the largest numbers of individuals affected by IRDs have mutations in genes of the phototransduction cascade. Because these genes encode mostly for photoreceptor-specific proteins, they cause nonsyndromic retinal degeneration such as retinitis pigmentosa, CRD, and CD including ACHM. Mutations in housekeeping genes often cause nonsyndromic IRDs even though these genes are expressed in most cells. This is because photoreceptors are among the most metabolically active cells in the body [48]. Since such mutations do not cause complete loss-of-function, the most metabolically active cells are affected first. Genes in this category encode for splicing factors and metabolic enzymes [22, 49–52].

Animal models of inherited retinal degenerations Animal models have played an important role in understanding the basic biology of genes involved in IRDs and in dissecting the process of vision in more detail [43]. Additionally, animal models have been an integral part in the development of therapeutic approaches, especially gene therapies [25, 43, 53, 54]. They not only allow one to establish a proof-of-concept but also to test the best route of delivery of gene therapy vectors, the serotype with the best tropism, the optimal promoter for cell type-specific expression and the best time of intervention. All these parameters are important components in order to optimize the efficacy of a gene therapy treatment. In general, the closer related an animal model is to humans the more likely therapeutic success in a model translates to humans. However, many other aspects such as differences in retinal architecture or viral tropism between species can influence the effectiveness of the therapeutic outcomes in humans [55]. In this section, we will discuss the anatomical eye differences of various vertebrate models, the pros and cons of each model [56] before focusing on the therapies established thus far.

Vertebrate models of IRD Vertebrate models for eye studies include zebrafish, chicken, sheep, pig, cat, dog, rat, mouse, and nonhuman primates (NHPs). Among these models, the ones mostly used for ocular gene therapy are mice and dogs due to the availability of disease models and the relative similarities to the human eye. Most gene therapy studies are first established in mouse and

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then tested in larger animals such as dogs. Paradoxically, the gene therapy treatment of the LCA2 dog preceded that of the LCA2 mouse model [57–60]. We will now briefly review the pros and cons of the different animal models [56] before focusing on the gene therapy studies performed for different IRDs. The advantage of the zebrafish and chicken model is their ex-utero development, which facilitates genetic manipulation in particular for the developmental studies. Both species have double cones and are cone dominant, which contrasts the rod-dominant human retina [61, 62]. In addition, chicken cones have oil droplets that are believed to function as cut-off filters for optimal light absorption [62]. Finally, the vasculature of the chicken retina consists of a specialized structure called pecten oculi that differs considerably from the human retinal vasculature [63]. While the retinal vasculature in zebrafish is more similar to the human one, it consists of only one stratified layer and not three [64]. What complicates the use of these two animal models for retinal gene therapy is their ability to regenerate lost photoreceptors from Muller glia cells. For this reason, disease progression can be quite different from mammalian species and therapeutic efficacy is difficult to interpret. While this regenerative ability is lost in adult chickens, it persists in zebrafish [61, 65, 66]. Consequently, few models of IRDs have been generated or identified in either species [21, 67, 68]. Sheep and pig are both rod dominant, have two types of cones, S (short wavelength) and M (medium wavelength) cones, and an area centralis, which is a cone-enriched region similar to the human macula [69–71]. In contrast to pig, human, and mouse, sheep have a dorsal tapetum lucidum, a reflective mirror-like structure behind the RPE that enhances light detection in the dark [72]. This structure can complicate the use of imaging modalities such as optical coherence tomography (OCT) and fundoscopy. Of the larger animals, the pig is the ideal model to explore gene therapy approaches for human IRDs. The presence of an area centralis, the lack of a tapetal zone, the similarity in eye size, and the presence of calyceal processes, which is a specialized structure surrounding the connecting cilium that is found also in human cones [73], make the pig particularly suited for the study of gene therapy approaches. Cats and dogs have both a dorsal tapetal zone [72], a vascularized rod-dominant retina, and a cone rich central region [74, 75]. Additionally, a region completely devoid of rods within the area centralis has recently been identified in dogs [76]. Although devoid of a foveal pit, the area centralis in dogs is in function very similar to the human fovea. Nonetheless, the pig remains in principle a slightly superior model for IRD studies when compared to the dog based on the other anatomical features. However, dogs are the large animal of choice because there are many more disease models available [25, 53, 54] and testing therapeutic outcome by behavioral assays is much easier with dogs than with pigs. The reasons for the availability of many disease models in dogs are twofold. First, excessive inbreeding for the purpose of generating and maintaining a variety of breeds has resulted in the accumulation of mutations in photoreceptor-specific genes, which due to their nonsyndromic nature and often later onset tend to accumulate faster. Second, the advent of cheap genomic sequencing has pushed breeders to eradicate mutations within their colonies, which has allowed identifying many human disease genes that cause blindness in dogs. Thanks to veterinary hospitals that maintain breeding colonies there is now a large selection of IRD dog models available [54] to test and optimize gene therapeutic intervention strategies for humans.

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Mouse and rat are the most commonly used laboratory animals since they are easy to maintain, are inexpensive, have a short generation time and intermediate litter size. Both have a rod-dominant retina with an almost constant density of cones across the retina [24]. This means there is no macular equivalent in either species. The eyes of the mouse and rat are dichromatic with S-cones in the ventral halve and M-cones across the entire retina, albeit expression of M-opsin is more prominent dorsally [77, 78]. Many ventral cones express both opsins. Similar to humans the retinal vasculature has three plexuses. By far the largest numbers of IRD models exist in mouse, either generated or identified [43]. The advent of genetic engineering and the introduction of the Cre-lox system [79] for cell type-specific gene deletion and the recently developed Crispr-Cas9 technology [80, 81] permits nowadays to generate any mouse strain needed to study any IRD. Consequently, the mouse has become the workhorse for proof-ofconcept gene therapy studies although its eyes are not the most ideal model. Among the NHPs, Macaques are the most commonly used species for ocular gene therapy approaches. Like humans they have three types of cones, a rod-rich retina and a cone only fovea with a foveal pit [82, 83]. However, because NHPs are not kept in large colonies and are thus often captured for research purposes there are no genetic models of IRD available, since a blind NHP has little survival chance in the wild. The lack of any genetic model precludes testing the efficiency of a gene therapy. Consequently, NHPs are generally used for safety and vector distribution studies, as their similarity to humans is more likely to reveal any flaws in the design of the therapeutic vector [84].

Gene therapies of IRDs Gene therapies that have been developed thus far have generally followed that path of least resistance in terms of translation. As such, simple gene augmentation approaches that address complete or partial (hypomorph) loss-of-function of a protein in autosomal recessive or X-linked IRDs are the easiest to develop and a lot of progress has been made on that front [2, 3, 25, 35, 36, 42, 85, 86]. More challenging approaches are knockdowns of dominant mutations, since these often require a simultaneous gene augmentation strategy unless knockdown of the mutant allele transcript is possible and one normal copy is sufficient for proper gene function. However, many genes have more than one dominant mutation. In such cases, mutation-independent strategies that knock down the endogenous gene by shRNA, while delivering at the same time an shRNA-resistant version are better strategies. Such an approach has just recently been published for the rhodopsin gene in dogs [87]. Because the rhodopsin gene alone accounts for a quarter of all autosomal dominant retinitis pigmentosa cases and autosomal dominant retinitis pigmentosa accounts for 35% of all retinitis pigmentosa cases, the therapy established in dogs should be able to treat about 10% of all retinitis pigmentosa patients [22]. Another important aspect to consider is the ease of translating an approach into a human therapy. The easiest therapies to translate fulfill two criteria: severely impaired or almost nonexisting vision and very slow degeneration of the retina. The slow degeneration provides a greater time window for therapeutic intervention and generally increases the success of the therapeutic outcome as more cells can be treated. The almost complete absence of normal vision has the advantage that negative outcomes of a gene therapy do not worsen the patients’ condition, especially at a time where not enough safety data exists yet. Thus, it is no surprise

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that LCA2 (RPE65) was the first condition treated in humans, as besides fulfilling these two criteria it required a simple gene augmentation approach. Another favorable condition that meets these criteria is ACHM, which is characterized by impaired cone function and slow cone death. Mouse and dog models for the ACHM causing genes Cnga3 [88, 89], Cngb3 [90], and Gnat2 [91], and two sheep models of Cnga3 [92, 93] have all been treated with various degrees of success and clinical trials are ongoing for CNGA3, CNGB3. After the initial success with the easier approaches conditions that lead to fast central vision reduction have gained more attention, since no intervention results in early loss of high acuity vision. Choroideremia and retinoschisis, both X-linked recessive disorders due to lossof-function of the REP1 [36] and RS1 [38, 94] gene, respectively, cause blindness within the first three decades of life. Generation of animal models for choroideremia has proven complicated due to early lethality in mouse [39, 95, 96]. Subsequent use of the Cre-lox system has allowed determining that photoreceptors and RPE cells are the primary target cells for therapeutic intervention [97, 98]. Currently, nine clinical trials are ongoing and data published thus far suggest that the approach is beneficial [99]. In contrast to Rep1, Rs1 knockout mice are viable and were instrumental in showing that intravitreal delivery of AAVs carrying the transgene is sufficient to rescue the pathologies [100–102]. This has led to the initiation of two clinical trials that deliver the therapeutic vector intravitreally. Another X-linked gene that accounts for almost 70% of all X-linked retinitis pigmentosa cases is RPGR [86]. The protein is expressed in both photoreceptor cell types and localizes to the connecting cilium [103, 104]. While the pathology progresses generally as a CRD, it is often referred to as X-linked retinitis pigmentosa due to its heterogeneous clinical presentation in males caused by a multitude of mutations [86, 103]. Mouse [105–107] and dog [108, 109] models have been instrumental in understanding the role of the protein [110, 111] and establishing a gene therapy strategy [112–118], which has resulted in the first clinical trials for this condition. Finally, rodent models for LHON caused by mutations in ND4 were instrumental in showing that replacement of a mitochondrial protein by allotropic expression is feasible [119–122]. Because the disease affects primarily ganglion cells and treatments in rodent models were successful several clinical trials delivering AAV2-ND4 intravitreally have been initiated. Reports from the first trials are positive indicating that the approach works [123–125]. The more challenging treatments of IRDs are those where the therapeutic gene exceeds the packaging capacity of the AAV vector. Lentiviral vectors provide a good alternative for such therapies; however, their inefficiency in infecting photoreceptors has slowed progress for large genes such as ABCA4 and MYO7A. Gene therapy in animal models has worked with lentiviral vectors for both of these genes [126, 127]. However, translational outcomes in humans remain uncertain because both animal models do not develop severe photoreceptor degeneration making it difficult to evaluate the efficacy of the therapy in mouse [128, 129]. Thus, currently there is only one clinical trial registered for each gene to treat Stargardt disease and Usher syndrome by lentiviral gene augmentation of ABCA4 and MYO7A, respectively. Dual AAV vector approaches where half of the gene is packaged in one AAV vector and the other half in a second AAV vector, with enough overlap between the two halves to allow for recombination of both halves once they enter the same cell [130, 131], have also been tried for both genes in mouse [128, 132, 133]. However, success with this approach has been limited making lentiviral vectors the preferred treatment strategy for humans. An alternative approach to the use of lentiviral vectors for large genes is to identify a minimal

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functional domain of a gene (mini-gene) that is sufficient to provide some therapeutic benefit and small enough to be packaged in an AAV. One such approach has been recently published for CEP290, where overexpression of a minimal region within CEP290 successfully delayed photoreceptor death in a mouse model of LCA10 [134]. The most difficult gene therapies to translate are the ones where the underlying mutation is in a rod photoreceptor-specific gene and degeneration progresses very slowly. The reason for this is not the nature of the mutation but rather the appropriate time of intervention. Until most rods have died, daylight and color vision are barely affected in such individuals. Consequently, affected individuals are often diagnosed in their 40s if they don’t have a family history. Gene therapeutic intervention for those patients is futile as most rods have already died. These patients will lose sight since rod loss leads always to cone loss. Treating patients that are diagnosed early is complicated as well because the therapy needs to be administered at a time when color and high acuity vision is unaffected. Ideally, an individual that will develop blindness in their fifth or sixth decade of life needs to be treated in their second or third decade [22], as it remains unclear how many rods need to be saved in order to halt the progression of the disease. Earlier studies with chimeric mouse models were inconclusive [135, 136], while newer studies suggest that if expression of the therapeutic gene is high enough even less than 50% of rods should suffice [137]. Because infecting all rods requires a complete retinal detachment, which by itself causes a certain degree of photoreceptor loss, the procedure is risky for individuals that are diagnosed at a young age. One has to weigh the potential reward of delaying vision loss several decades later with the instantaneous risk of vision deterioration should the procedure fail. Thus, while several mouse and dog models of retinitis pigmentosa have been treated by gene augmentation [87, 138–143], there are currently only three clinical trials registered for this disease. An alternative approach for this category of IRDs is to develop a mutation-independent approach that saves the cones. Such a therapy could in principle be administered at any time during the disease progression (demise of cones), minimizing the risk of unwanted early side effects due to the therapy. While progress has been made in understanding the reasons for secondary cone death [20], which seems to be primarily a glucose shortage in cones [144–151] caused by the loss of rods [20], and some gene therapy approaches have been successful in mouse [152, 153], it remains unclear if they will work in larger animal models or humans.

Challenges There are still several challenges that need to be overcome in order for retinal gene therapy to become a tailor-made precision medicine that can be employed as needed. First and foremost, there is a need for more long-term safety data on intravitreal and subretinal delivery of AAVs. While this data will become available as more and more trials are conducted, lack of complicates direct treatment without preclinical studies for a condition where no animal model is available. The “holy grail” of photoreceptor gene therapy is to identify a vector that when injected intravitreally is able to infect photoreceptors efficiently. Currently, subretinal injections require the retina to be physically detached from the RPE in order to infect photoreceptors efficiently [154]. The problem with the procedure is that it always leads to some photoreceptor loss [155, 156], in particular if photoreceptors are already degenerating or if foveal cones, VI. Gene-based therapy

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which have much more intricate interactions with the RPE [157], are detached. It is unclear why a retinal detachment is necessary. The damage induced by the detachment might stimulate viral uptake during the resynthesis phase of the outer segment [10]. This idea is supported by findings that retinal pretreatment with the ciliary neurotrophic factor (CNTF), a growth factor that promotes intermediate deconstruction of the outer segment, improves viral infection [158]. The need to induce a retinal detachment complicates the treatment of RCD (e.g., retinitis pigmentosa) as in such cases treatment outcome depends on the number of infected rods [20], which means that ideally the entire retina needs to be detached. Alternatively, treating only the rod-rich ring that surrounds the foveal cones [159] should considerably slow disease progression [22]. Much time has been invested in identifying serotypes (naturally or engineered) that when delivered intravitreally infect photoreceptors in order to circumvent the aforementioned detachment problem [160]. One such example is AAV7m8, which efficiently infects photoreceptors when delivered intravitreally in mouse [161]. However, the same vector was not nearly as efficient in NHPs [84], although newer improved combinations look promising [12, 162]. Unfortunately, there is quite some variability from one species to the other with regards to cell type specificity for any given serotype [163]. Thus, therapies that are successful in mice or dogs with one serotype might work poorly in humans as infectivity is reduced. This means that vector distribution studies in NHPs are the only option to determine if a serotype is suitable for efficient infection of a desired cell type in humans [84]. Other species differences to consider when therapeutic outcomes do not translate from animals to humans are differences in the time of therapeutic intervention and species-specific disease differences [164]. In many cases, treatment in animals is done at an ideal time, which is often not possible with patients [25]. Additionally, some mutations cause different degrees of pathologies between mice, dogs, and humans thus success in the mouse or the dog does not guarantee success in humans [128, 129]. Despite these challenges, the field of retinal gene therapy has had a remarkable success over the last decades [2, 3, 85]. At the current rate, treatment for most IRDs might become a reality despite the overwhelming number of disease genes that can cause retinal degeneration.

References [1] G.M. Acland, et al., Gene therapy restores vision in a canine model of childhood blindness, Nat. Genet. 28 (1) (2001) 92–95. [2] A. Auricchio, A.J. Smith, R.R. Ali, The future looks brighter after 25 years of retinal gene therapy, Hum. Gene Ther. 28 (11) (2017) 982–987. [3] J. Bennett, Taking stock of retinal gene therapy: looking back and moving forward, Mol. Ther. 25 (5) (2017) 1076–1094. [4] J. Price, D. Turner, C. Cepko, Lineage analysis in the vertebrate nervous system by retrovirus-mediated gene transfer, Proc. Natl. Acad. Sci. U. S. A. 84 (1) (1987) 156–160. [5] J. Bennett, et al., Photoreceptor cell rescue in retinal degeneration (rd) mice by in vivo gene therapy, Nat. Med. 2 (6) (1996) 649–654. [6] R.R. Ali, et al., Restoration of photoreceptor ultrastructure and function in retinal degeneration slow mice by gene therapy, Nat. Genet. 25 (3) (2000) 306–310. [7] A. Puppo, et al., Retinal transduction profiles by high-capacity viral vectors, Gene Ther. 21 (10) (2014) 855–865. [8] M. Desrosiers, D. Dalkara, Neutralizing antibodies against adeno-associated virus (AAV): measurement and influence on retinal gene delivery, Methods Mol. Biol. 1715 (2018) 225–238. [9] M. Salganik, M.L. Hirsch, R.J. Samulski, Adeno-associated virus as a mammalian DNA vector, Microbiol. Spectr. 3 (4) (2015).

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C H A P T E R

18 Pleiotropy in eye disease and related traits Xiaoyi Raymond Gao Department of Ophthalmology and Visual Science, Department of Biomedical Informatics, and Division of Human Genetics, The Ohio State University, Columbus, OH, United States

Introduction Pleiotropy is a word with Greek origins, derived from the Greek words “pleion” meaning more and “trope,” meaning turning. The word entered our vocabulary in the 1930s, according to the English Oxford Living Dictionaries. In genetics, pleiotropy refers to the phenomenon that a single gene or genetic variant affects two or more seemingly unrelated phenotypic traits [1, 2]. Pleiotropy was first introduced in the scientific literature more than 100 years ago [2]. Before human genetics gained popularity, pleiotropy was mainly studied in model organisms and evolutionary biology. Over history, pleiotropy has evolved to have many different aspects. A number of diseases and syndromes with eye-related features have been found to be pleiotropic. A classic example is Bardet-Biedl syndrome (BBS), an autosomal recessive disorder [3] characterized principally by diverse phenotypic traits including retinitis pigmentosa, obesity, polydactyly, hypogonadism, and renal anomalies [4]. The prevalence of BBS varies among populations, with estimates of 1 in 160,000 persons affected in northern European populations and 1 in 13,500 persons affected in some Arab populations [5]. In all, 21 genes have been identified for BBS thus far [4]. Another example of a pleiotropic condition is Joubert syndrome (JBTS). JBTS is a rare autosomal recessive disorder that typically manifests very early in infancy with multiple neurological problems such as hypotonia, ataxia, developmental delay, and abnormal eye movements [6]. Other eye-related clinical features may include retinal dystrophy and ocular coloboma [7]. The prevalence of JBTS has been estimated at about 1 in 100,000 in the United States [7]. At least 34 genes have been identified for JBTS [8]. However, pleiotropy information was limited in human genetics until the widespread use of genome-wide association studies (GWASs), which make extensive pleiotropy investigation

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in the human genome feasible. Pleiotropy can now be inferred using the genotype-phenotype relationship discovered in GWASs [9–11]. Findings from these association studies are collected in the GWAS Catalog, a database of all published GWASs maintained by NHGRI and EBML-EBI [12]. Another method for uncovering pleiotropy is the phenome-wide association study (PheWAS) approach, that is, testing the association of a specific set of genetic variants with a wide range of phenotypes, typically using data available in electronic medical records or collected in large cohort studies [13, 14]. An advantage of GWAS is that phenotypes do not need to be measured in the same subjects, thus alleviating possible confounding due to environmental factors. Results from GWASs indicate that pleiotropy is ubiquitous across the phenotypic spectrum [9, 15]. In 2011, an analysis of the GWAS Catalog database [11] noted that 4.6% of single-nucleotide polymorphisms (SNPs) and 16.9% of genes have pleiotropic effects. A more recent study reported that 44% of genes in the GWAS Catalog are associated with two or more phenotypes [9]. As more genetic association studies are conducted and added to the GWAS Catalog, more and more pleiotropic relationships are being uncovered. Many of the pleiotropic relationships discovered through association studies, like GWASs, are more accurately described as cross-phenotype associations [16]. Solovieff et al. [1] described three major scenarios for cross-phenotype associations: biological pleiotropy, mediated pleiotropy, and spurious pleiotropy. Biological pleiotropy refers to a genetic variant or gene that has causal effects on two or more traits. Mediated pleiotropy occurs when a genetic variant manifests its effect on a trait through another trait. Spurious pleiotropy refers to a genetic locus that appears to be falsely associated with multiple phenotypes due to various biases. An additional type of pleiotropy that warrants mention is antagonistic pleiotropy, which refers to certain genes that may affect fitness differently at various life stages [17]. It is an important factor in aging and evolutionary biology. True biological pleiotropy typically requires functional validation [1], which can be very challenging and time consuming to verify in human subjects. Nevertheless, these cross-phenotype associations highlight important relationships between human diseases and traits. In this chapter, we will focus on the recent GWAS results to illustrate the interesting phenomenon of pleiotropy, examples in eye-related diseases and traits, and implications for genomic medicine.

Numerous genes show pleiotropic effects The GWAS Catalog provides an excellent resource for studying pleiotropy in humans. Using the GWAS Catalog (August 2018 version), we summarized a portion of noteworthy results in eye-related pleiotropy. Table 1 shows the top 20 high-ranking genes based on their number of reported associations. GCKR shows 84 connections, the maximum number of mapped traits and diseases. Several other popular genes in human genetics are also in the top 20 list, such as ABO, APOE, and CDKN2B-AS1. We use two protein-coding genes, ABO and APOE, and two nonprotein-coding ones, CDKN2B-AS1 and LOC105374007, to illustrate some eye-related pleiotropic effects. ABO (alpha 1-3-N-acetylgalactosaminyltransferase and alpha 1-3-galactosyltransferase) is a protein-coding gene located at chromosome 9q34.2. This gene encodes proteins that

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TABLE 1 Summary results for the top-ranking genes/regions displaying pleiotropic effects. Mapped gene/region

Number of phenotypes each gene is associated with

GCKR

84

HLA-DRB1-LOC107986589

81

ABO

75

CSMD1

65

HLA-DQB1-MTCO3P1

59

APOE

54

RBFOX1

53

FADS1

53

ABO-LCN1P2

50

PTPRD

47

SH2B3

44

TRIB1-LOC105375746

43

LPP

43

ATXN2

40

TERT

39

CDKN2B-AS1

39

FADS2

38

FTO

38

CDH13

38

APOC1-APOC1P1

37

determine the first discovered blood group system, ABO. ABO was reported to be associated with intraocular pressure (IOP) [18] and optic cup area measurement [19], but not glaucoma. It is interesting to note that the B blood group was associated with glaucoma, including primary open-angle glaucoma (POAG), in a Pakistani patient cohort [20]. ABO has been reported for associations with numerous other diseases and traits, such as breast carcinoma [21], coronary heart disease (CHD) [22], type I diabetes mellitus [23], type 2 diabetes mellitus [24], malaria [25, 26], pancreatic carcinoma [27–30], LDL cholesterol [31–33], total cholesterol [31–33], coronary artery disease [34–36], stroke [37], E-selectin [38], interleukin-6 [39, 40], and childhood ear infection [10, 41], to list a few. The pleiotropic effects of the ABO gene on E-selectin and lipid levels were also reported recently [42]. APOE, located at chromosome 19q13.32, encodes apolipoprotein E. Among the most important variants in this gene are those encoding three common haplotypes, ε2, ε3, and ε4, of the APOE gene. The ε3 allele is the most common allele with an estimated frequency of 68%–86% in subjects of European ancestry, compared to 4%–14% and 10%–23% for the

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ε2 and ε4 alleles, respectively [43]. APOE ε4 is the single most important genetic risk factor for Alzheimer’s disease (AD), where the addition of each ε4 allele shifts the age at onset younger [44, 45]. With increasing number of ε4 alleles, mean age at onset decreased from 84 to 68 years old. Compared to subjects without the ε4 allele, individuals with one copy of the ε4 allele had a 2.5-fold increased risk, and those with two copies of ε4 had a 3.6-fold increased risk of having AD at age 75 [44]. APOE has also been repeatedly studied over the years in CHD. APOE ε4 has been reported to moderately increase the risk of ischemic heart disease, while ε2 has shown reduced risk in several meta-analyses [46–50]. APOE is also associated with many quantitative traits, such as cerebral amyloid deposition measurement [51, 52], T-tau/Aβ42 measurement [53], total cholesterol measurement [31, 32, 54–57], LDL [31, 55–59], HDL [31, 32, 54], C-reactive protein measurement [31, 60], platelet count [61], and physical activity measurement [62]. APOE is also associated with blood pressure in a recent genetic study of over 1 million people [63]. Furthermore, subjects with APOE ε4/ε4 homozygotes had a 2.8fold increased risk of discontinuing statin therapy compared to those with the ε3/ε3 genotypes [64]. APOE is likely a promising drug target for CHD and AD [65]. In eye-related phenotypes, APOE has been reported for associations with age-related macular degeneration (AMD) [66]. Interestingly, APOE ε4 allele shows antagonistic pleiotropy, that is, it is reported as a risk factor for AD and heart disease, but also shows a protective effect on AMD [67]. CDKN2B-AS1 is a long, noncoding functional RNA located within the CDKN2B-CDKN2A gene cluster at chromosome 9q21.3 that regulates other genes at this locus. Associations with multiple glaucoma-related phenotypes have been reported for CDKN2B-AS1, such as with open-angle glaucoma [68–74], normal tension glaucoma [75], optic cup area [19, 76], optic disc size [77], and cup-to-disc ratio [19]. It was hypothesized that glaucoma risk variants at this locus may predispose retinal ganglion cells to gradual apoptosis [68]. Local ancestry in CDKN2B-AS1 is also associated with POAG in an African American cohort [78]. Furthermore, CDKN2B-AS1 is associated with macular thickness, measured using spectral-domain optical coherence tomography [79]. Associations with non-eye diseases/traits have also been reported, such as with basal cell carcinoma [80–82], lung carcinoma [83, 84], skin carcinoma [85], prostate carcinoma [83, 86, 87], estrogen-receptor negative breast cancer [88, 89], central nervous system cancer [90–92], coronary artery disease [22, 35, 36, 93–101], stroke [102], endometriosis [103, 104], interleukin-6 measurement [105], type 2 diabetes mellitus [106, 107], glioma [90, 108, 109], and brain aneurysm [110–114]. LOC105374007 is an uncharacterized ncRNA located at chromosome 3q12.1. LOC105374007 has been reported for associations with six traits/diseases, four of which are eye related. It is associated with AMD [66], cup-to-disc ratio [19], optic cup area [19, 76], optic disc area/size [19, 77], childhood ear infection [10, 41], and heart rate (RR interval) [115].

GWAS SNPs show pleiotropic effects Instead of examining associations with individual genes and loci, we can study variants with genome-wide statistically significant associations for a trait/disease all at once. This makes it possible to have an overview of what diseases and traits are connected to the phenotype of interest. Using 115,486 participants of European ancestry in the UK Biobank (UKB) cohort, Gao et al. [116] identified 671 directly genotyped variants, which mapped to 149 loci that are associated with IOP at the genome-wide significant level P < 5  10 8. We examined VII. Big data and precision medicine

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pleiotropy with other diseases/traits among these genetic loci in the GWAS Catalog. Of the 671 variants, 68 SNPs had exact ID matches in the GWAS Catalog (January 2018 version). We constructed the pleiotropy network using the genotyped GWAS significant variants, the August 2018 version of the GWAS Catalog, and the PleioNet algorithm [117]. We found that 76 SNPs had exact matches in the catalog. Fig. 1 shows the pleiotropy network of 92 related diseases/traits for these 76 SNPs. In the figure, yellow, orange, and red ovals denote SNPs, individual traits, and categories, respectively. Numerous eye diseases/traits were associated

FIG. 1 A network of the pleiotropic effects for significant intraocular pressure SNPs in the GWAS Catalog. A network of traits and diseases for 76 SNPs that matched directly to other phenotypes through the GWAS Catalog (August 2018 version). Yellow, orange, and red ovals denote SNPs, individual traits, and categories, respectively.

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with the included SNPs, such as open-angle glaucoma, primary angle closure glaucoma, AMD, Fuchs’ endothelial dystrophy, axial length, central corneal thickness, optic cup area, optic disc size, and eye color. Furthermore, these variants mapped to cancer, cardiovascular disease, metabolic disorder, immune system disorder, digestive system disorder, drug response, and a variety of other measurements, including breast cancer, prostate carcinoma, acute lymphoblastic leukemia, coronary artery disease, atrial fibrillation, hypertension, metabolic syndrome, type 2 diabetes mellitus, psoriasis, Crohn’s disease, celiac disease, inflammatory bowel disease, response to platinum-based chemotherapy, blood pressure, bone density, skin pigmentation, and forced expiratory volume. Eight additional matches are present in the GWAS Catalog August 2018 version. It is clear that as more GWAS associations are reported, we are likely to find additional pleiotropic associations. These IOP genetic loci show both a polygenic and pleiotropic nature [116]. Using available spectral-domain optical coherence tomography and imputed genetic data from the UKB cohort, Gao et al. [79] identified 139 loci significantly associated with macular thickness. Among the genome-wide significant variants identified, 274 had previously been associated with other phenotypes in the GWAS Catalog (May 2018 version). Numerous ocular diseases/traits overlapped with macular thickness SNPs, such as AMD, POAG, myopia, and optic nerve head measurements. Additionally, many macular thickness SNPs mapped to neurological diseases, such as AD, Parkinson’s disease, and schizophrenia; cancer, such as lung, breast, and ovarian cancers; and metabolic traits, such as type 2 diabetes, body mass index, and waist circumference. It is interesting to note that thinner retinal thickness has been observed in individuals with AD [118], Parkinson’s disease [119], and schizophrenia [120]. Avastin (bevacizumab), a cancer drug, can also be used to treat macular disease, for example, AMD. The metabolic factors have previously been reported for associations with both risk and progression of AMD [121, 122].

Genetic risk scores show pleiotropic effects Genetic risk scores (GRSs) aggregate many individual variants into a single measure, typically weighted by their effect sizes [123]. In addition to predicting quantitative traits and disease risk, GRSs can be used for studying pleiotropy. Grassmann et al. [124] investigated the association of GRSs of 60 diseases/traits with AMD and found genetic pleiotropy between GRSs of 16 complex diseases/traits and AMD. The study included 16,144 subjects with late AMD and 17,832 controls of European ancestry. The authors collected all variants that reached genome-wide significance (P < 5  10 8) for each of the 60 diseases/traits and constructed a weighted GRS for each disease/trait. Several different weighting approaches were used depending on the type of data: betas for continuous traits, log-odds ratio for binary traits, and log hazard ratio for survival data. The 60 diseases/traits mainly belonged to nine categories: complex eye diseases/traits, neurological diseases, cardiovascular diseases, organ function, metabolic traits, autoimmune diseases, immune system-related traits, cancer, and anthropomorphic traits. Subjects with an increased risk for autoimmune-related diseases were found to have a higher risk for AMD. Interestingly, subjects with a reduced risk for cardiovascular diseases, such as coronary artery disease and hypertension, were at an increased

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risk for AMD. Several GRSs associated with favorable lipid levels, for example, higher HDL and lower LDL, showed an increased risk for AMD. This seemingly contradictory pattern of fitness is an excellent example of antagonistic pleiotropy. Some beneficial pleiotropic genetic factors at younger ages may become unfavorable when people become older. Higher HDL and lower LDL can result in better cardiovascular health and survival to old age. However, the same processes may cause people to be more prone to late-stage AMD [124]. This poses challenging questions to gene/genome manipulations, since we need to consider the possible pleiotropic effects of the target sites, and whether they show antagonistic pleiotropy. Pleiotropic information also helped researchers identify novel genetic loci associated with AMD. Based on the substantial overlap between the GRSs of these complex diseases/traits, Grassmann et al. [124] further hypothesized that shared pathways exist and helped identify novel AMD loci. From the genetic variants used for constructing 60 GRSs, Grassmann et al. [124] identified 20 new loci associated with AMD.

Implications for genomic medicine Widespread pleiotropy poses important implications for genomic medicine. On the one hand, pleiotropic information expands our knowledge of the biology of complex diseases/ traits and helps reduce phenotype misclassification [1]. Drugs developed for treating one disorder may also be repositioned to treat other disorders that share common biology [1]. Due to the effects of pleiotropy, it is no longer adequate to just focus on the effect of a variant on a single disease [125]. On the other hand, specific genetic variants may also show association effects on other diseases/traits, sometimes with antagonistic effects, like that observed in the variants affecting cardiovascular diseases, lipid levels, and AMD as noted above. This raises important questions about genome editing. Sequence alterations may have downstream effects on multiple phenotypes, with the effects resulting in changes to multiple mediating pathways. It is very challenging to know the exact consequences of editing without extensive genome-phenome association results. Some drugs may also have off-target effects due to shared pathways [1]. For example, aromatase inhibitor use among women with hormonereceptor-positive breast cancer was associated with a decrease in retinal nerve fiber layer thickness [126]. Furthermore, many phenotypes have not yet been explored in GWAS and therefore, we do not know the effects of any genetic variants on them. For example, although macular thickness is a highly heritable trait, there have been no reported GWASs of macular thickness until recently [79]. Existing large-scale biobank cohorts, such as the UKB, can help illuminate much of what is unknown about pleiotropy. Biobanks typically have a large collection of phenotypes, coupled with genetics data, which provide invaluable opportunities to study and enhance our knowledge of pleiotropy. UKB is a population-based prospective cohort of 500,000 individuals living in the United Kingdom, who were ages 40–69 years at enrollment [127, 128]. The biobank collected a wide range of phenotypes along with follow-up of the participants’ health outcomes. Importantly, environmental exposure, lifestyle factors, and imaging data as well as de-identified medical records are also available for many of the participants. Partnerships with pharmaceutical companies, such as Regeneron, are enabling complete whole exome

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sequencing of all 500,000 UKB participants by the end of 2019. Whole genome sequencing of 50,000 UKB participants funded by the Medical Research Council is also underway. A significant advantage of sequencing over GWAS is that it covers the whole spectrum of genetic variants, including rare and structural variants. All of this data will generate numerous new insights in health and disease and the complex pleiotropy network. Of note, however, is that 95% of the UKB participants are white.

Other aspects of pleiotropy Many statistical methods have been developed to detect pleiotropy in complex human traits. These methods broadly fall into three categories, that is, genome-wide, regional, or single variant, depending on the level of coverage of the genome [129]. Many multivariate methods are available to detect pleiotropy, such as GCTA [130], PET [131], and PleiotropySNP [132]. Mendelian randomization can be used to detect mediated pleiotropy. BUHMBOX [133] is designed specifically for detecting spurious pleiotropy. Readers interested in statistical methods can consult the recent review by Hackinger and Zeggini [129]. Recent user-friendly tools have also greatly reduced the technical barriers for exploring pleiotropy. The vast amount of pleiotropic information may be best visualized using a network approach. A newly developed web-based visualization tool, PleioNet [117], makes exploring pleiotropy using the GWAS Catalog results as simple as browsing a website.

Conclusions Pleiotropy plays an important role in furthering our understanding of human biology and disease. GWAS results have provided numerous interesting and sometimes counterintuitive pleiotropic connections between human diseases and traits. Future results will continue to enhance our knowledge in these areas as more studies are conducted. Results from every GWAS indicate that pleiotropy is ubiquitous across complex human diseases and traits. Pleiotropy also has important implications for human genetics and health, such as the classification of diseases, genetic risk profiling, drug development, and genome editing. With the onset of large-scale biobank cohorts containing extensive genotype and phenotype information, researchers can now study human genetics in more holistic ways, instead of focusing on a single disease/trait at a time. More and more pleiotropic effects continue to be uncovered across the breadth of human disease and traits. Novel software tools are also contributing to the greater accessibility and exploration of pleiotropic information. Complex pleiotropic networks can be browsed as easily as a website. Through various methods, human pleiotropy networks are beginning to be revealed, including many pertaining to eye-related diseases/traits.

Acknowledgments This work was supported in part by the National Institutes of Health (NIH; Bethesda, MD, USA) grants R01EY022651, R01EY027315 and RF1AG060472. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.

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Web Resources The URLs for downloaded data and programs GWAS Catalog, https://www.ebi.ac.uk/gwas/. PleioNet, http://www.pleionet.com/.

VII. Big data and precision medicine

C H A P T E R

19 Advancing to precision medicine through big data and artificial intelligence Xiaoyi Raymond Gaoa,b,c, Colleen M. Cebullaa, Matthew P. Ohra a

Department of Ophthalmology and Visual Science, The Ohio State University, Columbus, OH, United States bDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, United States cDivision of Human Genetics, The Ohio State University, Columbus, OH, United States

Introduction Precision medicine received widespread attention when President Barack Obama announced the launch of the Precision Medicine Initiative (PMI) in his State of the Union address on January 20, 2015 [1]. Precision medicine is a revolutionary approach for disease prevention and treatment that takes into account individual differences, such as genetics, environment, lifestyle, and socioeconomics. It is the alternative to “one-size-fits-all” medicine and promotes health care tailored to the individual. Precision medicine is also sometimes referred to as precision health [2]. It has been recognized that data and technology can offer novel insights in disease and health [3, 4]. By contributing to the prevention and treatment of disease and improved health, big data and artificial intelligence (AI) play critical roles in precision medicine [5, 6]. Data are the fundamental inputs for precision medicine, and technology allows us to generate and collect more data than ever before. All major industries have embraced big data to gain insights [7]. Genomics data are an excellent example of big data in human genetics. With advances in the human genome and high-throughput sequencing technology, it has become possible to generate large volumes of sequencing, such as whole-genome sequencing (WGS) data, at a relatively low cost [8]. International precision medicine programs are aiming to

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sequence millions of volunteers [9, 10]. Genomics is expected to surpass astronomy, one of the biggest players in big data domains, in 2025 and as much as 20–40 exabytes (one exabyte is equal to one billion gigabytes) of storage will be needed for the human genomes [11]. Widespread adoption of electronic health records (EHRs) has led to a rapid accumulation of EHR data, which has created a rich resource for genomics research [12] as well as for other biomedical research areas, including drug-drug interactions [13]. The combination of genomics with EHR data has propelled a new wave of novel discoveries [14]. New technology has also enabled the collection of massive amounts of data from the internet, social media, mobile wearable devices, or other tools containing data about an individual’s lifestyle choices, environmental exposures, and socioeconomic determinants. The reach of big data does not stop with collection. Big data acquires its utility from big data analysis and interpretation. Significant leaps will come from the inferences derived from the data [3]. Widely used in many fields, AI is a key technology in the explosion of the big data era. Some examples of AI applications include robots, facial recognition, voice identification, handwriting recognition, natural language processing, and self-driving cars. AI is becoming integral to our daily life, although we may not be aware of the algorithms at work in the devices we are using. AI is also gradually changing medicine. AI has been applied to imagebased diagnosis in many medical specialties, including ophthalmology, radiology, pathology, and dermatology. Furthermore, AI has achieved expert-level accuracy [15]. At the Aravind Eye Hospital in India, a Google AI system is used to diagnose retinal abnormalities from retinal photographs of diabetic patients [16]. AI has also excelled in genetic variant calling for the next-generation sequencing data [17]. The increasing power of AI is positioned to revolutionize medicine.

Precision medicine What is precision medicine? There are many definitions of precision medicine. According to the PMI [18], precision medicine is “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” According to the Centers for Disease Control (CDC) [19]: “Precision medicine, sometimes called personalized medicine, is an approach for protecting health and treating disease that takes into account a person’s genes, behaviors, and environment. Interventions are tailored to individuals or groups, rather than using a one-size-fits-all approach in which everyone receives the same care.” According to the United Kingdom National Health Service (UK NHS) [20]: “Precision (or personalised) medicine is defined as the application of emergent technologies to better manage patients’ health and to target therapies to achieve the best outcomes in the management of a patient’s disease or predisposition to disease. Used properly, precision medicine should both improve patient outcomes and deliver benefits to the health service - including reducing the cost of ineffective treatment and multiple tests.” Harnessing all data and technologies, precision medicine develops and delivers more precise diagnosis, treatment, and prevention. Many countries have launched precision medicine programs [21, 22], which have included large investments in genomics and big data collection backed up by a significant amount of funding. The impact of these activities on biomedicine will be far reaching. Here we give examples from the United Kingdom, the United States, and China.

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In late 2012, the UK government launched the 100,000 Genomes Project with the aim to sequence the whole genomes from NHS patients and to create a new genomic medicine service for the NHS [23]. EHR data on the patients have also been collected and linked to the genomics data. De-identified data are accessed and analyzed within a secure and monitored environment. In 2018, the UK government committed £210 million to their precision medicine challenge to improve data use to support earlier diagnosis and the advancement of precision medicine [24]. With this investment, the United Kingdom is funding the genome sequencing of half a million volunteers to enhance the understanding of disease processes. This initiative is also utilizing AI technology for earlier diagnosis of diseases and using AI advances to improve outcomes for patients [25]. In 2015, the US PMI was launched with a budget of US $215 million [26, 27]. The All of US Research Program is a key component of the PMI. It aims to recruit one million or more participants from all walks of life. The participants contribute different kinds of health-related information, including genetics, EHRs, urine samples, and other information from surveys and fitness trackers. As of March 2019, more than 117,000 individuals have provided samples according to Dr. Francis Collins, the National Institutes of Health (NIH) director [28]. The All of US Research Program seeks to extend precision medicine to all diseases by building a large national research cohort. In 2016, the Chinese government launched a much larger and more expensive PMI than that of the US PMI [10]. This initiative is a 15-year project with a CN ¥60-billion (US $9.2 billion) budget that plans to sequence 100 million human genomes by 2030 [29]. In addition to creating an integrated and centralized data platform for precision medicine, the Chinese government also supports precision medicine projects that employ big data analytics, including machine learning (ML) and AI, to improve disease diagnosis and treatment [30, 31].

Advancing precision medicine with big data Big data is a term often used to describe large volume data sets not easily processed with conventional methods. A popular description of big data is represented by the “5Vs,” i.e., volume, velocity, variety, verification/veracity, and value [32]. There are many examples of big data in our daily living: for example, YouTube videos, cell phone GPS (global positioning system) signals, purchase transaction records, and social media [11, 33]. There are also many examples of big data related to human genetics, such as genomic sequencing, EHRs, and medical imaging data [34, 35]. Genomics is a big data science. A single human genome contains 3 billion base pairs of nucleotides. The Human Genome Project (HGP) was a 13-year international research program that sequenced and mapped all the genes of the human genome [36]. It cost US $3 billion dollars and concluded in 2003. The success of the HGP led to a large number of genome-wide association studies (GWASs) that enabled numerous discoveries in human genetics [37]. Findings from these studies are collected in the GWAS Catalog database maintained by the National Human Genome Research Institute (NHGRI) and the European Bioinformatics Institute (EMBL-EBI) [37]. These GWASs have greatly increased our understanding of the genetics and genomics of human diseases and traits, including those related to the eye, as well as

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contributed to the creation of new drug targets [38]. The sample sizes for GWASs have increased dramatically from fewer than 150 subjects [39] to over one million individuals [40], since the first successful GWAS (defined as at least 100 K single nucleotide polymorphisms), which resulted in the discovery of the Complement Factor H for age-related macular degeneration (AMD) in 2005 [39]. The cost of WGS has also dropped dramatically over the last 15 years or so from US $3 billion to several hundreds of dollars. The ever decreasing cost of sequencing enables many studies to use this method, including the All of Us Research Program, which plans to sequence one million U.S. individuals. The UK Biobank (UKB), a population-based prospective study of 500,000 study participants living in the United Kingdom [41, 42] has recently released whole-exome sequencing (WES) data for the first portion of 50,000 individuals [43]. WES of all 500,000 UKB participants is expected to finish by the end of 2019 [44]. UKB WGS of 50,000 participants is also underway [45]. These sequencing studies are anticipated to generate novel insights for numerous human diseases and traits. The EHR is another kind of big data and also a gold mine for biomedical research. Until about 10 years ago, patient medical records were stored on paper. Endeavors to change the records systems to electronic form and the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 in the United States led to widespread adoption of certified EHR systems [46]. EHR adoption rates have also increased worldwide [47, 48]. EHRs provide a large collection of patient medical information for biomedical research in addition to permitting the integration of AI into the clinical workflow [15]. EHRs are also economically beneficial compared with traditional ways to collect data. For example, the Framingham Heart Study (n ¼ 5209 adults, enrollment in 1948 and followed up since) cost $140 million, the Atherosclerosis Risk in Communities study (n ¼ 16,000 adults, 4 follow-up visits) cost $189 million, and the Multi-Ethnic Study of Atherosclerosis (n ¼ 6800 adults, 5 follow-up visits) cost $121 million in research funding [49]. In contrast, obtaining data from the EHR is much more economical and cost only about $50,000 on the Geisinger EHR data using an extract algorithm [49]. Moreover, the EHR data is continuous in nature so researchers can perform longitudinal studies involving the important details of detection and progression. The EHR data includes various types of information, such as demographics, health behavior, laboratory results, medication, imaging, diagnosis, and clinical notes [49, 50]. Though repurposing EHR data for research may be limited [12, 51], by such factors as incompleteness of patient records, lack of high-quality data, irregular follow up, and large amounts of unstructured information, EHRs provide a broad and deep data source of phenotypes for genomic discovery. Numerous studies have successfully used phenotypes extracted from the EHR to conduct genetic association studies [52–54]. EHR-linked biobanks are propelling a new wave of genetics discoveries [14]. GWASs have evolved from investigations of single phenotypes to assessments of a spectrum of phenomes, such as those in phenome-wide association studies (PheWAS) [52]. In the genotype-phenotype association equation, the steadily increasing coverage of the genome is an important factor. Still, there is a great need to study a multitude of phenotypes. The combination is significant to understand the genetic architecture of human diseases and traits as well as the pleiotropy information [55–57]. There are many excellent examples of EHR-linked biobanks, such as Kaiser Permanente’s Research Program on Genes, Environment and Health (RPGEH), the NIH’s Electronic Medical Records and Genomics (eMERGE) Network, and the

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UKB [14]. RPGEH of Kaiser Permanente enables large-scale studies of genetic and environmental factors that influence human disease. About 100,000 samples have matched EHR and genetics data [58, 59]. Many studies in ocular diseases have been conducted using data from the Kaiser Permanente program, such as a GWAS of primary open-angle glaucoma [60], common coding variants of AMD [61], differences in genetics of AMD subtypes [62], and the association of refractive error with glaucoma [63]. The eMERGE Network, funded by the NHGRI, combines DNA biorepositories with EHR data for genetic research and genomic medicine studies. There are close to 84,000 samples in eMERGE which have matched EHR and genetic data available [14, 64, 65]. Many ocular disease studies have been conducted using the eMERGE Network data. For example, a GWAS of cataract [66] and gene-gene interactions of glaucoma [67] and age-related cataract [68] all used the eMERGE Network data. Another notable example is the UKB dataset, which is accessible to researchers all over the world. The UKB data is one of the largest biobank databases in existence. Baseline data, collected between 2006 and 2010, consists of lifestyle, nutritional habits, medical history information, and various physical measurements, as well as blood, saliva, and urine samples [41, 42]. The current release includes 500,000 volunteers from 40 to 69 years of age, with GWAS data available on 488,377 participants [69]. Additionally, close to 118,000 subjects participated in the eye and vision component of the study. To date, over 700 published papers have utilized the UKB dataset [70]. With the use of the UKB data, research groups have reported genetic variants for intraocular pressure [71–73], glaucoma [74], myopia [75], polygenic risk scores of intraocular pressure and glaucoma [76], and macular thickness [77]. Big data requires not only the data itself, but also the use of effective data analytics. Eric Lander famously summed up the results of the HGP in seven words: “Genome: Bought the book; hard to read.” [78] Without advanced analytics tools to distill the hidden information and patterns in big data, the data itself is not very useful [4]. Harnessing the power of big data rests in the ability to understand it and extract necessary information for effective decision making.

Spearheading precision medicine with AI AI has a long history that traces its roots to concepts laid out by Alan Turing [79, 80]. The term “artificial intelligence” was first used at a Dartmouth conference by John McCarthy in 1956 [80]. AI involves machines that can perform tasks which are typically associated with human intelligence, such as learning, reasoning, and making a medical diagnosis. [81]. Arthur Samuel first coined the term “machine learning” in 1959, while working at IBM [82]. AI and ML are sometimes used interchangeably. ML is a way to achieve AI. In traditional computer programming, humans write a specific algorithm that enables machines to conduct a specific task. However, for complex tasks, like image recognition, specific written rules may not be comprehensive or realistic. In contrast, ML can give computers the ability to learn the rules automatically without being explicitly programmed [82–84], thus making ML the preferred architecture for building AI applications. ML is primarily categorized into three classes: supervised learning, unsupervised learning, and reinforcement learning [83, 85–87]. In general, supervised learning aims to build a model

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from labeled (such as the presence or absence of glaucoma) training samples to predict and estimate for unlabeled testing samples. Unsupervised learning aims at inferring underlying patterns, such as subclusters, outliers, or low-dimensional representations from unlabeled data. Reinforcement learning aims to find a policy to maximize expected rewards [87]. AlphaGo, developed by Google’s London-based DeepMind team, defeated the 18-time world champion, Lee Sedol, in a five-game Go match in 2016 [88] and is a representation of successful ML applications demonstrating that machines can learn without human expert knowledge and surpass what human experts can achieve in certain areas [89, 90]. Deep learning, which involves training artificial neural networks through many layers of connected ‘neurons’ (i.e., ‘deep’ neural networks) [85, 91], represents the most successful automatic ML methods. It allows machines to automatically find representations and learn patterns from raw data. There are several types of deep neural networks, including convolutional and recurrent [15, 92]. The neural network methods outperform many conventional ML methods in areas of image analysis, voice recognition, and natural language processing tasks. Deep-learning algorithms have shown to be promising tools to discover complicated patterns and structures from high-dimensional and large volumes of data. Many impressive advances have been made in AI/ML applications, such as in image-based diagnosis, genome interpretation, and data-driven medicine. Some select examples are listed in the following. AI has excelled in image-based diagnosis. Fundus photography is a noninvasive tool that captures images of the retina, macula, and optic disc. It can be used to detect and monitor eye diseases such as diabetic retinopathy (DR), AMD, and glaucoma. Traditionally, fundus photos are reviewed and analyzed by ophthalmologists. Gulshan et al. developed a deep learning algorithm for the detection of DR using 128,175 retinal images and achieved a comparable performance to ophthalmologists in two independent testing datasets [93]. Burlina et al. used deep convolutional neural networks to automatically grade AMD from fundus photos and achieved an accuracy nearly as high as that of expert ophthalmologists [94]. Raghavendra et al. applied a deep convolution neural network to fundus images to aid the detection of glaucoma [98]. Optical coherence tomography (OCT) is another noninvasive imaging method commonly used by retina and glaucoma specialists. Kermany et al. built a deep-learning tool utilizing transfer learning that could identify diabetic macular edema and choroidal neovascularization [95]. Transfer learning reuses a previously trained model in a similar domain as the starting point for a second task and therefore requires significantly fewer training examples. Using 37,206 OCT images with choroidal neovascularization, 11,349 with diabetic macular edema, 8617 with drusen, and 51,140 normals, the algorithm was able to achieve comparable results to those of trained experts. De Fauw et al. applied a novel deep learning framework to three-dimensional (3D) OCT image data [96]. After training on less than 15,000 scans, the algorithm was able to perform tissue segmentation and provide competitive and consistent diagnoses and referral recommendations, especially for sight-threatening retinal diseases. With an error rate of only 5.5%, AI performed on a par with two leading retina specialists who participated in the study. Notably, the AI algorithm did not miss a single urgent case. A unique aspect of this study is that the algorithm provided users with the information on the portions of scans that were used for reaching the diagnosis along with its level of confidence. This increased the transparency of the neural networks used and alleviated the ‘black-box’ issue of artificial neural networks. In April 2018, the FDA approved the first authorized AI software, IDx-DR, for DR diagnosis. The IDx-DR analyzes OCT

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screening images and provides clinical decision-making assistance for mild and severe DR [97]. Although these fundus photography and retinal OCT image studies have centered mainly on eye conditions, these images can also provide a window for other health conditions, such as the early diagnosis of dementia [99] and the prediction of cardiovascular risk factors [100]. AI methods have also performed well in many areas of genome interpretation. Accurate variant calling from the next-generation sequencing data is a challenging endeavor. The widely used GATK (Genome Analysis Toolkit) algorithm uses a combination of conventional statistical methods, such as logistic regression, hidden Markov models, and naı¨ve Bayes classification to achieve high accuracy [17, 101]. Interestingly, Poplin et al. transformed read base-pair mapping into red-green-blue image classification problems and developed DeepVariant, a deep convolutional neural network approach that achieved better performance than GATK [17]. DeepVariant discovers statistical relationships between ground-truth genotype calls and images of read pileups surrounding putative variant sites, and calls genetic variants. Deep neural networks can also be used to annotate the pathogenicity of variants and to quantify the function of DNA sequences outperforming conventional methods [102–105]. In recent years, clustered regularly interspaced short palindromic repeats (CRISPR) [106–108], as a revolutionary technique, has been widely applied to genome editing [109]. However, it is challenging to accurately predict on-target efficacy and off-target profile. Chuai et al. recently developed a comprehensive computational platform, DeepCRISPR, to improve the on-target and off-target site prediction in a data-driven manner [110]. DeepCRISPR uses deep convolutional neural networks for representation learning and prediction, outperforming other in-silico tools. In December 2018, Google’s DeepMind team took top honors in the 13th Critical Assessment of Structure Prediction (CASP), a worldwide competition for protein 3D structure prediction, even though this was the team’s first appearance in the competition [111]. Based solely on the genetic sequence, the team used its AI software, AlphaFold, to predict the 3D structure of a protein. Information on misfolded proteins is critical in understanding their biological functions and their contribution to diseases. AI also shows great potential and applicability to data-driven medicine. Mehta et al. [112] applied ML algorithms to study responses of neovascular AMD to treatment with intravitreal vascular endothelial growth factor inhibitors (anti-VEGF treatments) using global registries. This study included using “real-world” outcomes rather than randomized controlled trials. The research group found that ML can extract applicable insights from bigdata registries and serve as an alternative to time-consuming and more costly phase 4 clinical trials. SOPHiA™ GENETICS, Inc. (Boston, MA) is applying AI to genomics and to advance data-driven medicine [113]. A number of pharmaceutical companies, such as Pfizer, Merck, GlaxoSmithKline, and Novartis, have been bolstering their AI capacities for datadriven medicine as well [114]. With all kinds of data becoming available in an environment of advanced AI, we are going to see more and more exciting advances of AI in medicine from both academia and industry. Despite all the promises of AI, it has limitations as well. For example, ML algorithms require large amounts of ground-truth training data. There have been some black-box issues, especially in the case of deep neural networks. Sometimes it is challenging to understand how the outputs are determined by the algorithm. Privacy and security of the data need to be

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assured. Inequities of representation are problematic in health care today, contributing to a dearth of minorities in datasets. Interested readers can refer to the excellent reviews of this topic by Yu et al. [15] and Topol [115].

Ancient wisdom on medicine Before we close this chapter, let us take a look at what the ancient Chinese medical wisdom said in the book, Huangdi Neijing (an English translation title: The Yellow Emperor’s Classic of Medicine) [116, 117]. The idea of customized treatment taking into account individual differences, similar to precision medicine, can be traced to about 2500 years ago. Ancient Chinese medicine had already discovered that the same disease may be treated differently in different patients. For example, the following is a dialogue between the Yellow Emperor, Huang Di, and the Yellow Emperor’s minister/physician, Qi Bo, Huang Di: “When physicians treat diseases, one identical disease may be treated differently in each case, and is always healed. How is that?” Qi Bo: “Physical features of the earth let it be this way.” In the Huangdi Neijing, it was also explained that a superb doctor should gather all techniques and consider all variables in the treatment of a condition, “A superior doctor is able to gather all techniques and use them either together or separately, to flexibly adapt to a changing environment, lifestyle, and geography, and to consider many variables in the treatment of a condition. Thus, it is understood that even though treatment methods are different, all can succeed in healing a condition. This is dependent on the ability of the doctor to consider all variables and select the proper principle of treatment.” Today, the use of big data and AI shares this same philosophy. The ability to handle the complexity of enormous amount of data sets good physicians apart from the rest [4]. The Huangdi Neijing also recognizes that the human body is a holistic entity (in contrast to fragmented pieces as patients are sometimes treated in modern medicine) governed by natural laws [116]. Health and disease are influenced by physical features, the natural aging process, and the environment [116]. Modern precision medicine seems to be a continuation of this ancient wisdom. In our advancement to precision medicine, there are many other insights that we can possibly borrow from the ancient wisdom, such as “The sage did not treat those already ill, but treated those not yet ill” and “To treat diseases, one must search for the basis” [117].

Conclusion Medicine is defined as “The science or practice of the diagnosis, treatment, and prevention of disease” according to the English Oxford Living Dictionaries. The idea for sizeable amounts of data and precise diagnosis and treatment in medicine can be traced to ancient Chinese medicine practices described in the Huangdi Neijing. With the advancement of new techniques, we are accelerating our understanding of medicine and genomics with increasing precision.

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Consistent with the ancient wisdom, we should harness all available techniques and data in diagnosis, treatment, and prevention to improve human health. Precision medicine is enhanced by fresh minds and innovative approaches, such as automatic learning from big data to understand subtle patterns. We wish our readers good health and a long life.

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VII. Big data and precision medicine

Index Note: Page numbers followed by f indicate figures, and t indicate tables.

A ABCA1 gene, 169, 189 ABCA4 gene, 79–82, 84–85, 243–246, 248, 286–287, 303–304 ABO gene, 190, 316–317 Achromatopsia (ACHM), 72, 287, 299, 302–303 Actin filament-associated protein 1 (AFAP1) gene, 189 ADAMTS2 gene, 225 ADAMTS8 gene, 190, 225 Adeno-associated virus (AAV), 280, 285–287, 289, 297–298, 303–304 ADP ribosylation factor-like GTPase 4C (ARL4C), 209 Advanced dry age-related macular degeneration, 155 Advanced glycation end products (AGEs), 204–205 Aflibercept (EYLEA), 171 Age-related cataract and diabetic retinopathy, 265 Age-Related Eye Disease Study (AREDS), 170–171 Age-related macular degeneration (AMD), 11–13, 57–58, 260–261, 266–267, 317–318, 320–321 CFH p.Tyr402His variant, effect of, 165–166 common variants’ effect at ARMS2/HTRA1 locus, 166 genetics of, 260 genetic variants’ effect on complement system, 167–168 on gene expression, 166–167, 167t on lipoprotein homeostasis, 168–169 genetic variants’ effect on treatment response, 170–172 anti-VEGF treatment, 171 complement inhibitors, 171–172 dietary supplementation, 170–171 genomic studies in, 157–165 case-control studies for rare variants, 159–160, 160–161t contribution of common vs. rare variants, 165 effect sizes of common vs. rare variants, 164–165, 164f family-based studies for rare variants, 161–164, 162–163t functional effect of common vs. rare variants, 165 genome-wide association studies (GWASes), 157–159, 157–158t omics studies in, 170

risk scores in, 260–261 variants, effect of on extracellular matrix remodeling, 169 on neovascularization, 169–170 Agilent SureSelect, 32 AGPAT1, 262 Aldose reductase (AR), 204–205 Allele score, 260 Allelic heterogeneity in IRD, 82 ALX4, 191–192 Alzheimer’s disease (AD), 317–318 American College of Medical Genetics and Genomics (ACMG), 242–243 Amlexanox, 104–105, 111–112 Amplicon sequencing, targeted, 30–31 Amyotrophic lateral sclerosis (ALS), 102–103, 184 ANF385B, 191–192 Angiotensin-converting enzyme (ACE), 207 ACE2, 207 Angle-closure glaucoma (ACG), 95, 261 Animal models, gene therapy in challenges, 304–305 inherited retinal degenerations (IRDs), 298–304 basic biology of common IRDs, 299–300 classification, 298–299 gene therapies of, 302–304 vertebrate models of, 300–302 Ankyrin repeat and SOCS box containing 10 (ASB10), 182–184 ANRIL gene, 59–60 Antagonistic pleiotropy, 316–318, 320–321 Anterior segment ultrasonic biomicroscopy, 220 Antioxidants, dietary intake of, 157 Antisense oligonucleotides (AONs), 288 Anti-VEGF treatment, 171 APOE gene, 169, 316–318 Apoptosis-driven photoreceptor death, 124 Aqueous humor (AH) flow, 182 ARF, 145 Argonaute (AGO) protein, 56 ARL6 (BBS3), 126–127 ARMS2, 164, 166

351

352

Index

ARMS2/HTRA1, 157–159, 166–167, 171, 260–261 Array-based capture methods, 15, 32 Arrayed primer extension reaction (APEX), 27, 240–242 Array sequencing, 27 Artificial intelligence (AI), 338–339, 341–344 ASB10 gene, 182–184 ASN480LYS mutation, 97–98 Assay for transposase-accessible chromatin using sequencing (ATAC-seq), 15–16 Ataxin 2 (ATXN2) gene, 190 ATF6 gene, 78–79 Autosomal dominant inheritance, 36, 97–99 Autosomal dominant RP (adRP), 78–79 Autosomal recessive RP (arRP), 78–79

B

BAC-hOPTNE50K transgenic mice, 104–105 BANCR, 58, 61 BAP1 gene, 140–142, 141t, 143t BAP1 tumor predisposition syndrome (BAP1-TPDS), 141–142 deubiquitinase function of, 141–142 ubiquitin C-terminal hydrolase (UCH), 141–142 Bardet-Biedl syndrome (BBS), 77, 117–120, 286, 315 clinical diagnosis, 119 Joubert syndrome (JBTS), 126 Leber congenital amaurosis (LCA), 126 mode of inheritance, 117–118 nonsyndromic retinal degeneration, 126–127 pleotropic phenotypes, 119–120 research and advancing biotechnology, 127–128 retinal degeneration in, 120–125 in vitro molecular mechanisms of BBS, 124–125 transcriptional variation, 125 using animal models to study, 121–124 Senior-Løken syndrome (SLS), 126 Batten disease. See Juvenile neuronal ceroid lipofuscinosis BaySeq, 45 bb9 zebrafish knockdown model, 124 BBS1, 77, 118, 121–123t, 126–127, 243–245, 286 BBS2, 77, 121–123t BBS21, 121–123, 121–123t, 126–127 BBS3, 121–123t, 125–127 Bbs3l, 125 BBS4, 83–84, 121–123t, 286 BBS5, 121–123t, 125 BBS5L, 125 BBS6, 121–123t BBS7, 121–123t BBS8, 77, 84, 121–123t, 125–127 BBS9, 77, 121, 121–123t BBS10, 121–123t, 243–245

BBS11, 121–123, 121–123t BBS12, 121–123t BBS13, 121–123, 121–123t, 126 BBS14, 121–123t, 126 BBS15, 121–123, 121–123t BBS16, 121–123, 121–123t, 126 BBS17, 121–123t, 124 BBS18, 121–123t BBS19, 121–123, 121–123t, 127 BBS20, 121–123, 121–123t, 127 BBSL, 125 BBSome, 77, 117, 121–125, 127 BDNF-AS downregulation, 59, 61 BEST1, 73, 81–82 Best-corrected visual acuity (BCVA), 288 Bevacizumab (Avastin), 171, 320 Big data, advancing precision medicine with, 339–341 Binary Alignment/Map (BAM) format file, 36, 43 Biobanks, 321–322, 340–341 Birt-Hogg-Dube Syndrome (BHDS), 145 Blindness, 59, 119–120, 126–127, 299, 303 Blue cone monochromacy (BCM), 72 Body mass index (BMI), 119–120 Bonferroni adjustment, 13 Bowman’s layer, 219–221, 226–227 BRCA1, 140, 141t, 144 BRCA1-associated protein 1 (BAP1), 141–142 BRCA2, 140, 141t, 142–144 Breast cancer, 140, 142–144 Bridge amplification, 28–29 Brown-eye color allele of rs12913832, 146 Bruch’s membrane (BM), 156f, 169 BUB2, 209

C C2 gene, 159, 167, 172 C3 gene, 159, 167–168, 172 C3b binding, 168 C6orf223 gene, 159 C8orf37 (BBS21), 126–127 C9 gene, 159, 168, 172 CA4, 83–84 CACNA1A, 262 CACNA1F, 80 Calcium/calmodulin-dependent protein kinase IV (CAMK4), 209 Canavan disease, 248–249 Cancer predisposition, 140 Candidate gene studies, 204–208 aldose reductase (AR), 205 angiotensin-converting enzyme (ACE), 207 C-reactive protein (CRP), 208 endothelial nitric oxide synthase (eNOS), 205

Index

growth factor receptor-bound protein 2 (GBR2), 207 high-mobility-group A1 (HMGA1) protein, 208 insulin receptors (INSRs), 207 P-selectin (SELP), 208 receptor for advanced glycation end (RAGE) products, 205–206 solute carrier family 19 member 2/3 (SLC19A2/3), 208 vascular endothelial growth factor (VEGF), 206–207 rs10738760, 206–207 rs1570360, 206–207 rs2010963, 206 rs2146323, 206–207 rs3025039, 206 rs6921438, 206–207 rs699947, 206 rs833061, 206 Canonical miRNA gene, 56 Capture enrichment, 33 Carboxyethylpyrrole (CEP), 170 CAST, 223–224, 227 Cataract, 222 Cats, gene therapy studies in, 301 Caveolins 1 and 2 (CAV1 and CAV2), 187–188 CDC16 domain family member 4 (TBC1D4), 209 CDHR1, 83–84 CDK4, 138–139, 145 CDKN2A, 138–140, 141t, 145 CDKN2B antisense RNA 1 (CDKN2B-AS1) gene, 188–189, 318. See also ANRIL gene CDKN23, 191–192 Cellular retinaldehyde-binding protein (CRALBP), 285 Central corneal thickness (CCT), 190–191, 225 Centrosomal protein 162 (CEP162), 210 CEP290, 77, 82, 84, 126, 246, 288, 298–299, 303–304 CERKL gene, 82 CETP gene, 159, 166–167, 169 CFB gene, 159, 167–168, 172 CFH gene, 157–159, 161–168, 171–172, 260–261 CFHR1, 166–167 CFHR3, 166–167 CFHR4, 166–167 CFI gene, 159, 161–164, 168, 172 cGMP-gated Na+ channels, 78–79 Chain-termination sequencing, 13–14 Charge-coupled device (CCD) camera, 35 Chemical chaperones, 99, 111 Chicken, gene therapy studies in, 301 CHM gene, 81–83, 85, 246, 287–288 Choroid, 72 Choroidal neovascularization (CNV), 260–261, 342–343 Choroideremia, 83, 287–290, 299, 303 Chromatin immunoprecipitation sequencing (ChIP-seq), 15–16

353

Chromosomal aberration, 247–248 Chromosomal rearrangements leading to structural variants, 247f Chromosome-specific multipoint linkage analysis, 11–12 CIB2, 83–84 Ciliary genes, 77 Ciliary neurotrophic factor (CNTF), 304–305 Ciliary transport and intracellular trafficking, 77 Ciliopathies, 76f, 77, 117 Cilium, 117 Classical segregation analysis. See Simple segregation analysis Clinical Labs Information Act (CLIA), 110 CLN3, 83–84 CLPTM1L, 146 Cluster generation, 28–29 CNGA3 gene, 78–79, 82, 287, 302–303 CNGB3 gene, 78–79, 243–245, 287, 302–303 CNNM4 gene, 83–84 Cockayne syndrome, 248–249 Coding variation, detecting, 241–245 Coiled-coil domain-containing protein 101 (CCDC101), 209 COL2A1, 246 COL4A3, 226–227 COL4A4, 226–227 COL5A1, 225 COL6A2, 225 COL8A1, 159, 169 COL8A2, 225 COL11A1, 246 COL12A1, 225 COLA4, 226–227 Collaborative Ocular Melanoma Study Group (COMS), 140 Collagen fibrils, 219–220 Collagenous stroma, 219–220 Collectin subfamily member 12 (COLEC12), 209 Color blindness, 4, 72 “Common disease-common variant” hypothesis, 12 Common-disease rare-variant hypothesis, 159 Common ocular diseases, 259, 265 Common variants, 157–159, 161–165 at ARMS2/HTRA1 locus, 166 with high allele frequencies, 36 Common versus rare variants contribution of, 165 effect sizes of, 164–165, 164f functional effect of, 165 Comparative genomic hybridization (CGH), 247–248 Complement factor H (CFH) gene, 11–13 Complement inhibitors, 171–172

354

Index

Complement system, effect of genetic variants on, 167–168 Complex segregation analysis, 7–8 Complotype, 167–168 Computational analysis, 48 Computer-assisted topography analysis, 220 Concordance index (C-index), 260 Cone dystrophy (CD), 298 Cone pedicles, 80 Cone-rod dystrophy (CRD), 72, 73t, 82, 126–127, 298–299 Congenital glaucoma, 95 Congenital stationary night blindness (CSNB), 72, 78–79, 82 Connecting cilium (CC), 77, 117, 124 Consanguineous marriage, 222 Consortium for Refractive Error and Myopia (CREAM), 12–13, 263–265 Contact lenses, 221 Copy number variation (CNV), 31, 83–84, 105, 239–240, 247–248 Cornea anatomy, 219–220 Corneal epithelium, 219–220 Corneal opacities, 248–249 Corneal thinning, 220 CRB1, 80–81 C-reactive protein (CRP), 166, 208 Cre-lox system, 302–303 CRISPR/Cas9 genome editing system, 104–105, 111 CRISPR-guided tools, 4 CRX, 73, 77–78, 299–300 CTNND2, 191–192 CTNS, 248–249 Cufflinks, 43 Cup-to-disk ratio (CDR), 181–182 Cutaneous melanoma (CM), 137, 139–140 environmental basis of, 138 CWC27, 80, 82 CYP1B1, 191–192 Cytomegalovirus (CMV)-based promoter, 285–286

D Data-driven medicine, 343 Data interpretation, 36 DCLK3, 191 DCN, 225 Deep anterior lamellar keratoplasty (DALK), 221 DeepCRISPR, 343 Deep-intronic variants, 245–246 Deep learning, 342–343 DeepMind, 341–343 Deep neural networks, 342–343 DeepVariant, 343 Denaturing gradient gel electrophoresis (DGGE), 240

Descemet’s membrane, 219–221 DESeq, 44–45 DESeq2, 45 DHDDS mutation, 82 DHICA, 146 DHX38, 80 Diabetic macular edema (DME), 203 Diabetic retinopathy (DR), 58–59 candidate genes for, 204t, 207t candidate gene studies, 204–208 aldose reductase (AR), 205 angiotensin-converting enzyme (ACE), 207 C-reactive protein (CRP), 208 endothelial nitric oxide synthase (eNOS), 205 growth factor receptor-bound protein 2 (GBR2), 207 high-mobility-group A1 (HMGA1) protein, 208 insulin receptors (INSRs), 207 P-selectin (SELP), 208 receptor for advanced glycation end (RAGE) products, 205–206 solute carrier family 19 member 2/3 (SLC19A2/3), 208 vascular endothelial growth factor (VEGF), 206–207 epigenetics, 210–211 DNA methylation, 210–211 histone modifications, 211 microRNAs, 211 genetic linkage analysis, 204 genome-wide association studies (GWAS), 208–210, 209t mitochondrial DNA, 212 whole-exome sequencing, 210 DICER1, 83 Dicer endonuclease, 56 Dideoxynucleotides, 13–14 Dietary supplementation, 170–171 Differential expression analysis, 45 Differential miRNA expression, 61 Dipivefrin, 99–100 Discoveries, 3–4 of the 1800s and earlier, 4f of the 1900s, 5f of the 2000s, 5f Dizygotic twins (DZ), 222 DNA fragments, 16, 28, 31 DNA methylation, 210–211 DNA microarrays, 12, 15–16 DNA nanoball sequencing, 30 DNase I hypersensitive sites sequencing (DNase-seq), 15–16 DNA sequencing, 13–14 DNA synthesis, 13–14 DOCK9, 223–224

Index

Dogs, gene therapy studies in, 301 Down syndrome, 222 Drusen, 57, 155 “Dual rAAV” method, 284 Dystrophy, type of, 298

E Edger, 45 EGF-containing fibulin extracellular matrix protein 1 (EFEMP1), 182–184 Ehlers-Danlos syndrome, 222 Electronic health records (EHRs), 337–338, 340 Electrophysiological examinations, 72 ELMO1, 191–192 eMERGE Network, 340–341 ENCODE project, 45–46 Endophenotypes, 190–191 central cornea thickness (CCT), 190–191 intraocular pressure (IOP), 190 retinal nerve fiber layer (RNFL) thickness, 191 vertical cup-to-disk ratio (CDR), 191 Endoplasmic reticulum (ER), 141–142 Endothelial nitric oxide synthase (eNOS), 204–205 Endothelium, 219–220 End-stage renal disease in BBS, 120 Enhanced S-cone syndrome (ESCS), 77–78 Enrichment techniques, targeted, 30–31, 30f amplicon sequencing, targeted, 31 hybridization-based enrichment, 31 molecular inversion probes (MIPs), 31 Epigenetic metabolic “memory”, 210 Epigenetics, 210–211 DNA methylation, 210–211 histone modifications, 211 microRNAs, 211 ε3 allele, 317–318 Equine infectious anemia virus (EIAV), 285–286 Eumelanin, 146 Exfoliation glaucoma (XFG), 261 Exfoliation syndrome (XFS), 262 Exome arrays, 27, 159 Exome sequencing, 34. See also Targeted exome sequencing (TES); Whole exome sequencing (WES) Exon skipping, 244f Expressed sequence tags (ESTs), 41 Expression quantitative trait loci (eQTL) datasets, 166–167 Extracellular matrix (ECM), 80–81, 219–220 Extracellular matrix remodeling, effect of variants on, 169 Ex-utero development, 301 EYS, 83–84, 248

355

F FAM46A, 225 FAM53B, 225 Familial cancer clustering, 138 Familial uveal melanoma (FUM), 138–139 Family with sequence similarity 132 member A (FAM132A), 210 FANCA gene, 140 FANCM gene, 140 fas-activated serine/threonine kinase (FASTK), 210 FBN1 gene, 225 FBN2 gene, 161–164 FGD6 gene, 159 Fisher’s exact test, 226–227 FLCN gene, 140, 141t, 145 Fleischer’s ring, 220 Flow cell, 28–29 Fluorescent dye, 28–29 FNDC3B, 225 Forkhead box C1 (FOXC1), 189 Formin 1 (FMN1), 209 Founder effect, 82 454 platform, 30 FOXO1, 225 Fragments per kilobase per million mapped (FPKM), 44–45 Franceschetti’s oculo-digital sign, 126 FRMD8, 191 Fuchs corneal endothelial dystrophy (FECD), 265–266 Functional noncoding genes, 55 Fundoscopy, 301 Fundus photography, 342–343

G Galactosemia, 248–249 GATK (Genome Analysis Toolkit) algorithm, 343 GCKR, 316 GDP-mannose 4,6-dehydratase (GMDS), 189 Gene-directed therapies, 110–112 myocilin (MYOC)-associated glaucoma, targeted therapies for, 111 optineurin (OPTN) and TANK-binding kinase 1 (TBK1)-associated glaucoma direct therapies, 111–112 Gene expression, 43 effect of genetic variants on, 166–167, 167t variants leading to altered gene expression, 246 Gene panel vs. whole-exome sequencing (WES), 33 General Feature Format (GFF) file, 43 Gene therapy treatments, 243–245 Genetic heterogeneity, 27 in inherited retinal disease (IRD), 81–83 allelic heterogeneity, 82

356

Index

Genetic heterogeneity (Continued) incomplete penetrance in RP11, 83 monogenic IRDs, 81–82 Genetic information, 3 Genetic linkage analysis, 9, 204 Genetic materials, 3 Genetic risk scores (GRS), in complex eye disorders age-related cataract and diabetic retinopathy, 265 age-related macular degeneration (AMD), 260–261 genetics of, 260 risk scores in, 260–261 clinical utility of risk scores, 267–268 Fuchs corneal endothelial dystrophy (FECD), 265–266 glaucoma, 261–263 genetics of, 261–262 risk scores in, 262–263 myopia and refractive error, 263–265 genetics of, 263–264 risk scores in, 264–265 polygenic risk scores (PRS), 266–267 risk scores and their applications, 259–260 Genetic testing of various eye disorders altered gene expression, variants leading to, 246 coding variation, detecting, 241–245 deep-intronic variants, 245–246 genetic techniques, 240 inherited retinal degenerations (IRDs), genetic modifiers of phenotypic severity of, 248 non-canonical splice site (NCSS) variants, 245 ocular phenotype, 248–249 structural variants, detecting, 247–248 Genetic variants effect of on complement system, 167–168 on gene expression, 166–167, 167t on lipoprotein homeostasis, 168–169 effect on treatment response, 170–172 anti-VEGF treatment, 171 complement inhibitors, 171–172 dietary supplementation, 170–171 Genetic versus environmental basis of UM, 138 Gene Transfer Format (GTF) file, 43 Genome Analyzer, 28–29 Genome editing, 111 Genome-scan meta-analysis (GSMA), 11–12 Genome-wide association studies (GWAS), 10–12, 16, 60, 146, 157–159, 157–158t, 164–169, 171, 185–190, 186–187t, 208–210, 209t, 262, 315–316, 339–340 actin filament-associated protein 1 (AFAP1) gene, 189 advantages and disadvantages of, 12–13 Ataxin 2 (ATXN2) gene, 190 ATP-binding cassette subfamily A member 1 (ABCA1), 189

caveolins 1 and 2 (CAV1 and CAV2), 187–188 CDKN2B antisense RNA 1 (CDKN2B-AS1) gene, 188 examples, for eye diseases, 13 GDP-mannose 4,6-dehydratase (GMDS) and forkhead box C1 (FOXC1), 189 in keratoconus (KC), 224–225, 224t single-nucleotide polymorphisms (SNPs) showing pleiotropic effects, 318–320 SIX homeobox 6 (SIX6) protein, 188–189 thioredoxin reductase 2 (TXNRD2), 189–190 transmembrane and coiled-coil domain 1 (TMCO1), 188 Genome-wide linkage studies in KC, 223–224, 223t Genome-wide sequencing efforts, 15–16 Genomic medicine, implications for, 321–322 Genotype Tissue-Expression (GTEx) project, 246 Geographic atrophy, 155 GJD2 gene, 263–264 GLA, 248–249 Glaucoma, 13, 59–60, 261–263 core features of, 95 gene-directed therapies, 110–112 myocilin (MYOC)-associated glaucoma, targeted therapies for, 111 optineurin (OPTN) and TANK-binding kinase 1 (TBK1)-associated glaucoma direct therapies, 111–112 genetics of, 261–262 juvenile-onset open-angle glaucoma (JOAG), 95–97 age of onset, 96 epidemiology of, 95–96 genetic testing and, 108–110 inheritance pattern, 97 intraocular pressure (IOP), 96 myopia, 96 optic disc morphology, 96 response to therapy, 96 myocilin (MYOC)-associated glaucoma clinical phenotype juvenile-onset open-angle glaucoma (JOAG), 97–98, 98t primary open-angle glaucoma (POAG), 98–99 myocilin (MYOC)-associated juvenile-onset openangle glaucoma (JOAG) case report, 99–101 myocilin (MYOC) pathophysiology, 99 optineurin (OPTN), 101–105 and amyotrophic lateral sclerosis (ALS), 102–103 effects of OPTN mutations, 103 function of, 102 optineurin (OPTN)-associated glaucoma case report, 105 clinical phenotype, 101–102

Index

knock-in mouse model of, 104–105 transgenic mouse models of, 103–104 pathogenesis, 97, 102–103 POAG genetics (see Primary open-angle glaucoma (POAG)) risk scores in, 262–263 TANK-binding kinase 1 (TBK1), 105–108 effects of TBK1 duplications, 107–108 function of, 107 TANK-binding kinase 1 (TBK1)-associated glaucoma case report, 108, 109f clinical phenotype, 105–107 transgenic mouse model of, 108 Glaucomatous eye, 59–60 GLC1A, 97 GLN368STOP mutation, 98–99 GLU478GLY mutation, 103 GLU50LYS mutation, 101–105, 106f, 111–112 GLY364VAL mutation, 97 GLY367ARG mutation, 100f, 101 GNAT1 gene, 82 GNAT2 gene, 78–79, 302–303 Goldmann perimetry, 99–100 Gomafu. See Rncr2 G-protein-coupled receptors, 124–125 Green cone opsin mislocalization, 125 Growth factor receptor bound protein 2 (GRB2), 207–208 Guanine-cytosine (GC) content, 15 Gyrate atrophy, 71–72

H H141N variant, 189 Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, 340 Hemorrhages, 58 Heparan sulfate 6-O-sulfotransferase 3 (HS6ST3), 209 HERC2/OCA2 genes, 146 Hereditary nonpolyposis colorectal cancer (HNPCC). See Lynch syndrome HGF, 224–225, 227 High-microsatellite instability (MSI-High), 144 High-mobility-group A1 (HMGA1) protein, 208 High molecular weight (HMW) DNA molecules, 36 High-throughput sequencing technologies, 15. See also Next-generation sequencing (NGS) technologies Histone modifications, 211 HMCN1, 161–164 HMMSplicer, 44 Homologous recombination (HR), 141–142 HOTAIR, upregulation of, 61 HRAS gene, 140 HTR2A, 191–192

357

HTSeq, 43 Huangdi Neijing, 3, 344–345 Human Genome Project (HGP), 33–34, 339–340 Human immunodeficiency virus (HIV), 285–286 Human leukocyte antigen (HLA), 204 Humphrey’s perimeter, 105 Hybridization-based enrichment, 31 Hybridization-based molecular inversion probes (MIPs), 30 Hybrid lenses, 221 Hyperglycemia, 205 Hypomorphic alleles in genes, 77

I Identical by descent (IBD), 10 IFT81, 77 IFT140, 77, 82 IFT172, 77 IL1B promoter, 227 IL1RN, 223–224 ILE477ASN mutation, 97–98 Illumina sequencing, 28–30, 29f, 32–33 IMPDH1, 82 IMPG1, 80–81 IMPG2, 80–81 Index variant, 165 Induced pluripotent stem cells (iPSCs), 127–128 Ingenuity Pathway Analysis (IPA), 225 Inheritance, 7–10, 117–118 Mendelian modes of, 7–9, 11 pattern, 73, 97 triallelic, 118 Inherited eye diseases, 27 Inherited retinal degenerations (IRDs), 9, 239–240, 243, 298–300 basic biology of common IRDs, 299–300 classification, 298–299 clinical nomenclature, 298–299 type of dystrophy, 298 gene therapies of, 302–304 genetic heterogeneity of, 242f genetic modifiers of phenotypic severity of, 248 vertebrate models of, 300–302 Inherited retinal disease, 281–283t achromatopsia, 287 choroideremia, 287–288 clinical features observed in patients with, 74f future perspectives, 289–290 genes associated with, 75–76t genetic heterogeneity in, 81–83 allelic heterogeneity, 82 incomplete penetrance in RP11, 83 monogenic IRDs, 81–82

358

Index

Inherited retinal disease (Continued) history, 280 IRD genes and associated pathways, 76f Leber congenital amaurosis, 288–289 limitations of gene therapy, 289 mutation spectrum of, 83–85 large DNA duplication and deletion in IRD, 83–84 regulatory and noncoding variants, 83 splice-site and alternative transcript variants, 84–85 nonsyndromic retinitis pigmentosa, 284–285 MERTK gene, 285 PDE6β gene, 285 RLBP1 gene, 285 RPGR gene, 284–285 photoreceptor degeneration, mechanistic pathways culminating in, 73–81 ciliary transport and intracellular trafficking, 77 interphotoreceptor matrix (IPM), 80–81 photoreceptor development, 77–78 phototransduction cascade, 78–79, 78f spliceosome complex, 80 synaptic transmission defects, 80 visual cycle, 79–80, 79f spectrum and evaluation of the clinical phenotype of, 72–73 inheritance pattern, 73 Stargardt disease, 286–287 syndromic retinitis pigmentosa, 285–286 Bardet-Biedl syndrome (BBS), 286 Usher syndrome, 285–286 vectors, 280–284 X-linked retinoschisis, 287 Inner segment (IS), 77 Insulin receptor, 124–125, 207 Integrated DNA Technologies (IDT), 32 Interleukin 20 receptor subunit β (IL20RB), 184 International Age-related Macular Degeneration Genomics Consortium (IAMDGC), 12–13, 261 International Glaucoma Genetics Consortium, 12–13, 262 Interphotoreceptor matrix (IPM), 80–81 Interphotoreceptor retinoid-binding protein (IRBP), 79–81 Intraflagellar transport (IFT), 77, 124 Intraocular pressure (IOP), 59–60, 95–96, 99–100, 182, 190, 262, 316–317 Intrastromal corneal ring segment (ICRS) insertion, 221 Ion Torrent, 30 IRBP, 80–81 iSeq, 28–29

J Joubert syndrome (JBTS), 126, 315 Juvenile neuronal ceroid lipofuscinosis, 248–249

Juvenile-onset open-angle glaucoma (JOAG), 95–98, 181–182 age of onset, 96 epidemiology of, 95–96 genetic testing and, 108–110 inheritance pattern, 97 intraocular pressure (IOP), 96 linkage mapping for, 11–12 myopia, 96 optic disc morphology, 96 response to therapy, 96

K KDR gene, 171 Kelch-like ECH-associated protein 1 (KEAP1) promoter, 211 KERA, 225 Keratoconus (KC), 220–221 clinical signs and diagnosis, 220–221 genetics of, 222–227 genome-wide association studies (GWAS), 224–225, 224t genome-wide linkage studies, 223–224, 223t Sanger sequencing/targeted genotyping, KC candidate genes identified by, 225–227, 226t human cornea anatomy, 219–220 treatment modalities, 221 Keratocytes, 219–220 KLF2 transcript, 59 Krabbe disease, 248–249

L Law of Independent Assortment, 9 Law of Segregation, 7 LC3, 102–104, 107–108 LCR (locus control region), 83 Leber congenital amaurosis (LCA), 72, 77, 81–82, 126, 222, 241–242, 280, 288–289, 299 LCA2 gene therapy, 297–298 Leber’s hereditary optic neuropathy (LHON), 299 Lecithin retinol acyl transferase (LRAT), 79–80, 299 Lentiviral vectors, 280, 285–286, 288, 303–304 Leptin receptor (LR), 124–125 Let-7, 58–59, 61 Library preparation, 42 Likelihood ratio, 10 Limma/voom, 45 LINC00152, 61 Linkage analyses, 9, 182–185, 183t, 223–224, 227 advantages of, 10–11 ankyrin repeat and SOCS box containing 10 (ASB10), 182–184 disadvantages of, 11

Index

EGF-containing fibulin extracellular matrix protein 1 (EFEMP1), 182–184 genetic linkage, 9 interleukin 20 receptor subunit β (IL20RB), 184 myocilin (MYOC), 182–184 neurotrophin 4 (NTF4), 185 for ocular traits and diseases, 11–12 optineurin (OPTN) and TANK-binding kinase 1 (TBK1) gene, 184–185 types of, 10 WD repeat domain 36 (WDR36), 185 Linked-Reads sequencing by 10 Genomics, 36 LIPC gene, 166–167, 169 Lipoprotein homeostasis, effect of genetic variants on, 168–169 Logarithm of the odds (LOD) scores, in linkage analyses, 10–11 Log-odds, 10 Long interspersed nuclear elements (LINE), 83–84, 248 Long intervening noncoding RNAs (lincRNAs), 46 Long noncoding RNAs (lncRNAs), 45, 47, 55–62, 83 Long terminal repeats (LTRs), 83–84, 248 Low-density lipoprotein receptor-related protein 2 (LRP2), 209 Lowe syndrome, 248–249 LOX, 227 LOXL1, 262 LOXL1-AS1, 60 LOXL2, 225 LPAR1, 225 LRAT gene, 79–80 LTBP1, 225 LUM, 225 LUM/DCN/KERA, 225 Lynch syndrome, 144

M M (medium wavelength) cone, 301 M390R mutations, 118, 126–127, 286 Macaques, 302 Machine learning (ML), 339, 341–343 Macular dystrophy (MD), 72 Macular thickness, 318, 320–321 MAK mutation, 82 MALAT1 dysregulation, 59–62, 191 Malondialdehyde (MDA), 166 Manganese superoxide dismutase (Mn-SOD) gene, 212 MBD4, 140, 141t, 143 MC4R, 119–120 MCOLN1, 248–249 Mediated pleiotropy, 316, 322 Medicine

359

ancient wisdom on, 344 defined, 344–345 MEG3, 59–61 Melanotan II, 119–120 Mendel, Gregor, 7 Mendelian disease, 27, 33–34, 83 Mendelian modes of inheritance, 7–9, 11 Mendelian mutations, 164–165 Mendel’s Law of Independent Assortment, 9 Mendel’s laws, 222 MERTK gene, 84, 284–285 Messenger RNAs (mRNAs), 56 MET98LYS mutation, 101, 103 “Metabolic memory” phenomenon, 210 Metamorphopsia, 155 Methylation profiling, 15–16 Miat. See Rncr2 Microaneurysms, 58 Microarray hybridization approach, 30 Microarrays, 41 MicroRNAs (miRNAs), 55–57, 62, 83, 211 gene regulation by, 56 pri-miRNA, 56 Minor allele frequency (MAF), 36 MiR-9, 58 MiR-21, 58–60 MiR-15ab, 58–59 MiR-34a, 58, 61 MiR-195, 58–59 MiR-125b, 58 MiR-126, 62 MiR-132, 58–59 Mir-146a, 58 MiR-146a/b, 58–59 MiR-155, 58–59 MiR-181a, 62 MiR-182, 59–60 MIR184, 223–224 MiR-200b, 58–59 MiR-351, 62 MiR-450, 59–60 MiR-4707, 59–60 Mismatch repair genes (MLH1 and MSH6), 144–145 Missing heritability, 267 Mitochondrial DNA, 4, 212 Mitochondrially encoded tRNA leucine 1 (MT-TL1) gene, 212 Mitochondrial ribosomal protein L19 (MRPL19), 210 Mitochondrial ribosomal protein S35 pseudogene 3 (MRPS35P3), 209 Mitochondrial uncoupling protein 2 (UCP2), 212 Mitophagy, 103–104, 107

360

Index

MKS1 (BBS13), 126 MLH1 gene, 140, 141t, 144–145 Model-based linkage analyses, 10 Model-free linkage analyses, 10 Modern genetics, principles for, 7 Molecular diagnosis, 27, 33–34 Molecular inversion probes (MIPs), 30–31 Monogenic IRDs, genetic heterogeneity in, 81–82 Monogenic Mendelian inheritance, 73 Monozygotic (MZ) twins, 222 M-opsin, 302 Morbid obesity, 119–120 Mouse, gene therapy studies in, 302 MSH2 gene, 144 MSH6 gene, 140, 141t, 144–145 Muller glia cells, 301 Multipoint linkage analyses, 10 M€ unson’s sign, 220 Mutation screening, 27 MYCN oncogene, 61 Myelin transcription factor 1 like (MYT1L), 209 MYO7A gene, 85, 285–286, 298, 303–304 Myocilin (MYOC), 108–110, 182–184, 191–192 -associated glaucoma juvenile-onset open-angle glaucoma (JOAG), 97–98, 98t primary open-angle glaucoma (POAG), 98–99 targeted therapies for, 111 -associated juvenile-onset open-angle glaucoma (JOAG) case report, 99–101 pathophysiology, 99 Myopia, 96 Myopia and refractive error, 263–265 genetics of, 263–264 risk scores in, 264–265 Myosin VIIa, 285–286

N Nanopore sequencing, 15, 35 National Center for Biotechnology Information (NCBI), 110 National Eye Institute Glaucoma Human genetics collaBORation Heritable Overall Operational Database (NEIGHBORHOOD) Consortium, 12–13 National Human Genome Research Institute (NGHRI), 15 NBN gene, 140 ND4 gene, 299, 303 NEAT1, 60–61, 191 Neovascular AMD, 155

Neovascularization, 58, 62 effect of variants on, 169–170 Neuronal ceroid lipofuscinosis-1, 248–249 Neurotrophin 4 (NTF4), 185 Nextera rapid capture exome kit, 32–33 Next-generation sequencing (NGS) technologies, 27–30, 28t, 28f, 240, 245. See also High-throughput sequencing technologies Illumina sequencing, 28–30 Nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 4 (NOX4), 210 NimbleGen, 32 Nitric oxide synthase 2 inducible (NOS2A), 205 NME/NM23 nucleoside diphosphate kinase 3 (NME3), 210 NMNAT1, 47–48, 243–245 NOIseq, 45 Non-canonical splice site (NCSS) variants, 245 Noncoding genome in eye disease, 55 age-related macular degeneration (AMD), 57–58 diabetic retinopathy (DR), 58–59 glaucoma, 59–60 long noncoding RNAs (lncRNAs) and the eye, 56–57 microRNAs and the eye, 56 noncoding RNAs and eye disease, 57 retinitis pigmentosa, 60–61 retinoblastoma, 61 Noncoding RNA (ncRNA), 45, 55, 83 Non-enzymatic sequencing method, 240 Nonhuman primates (NHPs), 302 Nonproliferative diabetic retinopathy (NPDR), 205 Nonsense-mediated mRNA decay (NMD), 244f Nonsyndromic retinal degeneration, 126–127 Normal tension glaucoma (NTG), 95, 101–103, 181–182 NovaSeq instrument, 28–29 NPHP1, 77, 83–84 NPHP4, 77 NPHP5, 126, 298 NR2E3, 77–78, 299–300 NRG3, 191–192 NRL, 77–78, 299–300 Nucleotides, 13–14

O OAT, 81–82, 246 Obesity, 119–120 OCA2, 146 Ocular diseases, 239–240, 246, 248–249 Ocular phenotype, 248–249 Ocular traits and diseases, 3 linkage analyses for, 11–12 segregation analyses for, 9 Odds ratio (OR), 157–159, 164–165, 262

Index

OFD1, 81–82, 246 Oligonucleotides, 15, 28–29 Omics studies in AMD, 170 Online Mendelian Inheritance in Man (OMIM) database, 239–240 OPA1, 83–84, 246 OPCML, 191–192 Open reading frame 15 (ORF15), 284–285 Ophthalmic genetics, timeline of key discoveries in, 3–6, 5f Ophthalmology, 4 OPN1LW, 83 OPN1MW, 83 OPSIN protein, 78–79 Optical coherence tomography (OCT), 72, 220, 301, 342–343 Optic atrophy, 248–249 Optic cup area, 262, 316–317 Optineurin (OPTN), 101–105, 108–110, 184–185, 191–192 and amyotrophic lateral sclerosis (ALS), 102–103 -associated glaucoma case report, 105 clinical phenotype, 101–102 knock-in mouse model of, 104–105 transgenic mouse models of, 103–104 effects of OPTN mutations, 103 function of, 102 and TANK-binding kinase 1, 184–185 and TANK-binding kinase 1 (TBK1)-associated glaucoma direct therapies, 111–112 Ornithine aminotransferase (OAT), 81–82, 246 Osteogenesis imperfecta, 222 Outer segment (OS), 77, 117, 124 Ovarian cancer, 143–144 Oxford Nanopore Technologies (ONT) sequencing, 35 Oxidative stress-related factors, 170 Oxr1, dysregulation of, 58–59

P Pacific Biosciences (PacBio), 35 single-molecule real-time (SMRT) sequencing by, 35 Pancreatic cancer, 143–144 PANDAR, 61 p.Arg1210Cys variant, 168 PCDH15, 83–84, 248 PDE6A gene, 78–79, 81–82 PDE6B gene, 78–79, 81–82 PDE6β gene, 285 PDE6C gene, 78–79 PDE6H gene, 78–79 Pecten oculi, 301 Pedigrees, 8–9, 97 PELI2 gene, 159

361

Penetrating keratoplasty (PK), 221 Phenocopies, 8 Phenome-wide association study (PheWAS) approach, 315–316, 340–341 4-Phenylbutyrate, 99, 111 Phosphodiesterase subunit beta (PDE6ß), 284 Photoreceptor (PR), 71–73, 77, 117, 124–125 development, 77–78 gene therapy, “holy grail” of, 304–305 Photoreceptor degeneration, mechanistic pathways culminating in, 73–81 ciliary transport and intracellular trafficking, 77 interphotoreceptor matrix (IPM), 80–81 photoreceptor development, 77–78 phototransduction cascade, 78–79, 78f spliceosome complex, 80 synaptic transmission defects, 80 visual cycle, 79–80, 79f Photoreceptor sensory cilium (PSC), 77 Phototransduction pathway, 78–79, 78f PI3K-Akt signaling axis, 59 Pig, gene therapy studies in, 301 Piggy back contact lenses (PBCL), 221 PleioNet, 322 Pleiotropic effects genetic risk scores (GRSs) showing, 320–321 genome-wide association studies single-nucleotide polymorphisms showing, 318–320 genomic medicine, implications for, 321–322 numerous genes showing, 316–318 Plexin domain-containing 2 (PLXDC2), 209 p.Lys155Gln variant, 168 p.Lys65Gln variant, 168 PMS2 gene, 144 Polycystic kidney disease in BBS, 120 Polydactyly, 119 Polygenic risk scores (PRS), 266–267 Polymerase-chain reaction (PCR) amplification, 28–29, 31, 42 POMP, 262 Population attributable risk (PAR), 260 Population-level parameters, 7–8 Population stratification, 12–13 POT1 gene, 140 Precision medicine, 338–339 ancient wisdom on medicine, 344 artificial intelligence (AI), 341–344 big data, 339–341 defined, 338 Predictive power of a GRS, 260, 266 Premature stop codon (PSC), 244f Pre-mRNA splicing, 80 Primary angle-closure glaucoma (PACG), 262

362

Index

Primary open-angle glaucoma (POAG), 9, 11–13, 95–96, 98–99, 101, 105, 182–192, 261, 266–267, 316–317 endophenotypes, 190–191 central cornea thickness (CCT), 190–191 intraocular pressure (IOP), 190 retinal nerve fiber layer (RNFL) thickness, 191 vertical cup-to-disk ratio (CDR), 191 genome-wide association studies (GWAS), 185–190, 186–187t actin filament-associated protein 1 (AFAP1) gene, 189 Ataxin 2 (ATXN2) gene, 190 ATP-binding cassette subfamily A member 1 (ABCA1), 189 caveolins 1 and 2 (CAV1 and CAV2), 187–188 CDKN2B antisense RNA 1 (CDKN2B-AS1) gene, 188 GDP-mannose 4,6-dehydratase (GMDS) and forkhead box C1 (FOXC1), 189 SIX homeobox 6 (SIX6) protein, 188–189 thioredoxin reductase 2 (TXNRD2), 189–190 transmembrane and coiled-coil domain 1 (TMCO1), 188 linkage analyses, 182–185, 183t ankyrin repeat and SOCS box containing 10 (ASB10), 182–184 EGF-containing fibulin extracellular matrix protein 1 (EFEMP1), 182–184 interleukin 20 receptor subunit β (IL20RB), 184 myocilin (MYOC), 182–184 neurotrophin 4 (NTF4), 185 optineurin (OPTN) and TANK-binding kinase 1 (TBK1) gene, 184–185 WD repeat domain 36 (WDR36), 185 POAG pathways, 191–192 symptoms and diagnosis, 181–182 therapies, 182 Pri-miRNA, 56 PRO370LEU, 97–98 Proliferative diabetic retinopathy (PDR), 203 Proliferative vitreoretinopathy (PVR), 62 PROM1, 246 Propensity score, 264–265 Protein-coding genes, 55 Protein kinase C (PKC), 204 Proteomics, 127 PRPF3, 80 PRPF31, 80–84, 246, 248 PRPF4, 80 PRPH2, 81–82 P-selectin (SELP), 208 Pseudo-exons, 245–246 PTCH1 gene, 140

R rAAV2-VMD2-hMERTK, 285 RAB3GAP1 gene, 224–225, 227 Rab escort protein 1 (REP1) gene, 287–288, 299 Rab geranylgeranyl transferase (RGGT), 287–288 Ranibizumab (Lucentis), 171 Rare variants case-control studies for, 159–160, 160–161t family-based studies for, 161–164, 162–163t RASGRF1 gene, 263–264 Rat, gene therapy studies in, 302 RB1, 61, 246 RBFOX1, 191–192 RBMS3, 262 RBP3, 80–81 RDH12 gene, 79–80 Reads per kilobase per million mapped (RPKM), 44–45 Receiver-operator characteristic (ROC) curve, 260 Receptor for advanced glycation endproducts (RAGE), 204–206 Recombinant, replication-incompetent, adenoassociated viruses (rAAVs), 280–284 Recombination fraction, 9–10 Renal disease in BBS, 120 REP1 gene, 303 Restriction fragment length polymorphism (RFLPs), 240 Retinal anatomy in normal and various AMD stages, 156f 11-cis-Retinal chromophore, 78–79 Retinal cone dystrophy, 222 Retinal degeneration, 248–249. See also Inherited retinal degenerations (IRDs) in Bardet-Biedl syndrome (BBS), 120–125 in vitro molecular mechanisms of BBS, 124–125 transcriptional variation, 125 using animal models to study, 121–124 Retinaldehyde-binding protein 1 (RLBP1), 284 Retinal diseases, 81–82 Retinal dystrophies, 289–290 Retinal ganglion cells (RCGs), 15–16, 60 Retinal neovascularization, 58 Retinal nerve fiber layer (RNFL) thickness, 189, 191 Retinal-pigmented epithelium protein 65 kilodaltons (RPE65), 297 Retinal pigment epithelium (RPE), 46, 57, 72 Retina. See Inherited retinal degenerations (IRDs) Retinitis pigmentosa (RP), 9, 60–61, 71–72, 73t, 81–82, 120, 126–127, 286, 298–299 nonsyndromic, 284–285 MERTK gene, 285 PDE6β gene, 285 RLBP1 gene, 285 RPGR gene, 284–285

Index

syndromic, 285–286 Bardet-Biedl syndrome (BBS), 286 Usher syndrome, 285–286 Retinitis pigmentosa GTPase regulator (RPGR), 84–85, 284 Retinoblastoma, 61 Retinoid isomerohydrolase, 288 Retinoschisin 1 (RS1) gene, 287 Retinoschisis, 299, 303 RGR, 83–84 Rhodopsin gene (RHO), 78–82 mislocalization, 123–124 Rho GTPase-activating protein 22 (ARHGAP22), 209 Ribosomal RNAs (rRNAs), 55 Rigid gas permeable (RGP) lenses, 221 RIMS1 (regulating synaptic membrane exocytosis 1), 80 Risk scores. See also Genetic risk scores (GRS), in complex eye disorders and applications, 259–260 clinical utility of, 267–268 RLBP1 gene, 82, 285 RNAeXpress, 44 RNA sequencing (RNA-Seq), 15–16, 42 aligners, 43 data analysis alignment and count generation, 42–44 downstream applications, 44–45 novel feature detection, 44 disease genes and therapeutic targets, 47–48 in the eye, 45–46 library preparation, 42 models of disease, 47 RNA-Seq by expectation maximization (RSEM), 43–45 Rncr2, 57 RNCR3 upregulation, 59 Rncr4, 57 ROBO1, 191–192 Roche, 32 Rod-cone dystrophy (RCD), 72, 119, 298–299 Rod spherules, 80 ROR, 62 RORβ, 299–300 Rotterdam study, 264–265 RP1, 77, 81–82 RP11-234O6.2, 58 RP25, 81–82 RPE65 gene, 79–82, 85, 243–245, 288, 299 RPGR gene, 77, 81–82, 284–285, 303 RPGRIP, 82 RPGRIP1, 77 RPGR/RP3 gene, 84–85 RS1 gene, 299, 303 rs429358 variant in APOE, 169

363

Rsubread featureCounts, 43 RXRA-COL5A1, 225 RYR3, 191–192

S S (short wavelength) cone, 301–302 Samtools, 43 Sanger sequencing, 27, 241–242, 245 KC candidate genes identified by, 225–227, 226t SAR421869, 285–286 SAR422459, 286–287 Scheimpflug optical cross-sectional analysis, 220 Schizophrenia research, 266–267 Schlemm’s canal (SC) endothelial cells, 188 Scleral lenses, 221 S-cone syndrome, 299–300 Scotoma, 155 SCYL1, 191 SDCCAG8, 77, 126 Segmental duplications (SDs), 83–84 Segregation analysis, 7 advantages of, 8 disadvantages of, 8 for ocular traits and diseases, 9 simple vs. complex, 7–8 SEMA4A, 82 SEMA6A, 262 Senior-Løken syndrome (SLS), 77, 126 Sequence Alignment/Map (SAM) format file, 43 Sequencing, 13–15 advantages and disadvantages of, 16 by ligation, 15 timeline of sequencing technologies, 14f types of, 15–16 Sequencing by synthesis (SBS) technology, 15, 28–29 Serial analysis of gene expression (SAGE), 41 Sheep, gene therapy studies in, 301 Shotgun sequencing, 13–14 shRNA, 302 Simple segregation analysis, 7–8 Single genetic polymorphism (SNPs), 10, 12–13 Single-molecule real-time (SMRT) sequencing, 15, 35 by Pacific Biosciences (PacBio), 35 Single nucleotide polymorphisms (SNPs), 31, 146, 185–187, 224–225, 224t, 240, 260–261 Single nucleotide variants (SNVs), 244f Single-point analyses. See Two-point linkage analyses Single-strand conformational polymorphism (SSCP), 240 siRNA, 141–142 SIRT1, 58–59 Six3os, 57 SIX homeobox 6 (SIX6) protein, 188–189, 191–192

364

Index

Skin-derived-induced pluripotent stem cells (iPSCs), 107–108 SLC16A8 gene, 159 SLC44A4 gene, 159 SLC4A11, 223–224 SLC5A9, 210 SMAD3, 225 Small nuclear ribonucleoprotein particle (snRNP)specific proteins, 80 Smoking, 15–16 SMRTbell, 35 SNRNP200 mutations, 80 SOD1, 225–226 SOLiD, 30 Solute carrier family 19 member 2/3 (SLC19A2/3), 208 Sorsby fundus dystrophy, 82, 169 SPACR (sialoprotein associated with cones and rods), 80–81 SPACRCAN (sialoproteoglycan associated with cones and rods), 80–81 SPATA7, 77, 82 SpliceGrapher, 44 Spliceosome complex, 80 Splicing regulation, 245–246 Spurious pleiotropy, 316 Standardized incidence ratios (SIRs), 139 Stargardt disease (STGD), 72, 73t, 82, 286–287, 299, 303–304 Statistical tool of genome-wide association studies (GWAS), 12–13 of linkage analysis, 9–10 of segregation analysis, 7–8 STGD1, 286 Stokes’ JOAG pedigree, 97–98 StringTie, 44 Stroma, 219–220 Structural variation, 35, 247–248 detecting, 247–248 Sunlight ultraviolet (UV) radiation, 138 Superoxide dismutase 2 mitochondrial (SOD2), 211 Synaptic transmission defects, 80 Synonymous variants, 245

T TANK-binding kinase 1 (TBK1) gene, 105–108, 184–185, 191–192 -associated glaucoma clinical phenotype, 105–107 case report, 108, 109f effects of TBK1 duplications, 107–108 function of, 107 optineurin (OPTN) and, 111–112 transgenic mouse model of, 108 Targeted amplicon enrichment, 30

Targeted exome sequencing (TES), 241t Targeted genotyping, 16 TBK1 gene, 102–103, 108–110 TCF4, 265–266 TERT/CLPTM1L, 146 TGFB2, 225 TGFβ1-induced epithelial-mesenchymal transition, 62 Tg-TBK1 mice, 108 Thioredoxin reductase 2 (TXNRD2), 189–190 Third-generation sequencing technology, 35. See also High-throughput sequencing technologies THR377MET mutation, 97–98 3’ blocker (3’-O-azidomethyl), 28–29 THSB2, 225 Timeline of key discoveries in ophthalmic genetics, 3–6, 5f of sequencing technologies, 14f TIMP3 gene, 159, 169 TIMP-3 Ser181Cys mutation, 82 Tissue-specific alternative splicing, 84–85 TMEM136, 262 Tomography, 220 Topical timolol, 99–100 Trabecular meshwork (TM), 59–60, 182–183 Transcriptional regulation, 246 Transcriptome analysis, 46–47 history of, 41–42 Transcriptomics, 127 Transfer RNAs (tRNAs), 55 Transgenic mice, 99 Transmembrane and coiled-coil domain 1 (TMCO1), 188 Transmembrane protein 217 (TMEM217), 210 Tre-2, 209 Trimmed mean of M-values (TMM), 44–45 TRPM1, 80 Truseq exome enrichment kit, 32–33 TRβ2, 299–300 TTC8 (BBS8), 126–127 Tuberous sclerosis, 222 Tug1, 57 23andMe, 263–265 Two-point linkage analyses, 10–12 Type 3 inositol-1,4,5-trisphosphate receptor (IP3R3), 141–142 TYR437HIS mutation, 97–99 Tyrosinase (TYR), 146

U UK Biobank (UKB), 318–322, 339–340 Ultraviolet (UV) radiation, 138 Ultraviolet corneal cross-linking (UV-CXL), 221 Unfolded protein response (UPR), 244f

Index

Untranslated regions (UTRs), 243–246 Upper quartile, 44–45 USH2A, 83–84, 246, 248 Usher syndrome, 285–286, 303–304 Uveal melanoma (UM) clinical features of, 139t clustering, with other cancers, 139–140 familial uveal melanoma (FUM), 138–139 genetic versus environmental basis of, 138 highly penetrance genes with reported germline mutations in, 140–145 Birt-Hogg-Dube Syndrome (BHDS), 145 BRCA1-associated protein 1 (BAP1), 141–142 breast cancer 2 (BRCA2), 142–143 CDKN2A/ARF and CDK4, 145 MBD4, 143 mismatch repair genes (MLH1 and MSH6), 144–145 low penetrant genes, 146 HERC2/OCA2, 146 TERT/CLPTM1L, 146

V Variable number of tandem repeats (VNTRs), 240 Variant call format (VCF) files, 36 Variants, effect of on extracellular matrix remodeling, 169 on neovascularization, 169–170 Variants of unknown significance (VUS), 243–245 Vascular endothelial growth factor (VEGF), 58–59, 155–156, 169–170, 204, 206–207 rs10738760, 206–207 rs1570360, 206–207 rs2010963, 206 rs2146323, 206–207 rs3025039, 206 rs6921438, 206–207 rs699947, 206 rs833061, 206 Vax2os1, 57–58 Vax2os2, 58 VEGFA gene, 169, 171 Vertical cup-to-disc ratio (VCDR), 191, 262 Viral load, 280–284 Visual cycle, 79–80, 79f

365

Vogt’s striae, 220 VSX1 mutation, 225–226

W Warburg Micro syndrome, 224–225 WD repeat domain 36 (WDR36), 185 Wet AMD, 155, 156f Whole exome sequencing (WES), 12, 15, 32–33, 159, 165, 210, 241t, 242–243, 339–340 commercially available kits, 32–33, 32t data analysis, 36 future direction/perspectives, 36–37 vs. gene panel, 33 vs. whole-genome sequencing (WGS), 33–34 Whole genome sequencing (WGS), 12, 15–16, 27, 33–36, 159, 165, 241t, 242–243 data analysis, 36 future direction/perspectives, 36–37 long-read technologies, 35–36 Linked-Reads sequencing by 10 Genomics, 36 Oxford Nanopore Technologies (ONT), nanopore sequencing by, 35 single-molecule real-time (SMRT) sequencing by Pacific Biosciences (PacBio), 35 whole-exome sequencing (WES) vs. WGS, 33–34 Williams-Beuren syndrome, 222

X xGen Exome Research Panel, 33 X-linked CSNB, 80 X-linked retinitis pigmentosa (XLRP), 284–285, 303 X-linked retinoschisis, 287

Y YSK4 gene, 224–225

Z Zebrafish, gene therapy studies in, 301 Zero-mode waveguide (ZMW), 35 Zinc, dietary intake of, 157 Zinc finger protein 600 (ZNF600), 210 ZNF469, 225 ZNF503-AS1, 58 ZW10 interacting kinetochore protein (ZWINT), 209