Genetics of Psychological Well-Being: The Role of Heritability and Genetics in Positive Psychology [First edition.] 9780199686674, 019968667X

Table of contents :
Machine generated contents note: Section 1 Basic concepts --
1. Human flourishing and salutogenetics / Corey L.M. Keyes --
2. The genetic and epigenetic essentials of modern humans / Steven R.H. Beach --
3. Methodology of quantitative behavior and molecular genetics / Amber M. Jarnecke --
4. Evolution and well-being / Bjørn Grinde --
Section 2 Quantitative behavioral genetics of psychological well-being --
5. The heritability of subjective well-being: review and meta-analysis / Espen Røysamb --
6. The heritability and development of positive affect and emotionality / Danilo Garcia --
7. Virtue, values, genes, and psychological well-being / Qiang Shen --
8. The heritability and genetics of optimism, spirituality, and meaning in life / Charlotte Booth --
9. The genetics and evolution of covitality / Michelle Luciano --
Section 3 Molecular genetics of psychological well-being --
10. Molecular genetics of psychological well-being / Bart M.L. Baselmans. Note continued: 11. Molecular genetics of resilience / Kathryn Lemery-Chalfant --
12. Vantage sensitivity: genetic susceptibility to effects of positive experiences / Jay Belsky --
13. Epigenetics and well-being: optimal adaptation to the environment / Michael Pluess --
14. Imaging genetics of positive psychology / Adam Anderson --
Section 4 Application and implication --
15. Genes, environment, and psychological well-being / Michael J. Meaney --
16. Genetics of psychological well-being: current state and future directions / Michael Pluess.

Citation preview

Genetics of Psychological Well-Being

Series in Positive Psychology Christopher Peterson and Ralf Schwarzer, Series Editors A Life Worth Living: Contributions to Positive Psychology Mihaly Csikszentmihalyi and Isabella Selega Csikszentmihalyi (eds) International Differences in Well-Being Ed Diener, Daniel Kahneman, and John Hellliwell Well-Being for Public Policy Ed Diener, Richard Lucas, Ulrich Schimmack, and John Helliwell Oxford Handbook of Methods in Positive Psychology Anthony D. Ong and Manfred H. M. Van Dulmen (eds) Positive Education: The Geelong Grammar School Journey Jacolyn M. Norrish A Primer in Positive Psychology Christopher Peterson Designing Positive Psychology: Taking Stock and Moving Forward Kennon M. Sheldon, Todd B. Kashdan, and Michael F. Steger (eds)

Genetics of Psychological Well-Being The Role of Heritability and Genetics in Positive Psychology Edited by

Michael Pluess

1

1 Great Clarendon Street, Oxford, OX2 6DP, United Kingdom Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries © Oxford University Press 2015 The moral rights of the author have been asserted First Edition published in 2015 Impression: 1 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by licence or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this work in any other form and you must impose this same condition on any acquirer Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America British Library Cataloguing in Publication Data Data available Library of Congress Control Number: 2015934705 ISBN 978–0–19–968667–4 Printed in Great Britain by Clays Ltd, St Ives plc Oxford University Press makes no representation, express or implied, that the drug dosages in this book are correct. Readers must therefore always check the product information and clinical procedures with the most up-to-date published product information and data sheets provided by the manufacturers and the most recent codes of conduct and safety regulations. The authors and the publishers do not accept responsibility or legal liability for any errors in the text or for the misuse or misapplication of material in this work. Except where otherwise stated, drug dosages and recommendations are for the non-pregnant adult who is not breast-feeding Links to third party websites are provided by Oxford in good faith and for information only. Oxford disclaims any responsibility for the materials contained in any third party website referenced in this work.

Foreword by Ed Diener

This excellent book presents the most comprehensive and up-to-date review of research on how genes influence psychological well-being. It is a must-read for anyone interested in the topic of well-being. For scholars who are entering this area of research, the book is absolutely essential because it thoroughly presents the open questions in need of future investigation. The world’s leading experts in this field clearly present what we know and do not yet know in this area. Chapters in the book first cover what is meant by psychological and subjective well-being and discuss various categories of well-being. The authors make it clear that understanding mental illness and ill-being is not enough, and that positive well-being goes beyond the absence of ill-being. The genetic roots of the two are likely to be partially independent as well. In terms of covering genetics, there is a strong selection of chapters on heritability, molecular genetics, gene– environment interactions, and epigenetics. In each case the authors describe what we know about the genetic effects on well-being, as well as what advances have been made recently, and what is unknown. Because this research area is relatively new, what is unknown is frequently substantial, and these instances are wonderful opportunities for future research. Although there are many important findings reviewed in the book, let me focus on just a few to give readers an idea of the breadth and cutting-edge nature of the book. A chapter by Nes and Røysamb (Chapter 5) provides a new meta-analysis of empirical studies on the heritability of subjective well-being. Importantly, they find significant and large variability in the heritabilities found across studies. This is important in that it counters the mistaken idea that there is a fixed value to the heritability of a characteristic. It is also noteworthy because it suggests that the size of genetic influences on well-being measures, as reflected in heritability estimates, will depend on the environment and socialization as well as the genetic features of the population studied. Another intriguing line of research mentioned is cross-species comparisons of the heritability of well-being, as well as the genetics involved. I and my colleagues have argued that humans might be genetically prone to mild positive feelings, in the absence of significant environmental triggers for affect (Diener, Kanazawa, Suh, and Oishi, 2014). We base this argument on the ubiquity of positive feelings in humans around the world, even in those living in very difficult circumstances, and to the many benefits that positive feelings seem to confer on people. However, our argument would be greatly strengthened if we understood the genetic underpinnings of positive and negative feelings across species, as well as the behavioral patterns that these feelings might undergird. Not only is the book filled with descriptions of what we need to know, there is also frequent specific mention of hypotheses to be tested in future research. For example, Szyf and Pluess (Chapter 13) hypothesize that the genome’s activity regarding well-being is programmed epigenetically by environmental factors at critical developmental junctures in life. Hypotheses such as this offer prime research opportunities, but also suggest a whole series of processes that might follow, and be studied. The authors repeatedly point to what is likely to be a complex set of genes influencing wellbeing, in intricate interactions with the environment. When Craig Venter helped decode the human genome, he saw it as the beginning of exciting research rather than the end. He wrote that it would now take 100 years to understand what the code meant. This view of a field that is

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FOREWORD BY ED DIENER

wide-open with research opportunities is evident throughout this book. Most chapters describe the many important questions for future research. Rather than taking this as a discouraging state of affairs, young scholars should see this as a wonderful opportunity—a field that has a great many open questions, and not one where virtually everything is already known. I expect many exciting discoveries over the decades ahead by those who read this book! Ed Diener

Reference Diener, E., Kanazawa, S., Suh, E. M., and Oishi, S. 2014. Why people are in a generally good mood. Personality and Social Psychology Review. Published online before print September 24, 2014, doi: 10.1177/1088868314544467.

Foreword by Richard Layard

Our well-being is the product of both nature and nurture. But, outside psychology, social scientists have largely ignored the role of nature. This has led to over-optimism about human perfectibility and egregious errors in policy. After sixty years of the welfare state, people are still no happier than they were then, perhaps because policy-makers have failed to address the heritable component of psychological well-being. Findings from behavioral genetics studies are of fundamental importance because they may help us to do much better in promoting psychological well-being. While we are not able to change a genetic predisposition, a better understanding of the genetic and environmental determinants, as well as the interplay between both, may enable us to make more of who we are. An excellent example how a better understanding of nature and nurture can inform the promotion of psychological well-being can be found in psychiatry. A key moment in research on schizophrenia was when in 1966 Leonard Heston provided evidence for the heritability of schizophrenia (Heston, 1966). The observed genetic contribution did not mean that nothing could be done about schizophrenia. On the contrary, instead of blaming the parents, it meant the field could focus on systematically addressing the symptoms and teaching those affected new skills for managing their problems. A great deal of modern clinical psychology proceeds in a similar way, taking the symptoms seriously and helping people to change them (Layard and Clark, 2014). The field of behavioral genetics surely has the potential to revolutionize social science research, as it has psychiatry and psychology, enabling us to give much better estimates of how family upbringing and schooling influence the development of well-being. However, statistical estimates of percentage heritability are not enough for this purpose. We need to identify the specific genes at work in order to include genetic factors in statistical models aimed at explaining well-being over the life-course. With such models in hand we can get much more valid estimates of the average effects of experience. Equally important we can investigate gene–environment interactions which will tell us whether some experiences will be of particularly influence for people with specific genetic characteristics. From existing studies we already have some evidence to believe that the same gene variants which make an individual unusually sensitive to bad experiences make the same individual also unusually sensitive to good experiences. This observation of general genetic susceptibility to environmental factors is enormously encouraging and strongly supports the targeting of resources at people in distress. For all these reasons, this is an extremely important book, well written by leading experts in the field. It takes psychological genetics beyond the study of psychopathology into the study of well-being, running from the lowest level of well-being to the highest. Since the genetic bases of well-being and psychopathology are imperfectly correlated, this is an important new development, and this book provides the most comprehensive summary of research on the genetics of well-being yet. Helpfully, the book first sets out the basics of quantitative behavior and molecular genetics, and presents more traditional twin study findings, before covering more complex topics including epigenetics, gene–environment interaction, and imaging genetics. Applying a critical approach when

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FOREWORD BY RICHARD LAYARD

presenting research findings, the authors highlight both the strengths and limitations of current research. Of particular importance is Chapter 13 on epigenetics, making a convincing case that environmental factors can determine whether genes are active or not through various epigenetic mechanisms. This suggests that genes may be less determining than has been assumed and that it may be possible to influence the effects of a genetic predisposition through intervention. Equally important, Chapter 15 emphasizes the intricate and complex relationship between environment and genes in the development of well-being, concluding that genes and environmental factors are unlikely to have independent effects on well-being. Finally, Chapter 16 excels at summarizing the overall state of research in this field, concluding that although this work is still in a relatively early stage, we have now enough empirical evidence to appreciate the importance of genetic factors in explaining individual differences in well-being. This is a high-quality volume and every social scientist should take note.

Richard Layard

References Heston, L. L. 1996. Psychiatric disorders in foster home reared children of schizophrenic mothers. British Journal of Psychiatry, 112, 819–825. Layard, R. and Clark, D. M. 2014. Thrive: The Power of Evidence-based Psychological Therapies. London: Penguin.

Preface

Over the last decade positive psychology has become an extremely popular topic, evidenced by the rapidly growing number of research papers and books on various aspects of psychological well-being. Although this is a fairly new field of psychology, it is important to acknowledge that many relevant studies had been conducted long before its official emergence at the beginning of the millennium. Some of this early research included quantitative behavioral genetics studies on well-being, reporting significant heritability estimates of 50% and higher (Tellegen et al., 1988). Consideration of such findings in seminal papers on well-being led to the broad acknowledgment of the substantial role genetic factors play in positive psychology, with many accepting the simple but often completely misinterpreted “happiness formula” according to which 50% of the population variance of well-being is determined by genes, 10% by circumstances, and 40% by intentional activities (Lyubomirsky, Sheldon, and Schkade, 2005). Over the last years, there have been substantial efforts in both quantitative behavioral and molecular genetics aimed at investigating questions regarding the genetics of psychological wellbeing more deeply, catalyzed by a number of significant methodological advances in analytical technology and bioinformatics. Hence, the time seemed right to collect and summarize the current knowledge on heritability and molecular genetics in positive psychology in an edited book. Besides providing a comprehensive overview of many different aspects related to the genetics of psychological well-being, the goal of this book is also to allow people with limited knowledge of genetics and the associated methodology to familiarize themselves with the basics of genetic research in order to be able to understand and interpret the presented research findings. First, though, it is important to clarify the well-being terminology used throughout the book. Readers familiar with the positive psychology literature will be aware of the inconsistent and often confusing use of a large number of different yet overlapping well-being terms and concepts. Consequently, psychological well-being is used throughout this book as a general term to describe any aspect of well-being that refers to the psychological domain. Based on this definition, psychological well-being includes: both hedonic and eudaimonic well-being; subjective well-being (SWB) as defined by Diener (1984); psychological well-being (PWB) as defined by Ryff (1989); and life satisfaction, positive emotionality, spirituality, and so forth. Specific terminology is used in those chapters and studies that clearly focus on specific constructs rather than general psychological well-being. It is important to highlight that considerable efforts went into the process of putting this book together in order to warrant high scientific quality. Not only does the book feature many of the leading experts and authorities in the field as well as the latest research, but each chapter was also peer-reviewed by three independent experts in addition to the editor, ensuring rigorous review of each single contribution. The book is divided into four sections, starting with a primer on basic concepts and ending with an integrative review and suggestions for future research. The first section includes an introduction to human flourishing and genetics by Corey Keyes, a chapter on the biological fundamentals of genetics and epigenetics by Robert Philibert and Steven Beach, an introduction to the methodology used in behavior and molecular genetics by Susan South and Amber M. Jarnecke, and an evolutionary perspective on well-being by Bjørn Grinde. The second section contains five chapters

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that focus primarily on quantitative behavioral genetics studies: a review and meta-analysis of studies on the heritability of subjective well-being by Ragnhild Bang Nes and Espen Røysamb; a chapter on the heritability of positive emotionality by Robert Cloninger and Danilo Garcia; a detailed discussion of the genetics of virtue by Richard Ebstein, Rachel Bachner-Melman, Yunfeng Lu, Soo Hing Chew, and Qiang Shen; a review of the heritability and genetics of optimism, spirituality and meaning in life by Elaine Fox and Charlotte Booth; and a chapter by Alexander Weiss and Michelle Luciano on the notion of covitality, the observation that many positive traits seem to co-occur in individuals. The third section of the book covers more recent molecular genetics studies, comprising a review chapter by Meike Bartels and Bart Baselmans on the molecular genetics of psychological well-being; an overview of the molecular genetics of resilience by Sierra Clifford and Kathryn Lemery-Chalfant; an introduction to genetic Vantage Sensitivity by Michael Pluess and Jay Belsky, presenting empirical evidence for the notion that some people benefit more from positive experiences than others due to genetic factors; a chapter on epigenetics by Moshe Szyf and Michael Pluess; and an introduction to imaging genetics, the combination of molecular genetics with brain imaging by Hans Melo and Adam Anderson. The fourth and final section of the book discusses more overarching themes regarding the roles of genes and environment in the development of well-being, as discussed in a chapter by Michael Pluess and Michael Meaney, as well as an integrative review and discussion by Michael Pluess of the current state of knowledge in and the projected future direction of this new and exciting field of inquiry. The richness and breadth of the research presented throughout the book is testament to the growing interest in the biological underpinnings of psychological well-being and shows how much more advanced our understanding of the genetics of well-being is today compared to the now rather naïve-appearing “happiness formula” posited 10 years ago. Current knowledge suggests that heritability estimates of well-being outcomes reflect the combined effect of many thousands of gene variants and that one has to consider the complex interplay between genetic and environmental factors in order to understand the development of and individual differences in psychological well-being. A better understanding of the complex ways in which genes and environment contribute to well-being will be crucial in order to effectively influence and promote well-being through intervention and public policy. I trust that this book will be of value to researchers, practitioners, and policy makers alike, providing a comprehensive introduction to and overview of the current knowledge on genetics of psychological well-being. In closing I would like to express my deepest gratitude to the many outstanding authors and expert reviewers whom I had the great privilege to work with to produce this first scientific book on such an important topic as the genetics and heritability of psychological well-being. It is very exciting to see how far this research has developed in such a short time, and it will be fascinating to see how the field evolves further in the coming years.

Michael Pluess

References Diener, E. 1984. Subjective well-being. Psychological Bulletin, 95(3), 542–575. doi: 10.1037//0033-2909.95.3.542 Lyubomirsky, S., Sheldon, K. M., and Schkade, D. 2005. Pursuing happiness: the architecture of sustainable change. Review of General Psychology, 9(2), 111–131. doi: 10.1037/1089-2680.9.2.111 Ryff, C. D. 1989. Happiness is everything, or is it? Explorations on the meaning of psychological well-being. Journal of Personality and Social Psychology, 57(6), 1069. Tellegen, A., Lykken, D. T., Bouchard, T. J., Wilcox, K. J., Segal, N. L., and Rich, S. 1988. Personality similarity in twins reared apart and together. Journal of Personality and Social Psychology, 54(6), 1031.

Contents

List of Contributors  xiii Glossary  xv Abbreviations  xxi

Section 1 Basic concepts 1 Human flourishing and salutogenetics  3

Corey L. M. Keyes

2 The genetic and epigenetic essentials of modern humans  23

Robert A. Philibert and Steven R. H. Beach

3 Methodology of quantitative behavior and molecular genetics  38

Susan C. South and Amber M. Jarnecke

4 Evolution and well-being  56

Bjørn Grinde

Section 2 Quantitative behavioral genetics of psychological well-being 5 The heritability of subjective well-being: review and meta-analysis  75

Ragnhild Bang Nes and Espen Røysamb

6 The heritability and development of positive affect and emotionality  97

C. Robert Cloninger and Danilo Garcia

7 Virtue, values, genes, and psychological well-being  114

Richard P. Ebstein, Rachel Bachner-Melman, Yunfeng Lu, Soo Hong Chew, and Qiang Shen

8 The heritability and genetics of optimism, spirituality, and meaning in life  132

Elaine Fox and Charlotte Booth

9 The genetics and evolution of covitality  146

Alexander Weiss and Michelle Luciano

Section 3 Molecular genetics of psychological well-being 10 Molecular genetics of psychological well-being  163

Meike Bartels and Bart M. L. Baselmans

11 Molecular genetics of resilience  177

Sierra Clifford and Kathryn Lemery-Chalfant

12 Vantage sensitivity: genetic susceptibility to effects of positive experiences  193

Michael Pluess and Jay Belsky

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13 Epigenetics and well-being: optimal adaptation to the environment  211

Moshe Szyf and Michael Pluess

14 Imaging genetics of positive psychology  230

Hans Melo and Adam Anderson

Section 4 Application and implication 15 Genes, environment, and psychological well-being  251

Michael Pluess and Michael J. Meaney

16 Genetics of psychological well-being: current state and future directions  266

Michael Pluess

Author Index  277 Subject Index  287

List of Contributors

Adam Anderson Department of Human Development, College of Human Ecology, Cornell University, Ithaca, USA

C. Robert Cloninger Department of Psychiatry, Center for Well-Being, Washington University School of Medicine in St. Louis, Missouri, USA

Rachel Bachner-Melman Department of Psychology, Hebrew University of Jerusalem, Jerusalem, Israel

Richard P. Ebstein Department of Psychology, National University of Singapore, Singapore

Meike Bartels Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands

Elaine Fox Department of Experimental Psychology, University of Oxford, Oxford, UK

Bart M. L. Baselmans Department of Biological Psychology, VU University Amsterdam, Amsterdam, The Netherlands

Danilo Garcia Department of Psychiatry, Center for Well-Being, Washington University School of Medicine in St. Louis, Missouri, USA

Steven R. H. Beach Department of Psychology, University of Georgia, Athens, USA Jay Belsky Department of Human Ecology, University of California Davis, Davis, USA Charlotte Booth Department of Experimental Psychology, University of Oxford, Oxford, UK Soo Hong Chew Department of Economics, National University of Singapore, Singapore Sierra Clifford Department of Psychology, College of Liberal Arts and Sciences, Arizona State University, Tempe, USA

Bjørn Grinde Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway Amber M. Jarnecke Department of Psychological Sciences, Purdue University, West Lafayette, USA Corey L. M. Keyes Department of Sociology, Emory University, Atlanta, USA Kathryn Lemery-Chalfant Department of Psychology, College of Liberal Arts and Sciences, Arizona State University, Tempe, USA Yunfeng Lu Department of Economics, National University of Singapore, Singapore

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LIST OF CONTRIBUTORS

Michelle Luciano Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, UK

Espen Røysamb Department of Psychology, University of Oslo, Oslo, Norway

Michael J. Meaney Department of Psychiatry and Neurology, McGill University, Montreal, Canada

Qiang Shen Department of Management of Science and Engineering, School of Management, Zhejiang University, Hangzhou, China

Hans Melo Department of Psychology, University of Toronto, Toronto, Canada

Susan C. South Department of Psychological Sciences, Purdue University, West Lafayette, USA

Ragnhild Bang Nes Division of Mental Health, Norwegian Institute of Public Health, Oslo, Norway

Moshe Szyf Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada

Robert A. Philibert Department of Psychiatry, University of Iowa, Iowa City, USA Michael Pluess Department of Biological and Experimental Psychology, School of Biological and Chemical Sciences, Queen Mary University of London, UK

Alexander Weiss Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, UK

Glossary

Acetylation  Addition of an acetyl group to a molecule Allele  Different version of a gene. An individual inherits two alleles for each gene, one from each parent. If the two alleles are the same, the individual is homozygous for that gene. If the alleles are different, the individual is heterozygous Amygdala  Brain region involved in processing of emotions Assortative mating  Nonrandom mating pattern. Individuals with similar characteristics mate with one another more frequently than would be expected under a random mating pattern Autosome  All chromosomes except the sex chromosome AVPR1  Gene coding for Arginine vasopressin 1a receptor Brain-derived neurotrophic factor (BDNF)  Protein (and associated gene) that supports the survival of existing brain cells and the growth of new brain cells Broad-sense heritability  Heritability reflecting both additive and non-additive genetic influences Candidate gene  A gene that is hypothesized to play an important role in the development of a specific physiological or psychological outcome based on biological considerations Chromatin  Complex of DNA and proteins that form chromosomes Chromosome  Thread-like structure inside the nucleus made up of tightly coiled DNA. Humans have 23 pairs of chromosomes Common disease common variants (CR-CV) hypothesis  Understanding that common diseases are associated with gene variants that are frequent in the human population Comorbidity  Simultaneous presence of multiple disorders or diseases COMT  Gene coding for catechol-O-methyltransferase, an enzyme involved in the degradation of catecholamines (e.g., dopamine, epinephrine, and norepinephrine) Copy number variation (CNV)  Variation in the number of copies of a particular gene between individuals Covitality  Co-occurrence of positive traits within the same individual CRH  Corticotropin-releasing hormone CRHR1  Gene coding for corticotropin-releasing hormone receptor 1 Cytokine  Small protein with specific effects on the interaction and communication between cells or the behavior of cells Developmental plasticity  Ability of the same organism to develop in a variety of ways in order to adapt to a specific environment Diathesis-stress  Model for individual differences in response to adverse experiences. Vulnerability (i.e., diathesis) describes the propensity to respond negatively to adversity whereas resilience reflects protective resistance from the same negative influence

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GLOSSARY

Differential susceptibility  Model for individual differences in general environmental sensitivity. High susceptibility is characterized by increased susceptibility in response to both negative and positive exposures whereas low susceptibility reflects psychological inertia to environmental influences independent of their quality Dizygotic twins (DZ)  Twins that share on average 50% of their genes. DZ twins can come in both same-sex (e.g., boy–boy) and opposite-sex (e.g., boy–girl) varieties DNA  Deoxyribonucleic acid, the molecule that encodes genetic information DNA methylation  Addition of a methyl group to specific regions of the DNA DNMT  DNA methyltransferases, group of enzymes that are involved in transferring methyl groups to DNA DRD2  Gene coding for the dopamine receptor D2 DRD4  Gene coding for the dopamine receptor D4 EEG  Electroencephalography, non-invasive method of recording electric brain activity along the scalp Endophenotype  Biological or psychological features that mediate the link between genes and complex behaviors Enzyme  Molecule that facilitates biochemical processes Epigenetic mechanisms  A variety of biochemical processes that determine gene expression Epigenetics  Study of changes in organisms caused by modification of gene expression rather than alteration of the DNA Epigenome  Chemical compounds that have been added to the entirety of one’s DNA (genome) as a way to regulate the activity (expression) of all the genes within the genome Epistasis  Interaction between one or more genes Etiology  Cause of a disease or condition Eudaimonic well-being  Aspect of well-being that refers to the creation of meaning and the experience of purpose in life Eukaryote  Organism whose cells contain a nucleus with DNA Exon  Protein-coding DNA or RNA sequence of a gene Fitness  Ability of an individual to reproduce offspring and successfully pass on genes to the future generation FKBP5  Gene coding for the FK506 binding protein 5 Functional magnetic resonance imaging (fMRI)  Non-invasive method for the measurement of changes in brain activity Gene  Sequence of DNA that contains the code for a specific protein Gene expression  Process by which the information encoded in a gene (DNA) is used to direct the assembly of a protein molecule Gene transcription  Process by which genetic information present in the DNA is copied onto messenger RNA (mRNA) Gene translation  Process by which genetic information present in mRNA is translated into a protein Gene–environment correlation (rGE)  Observation that genetic characteristics are correlated with specific environments

GLOSSARY

Gene–environment interaction (GxE)  Observation that genetic characteristics moderate the effects of environmental influences Genome  The complete genetic information of an individual Genome-wide association study (GWAS)  Exploratory hypothesis-free study testing associations between a large number of DNA variations across the whole genome and a specific outcome of interest Genomic tone  Balance of factors controlling the transcriptional repertoire for a cell at a given point of time Genotype  An individual’s genetic information Gestation  Developmental period from conception until birth Glucocorticoids  Class of steroid hormones GREML (also GCTA)  Genomic-relatedness-matrix restricted maximum likelihood (also genome-wide complex trait analysis), a technique to estimate the extent to which phenotypic variance can be explained by genetic variance across a large number of gene variants Haploid  Cells that contain only one complete set of chromosomes Haplotype  A set of DNA variations that are clustered and tend to be inherited together Hedonic well-being  Aspect of well-being that refers to pleasure and positive emotions Heritability  Proportion of phenotypic variation that can be attributed to genetic variation in a particular population Hippocampus  Brain region involved in formation of memory Histone  Proteins that are part of chromatin and provide structural support to chromosomes (DNA wraps around complexes of histone proteins) Histone modification  Biochemical modification of histones that influences the accessibility of DNA for transcription Histone octamer  Complex made up by eight proteins that functions like a spool around which DNA is wrapped Hominins  Human ancestor that appeared after the split from chimpanzees Hominoids  Biological superfamily that includes all modern great apes and humans as well as a number of their extinct ancestors and relatives Homozygous  Individuals that carry two copies of the same allele of a particular gene Hydroxylation  Addition of a hydroxyl group to a molecule Imaging genetics  Methodology focused on the investigation of associations between genetic variation and brain structure and function Intron  DNA or RNA sequence of a gene that does not contain coding information r/K-selection  K-selected species or populations invest their resources in high quality rearing of a few offspring whereas r-selected species or populations invest their resources in producing many offspring but provide limited care Linkage analysis  A technique aimed at detecting links between DNA and traits using family pedigrees in order to identify genes that map to behavioral or other outcomes Linkage disequilibrium (LD)  The nonrandom association between two or more alleles such that certain combinations of alleles are more likely to occur together on a chromosome than other combinations of alleles

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xviii

GLOSSARY

Locus  The site of a specific gene on a specific chromosome MAOA  Gene coding for monoamine oxidase A Messenger RNA (mRNA)  RNA molecule that carries a DNA copy from the nucleus of the cell to the sites of protein synthesis (the ribosomes) Meta-analysis  Statistical technique for combining the findings from multiple independent ­studies Methylation  Addition of a methyl group to a molecule Micro-array  Hundreds of thousands of microscopic DNA spots attached to a solid surface the size of a postage stamp that serve as probes for the measurement of gene-expression (RNA micro-arrays) or single-nucleotide polymorphisms (DNA-micro-array) Microsatellite  Genetic polymorphism with repetitive blocks of 2–5 base pairs Minisatellite  Genetic polymorphism with repetitive blocks of 10–60 base pairs referred to as variable number tandem repeats (VNTR) Missing heritability  Difference between heritability estimates from quantitative behavioral genetic studies and the generally much smaller variance explained by measured genes Mitochondrial DNA  Small amount of DNA that is located in the mitochondria in contrast to majority of DNA that is located in chromosomes Mononuclear cells  Any type of cells that has a single nucleus containing DNA Monozygotic twins (MZ)  Twins that share on average 100% of their genes. MZ twins always have the same sex MR  Gene coding for the mineralocorticoid receptor Mutation  Heritable change in DNA base-pair sequence Narrow-sense heritability  Heritability reflecting only additive genetic influences Natural selection  Evolutionary process by which heritable traits become either more or less common in a population as a function of the effect of inherited traits on the reproductive success Neurotransmitters  Molecules that transmit signals between brain cells Noncoding RNA  DNA that is transcribed into RNA but not translated into proteins Nonshared environment  Environmental influences that do not contribute to resemblance between family members NPY  Gene coding for neuropeptide Y NR3C1  Gene coding for the glucocorticoid receptor Nucleosome  The basic building block of chromatin consisting of DNA wrapped around a histone octamer Nucleotide  The basic building block of nucleic acids, a molecule composed of a nitrogen base, a sugar, and a phosphate group Nucleus  Membrane-enclosed organelle of eukaryotic cells that contains most of the cell’s DNA OXTR  Gene that codes for the oxytocin receptor Pathogenesis  Study of processes involved in the development of disease Penetrance  Proportion of individuals that carry a certain allele

GLOSSARY

Phenotype  Observed characteristics of an individual Phosphorylation  Addition of a phosphate group to a molecule Pleiotropy  Observation that a single gene affects a number of phenotypic traits in the same organism Polymorphism  Existence of different forms of the same gene in the population Positive psychology  The scientific study of positive human functioning, wellbeing, and flourishing Prefrontal cortex  Brain region involved in complex regulatory functions including problem solving and abstract thinking Promotive factor  Individual characteristic that predicts a higher probability of positive development regardless of risk level Protective factor  Individual characteristic that predicts a higher probability of positive outcome in the context of high risk or adversity Psychological well-being  (1) General term used to refer to well-being related to one’s psychological state, including subjective, hedonic, eudaimonic, and evaluative well-being, (2) specific measure of well-being (PWB) developed by Carol Ryff (1989) consisting of six facets: Autonomy, Environmental Mastery, Personal Growth, Positive Relations, Purpose in Life, Self-Acceptance Quantitative trait loci  A gene that contributes to quantitative (continuous) variation in a specific phenotype Resilience  Positive adaptation in the face of risk or adversity Retroposon  DNA sequence that does not originate in the DNA but in an mRNA that is transcribed back into the genomic DNA by reverse transcription Ribonucleic acid (RNA)  Single-stranded nucleic acid similar to DNA but having ribose sugar rather than deoxyribose sugar and uracil rather than thymine as one of the bases. RNA are complementary copies of DNA with multiple functions Ribosome  Organelle that catalyzes the translation of mRNA into proteins Risk factors  Individual characteristic that predicts the development of a negative outcome Salutogenesis  Study of processes involved in the development of positive health Shared environment  Environmental factors that make family members more similar to each other Single nucleotide polymorphism (SNP)  Genetic polymorphism with a single-nucleotide substitution of one base for another that occur in more than one percent of the general population SLC6A4  Gene coding for the serotonin transporter Somatic cell  All cells in a living organism apart from reproductive cells Stochastic factors  Processes that appear to be varying at random and can’t be predicted or attributed to specific mechanisms or biological processes Subjective well-being (SWB)  A person’s cognitive and affective evaluation of life as defined by Diener (1984) Telomere  Tip or end of a chromosome Transcription factor  Proteins that facilitate the process of transcribing DNA to RNA

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Transcriptome  RNA transcribed from all the DNA in the genome Trichostatin A  Organic compound that inhibits histone deacetylase Ubiquitination  Process of adding ubiquitin chains to a protein targeted for degradation Vantage sensitivity  Model for individual differences in response to positive experiences as a function of inherent characteristics. Vantage sensitivity describes the propensity to respond favorably to positive experiences whereas vantage resistance reflects the inability to benefit from supportive influences Variable number tandem repeat (VNTR)  Genetic polymorphism with repetitive blocks of 10–60 base pairs 5-HT1A  Gene coding for the serotonin transporter 1A 5-HTTLPR  Serotonin transporter linked polymorphic region, genetic polymorphisms (VNTR) located in the serotonin transporter gene

Abbreviations

ABM ACTH AD ADHD ASD AN ASQ ATR AVP AVPR1a BAS BDNF BEIP BP BIS CBT CELAM

Attention Bias Modification adrenocorticotropic hormone Alzheimer’s Disease attention deficit hyperactivity disorder autism spectrum disorder anorexia nervosa Attributional Style Questionnaire Australian Twin Registry Arginine Vasopressin arginine vasopressin 1a receptor Behavioral Activation System Brain-Derived Neurotrophic Factor Bucharest Early Intervention Project bipolar disorder Behavioral Inhibition System cognitive behavioral therapy Centre for Ethics, Law, and Mental Health CES-D Center for Epidemiology Studies Depression Scale CI confidence interval CNS central nervous system CNV copy number variation COMT catechol-O-methyltransferase COS Cognitive Orientation towards Spirituality COT Children of Twins CpG cytosine-guanine CRH corticotropin releasing hormone CRHR1 corticotropin-releasing hormone receptor 1 CTRA conserved transcriptional response to adversity DA dopamine DG Dictator Game DNA deoxyribonucleic acid DRD4 dopamine receptor D 4 DTI Diffusion Tensor Imaging DZ dizygotic EDS Experiential Dimension of Spirituality

EEG electroencephalography EFPTS East Flanders Prospective Twin Survey ESI Expressions of Spirituality Inventory EWAS Epigenome-wide association studies EWB Existential Well-Being fMRI functional magnetic resonance imaging FS flourishing scale GAD generalized anxiety disorder GC glucocorticoid GCTA genome-wide complex trait analysis GR glucocorticoid receptor GRE glucocorticoid response element GREML genomic-relatedness-matrix restricted maximum likelihood GWAS genome-wide association studies GxE gene–environment interaction HPA hypothalamic-pituitary-adrenal axis HXE heritability–environment interaction ITR Italian Twin registry LD Linkage disequilibrium LINES long interspersed nuclear elements lncRNA long non-coding RNA LOD logarithm of odds LOT Life Orientation Test LS Life Satisfaction MAF minor allele frequency MAOA monoamine oxidase A MATR Mid Atlantic Twin Registry MBSR mindfulness-based stress reduction MDD major depressive disorder MDE major depressive episode MHC-LF Mental Health Continuum long form MHC-SF Mental Health Continuum short form MIDUS Midlife Development in the United States MLQ Meaning in Life Questionnaire MMPI Minnesota Multiphasic Personality Inventory MPQ Multidimensional Personality Questionnaire

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ABBREVIATIONS

MR Mineralocorticoid Receptor mRNA mature RNA MTFR Minnesota Twin-Family Registry MTR Minnesota Twin Registry MWB mental well-being MZ monozygotic NA negative affect NEM negative emotionality NIPHTP Norwegian Institute of Public Health Twin Panel NPY Neuropeptide Y Gene NTR Netherland Twin registry OCD obsessive compulsive disorder OCEAN Oxford Centre for Emotions and Affective Neuroscience OXTR Oxytocin Receptor Gene PA positive affect PA panic attacks PAS Positive Affect Scale PBMCs peripheral blood mononuclear cells PET positron emission tomography PFC prefrontal cortex PGC Psychiatric Genomic Consortium PGG Public Goods Game PPA parahippocampal place area PTSD post-traumatic stress disorder PVQ Portrait Values Questionnaire PWB psychological well-being QoL Quality of Life QTL quantitative trait loci

RAPI

Religious Attitudes and Practices Inventory rGE gene-environment correlation RNA ribonucleic acid SAM S-adenosylmethionine SATSA The Swedish Adoption/Twin Study of Aging SCAN Social Cognitive and Affective Neuroscience SCZ schizophrenia SEM Structural Equation Models SHS Subjective Happiness Scale SIBS Sibling Interaction and Behavior Study SNP Single Nucleotide Polymorphism SOC sense of coherence SSGAC Social Sciences Genetic Association Consortium SVO Social Value Orientation SWB subjective well-being SWLS Satisfaction With Life Scale TCI Temperament and Character Inventory TET ten-eleven translocation TFTC The Finnish Twin Cohort tRNA transfer RNA TS Tourette’s syndrome TSS translation start site UG Ultimatum Game UTR untranslated region VETSA Vietnam Era Twin Registry Study VIA Values In Action scale VNTR variable number tandem repeats WHO World Health Organization

Section 1

Basic concepts

Chapter 1

Human flourishing and salutogenetics Corey L. M. Keyes

Human flourishing and salutogenetics: an introduction This chapter will introduce readers to the theory and conception of psychological well-being (PWB) as consisting of the hedonic and the eudaimonic traditions. Positive psychology, the scientific study of positive human functioning and flourishing (Seligman and Csikszentmihalyi, 2000), has only recently embraced the notion of well-being as including eudaimonic approaches that articulate ways in which individuals function well in life. However, the work on flourishing, which combines the hedonic with the eudaimonic measurement of well-being, has yielded findings indicating the additional benefits of functioning well in life in addition to feeling good about life (e.g., Keyes and Annas, 2009; Keyes and Simoes, 2012). In addition to my own model of flourishing, I will review the conceptions and measurement of three newer measures of flourishing. The public health benefits in terms of reduced mental illness and premature mortality attributable to flourishing have led to increased interest in the etiology of well-being. Behavioral genetics, with its focus on understanding the genetic and environmental etiology, has been disease-focused. When it has focused on happiness, behavioral genetics, like positive psychology, has primarily studied the hedonic facets of well-being. Yet, recent studies have shown that eudaimonic wellbeing is highly heritable and shares the same genetic sources as hedonic well-being (Keyes, Myers, and Kendler, 2010). As such, the field of behavior genetics could benefit from expanding its scope to include eudaimonic well-being. With this conceptual and theoretical expansion, behavioral genetics can produce important insights into positive mental (and physical) health through the study of salutogenetics. The salutogenic approach is supported by the mounting empirical evidence of the two-continua model at the phenotypic (Keyes, 2005a) and genetic (Kendler et al., 2011) levels. That is, health is more than the absence of illness and genetic risk; it is also the presence of well-being and the genetic propensity for well-being.

Pathogenesis and salutogenesis Salus is the Latin word for the presence of health and the name for the Roman goddess of positive health. Salus is equated with, or derived from, the Greek term, Hygeia. The Greek physicians and philosophers viewed health as more than the absence of illness in distinguishing health from illness. Hart’s (1965) classic article describes the origin myth of modern medicine to the cult of Asclepius, a deity in ancient Greece known as the father of medicine. The daughters of Asclepius represented the complementary branches of medicine. Panacea cured illness; Hygeia promoted good health. The snake winding itself around the staff of Asclepius symbolizes Hygeia, because snakes routinely shed their skin and thereby restore vitality. In taking the Hippocratic oath, today’s medical students unwittingly swear allegiance to protecting good health and not merely treating the presence of illness. Originating in ancient Greece, salutogenesis

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is the study of the etiology of positive health. Salutogenetics is therefore a branch of salutogenesis where the focus is on the genetic and environmental causes of positive health. What determines whether an approach or field of inquiry is salutogenic? Before positive psychology, several areas of research studied positive constructs. The field of resilience, for example, studied strengths. Such strengths in the resilience literature are called protective factors, because they buffered against illness or worse-than-average development in the face of adversities such as poverty, abuse, neglect, or household turmoil. However, including individual strengths (e.g., confidence) in a study does not make an approach salutogenic. The difference between a pathogenic and salutogenic model is the outcome, not the predictive or explanatory variables. A pathogenic approach to health measures the presence and absence of illness conditions, and views health as the absence of illness. A salutogenic approach to health measures the presence and absence of health, and conceives of health as the presence of positive attributes. For me, the rule that the dependent variable distinguishes pathogenic from salutogenic can hopefully clarify confusion surrounding the use of the term “salutogenesis.” Scholars often refer to their work as salutogenic when they are studying illness or negative attributes as the outcome. This was sometimes true of the sociologist Aaron Antonovsky, who popularized the term salutogenesis. His pioneering contribution to the literature was his sense of coherence (SOC) construct. By the outcome criterion, Antonovsky (1979) often used a pathogenic model, because he was studying whether the dimensions of SOC—i.e., comprehensibility, manageability, and meaningfulness—were protective of illness (e.g., mortality and heart disease) in the face of stress. Similarly, positive psychology is not always “positive” per se. It is tempting to view the constructs in the two primary handbooks of positive psychology (David, Boniwell, and Ayers, 2013; Lopez and Snyder, 2009) as solely positive and therefore salutogenic. Positive psychology can be salutogenic and pathogenic, and the latter justifies the former. The salutogenic mission of positive psychology seems more justified when positive constructs are shown to ameliorate, mitigate, or prevent social problems such as stress, illness, and mortality. That is, it seems even more important to study the origins of hope when hope is shown to prevent mental illness. To me, then, the positive and the negative—both salutogenesis and pathogenesis—are and should be intimately connected. Indeed, for many years, health has been seen as more than the absence of illness; it is, to quote Sigirest, “something positive” (Sigirest, 1941, p. 100). In 1948 the World Health Organization (WHO, 1948) adopted the definition of health as the presence of a positive state of human capacities and functioning, and not merely the absence of illness. In proffering this definition, the WHO implicitly relied on the two-continua model of health and illness. However, there was no scientific evidence supporting the two-continua model, because there were no measures of positive mental or positive physical health. Indeed, there was little interest in positive health throughout most of the twentieth century, because modern medicine created the zeitgeist of the single continuum where health was the absence of illness.

The underpinnings of positive psychology Many may credit positive psychology with moving forward the science of the study of positive health and human strength. While positive psychology has certainly brought attention and resources to the study of good health, strengths, and virtues, I would argue that seven scientific trends that occurred in the wake of World War II made it possible for the emergence of positive psychology. World War II woke the world and psychology to the need to understand what people were feeling and thinking. As such, introspection through self-report once again became a legitimate

HUMAN FLOURISHING

method of data collection. Gordon Allport declared that “[i]t is not enough to know how man reacts: we must know how he feels, how he sees his world, . . . why he lives, what he fears, for what he would be willing to die. Such questions of existence must be put to man directly” (Severin, 1965, p. 42). Second, the study of quality of life moved beyond objective and negative indicators such as crime and poverty to include large-scale surveys that asked citizens how happy and satisfied they felt about their lives (Gurin, Veroff, and Feld, 1960). Third, the humanistic psychology championed, among other things, the study of self-actualization and human potential (Maslow, 1968), which counterbalanced the prevailing Freudian and Behaviorist views of humans as faulty and reactive. Fourth, the study of risk factors expanded to include resilience (Werner and Smith, 1977), where scholars brought attention to strengths and assets that permitted individuals to develop normally despite exposure to adversity. Fifth, the study of stress expanded to include individuals’ capacity to cope with stress and the fact that perception of stressors could mitigate the event if viewed as a challenge rather than a threat (Lazarus and Folkman, 1984). Sixth, the study of person–environment fit emerged and brought attention to positive mental states (e.g., flow) that resulted from the matching individual skill and environmental challenge (Csikszentmihalyi, 1975). The study of flow counterbalanced the prevailing focus on negative mental states and problems of person–environment fit. Last, as life expectancy and longevity increased, gerontologists began to study the process of successful aging (Baltes, 1987), which countered the prevailing view of aging as inevitable declines in health and functioning. Two scholars published seminal articles in the 1980s that brought the study of well-being and its two traditions into the mainstream of psychology. The first was Ed Diener’s (1984) review article of the state of the first generation of research and theory on subjective well-being (SWB), which had focused mainly on hedonic—or emotional—well-being, defined by the combination of high life satisfaction, and high positive and low negative affect. Diener (1984) encouraged greater attention to top–down models of SWB, where personality and individual attributes were viewed as potential causes of SWB. Moreover, Carol Ryff (1989) operationalized the theory of PWB consisting of six facets (autonomy, self-acceptance, positive relations with others, personal growth, environmental mastery, and purpose in life). Ryff (1989) was the first to champion “eudaimonic” well-being as a neglected approach to research on well-being. Together, Diener and Ryff brought the two streams of research on well-being first reviewed by Gurin, Veroff, and Feld (1960) and Jahoda (1958) into mainstream psychology. I would argue that nearly all of the topics in the handbooks of positive psychology could be traced back to one or more of these trends. As such, all of us who now enjoy the umbrella of positive psychology owe much in recognition to the pioneers who brought us subjective quality of life, humanism, resilience, stress perception, coping, flow, successful aging, and happiness distinctions. The last contribution—the distinctions and study of kinds of happiness—is central to this chapter and the argument for a salutogenic approach in behavioral genetics. I therefore turn next to the topic of the two faces of happiness, or rather PWB, which is used in this edited book as the general term for well-being of human mind and soul (to be distinguished from Ryff ’s specific concept of PWB).

The two faces of psychological well-being Social and psychological scientists have created a variety of self-report measures to tap into people’s subjective sense of the quality of their lives. Although research shows that people use multiple criteria to evaluate their subjective experiences, there are two general lines of research that have

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evolved. According to one line of well-being research, evaluations of the degree of positive feelings (e.g., happiness) experienced and perceptions (e.g., satisfaction) toward one’s life overall constitute well-being (Diener et al., 1985). A second stream of well-being research specifies dimensions of positive functioning (Ryff, 1989) and social well-being (Keyes, 1998). Overall, PWB consists of two broad domains: hedonic and eudaimonic well-being (see Huta and Waterman, 2013 for an excellent review of various approaches to these two traditions).

Hedonic well-being The emotional well-being cluster of symptoms reflects the presence and absence of positive feelings about life operationalized as evaluations of happiness and satisfaction with life, and the balance of positive to negative affect experiences over a time period. Thus, emotional well-being can be conceptualized as the balance of feelings (positive and negative) experienced in life (Bradburn, 1969), and the perceived feelings (happiness and satisfaction) (Andrews and Withey, 1976). Most single-item measures of life satisfaction are adaptations of Cantril’s (1965) Self-Anchoring Scale, which asks respondents to “rate their life overall these days” on a scale from 0 to 10, where 0 meant the “worst possible life overall” and 10 meant “the best possible life overall.” Variants of Cantril’s (1965) measure have been used extensively in numerous studies worldwide, and have been applied to the measurement of avowed happiness with life (Andrews and Withey, 1976). Single-item indicators and multi-item scales of life satisfaction and happiness have also been developed and employed extensively (see Diener, 1984, p. 546 for a list of measures of emotional well-being). Most measures of positive and negative affect investigate the frequency or the duration of time that a respondent reports the experience of symptoms of positive and negative affect. For example, individuals are often asked to indicate how much of the time during the past 30 days they have felt six types of negative (e.g., blue) and six types of positive (e.g., calm and peaceful) indicators of affect. Response choices for the negative affect and positive affect items are usually “all,” “most,” “some,” “a little,” or “none of the time.” Estimates of internal reliability of the multi-item scales of satisfaction and happiness and positive and negative affect usually exceed .80 (see e.g., Diener, 1994). Studies also support a proposed factor structure of emotional well-being with a cognitive domain of avowed life satisfaction and an affective domain of happiness (Bryant and Veroff, 1982). In sum, hedonic well-being consists of two dimensions. The first dimension reflects declared emotion, where individuals indicated “this is what I typically feel” as measured by the positive affect items. The second dimension is evaluative, where individuals think about their lives and then use emotion words to evaluate how they feel about their lives. Because the evaluative dimension emphasizes how people think about their lives, it is sometimes referred to as the “evaluative” or “cognitive” aspect.

Eudaimonic well-being As mentioned earlier, Ryff ’s (1989) article was an important milestone for the field of happiness. Drawing inspiration from Aristotle’s view of happiness as eudaimonia and Jahoda’s (1958) review of perspectives on positive mental health, Ryff (1989) argued that happiness is not only about feeling good about life, but it is also about functioning well in life. In Ryff ’s PWB model, positive functioning encompasses six dimensions: self-acceptance, positive relations with others, personal growth, purpose in life, environmental mastery, and autonomy.

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Self-acceptance requires the maintenance of esteem for one’s self, while facing complex and sometimes unpleasant personal aspects of the self. In addition, individuals accumulate a past and have the capacity to recall and remember themselves through time. Healthy individuals perceive themselves positively across the life-course and accept all parts of themselves. Positive relations with others consist of the ability to cultivate warm, intimate relationships with others. It also includes the presence of satisfying social contacts and relations. Autonomy measures the degree to which people seek self-determination and personal authority, in a society that at times requires obedience and compliance. However, healthy individuals seek to understand their own values and ideals. In addition, healthy individuals see themselves guiding their behavior and conduct from internalized standards and values. Environmental mastery is the active engagement of the environment to mold it to meet one’s needs and wants. Healthy individuals recognize personal needs and desires and also feel capable of taking, and permitted to take, an active role in getting what they need from their environments. Purpose in life captures the adult’s perception of having direction in life, even when the world offers none or provides unsatisfactory alternatives. Healthy individuals see their daily lives as fulfilling a direction and purpose, and therefore, they view their personal lives as meaningful. Last, personal growth is the ability and desire to enhance existing skills and talents, and to seek opportunities for further personal development. In addition, healthy individuals are open to experience and have the capacity to identify challenges in a variety of circumstances.

Social well-being Keyes (1998) has asserted that positive functioning includes social challenges and tasks, and proposed five dimensions of social well-being. Whereas Ryff ’s PWB model (and its component, positive relations with others) represents more private and personal criteria for evaluation of one’s functioning, social well-being epitomizes the more public and societal criteria, whereby people evaluate their functioning in life. Put simply, Ryff ’s PWB model is premised on the pronouns “I” and “Me,” while social well-being is premised on the pronouns “We” and “Us.” These societal dimensions consist of social coherence, social actualization, social integration, social acceptance, and social contribution. Social integration is the evaluation of the quality of one’s relationship to society and community. Integration is therefore the extent to which people feel they have something in common with others who constitute their social reality (e.g., their neighborhood), as well as the degree to which they feel that they belong to their communities and society. Social contribution is the evaluation of one’s value to society. It includes the belief that one is a vital member of society, with something of value to give to the world. Social coherence is the perception of the quality, organization, and machinations of society, and it includes a concern for knowing about the world. Social coherence is the obverse of a sense of meaninglessness, and involves appraisals that society is discernible, sensible, and predictable. Social growth, sometimes referred to as social actualization, is the evaluation of the potential and the trajectory of society. This is the belief in the positive evolution of society and the sense that society has potential that is being realized through its institutions and citizens. Social acceptance is the construal of society through the character and qualities of other people in general. Individuals must function in a public arena that consists primarily of strangers. Individuals who illustrate social acceptance trust others, think that others are capable of kindness, and believe that people can be industrious. Socially accepting people hold favorable views of human nature and feel comfortable with others.

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Newer approaches to the measurement of flourishing Table 1.1 shows the dimensions of hedonic and eudaimonic well-being and the items measuring the four dominant models of flourishing. In what follows, strong agreement means that at least three of the four models measured the same (or similar) dimensions. Modest agreement means that two of the four models measured the same (or similar) dimensions. There are some distinctive dimensions that will be mentioned last. There is strong agreement that hedonic well-being consists of positive emotions and interest in (or engagement with) life. There is strong agreement that eudaimonic well-being consists of self-acceptance (or self-respect or self-esteem), a sense of personal mastery (or competence), warm interpersonal relationships, purpose in life, and optimism, where optimism can be directed toward the future in general or the future improvement of society. I have separated personal growth and accomplishment, because the former refers to improving oneself, while accomplishment is either about reaching goals (for Seligman) or a sense of accomplishment (for Huppert). Yet, a case could be made that growth and accomplishment belong to the same dimension, in which case one could argue that here, too, is strong consensus across models. In turn, there is modest agreement that eudaimonic well-being includes contribution. In terms of distinctiveness, only Huppert’s model includes a hedonic dimension that reflects what might be called vitality (calm and peaceful, and a lot of energy). Moreover, in terms of eudaimonic well-being, Huppert includes resilience, or the ability to get back to normal after challenges in life. My own model is distinctive in including autonomy, acceptance of others, and social coherence. Overall, then, there is considerably more agreement than distinctions in terms of dimensions that reflect hedonic and eudaimonic well-being. However, even where there is strong to moderate agreement, each model provides unique questions to operationalize each dimension. There have been no studies that have empirically examined some or all of the proposed measures of flourishing, so it is not possible yet to assess how much overlap exists among the four models. Seligman proposed his flourishing model in his 2012 book (Seligman, 2012). The acronym of Seligman’s model is PERMA, which refers to the core features of flourishing that consist of positive emotions, engagement, relationships, meaning, and accomplishment. The items in Table 1.1 for Seligman’s model are from the PERMA-Profiler (PERMA-P) by Butler and Kern (2013). The first peer-reviewed article on the psychometrics of the PERMA-P appeared in July of 2014 (Kern et al., 2014). Items were administered to a sample of 516 Australian male students from a private Anglican boys’ school. Factor analysis confirmed the presence of four of the five factors: positive emotions, engagement, relationships, and accomplishment (PERA). The items reflecting the meaning component (e.g., my life has purpose, and what I do is valuable and worthwhile) loaded on the “relationship” factor. In contrast to the PERMA-P items (three items per domain) in Table 1.1, Kern et al. (2014) investigated a wider array of potential items in their study. The positive emotions factor consisted of 13 positive emotions (cheerful, joyful, energetic, delighted, proud, fearless, calm, happy, excited, active, daring, strong, lively). The engagement factor consisted of six items (When I am reading or learning something new, I often lose track of how much time has passed; I often become completely absorbed in what I am doing; I get so involved in activities that I forget about everything else; When I see beautiful scenery, I enjoy it so much that I lose track of time; How often have you felt interested?; and, How often have you felt alert?). The relationship factor consisted of nine items (My relationships are supportive and rewarding; I actively contribute to the happiness and well-being of others; I generally feel that what I do in my life is valuable and worthwhile; When

HUMAN FLOURISHING

Table 1.1  Models and measure of human flourishing Keyes

Seligman

Diener

Huppert

Hedonic WellBeing

During the past month, how often did you feel (1–5; never to every day)

In general, how often have you felt (1–5; never to every day)

Response options: 1–7; strongly disagree to strongly agree

Response options: 1–7; strongly disagree to strongly agree

Positive Affect

satisfied, happy

joyful, positive, contented, and happy*

Taking all things together, how happy would you say you are? (0–10; extremely unhappy to extremely happy)

Vitality

In the past week, I had a lot of energy (1–4; none or almost none of the time to all or almost all of the time) In the past week, I felt calm and peaceful (1–4; none or almost none of the time to all or almost all of the time)

Interest, Engagement

interested in life

excited and interested in things absorbed in what you are doing

I am engaged and interested in my daily activities

I love learning new things (1–5; strongly agree to strongly disagree)

Response options: same as above

Response options: 1–5; strongly disagree to strongly agree

I am a good person and live a good life

In general, I feel very positive about myself

like you have lost track of time while doing something you enjoy Eudaimonic Well-Being

Response options: same as above

Self-acceptance, Respect, Esteem

that you liked most parts of your personality

Two response options: never to always; not at all to completely, both 0–10

People respect me Competence, Mastery

good at managing the responsibilities of your daily life

How often are you able to handle your responsibilities (0–10; never to always)

I am competent and capable in the activities that are important to me

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Table 1.1  (continued) Models and measure of human flourishing

Warm, Good, Intimate Relationships

Keyes

Seligman

Diener

Huppert

you had warm and trusting relationships with others

To what extent have you been feeling loved

My social relationships are supportive and rewarding

There are people in my life who really care about me

How satisfied are you with your personal relationships To what extent do you receive help and support from others when you need it

Personal Growth

you had experiences that challenged you to grow and become a better person

Accomplishment

How much of the time do you feel you are making progress toward accom­ plishing your goals

Most days I feel a sense of accomplishment from what I do

How often do you achieve the important goals you have set for yourself Resilience

When things go wrong in my life it generally takes me a long time to get back to normal (reverse score)

Autonomy, Confidence

confident to think and express your own ideas and opinions

Purpose in Life

your life has a sense of direction or meaning to it

In general, to what extent do you lead a purposeful and meaningful life In general, to what extent do you feel that what you do in your life is valuable and worthwhile To what extent do you generally feel you have a sense of direction in your life

I lead a purposeful and meaningful life

I generally feel that what I do in my life is valuable and worthwhile

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Table 1.1  (continued) Models and measure of human flourishing Keyes Contribution (to Others or Society)

you had something important to contribute to society

Social Integration

you belonged to a community (like a social group, school, neighborhood, etc.)

Optimism (Self or Society)

our society is a good place, or is becoming a better place, for all people

Acceptance of Others

that people are basically good

Social Coherence

that the way our society works made sense to you

Seligman

Diener

Huppert

I actively contribute to the happiness and well-being of others

I am optimistic about my future

I am always optimistic about my future

*Happiness is measured by asking, “Taking all things together, how happy would you say you are?” (0–10, not at all to completely)

something good happens to me, I have people in my life that I like to share the good news with; I have friends that I really care about; There are people in my life who really care about me; When I have a problem, I have someone who will be there for me; I feel that I am loved; and, I feel that my life has a purpose). Last, accomplishment included six items (I finish whatever I begin; Once I make a plan to get something done, I stick to it; I am a hard worker; I keep at my schoolwork until I am done with it; Most days I feel a sense of accomplishment from what I do; and, During the past two weeks, I have been pleased about completing something that was hard to do). The four PERA scales correlated positively with life satisfaction, hope, gratitude, school engagement, growth mindset, spirituality, physical vitality, and physical activity. The four PERA scales correlated negatively with somatic symptoms, depression, and anxiety. Clearly, scale development is under way, but at early stages. At the moment, only scores on each factor and a total score are reported, with a higher score on the total scale reflecting greater levels of flourishing. Diener and colleagues (Diener et al., 2010) published the first paper on their new flourishing scale. As seen in Table 1.1, the flourishing scale (FS) consists of eight items that measure positive functioning. Although Diener includes a scale of positive emotions and negative emotions, the hedonic aspects of well-being are not used in the assessment of flourishing. The FS has shown excellent reliability and, to some degree, construct validity (the latter being a matter of time for more studies to be done using that measure). Diener et al. (2010), however, correlated the FS with PWB, the latter being a key component of my own (Keyes, 2002; Keyes, Shmotkin, and Ryff, 2002) assessment of flourishing. The correlation of the FS with total PWB (all six scales summed

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together) was either .73 or .78 (it was not clear from the article which correlation referred to FS and PWB or FS and the Basic Needs Satisfaction scale). Huppert (Huppert and So, 2013) used existing measures of well-being and also created items that were intended to be the obverse of mental disorder. She identified ten items for a wellbeing module in the European Social Survey administered to a representative sample of 43,000 Europeans aged 15 years or older. Exploratory factor analysis yielded two factors underlying the ten items shown in Table 1.1. The first factor, called “positive characteristics,” consisted of item 1 (calm and peaceful), item 2 (how happy?), item 3 (a lot of energy), item 5 (positive about myself), item 8 (takes a long time to get back to normal), and item 10 (optimistic about my future). The second factor, called “positive functioning,” included item 4 (love learning new things), item 6 (people love and care for me), item 7 (sense of accomplishment), and item 9 (what I do is valuable and worthwhile). A categorical assessment was devised that required a high level of happiness (item 2: how happy?) combined with high levels on at least four of the remaining five items reflecting positive characteristics and at least three of the four items reflecting positive functioning. Results showed the highest prevalence of flourishing at 40.6% in Denmark compared with the lowest prevalence of 9.3% in Portugal (Huppert and So, 2013). There was no evidence provided for the construct validation the European Social Survey (ESS) scale of flourishing, although the scale has good face validity. The authors correlated it with an item measuring life satisfaction, and performed factor analyses on the flourishing items and included life satisfaction. When life satisfaction was not included, factor analysis yielded the two-factor solution described earlier. When including life satisfaction, Huppert and So (2013) found a three-factor solution that they called positive emotions, positive characteristics, and positive functioning, which is about the equivalent of the Mental Health Continuum “short form” (MHC-SF) questionnaire’s components of emotional, psychological, and social well-being. But, national differences in the rates of a measure said to reflect flourishing and a measure correlated with life satisfaction do not equal the standard of construct validation. In terms of scoring, two models provide categorical assessments of flourishing (Huppert, Keyes) and two used continuous scores without cut-points. Like the MHC model (Keyes, 2002), Huppert (Huppert and So, 2013) provides an algorithm for computing a categorical diagnosis of flourishing that consists of hedonic and eudaimonic facets of well-being. Seligman’s and Diener’s scales are continuous measures of flourishing, with no proposed cut-point or norms for determining flourishing categorically. However, as mentioned earlier, no studies proposing new scales of flourishing have included other measures of flourishing to determine distinctiveness of the news scales and what each contributes to this growing literature that was not measured by existing scales of flourishing. Hone and colleagues (Hone et al., 2014) have published a review of my model of flourishing with three more recently proposed models by Diener (Diener and Biswas-Diener, 2008), Huppert (Huppert and So, 2013), and Seligman (Seligman, 2012). Hone and colleagues (2014) note that all four models use the distinction of hedonic and eudaimonic well-being and share many commonalities in terms of indicators of flourishing. According to Hone et al. (2014), The most striking difference between the four [models] lies in the imbalance between the substantial body of cross-cultural empirical evidence supporting the psychometric properties and utility of Keyes’ model, and the relative paucity of published research behind the three more recently developed models. (p. 72)

With time and continued research, we will come to the best approach to flourishing, which is the one that scientifically best serves people and population mental health. Thus, in what follows, I

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review the literature on flourishing that uses my model, not because I want to be narrow in my review, but because this is the primary body of empirical knowledge on the construct to date.

From subjective well-being to mental health Mental health can be operationalized salutogenically under the rubric of SWB, or individuals’ evaluations of the quality of their lives. When such well-being is measured comprehensively, studies support the tripartite model consisting of emotional, psychological, and social well-being in U.S. adults (Gallagher, Lopez, and Preacher, 2009), college students (Robitschek and Keyes, 2009), and adolescents (Keyes, 2005b), as well as in various cultures. Thus, mental health can be measured in terms of the presence and absence of positive feelings toward one’s life and the presence and absence of positive functioning in various facets of life. The MHC-SF was created to address the problem of the diagnostic threshold and to create a version more efficiently administered in epidemiological surveillance. The MHC-SF derives from the MHC “long form” (MHC-LF) used in the Midlife in the United States (MIDUS) study (Keyes, 2002). While the MHC-LF consisted of 40 items, the MHC-SF consists of 14 of the most prototypical items representing the construct definition for each facet of well-being. Three items (happy, interested in life, and satisfied) indicate emotional well-being, six items measure the six dimensions of Ryff ’s PWB, and five items represent the five dimensions of social well-being. The response option for the short form was changed to measure the frequency (from “never” to “every day”) with which respondents experienced each sign of mental health during the past month, which provides a clear standard for the assessment and a categorization of levels of mental health that is similar to the survey assessment of mental Major Depressive Episode according to DSMcriteria. Hundreds of studies have investigated the correlates and causes of life satisfaction, positive affect, happiness, PWB, and social well-being separately. There are many justifiable reasons for focusing on specific aspects of well-being. However, I have been more interested in the concept of mental “health,” and health, like illness, does not consist solely of either feelings or functioning. It is the merger of feeling good about a life in which individuals are functioning well that constitutes the presence of good mental health (Keyes, 2002). In the same way that depression requires symptoms of an-hedonia, mental health consists of symptoms of hedonia. But, feeling good, in the same way as only feeling sad or losing interest in life, is not sufficient for the diagnosis of a clinical state. Rather, and in the same way that major depression consists of symptoms of mal-functioning, mental health must also consist of symptoms of positive functioning. The continuum of mental health ranged from languishing, to moderate, to flourishing mental health. Individuals with flourishing mental health report feeling at least one measure of hedonic well-being plus six or more of the measures of positive functioning almost every day or every day during the past month. Individuals with languishing mental health, however, report feeling at least one measure of hedonic well-being with six or more measures of positive functioning never or maybe once or twice during the past month. Languishing is the absence of mental health—a state of being mentally unhealthy—which is tantamount to being stuck and stagnant, or feeling empty or that life lacks interest and engagement. Individuals who are neither flourishing nor languishing are diagnosed with moderate mental health. Lamers and colleagues (Lamers et al., 2012) published a study that evaluated the measurement invariance of the MHC-SF using data from a representative sample of 1,932 Dutch adults who completed the MHC-SF at four time points over nine months. This study used item response theory and analytic techniques to examine differential item functioning (DIF) across demographics,

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health indicators, and time points. The results indicated differences in the performance of one item (social well-being) for educational level, one item (social well-being) for sex, and two items (PWB) for age. However, none of the items with differential performance was large enough to affect any mean comparisons. The MHC-SF is highly reliable over time, as there was no DIF on ten of the items across demographics, health indicators, and over several time points. The four items with DIF were low and did not affect mean comparison after appropriate adjustments, and the means and reliabilities of the subscales were consistent over time. The MHC-SF is a highly reliable and valid instrument to measure positive aspects of mental health. Joshanloo and colleagues (Joshanloo et al., 2013) found support for the factor structure (emotional, psychological, and social) and full metric invariance of the MHC-SF across three cultures: the Netherlands, South Africa, and Iran. Metric invariance means that the strength of the relationships between the MHC-SF items and latent factors are equivalent across groups and populations. Therefore, the relationship between the latent factors of the MHC-SF and other concepts (e.g., physical health) can be reliably compared across groups. Put simply, differences in the MHC-SF score and diagnosis between groups can be attributed to the group rather than the fact that the items function differently (i.e., the items mean different things to different people/groups) across groups. Moreover, differences in how the MHC-SF score and diagnosis predicts outcomes such as physical health by group can be attributed to group differences rather than the fact that the items function differently within each group.

From measurement to the two-continua model The importance of measuring mental health in the same way as mental illness cannot be overstated, because it allows us to finally adequately test the hypothesis that mental health and illness belong to two separate continua. Indeed, the argument for promoting mental health is premised on the two-continua model, because good mental health is presumed to belong to a separate continuum from mental illness. Yet, the studies that did exist on the subject have only measured the emotional aspects of life satisfaction or happiness (Greenspoon and Saklofske, 2001; Headey, Kelley, and Wearing, 1993; Huppert and Whittington, 2003; Masse et al., 1998; Suldo and Shaffer, 2008; Veit and Ware, 1983). Numerous studies in mainstream psychology of emotion have shown that positive and negative emotions belong to separate continua (e.g., Bradburn, 1969; Watson and Clark, 1997), but as mentioned earlier, emotional disturbance or emotional vitality do not, in themselves, constitute states of mental illness or mental health. Findings using the long and short forms of the MHC have supported the two-continua model. For example, the latent factors of mental illness and mental health correlated (r = −.53), but only 28.1% of their variance is shared in the MIDUS data (Keyes, 2005a). Recently, this model has also been replicated in a random sample of U.S. adolescents (aged 12 to 18 years) with data from the Panel Study of Income Dynamics’s Child Development Supplement (Keyes, 2006), in Dutch adults (Westerhof and Keyes, 2008, 2010), Setswana-speaking South African adults (Keyes et al., 2008), Polish youth and adults (Karas, Cieciuch, and Keyes, 2014), South Korean adolescents (Lim, 2014), Italian adults (Petrillo et al., 2014), and several other cultures such as China, Iran, Norway, and Australia (Keyes, 2013). There are several important implications of the two-continua model. First, even if all mental illness were cured tomorrow, this would not mean that everyone is mentally healthy or flourishing. This implication flows from the fact that the absence of mental illness does not imply the presence of mental health. In the American adult population between age 25 and 74 years, just over 75% were free of three common mental disorders during the past year (i.e., major depressive

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episode—MDE, panic attacks—PA, and generalized anxiety disorder—GAD). However, while just over three-quarters were free of mental illness during the past year, only about 20% were flourishing. A second implication of the two-continua model is that the presence of mental illness does not imply the absence of mental health. Of the 23% of adults with any mental illness, 14.5% had moderate and 1.5% had flourishing mental health, while only 7% were languishing and had a mental illness. Thus, about 70% of adults with mental illness (i.e., MDE, GAD, or PA) had moderate or flourishing mental health (Keyes, 2002, 2005a, 2007). The absence of mental illness does not mean the presence of mental health, but the presence of mental illness does not imply the absence of some level of good mental health. A third implication of the two-continua model is that level of mental health should differentiate level of functioning among individuals free of, and those with, a mental illness. Put differently, anything less than flourishing mental health is associated with impairment for persons with a mental illness and persons free of a mental illness. Findings consistently show that adults and adolescents who are diagnosed as not flourishing are doing worse in terms of physical health outcomes, healthcare utilization, missed days of work, conduct problems, and psychosocial functioning (Keyes, 2002, 2005a, 2006, 2007). Over all outcomes to date, individuals who are flourishing function better (e.g., fewer missed days of work) than those with moderate mental health, who in turn function better than languishing individuals—and this is true for individuals with a recent mental illness and for individuals free of a recent mental illness. Regardless of whether they have a high level of burnout, medical students who are not flourishing are more likely to seriously consider dropping out of school and to engage in suicide than students who are flourishing (Dyrbye et al., 2012). Dyrbye et al. (2012) also found higher rates of unethical conduct (e.g., said a medical test was pending when it had not been ordered) and fewer humanitarian values (e.g., personally want to provide care to the underserved) among students who were not flourishing. A fourth implication is that changes in positive health may precede and increase risk of illness. Studies have shown that changes in the direction of flourishing lower the future risk of mental illness (Grant, Guille, and Sen, 2013; Keyes, Dhingra, and Simoes, 2010); compared to those flourishing, individuals not flourishing are at elevated risk at all ages between 25 and 74 of ten-year allcause mortality (Keyes and Simoes, 2012) and suicidal ideation, plans, and attempts (Keyes et al., 2012). Moreover, and compared to individuals who are depressed, those not flourishing do not experience any more positive emotion as a result of doing more positive daily activities; however, individuals who are flourishing experience much more positive emotions when they do more of the same positive daily activities (Catalino and Fredrickson, 2011). The problem is that, in the U.S., over half of adolescents, college students, and adults are not flourishing, but at the moment do not have a mental illness. Such individuals who are not flourishing are at increased risk of problems that cannot be fixed and for which treatments, in the case of mental illness, are costly and palliative (Insel and Scolnick, 2006). Worldwide, there is too much mental illness and not enough people are flourishing. Mental illness is a burden to society and so is the absence of flourishing. If we promote flourishing, we have found that you get more of what we want (i.e., good mental health) and less of what we do not want and cannot fix (i.e., mental illness). Yet, most nations continue to argue for providing more treatment as a way to achieve better mental health in their populations (see, e.g., London School of Economics and Political Science Centre for Economic Performance Mental Health Policy Group, 2006). The catch and last implication of the two-continua model is that the things we know can lower the bad (e.g., lowering stress to lower depressive symptoms) do not necessarily increase the good (i.e., levels of positive mental health). Put another way, the genetic and environmental causes

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of mental illness may not be shared with the genetic and environmental causes of flourishing. Indeed, we have found this to be the case (Kendler et al., 2011) and will therefore turn next to the evidence that supports the need for a salutogenetic approach in behavioral genetics.

The need for salutogenetics With the exception of two studies I could locate (Archontaki, Lewis, and Bates, 2013; Gigantesco et al., 2011), both of which considered the six dimensions of Ryff ’s PWB, the literature on the genetic and environmental etiology of well-being has focused on some aspects of emotional well-being—satisfaction with life, happiness, or positive affect. The evidence suggests that a common set of genes underlies both life satisfaction and positive affect (Bartels and Boomsma, 2009; Lykken and Tellegen, 1996; Nes et al., 2006; Røysamb et al., 2002; Stubbe et al., 2005; Tellegen et al., 1988). Broad heritability estimates in these studies have ranged from 36% to 56%. No study found evidence for strong effects of the family environment, and one found support for sex-specific effects, with females reporting a slightly higher (8%) heritability estimate than males (Røysamb et al., 2003). Based on the long form of the MHC measure and on the 670 pairs of same-sex twins from the MIDUS sample of U.S. adult twins, we have found strong support for the heritability of positive mental health and for the two-continua model at the genetic level (Kendler et al., 2011; Keyes, Myers, and Kendler, 2010). First, the common pathway model was the best fitting model to the three phenotypic measures of positive mental health—emotional, psychological, and social well-being. In other words, the three measures of well-being all share a single common source of genetic variance that may be referred to as the latent propensity for good mental health. The latent factor of positive mental health was quite heritable at 72% among the population. We also found no evidence that the magnitude of genetic and unique environmental effects on any kind of well-being differed for males and females (Keyes, Myers, and Kendler, 2010). Next, we investigated whether and how much of the highly heritable construct of positive mental health was shared in common with the genetic variance of the mental disorders measured in the MIDUS. The MIDUS twins received the same measures of well-being and past-year mental illness (i.e., MDE, GAD, PA) as the nationally representative sample of MIDUS adults. A common pathway model was the best fitting model to the three MIDUS measures of mental illness, as these measures of mental illness represent internalizing mental disorders. The latent factor for mental illness was also highly heritable, with 61% attributable to additive genetic effects among the population. We found that exactly 50% of the genetic influences of the common factor of mental health were shared with the genetic influence for the common factor of mental illness. In other words, half of the genetic influences on mental health and on mental illness are independent of each other. Moreover, less than 10% of the environmental influences on the common factor of mental health were shared with the common factor of mental illness, which means that the majority of the environmental causes of mental illness and of mental health are independent of each other. This evidence for the two-continua model has been replicated in recent studies that have focused on depression and the emotional facets of well-being (Franz et al., 2012; Nes et al., 2013). As such, the two-continua model appears to be encoded in our DNA. Because there is some genetic overlap of mental illness and health, our findings suggest that it may be somewhat more difficult to reach high levels of well-being if one inherits strong genetic risk factors for depression or an anxiety disorder. However, high genetic liability to mental illness does not preordain

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individuals to low levels of SWB. Conversely, inheriting a low level of genetic risk for mental illness by no means guarantees that an individual will flourish in life. In short, the two-continua model appears to arise from the fact that as much as half of the genetic propensity for, and nearly all of the environmental causes of, positive mental health are independent of the genetic liability for, and environmental causes of, common internalizing mental disorders. At the phenotypic level, the absence of mental illness does not mean the presence of mental health, because the absence of genetic risk for internalizing mental illness does not mean the presence of high genetic potential for flourishing.

Conclusion There is now substantial empirical evidence that PWB in the broad sense includes dimensions of positive functioning in addition to positive affect (or feeling good about life). Flourishing is the combination of feeling good about a life in which one is also functioning well. This distinction and the merger of feeling good and functioning well is important not merely for conceptual reasons that go back to ancient Greek philosophical distinctions about the good life or factor analytic evidence supporting the distinction. Rather, and until very recently, the fields of positive psychology and behavioral genetics have focused almost exclusively on hedonic forms of well-being. Behavioral genetic evidence suggests that eudaimonic and hedonic measures share a single common source of genetic variance for men and women in the U.S. (Keyes, Myers, and Kendler, 2010). Yet, most studies using flourishing as the standard reveal that over half of youth and adults have not found a way to function well in a life where they feel quite good about life. When asked if they feel “very” or “quite” happy about life, most people in the U.S. and the U.K. (over 90%; Layard, 2011) feel good about life. However, many individuals feel good about life but are not functioning well, and they have worse outcomes in terms of mental illness (Keyes and Annas, 2009) and mortality (Keyes and Simoes, 2012) than those who are flourishing. The same is true of individuals who are functioning well but are not feeling very good about life, who have worse outcomes than those who are flourishing. Americans have always thought of themselves as pragmatists, and I suspect that is true of most people around the globe. That is, we should stick with things that work. That does not ring true when it comes to health care today, especially the mental health of populations around the world. Despite living longer, we are parking more ambulances at the bottom of the cliff because more people are breaking down with mental illness. Two notions get in the way of progress: first, “if it’s not broken, there is no need to fix it”; second, “if it breaks, we can fix it” (or find a fix for it). Anxiety and mood disorders are called “common” disorders because they are prevalent and start early, roughly between the ages of 15 and 26. Such mental disorders are recurrent because risk of a future episode increases with each new episode. Worse, all treatments for mental illness are palliative—they address the symptoms, not the causes. Whether it is physical or mental health, we say we want to “go to a destination called health,” but our roadmap is flawed, overly skewed toward treating and managing illness. Our stubborn beliefs that we can fix or manage problems are getting us nowhere in terms of reducing the amount of mental illness. We are lost, and either we do not know it or we only ask for direction from the experts trained in the broken system of treating our way out of health problems. Science supports the two-continua model where the absence of mental illness does not translate to the presence of mental health. Increasing the good, when it comes to positive mental health, appears to protect against an increase in mental illness. That is, movement away from flourishing appears to increase, and movement toward flourishing appears to decrease, the risk of future

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mental illness. Unless you are diagnosed as ill, health care policy and programs provide little to nothing to help people who are not flourishing. This has to change, and this is where positive psychology and salutogenetics become important, because both seek to understand and promote positive health. There are enough challenges that lie ahead for those who care about mental health and those who care about mental illness. There is too much mental illness and there is too little flourishing. Mental illness is a burden to society and so is the absence of flourishing. We will always need the best available treatment, because prevention of illness will never be infallible. But, if we promote flourishing, we just might get more of what we want (i.e., good health) and less of what we do not want (i.e., illness). We need promotion of positive mental health now more than ever to complement the treatment of mental illness. How do we get to this promised land of the presence of good mental health? We now know that flourishing is as genetic as mental illness. When many people hear this they panic and wonder if they have the genes for flourishing. My response has been, “Don’t panic, genes do not determine the outcome” (see also Chapters 15 and 16). The scientific evidence now suggests that the quality of the environment activates “what’s inside.” Epigenetically, a sick society activates genetic risks; a healthy society should activate the genetic potential to flourish (see Chapter 13 on epigenetics). The genetic potential for flourishing operates largely independently of genetic risk for conditions like depression. This means that the two-continua model is in our DNA; we are wired for two possibilities, and the one that wins is the one that our environment activates. Which one wins? There is a Native American Indian story of an elder explaining to a young boy the two sides of human nature. “Son,” the elder says, “we are made up of two wolves and there is a battle going on inside us all. One is negative and evil, the other is positive and kind.” The son asks, “Which one wins?” The elder replies, “The one you feed.” We are feeding the wolf of illness and death, not health and life. We need different tools in our hands if we want more people to flourish in life. Everything is a nail when all we have are hammers in our hands. Everyone is a patient if nations only have hospitals, doctors, and medical procedures for the care of citizens’ health. We will need to imagine and build a system with a new kind of professional trained in the art and science of protecting against the loss of flourishing and promoting it. To get to that place, we need to support the best minds from around the world and from multiple disciplines to study the salutogenetic side of human nature. The contributors to the chapters in this book are those pioneers whose research is building the case for salutogenetics.

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Butler, J. and Kern, M. L. 2013. The PERMA-Profiler. Available from: [September 29, 2014]. Cantril, H. 1965. The Pattern of Human Concerns. New Brunswick, NJ: Rutgers University Press. Catalino, L. I. and Fredrickson, B. L. 2011. A Tuesday in the life of a flourisher: the role of positive emotional reactivity in optimal mental health. Emotion, 11(4), 938–950. Csikszentmihalyi, M. 1975. Beyond Boredom and Anxiety. San Francisco: Jossey-Bass. David, S. A., Boniwell, I., and Ayers, A. C. (eds) 2013. Oxford Handbook of Happiness. London: Oxford University Press. Diener, E. 1984. Subjective well-being. Psychological Bulletin, 95, 542–575. Diener, E. 1994. Assessing subjective well-being: progress and opportunities. Social Indicators Research, 31(2), 103–157. Diener, E., Emmons, R. A., Larsen, R. J., and Griffin, S. 1985. The satisfaction with life scale. Journal of Personality Assessment, 49(1), 71–75. Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D. W., Oishi, S., and Biswas-Diener, R. 2010. New well-being measures: short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143–156. Diener, E. and Biswas-Diener, R. 2011.Happiness: Unlocking the Mysteries of Psychological Wealth. New York: John Wiley & Sons. Dyrbye, L. N., Harper, W., Moutier, C., Durning, S. J., Power, D. V., Massie, F. S., Eacker, A., et al. 2012. A multi-institutional study exploring the impact of positive mental health on medical students’ professionalism in an era of high burnout. Academic Medicine, 87(8), 1024–1031. Franz, C. E., Panizzon, M. S., Eaves, L. J., Thompson, W., Lyons, M. J., Jacobson, K. C., Tsuang, M., Glatt, S. J., and Kremen, W. S. 2012. Genetic and environmental multidimensionality of well- and illbeing in middle-aged twin men. Behavior Genetics, 42(4), 579–591. Gallagher, M. W., Lopez, S. J., and Preacher, K. J. 2009. The hierarchical structure of well-being. Journal of Personality, 77(4), 1025–1049. Gigantesco, A., Stazi, M. A., Alessandri, G., Medda, E., Tarolla, E., and Fagnani, C. 2011. Psychological well-being (PWB): a natural life outlook? An Italian twin study on heritability of PWB in young adults. Psychological Medicine, 41(12), 2637–2649. Grant, F., Guille, C., and Sen, S. 2013. Well-being and the risk of depression under stress. PLoS ONE, 8(7), e67395. Greenspoon, P. J. and Saklofske, D. H. 2001. Toward an integration of subjective well-being and psychopathology. Social Indicators Research, 54(1), 81–108. Gurin, G., Veroff, J., and Feld, S. 1960. Americans View their Mental Health. New York: Basic Books. Hart, G. D. 1965. Asclepius: God of medicine. Canadian Medical Association Journal, 92(5), 232–236. Headey, B., Kelley, J., and Wearing, A. 1993. Dimensions of mental health: life satisfaction, positive affect, anxiety, and depression. Social Indicators Research, 29(1), 63–82. Hone, L. C., Jarden, A., Schofield, G. M., and Duncan, S. 2014. Measuring flourishing: the impact of operational definitions on the prevalence of high levels of wellbeing. International Journal of Wellbeing, 4(1), 62–90. Huppert, F. A. and So, T. T. 2013. Flourishing across Europe: application of a new conceptual framework for defining well-being. Social Indicators Research, 110(3), 837–861. Huppert, F. A. and Whittington, J. E. 2003. Evidence for the independence of positive and negative wellbeing: implications for quality of life assessment. British Journal of Health Psychology, 8(1), 107–122. Huta, V. and Waterman, A. S. 2013. Eudaimonia and its distinction from hedonia: developing a classification and terminology for understanding conceptual and operational definitions. Journal of Happiness Studies, 14(6), 1–32.

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Insel, T. R. and Scolnick, E. M. 2006. Cure therapeutics and strategic prevention: raising the bar for mental health research. Molecular Psychiatry, 11(1), 11–17. Jahoda, M. 1958. Current Concepts of Positive Mental Health. New York: Basic Books. Joshanloo, M., Wissing, M. P., Khumalo, I. P., and Lamers, S. M. A. 2013. Measurement invariance of the Mental Health Continuum–Short Form (MHC-SF) across three cultural groups. Personality and Individual Differences, 55(7), 755–759. Karaś, D., Cieciuch, J., and Keyes, C. L. M. 2014. The Polish adaptation of the Mental Health Continuum– Short Form (MHC-SF). Personality and Individual Differences, 69, 104–109. Kendler, K. S., Myers, J. M., Maes, H. H., and Keyes, C. L. M. 2011. The relationship between the genetic and environmental influences on common internalizing psychiatric disorders and mental well-being. Behavior Genetics, 41(5), 641–650. Kern, M. L., Waters, L. E., Adler, A., and White, M. A. 2014. A multidimensional approach to measuring well-being in students: application of the PERMA framework. Journal of Positive Psychology (ahead-ofprint, doi: 10.1080/17439760.2014.936962), 1–10. Keyes, C. L. M. 1998. Social well-being. Social Psychology Quarterly, 61(2), 121–140. Keyes, C. L. M. 2002. The mental health continuum: from languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207–222. Keyes, C. L. M. 2005a. Mental illness and/or mental health? Investigating axioms of the complete state model of health. Journal of Consulting and Clinical Psychology, 73(3), 539–548. Keyes, C. L. M. 2005b. The subjective well-being of America’s youth: toward a comprehensive assessment. Adolescent and Family Health, 4, 3–11. Keyes, C. L. M. 2006. Mental health in adolescence: is America’s youth flourishing? American Journal of Orthopsychiatry, 76(3), 395–402. Keyes, C. L. M. 2007. Promoting and protecting mental health as flourishing: a complementary strategy for improving national mental health. American Psychologist, 62(2), 95–108. Keyes, C. L. M. (ed.) 2013. Mental Well-being: International Contributions to the Study of Positive Mental Health. Dodrecht, NL: Springer-Verlag. Keyes, C. L. M. and Annas, J. 2009. Feeling good and functioning well: distinctive concepts in ancient philosophy and contemporary science. Journal of Positive Psychology, 4(3), 197–201. Keyes, C. L. M., Dhingra, S. S., and Simoes, E. J. 2010. Change in level of positive mental health as a predictor of future risk of mental illness. American Journal of Public Health, 100(12), 2366–2371. Keyes, C. L. M., Eisenberg, D., Perry, G. S., Dube, S. R., Kroenke, K., and Dhingra, S. S. 2012. The relationship of level of positive mental health with current mental disorders in predicting suicidal behavior and academic impairment in college students. Journal of American College Health, 60(2), 126–133. Keyes, C. L. M., Myers, J. M., and Kendler, K. S. 2010. The structure of the genetic and environmental influences on mental well-being. American Journal of Public Health, 100(12), 2379–2384. Keyes, C. L. M., Shmotkin, D., and Ryff, C. D. 2002. Optimizing well-being: the empirical encounter of two traditions. Journal of Personality and Social Psychology, 82(6), 1007–1022. Keyes, C. L. M. and Simoes, E. J. 2012. To flourish or not: positive mental health and all cause mortality. American Journal of Public Health, 102(11), 2164–2172. Keyes, C. L. M., Wissing, M., Potgieter, J. P., Temane, M., Kruger, A., and van Rooy, S. 2008. Evaluation of the Mental Health Continuum–Short Form (MHC-SF) in Setswana speaking South Africans. Clinical Psychology and Psychotherapy, 15(3), 181–192. Lamers, S. M. A., Glas, C. A., Westerhof, G. J., and Bohlmeijer, E. T. 2012. Longitudinal evaluation of the Mental Health Continuum–Short Form (MHC-SF). European Journal of Psychological Assessment, 28(4), 290–296. Layard, R. 2011. Happiness: Lessons from a New Science. London, UK: Penguin. Lazarus, R. S. and Folkman, S. 1984. Stress, Appraisal, and Coping. New York: Springer.

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Lim, Y. J. 2014. Psychometric characteristics of the Korean Mental Health Continuum–Short Form in an Adolescent Sample. Journal of Psychoeducational Assessment, 32(4), 356–364. London School of Economics and Political Science Centre for Economic Performance Mental Health Policy Group. 2006. The depression report: a new deal for depression and anxiety disorders. Available from: [June 17, 2014]. Lopez, S. J. and Snyder, C. R. (eds) 2009. Oxford Handbook of Positive Psychology. New York: Oxford University Press. Lykken, D. and Tellegen, A. 1996. Happiness is a stochastic phenomenon. Psychological Science, 7(3), 186–189. Maslow, A. H. 1968. Toward a Psychology of Being. Princeton: Van Nostrand Reinhold. Masse, R., Poulin, C., Dassa, C., Lambert, J., Belair, S., and Battaglini, A. 1998. The structure of mental health higher-order confirmatory factor analyses of psychological distress and wellbeing measures. Social Indicators Research, 45(1–3), 475–504. Nes, R. B., Czajkowski, N. O., Røysamb, E., Ørstavik, R. E., Tambs, K., and Reichborn-Kjennerud, T. 2013. Major depression and life satisfaction: a population-based twin study. Journal of Affective Disorders, 144(1), 51–58. Nes, R. B., Røysamb, E., Tambs, K., Harris, J. R., and Reichborn-Kjennerud, T. 2006. Subjective wellbeing: genetic and environmental contributions to stability and change. Psychological Medicine, 36(7), 1033–1042. Petrillo, G., Capone, V., Caso, D., and Keyes, C. L. M. 2014. The Mental Health Continuum–Short Form (MHC–SF) as a measure of well-being in the Italian context. Social Indicators Research, 121(1), 1–22. Robitschek, C. and Keyes, C. L. M. 2009. The structure of Keyes’ model of mental health and the role of personal growth initiative as a parsimonious predictor. Journal of Counseling Psychology, 56(2), 321–329. Røysamb, E., Harris, J. R., Magnus, P., Vittersø, J., and Tambs, K. 2002. Subjective well-being. Sexspecific effects of genetic and environmental factors. Personality and Individual Differences, 32(2), 211–223. Røysamb, E., Tambs, K., Reichborn-Kjennerud, T., Neale, M. C., and Harris, J. R. 2003. Happiness and health: environmental and genetic contributions to the relationship between subjective well-being, perceived health, and somatic illness. Journal of Personality and Social Psychology, 85(6), 1136–1146. Ryff, C. D. 1989. Happiness is everything, or is it? Explorations on the meaning of psychological well– being. Journal of Personality and Social Psychology, 57(6), 1069–1081. Seligman, M. E. P. 2012. Flourish: A Visionary New Understanding of Happiness and Well-being. New York: Simon and Schuster. Seligman, M. E. P. and Csikszentmihalyi, M. 2000. Positive psychology. An introduction. American Psychologist, 55(1), 5–14. Severin, F. T. 1965. Humanistic Viewpoints in Psychology. New York: McGraw-Hill. Sigerist, H. E. 1941. Medicine and Human Welfare. New Haven, CT: Yale University Press. Stubbe, J. H., Posthuma, D., Boomsma, D. I., and De Geus, E. J. 2005. Heritability of life satisfaction in adults: a twin-family study. Psychological Medicine, 35(11), 1581–1588. Suldo, S. M. and Shaffer, E. J. 2008. Looking beyond psychopathology: the dual-factor model of mental health in youth. School Psychology Review, 37(1), 52–68. Tellegen, A., Lykken, D. T., Bouchard, T. J., Wilcox, K. J., Segal, N. L., and Rich, S. 1988. Personality similarity in twins reared apart and together. Journal of Personality and Social Psychology, 54(6), 1031–1039. Veit, C. T. and Ware, J. E. 1983. The structure of psychological distress and well-being in general populations. Journal of Consulting and Clinical Psychology, 51(5), 730–742.

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Watson, D. and Clark, L. A. 1997. Measurement and mismeasurement of mood: recurrent and emergent issues. Journal of Personality Assessment, 68(2), 267–296. Werner, E. E. and Smith, R. 1977. Kauai’s Children Come of Age. Honolulu: University of Hawaii Press. Westerhof, G. J. and Keyes, C. L. M. 2008. Geestelijke gezondheid is meer dan de afwezigheid van geestelijke ziekte [Mental health is more than the absence of mental illness]. Maandblad Geestelijke Volksgezondheid, 63(10), 808–820. Westerhof, G. J. and Keyes, C. L. M. 2010. Mental illness and mental health: the two continua model across the lifespan. Journal of Adult Development, 17(2), 110–119. World Health Organization. 1948. World Health Organization Constitution, in Basic Documents. Geneva, Switzerland: World Health Organization.

Chapter 2

The genetic and epigenetic essentials of modern humans Robert A. Philibert and Steven R. H. Beach

The genetic and epigenetic essentials of modern humans: an introduction After an epic round-the-world journey in the HMS Beagle, Charles Darwin set down in print a manuscript entitled The Origin of Species, revolutionizing our conceptualization of the way in which the environment influences organisms and their behaviors through natural selection (Darwin, 1860). Remarkably, he did this without knowing of the existence of genes and the mechanisms through which the information encoded in the genome interacts with the environment to effect organismic size, shape, composition, and behavior. Nevertheless, the “evolutionary” theory he described exactly captured many salient phenomena and is generally regarded as one of the most successfully validated hypotheses in the history of science. Today, thanks to a series of recent breathtaking advancements in genetic sequencing technology and bioinformatics, the common understanding of the fundamentals of human genetics is significantly better than only a few years ago and we have easier access to genetic information than ever before. As a consequence of this explosion of information, those interested in genetic and epigenetic contributions to behavior are now well placed to examine the biological underpinnings of the selection processes observed by Darwin, and in addition, to examine how these factors interact to influence human behavior at the individual, group, and societal levels. Not surprisingly, the sheer amount and complexity of the information that is available can be discouraging to potentially interested investigators who may have strong backgrounds in behavioral science but whose biological training may be dated. Still, it should be appreciated that even the most well-rounded geneticists have broad gaps in their mastery of the field due to the breathtaking speed at which the study of genetics and epigenetics is expanding. Therefore, a broad framework through which to organize the vast amounts of available information can be helpful to almost any investigator in furthering their understanding of human genetics. In this chapter, we provide a framework for new investigators that will help them assimilate and understand the genetic and epigenetic factors of potential relevance to their studies. In order to accomplish this, we will first review the fundamentals of the human genome giving particular emphasis to types of genetic variation and the mechanisms through which variation can affect behavioral outcomes. Next, we will introduce the reader to epigenetic variation with a particular focus on the role of DNA methylation and histone modifications in regulating gene transcription. Finally, we will introduce the concept of “genomic tone” and discuss how interactions of the environment manifest their effects on this meta-construct to influence cellular, tissue, and organismic behavior.

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The human genome The fundamentals of the human genome are relatively straightforward. The human genome consists of four nucleotides, which are nitrogenous bases: adenine, guanine, cytosine, and thymine (see Figure 2.1). Each base is coupled to both deoxyribose and between one and three phosphate groups. Two strings of base pairs (bp, i.e., either adenine paired with guanine or cytosine paired with thymine) intertwine to create the classic “double helix” structure that we associate with DNA. DNA is further packaged into chromosomes. There are 22 autosomes (any chromosome that is not a sex chromosome) and one sex chromosome in the average human haploid (i.e., having a single copy of each gene locus) genome (see Figure 2.2). Together these comprise approximately 3.2 billion nucleotide base pairs. However, these strands of nucleic acids make up only half of the nucleic acid/protein assembly referred to as chromosomes. An additional family of proteins referred to as histones, which will be covered later in this chapter, provide a malleable secondary scaffold (or infrastructure) around which DNA can wind, either more tightly or more loosely, and create an erasable “tablet” on which modifications to DNA accessibility can be written. This histone scaffold has a dramatic impact on “gene transcription,” i.e., the extent to which genes are expressed in a given cell and ultimately can have substantial functional or structural impact. The largest chromosome, Chromosome 1, is approximately 250 million bp in length, while the smallest, Chromosome 21 (not 22 or Y) is approximately 48 million bp in length. Since most cells in the human body are diploid (i.e., contain two copies of each gene locus), the typical cell has 46 chromosomes including 22 pairs of each autosome and two sex chromosomes (either two X or both an X and a Y chromosome). Therefore, the average somatic cell has approximately 6,000,000,000 DNA base pairs in total.

Figure 2.1 Graphical illustration of the double helix of deoxyribonucleic acid (DNA) and the four nucleic bases: Adenine, Thymine, Guanine, and Cytosine. DNA is wrapped around histones (illustrated by the balls in the centre) and packaged into chromosomes (top right).

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Figure 2.2 An illustration of the complete set of human chromosomes. The first 22 pairs are numbered by size and are the same in both sexes. These are called autosomes. Females have two copies of the larger sex chromosome called X, males have one X and the smaller Y chromosome. With kind permission of The U.S. National Library of Medicine.

The most fundamental unit of organization of the genome is the gene. Since its original conceptualization as the “fundamental unit of heredity” nearly one hundred years ago, our understanding of what constitutes a “gene” has evolved and is still somewhat fluid. A somewhat more advanced conceptualization of the term would be that of “a unit of DNA that gives rise to a functional ribonucleic acid (RNA) transcript.” However, this definition does not take into account the recent discovery of DNA sequences giving rise to often irregularly delineated regulatory transcripts such as long non-coding RNAs (lncRNA), whose functionality in many cases has not been established. Hence, the organization of the genome can best be described by viewing DNA sequence through the lens of RNA transcription. RNA transcription (i.e., the copying of the DNA template into RNA) and associated “gene expression” (i.e., the translation of RNA into proteins, see Figure 2.3) are of special interest to behavioral researchers given that these processes reflect changes produced by environmental manipulations or experience. Changes in gene expression or transcription, in turn, give rise to downstream outcomes of interest such as changes in developmental trajectories, structural changes in organ systems with biological significance, or changes in cellular environments that alter post-translational products of genes. There are several different mechanisms that influence gene expression, beginning with DNA sequence variation and also including epigenetic modifications, each of which is described in the next section.

Viewing organization via the lens of transcription: the RNA Pol II gene Humans have three RNA polymerases (I, II, and III), which are large enzymatic complexes that transcribe DNA into RNA. Given the fact that the composition of each of these polymerases differs dramatically, it is not surprising that the organization of the genes that they transcribe also differs. RNA polymerase II (Pol II) is the polymerase responsible for transcription of all

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Nuclear membrane

Cell membrane

Nucleus DNA

Transcription

mRNA transcript

G A T U C G C TA A C G

mRNA transported out of nucleus

Ribosome

Growing polypeptide Translation

Protein chain released

Figure 2.3 A graphical illustration of gene transcription and translation. DNA is transcribed into mRNA and transported out of the nucleus. Ribosomes then translate mRNA into protein. This material was originally published as Figure 2.8 on page 34 in Evolution and Genetics for Psychology by Daniel Nettle, and has been reproduced by permission of Oxford University Press: http://ukcatalogue.oup.com/product/9780199231515.do. For permission to reuse this material, please visit http://www.oup.co.uk/academic/rights/permissions.

messenger RNA (mRNA) encoding “genes” whose sequences are then translated by ribosomes into proteins. As such, it is the polymerase with which most biologists are familiar. From the viewpoint of RNA Pol II transcription (see Figure 2.4), a gene consists of a promoter and a variable number of exons (i.e., DNA sequences represented in the mature mRNA transcripts) and introns (i.e., DNA sequences not represented in the mature mRNA). Promoters of RNA Pol II transcribe genes that are essential for the regulation of gene transcription. The core portion of this promoter region, which in the past was referred to as the minimal essential promoter, typically spans ~300 bp. The boundary between the promoter and the first exon (which is the portion of the DNA template that is represented in the mature mRNA) is defined by the transcription start site of RNA transcription. In contrast to bacterial promoters, the regulatory elements of human Pol II transcribed genes can be 1,000 or 100,000 base pairs upstream of the transcription start site. This ability to use larger sections of the genome to regulate gene transcription in humans is a consequence of two properties common to all eukaryotes. The first is the size and complexity of the Pol II transcriptional apparatus (12 proteins) which, when active, binds a ~25 protein co-activator assembly referred to as the Mediator complex (Myer and Young, 1998; Ohlsson, 2010). The second is the ability of DNA sequence to “loop” and form tertiary structures that stabilize both active and inactive transcriptional states. As a consequence, defining the exact boundaries of the promoters of Pol II mediated genes is quite complex and the form of most genes is not known with certainty.

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Typical Gene Transcribed by RNA Pol II CpG Island 5’ UTR TSS Promoter

Gene Body

3’ UTR

Translation start

Figure 2.4 The typical organization for a gene transcribed by RNA Pol II. Exonic sequence is in black while the minimal promoter region is in grey (to the left). The translation start site (TSS) marks the boundary of the non-transcribed promoter to the transcribed portion (body) of the gene. The translation start point marks the start site of ribosomal translation into protein for the future mature mRNA. A stop codon, TAG, UAA, or UGA, marks the endpoint for protein translation and transition into the 3’ UTR. Transcription termination signals exist in last portions of the gene and serve to terminate the association of RNA Pol II with the gene and signal for the addition of the poly A tail which characterizes mRNA.

It is important to note that not all exons in a mature mRNA will provide sequence information for proteins. In fact, the first exons of almost all genes contain sequences, referred to as the 5 prime (5’) untranslated region (UTR) that regulates the number of times a particular mRNA can be copied into protein. Accordingly, the first exon is often important in regulating transcriptional activity. Additionally, the sequences from the last exons of a gene are not typically translated into protein but instead code for the 3’ UTR for the mature mRNA whose function is to control the stability (protect against degradation) and targeting (the portion of the cell to which the mRNA will be transported to be translated) of the mRNA. The human genome contains about approximately twenty-two thousand protein-coding genes, with the average length between the foremost promoter regulatory elements and the most distal exons being about thirty thousand base pairs (30 kbp). But some genes, such as dystrophan, can span millions of base pairs. The number of exons is highly flexible and can range from one to more than three hundred (e.g., TTN, which codes for Titin, a muscle protein). As a rule, the larger the gene, the larger the size of the introns. The minimum size for an intron is about 50 bp, with some introns spanning more than 100 kb (Wang and Yu, 2011).

The organization of Pol I and Pol III transcribed genes In contrast to the diversity of Pol II transcribed genes, Pol I and Pol III transcribed genes are easy to conceptualize. In contrast to the diverse set of transcripts from more than twenty-two thousand Pol II transcribed genes, Pol I produces only one polycistronic (multiunit) “pre-RNA” transcript that encodes the sequence for the 18S, 5.8S, and 28S RNA subunits of the ribosome, the machinery that translates mRNA transcripts into protein (Russell and Zomerdijk, 2006). Unlike most Pol II transcribed genes, these Pol I transcribed genes are largely copies of one another, do not contain introns, and are arranged in clusters in a head-to-tail fashion. However, the genome contains hundreds of copies of these genes, with clusters of ribosomal transcriptional tracks being found on Chromosomes 13, 14, 15, and 22. Similarly, Pol III also transcribes ribosomal RNA including the 5S ribosomal RNA and transfer RNA (tRNA). In addition, it transcribes a number of other small RNAs including certainly

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recently discovered microRNA genes. As such, the number of unique genes transcribed by Pol III appears to be considerably higher than that for Pol I (>500), with the number continuing to grow as our understanding of the transcriptome increases (Canella et al., 2010). Furthermore, unlike genes transcribed by Pol II, genes transcribed by Pol III typically do not have upstream promoter regions and instead rely on downstream sequences (i.e., sequences internal to the transcript itself) to regulate transcription.

Intergenic and other non-coding regions of the genome Before the turn of the century, the classical view of the human genome held that genes were islands in a sea of non-transcribed or “junk” DNA. This viewpoint has dramatically changed in the past decade with high-throughput RNA sequencing studies demonstrating that the vast majority of the space between genes or “intergenic” regions is either transcribed or involved in regulating the expression of coding regions. In fact, recent studies have demonstrated that up to 75% of the genome may be transcribed at some point of development and that large portions of the nontranscribed genes may bind to “transcription factors” which regulate the level of transcription up or down (Djebali et al., 2012; Gerstein et al., 2012). Taken together, the current data suggest a vast interactive, interwoven network of gene–gene interactions. Although mapping of gene-networks is well under way, it can be expected that over the next several decades, advances in bioinformatics will further sculpt our understanding of regulatory interplay in these complex systems.

Telomeres The ends and mid-sections of chromosomes have distinctive functions not related to RNA polymerase function and possess a distinct sequence that differentiates them from the rest of the genome. The ends of the chromosome, which are referred to as “telomeres,” are essential for chromosomal stability (Lu et al., 2013). By and large, these areas are rich in repetitive DNA elements, such as 6 bp telomere repeats discussed later, which compensate for the loss of chromosomal ends during DNA replication. With some important exceptions, telomeres are also relatively devoid of gene content. However, because of the need to protect against viral incursions and dysfunctional cell proliferation caused by fusion to other chromosomes, free ends of DNA are not tolerated. Instead, the telomeres are bundled by a complex of proteins referred to as “shelterin” and thereby protected against degradation.

Types of DNA sequence variation At all levels of the human genome, sequence differences or “polymorphisms” between individuals are evident. The easiest ways to classify these differences is by their scale. In this system, the largest of these differences are referred to as copy number variation (CNV). One of the most interesting observations revealed by the human genome project was the discovery that the length of the genome between individuals can vary by up to 100 million base pairs. The largest portion of this variation is caused by CNVs. CNVs are regions throughout the genome as small as 1 kb and as large as several hundred kb that are duplicated or deleted. Sometimes this variation is associated with disease, such as autism and schizophrenia (Levinson et al., 2011). In fact, up to 5% of all vulnerability to schizophrenia may be secondary to CNV. Alternatively, CNV can have distinct positive and negative effects on traits of evolutionary significance (Almal and Padh, 2012). Often the effect of CNV appears to be without currently observable consequence. However, over the

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next several decades, it is likely that mass sequencing combined with improved bioinformatics will elucidate the more subtle effects of this “silent” variation. Linked closely to copy number variation is the phenomenon referred to as “retroposon variation.” It is now well understood that much of the human genome is of viral origin, resulting from insertion of DNA from viruses into the DNA of our distant ancestors. In humans, these remnants of viruses are recognized as long interspersed nuclear elements (LINES) and alu repeats, which are approximately 6 kb and 350 bp in length, respectively (Hancks and Kazazian Jr, 2012). Approximately five hundred thousand copies of LINES and over 1 million copies of alu elements can be found interspersed throughout the genome. Often these elements have been co-opted to serve in regulatory capacities for other portions of the genome. Under certain circumstances, some of these elements can be transcribed and give rise to functional proteins. In fact, the activation of LINES elements is sometimes followed by the copying and insertion of that LINE into new portions of the genome. In several cases, this has been associated with disease. A third large-scale genetic variation is that of chromosomal inversion (Martínez-Fundichely et al., 2014). Though not as common as CNV, inversions can have large effects by invoking ­position-sensitive changes on gene transcription. Surprisingly, secondary to our limited ability to detect sequence inversion, the exact frequency and impact of this phenomenon is not well understood. However, over two hundred inversions found to date and several large-scale inversions of 18 p have been observed and they are sometimes associated with illness (Weischenfeldt et al., 2013). On scales less than 500 bp, three distinct classes of common variation are evident: minisatellite, microsatellite, and single nucleotide polymorphisms.

Genetic variation: minisatellites Minisatellite repeats, which are also referred to as variable number tandem repeats (VNTR), consist of blocks of repetitive DNA whose core unit is between 10 and 100 base pairs (see Figure 2.5). The exact number of these repeats in the human genome is not known, but hundreds have been characterized. Several of these VNTR elements are perhaps the best characterized variation in human genome and have high relevance to behavioral studies. For example, the serotonin transporter promoter polymorphism referred to as 5-HTTLPR is a VNTR that consists of between 14 and 20 repeats of a ~22 bp sequence (Vijayendran et al., 2012). Similarly, another well-studied VNTR is the dopamine type 4 receptor (DRD4) exon 7 variant that consists of between two and ten repeats of 48 bp (16 amino acids) unit. Third, the promoter for monoamine oxidase A (MAOA) contains two linked variable nucleotide repeats, one of 30 bp and one of 10 bp, the latter of which appears to be more critical for gene expression (Philibert et al., 2011). Therefore, although relatively uncommon, this class of sequence variation is of critical interest to behavioral scientists.

Genetic variation: microsatellites Microsatellite (or short tandem repeat) variation is much more common. Microsatellites are repetitive blocks of repeats whose core unit varies between 2 and 9 bp (see Figure 2.5). Dinucleotide repeats, usually (CA)N or (CT)N, are the most common. These repeats, which are perhaps the most polymorphic in the genome, were used during the early phase of human genome mapping as markers for association analyses and map construction. Dinucleotide repeats which occur in the 3’ UTR of genes are particularly relevant to those seeking to understand the relationship between sequence variation and gene expression because of their ability to bind proteins that can

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stabilize RNA transcripts and thereby protect them from degradation. However, given the abilities of dinucleotide repeats to expand or bind transcription factors, any intronic or promoter-related dinucleotide repeat is of potential interest. Trinucleotide repeats, in particular (CAG)N, have long captured the attention of biologically minded behavioral scientists. Huntington’s chorea is caused by a CAG repeat expansion in exon 1 of the huntington gene and at least 11 forms of spinocerebellar ataxia are caused by CAG repeat expansion (Durr, 2010). However, other types of trinucleotide repeat expansion also are relevant to behavioral scientists. Fragile X syndrome is caused by the expansion of a CGG repeat in the 5’ UTR of the FMR1 mRNA transcript. Friedreich’s ataxia is caused by the expansion of an intronic GAA repeat. The most common form of Myotonic dystrophy is caused by the expansion of a CTG repeat in the 3’ UTR of dystrophia myotonic protein kinase transcript. Tetranucleotide repeats (e.g., (ATCA)N) are still used in some applications because of their potential to be highly polymorphic and the ease with which they can be genotyped. However, occasionally, they also can be causes of disease such as a less common form of myotonic dystrophy (CCTG)N whose copy number can exceed 11,000 repeats. Finally, other classes of microsatellite repeats are also worthy of consideration. Pentameric repeats, e.g., (TAGAA)N, are also occasional causes of illness. However, hexameric repeats are of perhaps greater interest to behavioral psychologists because the presence of thousands of telomeric TTAGGG repeats is essential for the stability of chromosomes. These repeats are necessary for the health of the cell because DNA polymerases, which are responsible for copying DNA replicating cells, need a template in order to copy DNA and thereby cannot copy the ends of chromosomes. Ordinarily, this would lead to the gradual shortening of chromosomes. To compensate for this, large numbers of TTAGGG units are enzymatically added to the ends of chromosomes (telomeres) by telomerase. This enzymatic activity is absolutely critical to cellular health with loss of this repetitive telomeric cap or telomerase activity is associated with cell senescence, aging, and cancer.

Genetic variation: Single Nucleotide Polymorphisms (SNPs) Thirty years ago, SNPs, which are defined as a substitution of one nucleotide for another nucleotide at the same position of gene (e.g., GAATG vs GAAAG; see Figure 2.5), were thought to be relatively infrequent in the human genome. However, the advent of the human genome project has been accompanied by the revelation that the genome contains up to 10 million common (minor allele frequency >5%) SNP variants. Over the past decade, the use of SNP genotyping technologies has rapidly progressed and found usefulness in a wide variety of commercial applications such as ancestry determination, paternity testing, and forensic analyses. However, by and large, there has been a revolution in the number of these SNPs associated with vulnerability to various illnesses. Genome-wide association studies (GWAS) analyses for complex traits such as type 2 diabetes mellitus, height, and weight have shown that hundreds of common individual SNP variants contribute small to modest relative risk of or protection against a given illness or trait. Less common SNP variation (35,000 cases compared with a maximum of ~17,000 cases for any other disorder). Importantly, sample size is only one of several factors to be considered in GWAS. Another significant influence is the distribution of the phenotype in the population, with studies focused on less frequent phenotypes having generally more power to detect SNPs (Wray et al., 2012). Higher ratings of PWB are likely to be more prevalent in the population than most psychiatric disorders. As a consequence, the sample size to detect SNPs associated with well-being should be bigger than any existing GWAS of psychiatric illness.

Genome-wide association studies and well-being To accomplish such an enormous sample size, a large genome-wide association meta-analysis on PWB is currently under way within the Social Sciences Genetic Association Consortium (SSGAC) (). This study consists of at least 43 cohorts and will include ~200.000 individuals, which will yield a power of approximately 50% to detect SNPs with a minor allele frequency (MAF) of 3%. After the discovery and replication phase, meta-analyses on positive affect and life satisfaction will be conducted in order to explain individual differences in PWB caused by genetic factors.

The path between genes and behavior As already mentioned, quantitative behavioral genetic research consistently reports that heritable factors contribute substantially to the population variance in well-being (Chapter 5 and

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Bartels, 2015), but these studies do not give us insight into the specific genomic regions that are responsible for these differences. Molecular genetic research, which aims at identifying the specific genes responsible for the heritability estimates of well-being, are showing mixed results. However, although present efforts are somewhat disappointing, we should not forget that molecular research pertaining to PWB is still in its infancy. In summary, results of significant heritability estimates suggest that DNA variation is involved in behavioral variation and we (as behavioral genetics) need to puzzle out which genomic regions are associated with these complex traits in order to understand the molecular mechanisms, which requires not only the identification of genomic regions associated with well-being but also investigation of how these regions affect complex behavior.

Gene expression A possible way to bridge this gap is the study of gene expression as a mechanism underlying genetics associations with complex traits (see Chapters 2 and 13 for more information). The genes that are expressed or transcribed from genomic DNA—sometimes referred to as the transcriptome— represent the major determinants of cellular phenotype and function. Transcription of genomic DNA to produce messenger RNA (mRNA) is the first step in the process of protein synthesis, and differences in gene expression are responsible for both morphological and phenotypic differences as well as being indicative of cellular responses to environmental stimuli and perturbation. Unlike the genome, the transcriptome is highly dynamic and changes rapidly and dramatically in response to perturbations, or even during normal cellular events such as DNA replication and cell division (Cho et al., 1998; Spellman et al., 1998). With the development of micro-arrays, expression of all genes in the genome can be assessed simultaneously using RNA transcripts as an outcome measurement.

Gene expression and well-being To date, there is only one gene expression study involving PWB (Fredrickson et al., 2013). In their study, the conserved transcriptional response to adversity (CTRA) gene expression profile was used as a molecular reference space in which to map potentially distinct biological effects of hedonic and eudaimonic well-being. Hedonic well-being represents the sum of an individual’s positive affective experience (Ryff, Singer, and Dienberg Love, 2004), whereas eudaimonic wellbeing results from striving toward meaning and a noble purpose beyond simple self-gratification (Ryan and Deci, 2001). Although eudaimonic well-being is sometimes explained as a “deeper” form of well-being, these two forms are highly correlated (r = .70) and tend to reciprocally influence one another (Keyes, Shmotkin, and Ryff, 2002; Waterman, 1993). CTRA is characterized by an increased expression of genes involved in inflammation (e.g., proinflammatory cytokines such as IL1B, IL6, IL8, and TNF), whereby genes involved in type I interferon antiviral responses (e.g., IF1-, OAS-, and MX-family genes) and IgG1 antibody synthesis (e.g., IGJ) are down-­regulated. Activation of the CTRA response is associated with several pathological phenotypes, such as inflammatory mediated cardiovascular and neurodegenerative diseases, and impaired host resistance to viral infections (Cole et al., 2007; Irwin and Cole, 2011). Differential expression of the leukocyte CTRA was assessed in genome-wide transcriptional profiles of peripheral blood mononuclear cells (PBMCs) in 80 participants for whom hedonic and eudaimonic well-being was measured using the eight-item Short Flourishing Scale (Diener et al., 2009). It was shown that hedonic and eudaimonic well-being, although strongly correlated with each other, have divergent gene transcriptional correlates in human immune cells. Eudaimonic

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well-being was associated with decreased expression of the CTRA transcriptome (e.g., less antiviral responses and antibody synthesis), whereas CTRA gene expression was significantly up-regulated in association with increasing levels of hedonic well-being (e.g., increase in proinflammatory cytokines). Although high levels of hedonic and eudaimonic well-being seem to have a divergent geneexpression profile in human immune cells, these results should be interpreted with caution and are subject to debate (Brown et al., 2014; Cole and Fredrickson, 2013, 2014; Coyne, 2013). The discussion focuses primarily on the fact that the complex analyses used in the study depend entirely on distinguishing the highly correlated constructs hedonic and eudaimonic well-being with a self-report measurement conducted in a small sample. The question that arises, therefore, is whether proper psychometric conditions could be met to see these two philosophic definitions of well-being as two independent constructs.

Strengths, weaknesses, and opportunities Strengths and weaknesses One of the most exciting directions for genetic research in PWB involves harnessing the power of molecular genetics to identify the specific genes responsible for the, consistently reported, influence of genetics on well-being outcomes. The two major strategies for identifying genes associated with PWB are allelic association and linkage studies. Allelic association has a rather simple design and calculates the correlation between an allele and well-being. Linkage is—like association within families—tracing the co-inheritance of a DNA marker and well-being within families. A great strength of the linkage approach is that it systematically scans the genome with only a few hundred markers in order to test for violations of Mendel’s law of independent assortment between well-being and a DNA marker. However, in most complex traits, like PWB, it is likely that many genes with small effect sizes are involved. Therefore, using linkage is like using the Hubble telescope: it can scan planets in our galaxy (large QTL effects), but will go out of focus when trying to detect the Apollo landing sites on the moon (small QTLs). Furthermore, linkage studies are difficult to replicate, which was demonstrated in a review study by Altmüller et al. (2001), who found that many studies of the same disease were often showing inconsistency in their results. In addition, if linkage can be compared to the Hubble telescope, candidate gene studies are more like a microscope with theoretically enough power to detect genes with small effect sizes. However, a major drawback of this approach is that these studies require the ability to predict functional candidate genes a priori—knowledge which is still limited despite our increasing understanding of biochemical pathways and the etiology of quantitative traits. For instance, in existing candidate studies on well-being (5-HTTLPR and MAOA), the candidate gene of interest were chosen based on biological pathways that—at best—are only indirectly linked to well-being. Furthermore, just as with linkage, candidate genes studies are extremely difficult to replicate (Tabor, Risch, and Myers, 2002). Most likely, the failure of replication is due to the fact that the largest effect sizes of genes involved in complex traits are still much smaller than initially expected. In other words, the existing candidate gene studies on well-being were most likely underpowered to defect any genetic effects in the first place. In contrast, GWAS are hypothesis free and provide a relatively unbiased screening of the human genome, thereby enabling the discovery of previously unsuspected genetic variants. At time of writing, the upcoming GWAS (~150 K) on PWB is still in the pipeline, but it will be exciting to see the first results in the near future, which will hopefully bring us a step closer to the identification of genes associated with PWB.

MOLECULAR GENETICS OF WELL-BEING

Opportunities As mentioned, the field of molecular genetics and PWB is still in its infancy, meaning that there are many opportunities left to unravel the genetic architecture of this increasingly popular topic. An interesting approach that recently generated much interest is polygenic score analysis. Polygenic scores are created based on the weighted sum of multiple alleles associated with the outcome of interest in a discovery sample. It is then tested whether the same score predicts the outcome in an independent replication sample (Dudbridge, 2013). There is increasing evidence that a substantial proportion of the phenotypic variation might be better explained by a combination of multiple genetic variants rather than individual variants that often fail to reach significance in large GWAS. For instance, significant associations between polygenic scores and well-being would imply that a genetic signal is indeed present among the included markers. It would therefore be very interesting to construct such a polygenic risk score from the forthcoming GWAS meta-analysis on PWB. Furthermore, and as mentioned at the very beginning of this chapter, PWB is influenced by both genetic and environmental factors (see also Chapter 15). Despite a rich epidemiologic literature that is focused either on environmental and social influences or genetic factors, few studies to date have examined the dynamic interplay between genetics and environment in the prediction of PWB. It would therefore be very interesting to (1) investigate whether genetic factors (based on promising SNPs of the forthcoming GWAS on PWB) predict specific preferences for particular (social) environment (gene–environment correlation), and (2) whether genetic factors predict different degrees of environmental sensitivity, including the sensitivity to positive exposures (see Chapters 11 and 12 for a detailed description of gene–environment interaction). To conclude, the field of molecular genetics is a scientific field that is constantly in development and changes very rapidly. Technical advances will ensure that we will be increasingly able to explain genetic variance that is associated with PWB.

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Chapter 11

Molecular genetics of resilience Sierra Clifford and Kathryn Lemery-Chalfant

Molecular genetics of resilience: an introduction Resilience has been defined in many ways, but fundamentally, it refers to positive adaptation in the face of adversity. Such adaptation can take many forms, including the capacity to “bounce back” or recover following an acute stress or trauma, the maintenance of positive functioning under prolonged stress, or even the capacity for personal growth as a consequence of adversity (Masten, 2011). Resilience is not simply the absence of psychopathology or other negative outcomes, nor does it refer to positive outcomes in the absence of risk. Additionally, resilience does not mean that one is unaffected by adversity, or experiences no negative effects of trauma. Often individuals will show both adaptive and maladaptive responses to adversity (Luthar, 2006). For instance, although a resilient individual might bounce back from a traumatic event without ever developing symptoms of post-traumatic stress disorder (PTSD), a person who develops PTSD but—at the same time—maintains social connections and actively pursues valued goals is also showing resilience. Thus, rather than an “either/or” perspective in which an individual is either resilient or not, resilience research necessarily focuses on the complex, holistic nature of individual strengths and vulnerabilities. We define resilience not as a trait or an outcome but as a developmental process, unfolding across the lifespan and dependent on individual history and context (Masten, 2011). Just as pathology can be the product of a succession of maladaptive responses to risky environments, resilience in the present moment may rest on a history of positive functioning, including successful coping with previous challenges (i.e., “steeling effects”; Rutter, 1987). For instance, a positive early environment may provide the physiological and psychological foundation for successfully adapting to later stressors, and the experience of overcoming previous stressors may provide psychological resources (e.g., coping skills) which contribute to future adaptation to adversity (Zimmerman et al., 2013). At the same time, developmental transitions (e.g., puberty, the transition to work or college) bring new risks, challenges, and opportunities, and with these changes, the opportunity to shift from a trajectory of risk to a trajectory of resilience or vice versa (Masten, 2011). Thus, resilience is the product of a series of transactions between the person and the environment over time, spanning genetic, biological, and social levels (Feder, Nestler, and Charney, 2009). Although resilience is not a trait, and multiple pathways toward and away from resilient functioning exist, certain individual and social factors are consistently found to predict resilience, including but not limited to effective biological stress response, executive functioning, and social support and affiliation (Luthar, 2006). Stress response systems such as the sympathetic adrenal medullary pathway (“fight” or “flight”) and the hypothalamic-pituitary-adrenal (HPA) axis enable the mobilization of resources to deal with challenges in the environment, but chronic activation of these systems puts individuals at risk of long-term neurobiological dysregulation and mental and physical health consequences (Juster et al., 2011). Efficient stress recovery protects against the

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deleterious effects of prolonged activation of one or both of these systems. Executive functions such as attentional shifting and focusing, inhibitory control, and working memory provide the foundation for the effortful modulation of emotion and behavior (Zhou, Chen, and Main, 2012), which is associated with behavioral and academic resilience in high-stress conditions (Luthar, 2006). Finally, positive social relationships with adults and competent peers not only provide emotional and material support, but also an opportunity to acquire skills and adaptive behaviors not learnt in adverse home environments. Thus, individual differences exist in response to adverse environments, and genetic influences on protective factors (factors protecting against the negative effects of risk exposure) and promotive factors (factors promoting positive outcomes regardless of risk) are likely to be one component underlying the ability of some individuals to withstand or recover from even severe adversity. Genetic influences on protective or promotive factors may have direct effects or interact with environmental factors. At the neurobiological level, genes may influence the functioning of systems and mechanisms promoting resilience, such as more efficient stress response and recovery, or neural circuitry implicated in the top–down regulation of emotion and behavior (Feder, Nestler, and Charney, 2009). For instance, catechol-O-methyltransferase (COMT) is an enzyme important for the degradation of dopamine in the prefrontal cortex (Witte and Floel, 2012). A common valine (Val) to methionine (Met) substitution in the rs4680 SNP on the COMT gene leads to functional differences in enzyme activity, hypothesized to result in a trade-off between cognitive efficiency (Met) and cognitive flexibility (Val). The low-activity Met variant is associated with advantages in attentional focusing and working memory, core skills underlying a range of executive and regulatory processes (Zhou, Chen, and Main, 2012). In contrast, the high-activity Val variant may promote increased capacity to shift attention and update information in working memory (Witte and Floel, 2012), skills which are important for disengaging from stressors and flexibly shifting behavioral strategies and likely protecting against internalizing (anxiety and depression) problems (Degnan and Fox, 2007). Alternately, some genetic influences may be socially mediated through gene–environment correlation (the non-independence of genes and environments; see Chapters 3 and 15). For example, polymorphisms in the oxytocin receptor gene (OXTR) have been related to the ability to send and receive social cues (Ebstein et al., 2012), potentially contributing to resilience by evoking social support from others. At the same time, few traits are universally positive (i.e., adaptive) or negative (i.e., maladaptive). Genetic variability allows a species to maintain the flexibility to adapt to changing environmental circumstances, and even on an individual level, the extent to which a trait is adaptive often depends on the social opportunities, expectations, and restrictions of the environment. Accumulating evidence from medical, psychological, and animal research suggests that gene– environment interaction (GXE) is widespread and likely to play an important role in physical and mental health and functioning, including individuals’ ability to withstand and adapt to stress (Rutter, Moffitt, and Caspi, 2006). GXE refers to any situation in which genetic effects vary depending on the environment, or alternately, in which genetically different individuals respond to the same environment in different ways. Although multiple models of gene–environment interaction exist (Shanahan and Hofer, 2005), molecular genetic research to date has primarily operated under a diathesis–stress framework (see Figure 11.1), with many putative “risk” polymorphisms only associated with greater risk for negative outcomes such as depression or antisocial behavior in adverse environments (see Uher, 2014 for review). Without exposure to an environmental trigger, the genetic diathesis (i.e., vulnerability) will never manifest. A more recent model of interaction, differential susceptibility, is distinct from diathesis–stress in that it describes an interaction in which “plastic” genetic polymorphisms (and associated physiological and psychological traits)

Molecular genetics of resilience

Depression

Genetic Diathesis No Diathesis

No Risk Exposure

Risk Exposure

Figure 11.1 The diathesis–stress model of gene–environment interaction: genetic susceptibility for a negative outcome (e.g., depression) only manifests under exposure to adversity. In the absence of environmental risk, no difference is evident between genetically “vulnerable” and “resilient” individuals.

confer sensitivity to both negative and positive environmental influences, such that the same individuals who are most at risk in adverse conditions also have the greatest capacity to benefit when environments are supportive (Belsky and Pluess, 2009). Differential susceptibility is consistent with the principle that genetic variation is not “good” or “bad” but dependent on the context, and offers one explanation for why putative “risk” variants are maintained at high levels in the population (see Chapter 12). The most important message of molecular genetic research on psychological traits to date is that neither genes nor environments are deterministic, suggesting that genetic risk for many psychological disorders can be offset by positive environmental factors, and that genetic factors can be one source of protection against the effects of environmental stress (Rutter, Moffitt, and Caspi, 2006). This message is relevant to both behavior genetic and environmentally focused research, and has contributed to a more nuanced understanding of gene–environment interplay in psychology as a whole. However, molecular genetics is a relatively new field, with advances in theory and methodology still ongoing, and certain methodological shortcomings must be taken into account. First, false-positive findings are a serious problem in the molecular genetic literature. This is due to multiple factors, including underpowered research, small genetic effect sizes, heterogeneous samples, and multiple testing of genetic associations (Rutter, Moffitt, and Caspi, 2006), as well as poor measurement of trait-relevant environmental factors (Uher and McGuffin, 2010). Consequently, findings that are not replicated or rely on small samples may lay the groundwork for future research, but must be taken with caution. There is also increasing awareness that genes often operate differently in males and females and across developmental periods, with recent imaging genetics findings suggesting that age should be included as a second moderator in GXE models (Schmitt et al., 2014). Finally, phenotypes of interest to psychologists are often not only polygenic (i.e., the combined function of many individual genes), but also genetically and environmentally heterogeneous, contributing to inconsistent genetic associations (Levy and Ebstein, 2009). More recent research has worked to address all these issues, with the following recommendations for GXE research: (1) recruitment of large samples with sufficient power to detect small effects; (2) the use of general population samples (as opposed to case-control designs) containing an adequate range of both risk exposure and outcome; and (3) research based on plausible biological pathways linking genes to behavior (Rutter, Moffitt, and Caspi, 2006). To better capture gene–environment interplay given complex, multi-determined phenotypes, research has increasingly taken an endophenotype approach, attempting to isolate more proximal,

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heritable processes that exist along the pathway from genes to phenotypes (Gottesman and Gould, 2003). These endophenotypes (or intermediate phenotypes) are measurable traits that may be of neurophysiological, endocrine, cognitive, or even psychological nature (e.g., temperament and personality), and are thought to be more closely linked to genetic influence. However, although biological traits such as cortisol stress response and recovery (Zannas and Binder, 2014) or amygdala reactivity to threat and novelty (Murphy et al., 2013) do represent more proximal links in the chain between genes and behavior, they are still polygenic and subject to environmental influence. Nevertheless, relations between genes and neurophysiological endophenotypes are sometimes more consistent than relations with diagnosed psychopathology (e.g., Munafò, Brown, and Hariri, 2008, but see Murphy et al., 2013), and an endophenotype approach may clarify mechanisms linking genes and psychological phenotypes. Consequently, rather than conceptualizing single genetic variants as conferring greater or lesser resilience across all contexts, it may be more valuable to focus on neural or physiological endophenotypes, and elucidate the pathways through which such processes may be associated with specific aspects of resilience within particular environments. Finally, an understanding of genetic influences on resilience requires attention not only to the absence of negative outcomes, but also to positive outcomes. Although severe adversity such as child abuse is not expected to promote positive outcomes, individuals exposed to adversity also vary in aspects of positive functioning such as social or cognitive competence (Luthar, 2006). However, with some exceptions (e.g., Ciccetti and Rogosch, 2012), molecular genetic research has largely operationalized resilience as the absence of psychopathology (e.g., Feder, Nestler, and Charney, 2009) with no consideration of positive functioning. Although simplistic conceptions of adaptation and maladaptation have come under critique (Belsky and Pluess, 2013), and emphasis on positive outcomes under supportive conditions according to the differential susceptibility and vantage sensitivity frameworks is emerging (see Chapter 12), it will be important for future research aimed at understanding genetic influences on resilience processes to assess a full range of outcomes, including positive adaptation to stress.

Review of empirical evidence The molecular genetic literature contains many examples of gene–environment interplay, across a diverse array of genes and outcomes, and this review is not intended to be exhaustive. Instead, we focus on selected genes (SLC6A4, BDNF, CRHR1, FKBP5, NPY, and OXTR) which are likely to be of interest to the study of resilience due to their involvement in biological systems, and their prominence in the literature.

The serotonin transporter gene-linked polymorphic region (5-HTTLPR) The gene coding for the serotonin transporter (SLC6A4) contains a VNTR polymorphism in the promoter region (referred to as 5-HTTLPR), which is among the most widely studied polymorphisms in the molecular genetic literature. 5-HTTLPR contains two common variants, the 14-repeat short (S) variant and the 16-repeat long (L) variant. Relative to the L variant, the S variant displays reduced transcriptional efficiency, impaired inhibitor binding, and lower serotonin reuptake in vitro (Lesch et al., 1996), although evidence for differences in vivo is mixed (Jonassen and Landrø, 2014). Additionally, 5-HTTLPR contains an A to G SNP (rs25531) which reduces the transcriptional efficiency of the LG variant to a level similar to that of the S variant, and multiple other SNPs within 5-HTTLPR may also influence expression (Jonassen and Landrø, 2014). The low-activity S variant is associated with risk for internalizing disorders in the context of early or recent stress (Caspi et al., 2010), although whether this interaction represents a true

Molecular genetics of resilience

finding is contentious (Karg et al., 2011; Munafò et al., 2009). Evidence is stronger at the level of endophenotypes, including heightened amygdala reactivity (Munafò, Brown, and Hariri, 2008; Murphy et al., 2013), and cortisol stress response (Miller et al., 2013). Taken together, these findings suggest the S variant contributes to elevated stress reactivity (Caspi et al., 2010). Given such heightened vulnerability, the S variant is often labeled a risk variant, and conversely, the L variant a resilience variant (e.g., Feder, Nestler, and Charney, 2009). Indeed, L homozygotes self-report higher ability to maintain adaptive functioning under adverse conditions and recover from stress (Stein, Campbell-Sills, and Gelernter, 2009). However, conceptualizing the S and L variants as conferring risk and resilience, respectively, is an oversimplification. First, although the L variant is most often associated with lower risk for internalizing outcomes, a small number of studies find this variant to be associated with higher risk (e.g., Chipman et al., 2007; Laucht et al., 2009), and both the L and the S variants have been associated with risk for externalizing problems (Yildrim and Derksen, 2013). Inconsistent associations may indicate false positive findings, but also suggest the possibility of different pathways to risk and resilience. Second, preliminary evidence suggests that the S variant is related to executive functioning advantages in a fashion similar to the COMT Met variant, although findings are mixed, particularly for tasks involving cognitive flexibility or an emotional component (Jonassen and Landrø, 2014). Thus, although the L variant may be related to resilience against internalizing psychopathology under high-stress conditions, the S variant is not necessarily disadvantageous for all outcomes across all contexts. Both variants may be associated with specific strengths and vulnerabilities, and it would be valuable for future research to consider specific environmental influences which best promote adaptive functioning in individuals with either a high-expressing or low-expressing genotype.

The Brain-Derived Neurotrophic Factor gene (BDNF) BDNF is a neuroprotective and proliferative factor that serves a number of functions important for memory, learning, and synaptic plasticity across the lifespan (Rothman and Mattson, 2013). Changes in BDNF levels and signaling have been implicated in the pathophysiology of depression, perhaps by virtue of BDNF’s role as a modulator of neural plasticity (Groves, 2007). Additionally, BDNF may mediate the beneficial neural and cognitive effects of exercise and cognitive challenge, including protection against and recovery from CNS damage (Rothman and Mattson, 2013), making this protein a potentially important factor for promoting and sustaining resilience. The BDNF polymorphism which has received the most attention in the literature is a Val to Met substitution at codon 66 (Val166Met, rs6265), with reduced BDNF production in Metcarriers (e.g., Egan et al., 2003). The Met variant has been associated with cognitive and memory deficits, along with reduced hippocampal activity during memory tasks (e.g., Dempster et al., 2005; Schofield et al., 2009). Conversely, carrying a copy of the Val variant has been associated with greater hippocampal, amygdala, and prefrontal volume (Hajek, Kopecek, and Hoschi, 2012; Ninan, 2014). Both cognitive and neurophysiological findings are mixed (e.g., Dodds et al., 2013; Karnik et al., 2010), but a meta-analysis concluded that the Val variant is associated with lower risk for depression in conjunction with recent stress, though only marginally in conjunction with childhood adversity (Hosang et al., 2014). Importantly, support for the Val/Val genotype as being protective is based not only on the absence of psychopathology, but also on indices of positive functioning. For instance, Val homozygotes in a large community sample of older adults reported a higher sense of coherence, defined as a sense that life is more comprehensible, manageable, and meaningful (Surtees et al., 2007), and Val homozygotes also reported higher subjective social support in both clinically depressed and

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non-depressed adults (Taylor et al., 2008). Genetically influenced differences in perception may affect individuals’ willingness to seek support, suggesting the possibility that protective effects of the Val/Val genotype may be socially mediated through active gene–environment correlation (rGE) as well as more direct influences on neural structure or function.

HPA-axis genes: CRHR1, FKBP5, and NPY The hypothalamic-pituitary-adrenal (HPA) axis is one of the body’s primary stress response systems, responsible for the production and release of the steroid hormone cortisol over the course of the day and in response to stress or challenge (Juster et al., 2011). Here, we focus on three genes (CRHR1, FKBP5, and NPY) which may be important for resilience due to their role in the initiation and termination of, and recovery from, the effects of the HPA axis stress response, respectively.

The corticotropin-releasing hormone receptor 1 (CRHR1) gene The corticotropin-releasing hormone receptor 1 (CRHR1) plays a key role in the initiation of the neuroendocrine cascade culminating in cortisol release in response to stress (Juster et al., 2011). CRHR1 is also heavily expressed in corticolimbic regions, including the amygdala and hippocampus, and CRH signaling in these regions may mediate threat-response and anxiety (Hauger et al., 2006), whereas CRHR1 expressed on dopaminergic neurons in the ventral tegmental area and substantia nigra may have anxiolytic effects (Refojo et al., 2011). The most studied variants in the gene coding for CRHR1 are three SNPs in intron 1 (rs7209436, rs110402, and rs242924), which form two common haplotypes, the more common CGG and the rarer TAT (Bradley et al., 2008). Some evidence exists for differences in stress reactivity associated with the TAT haplotype, which is related to lower peak cortisol response in healthy adults following a laboratory stress test (Mahon et al., 2013), and interacts with retrospectively reported maltreatment to predict lower cortisol reactivity to chemical challenge (e.g., Tyrka et al., 2009). However, a community study of preschoolers found no effect on cortisol reactivity (Sheikh et al., 2013). Studies of physical health and substance use in high-risk patients have found a protective effect of the TAT haplotype in individuals retrospectively reporting maltreatment, such that individuals carrying one or two copies of the TAT haplotype have lower symptoms and diagnoses of disorders, including depression (e.g., Bradley et al., 2008; Polanczyk et al., 2009) and substance use (Ray et al., 2013). However, several non-replications exist, particularly in studies using prospective measurement (e.g., Polanczyk et al., 2009), and findings are complicated by heterogeneous sample demographics (i.e., gender, ethnicity, level of risk exposure) and measurement of stress and outcome. At this point, despite promising findings at the level of cortisol reactivity, this research is still preliminary, and support for a protective role of the TAT haplotype is mixed. Two studies within a single sample have examined concurrently assessed maltreatment and adjustment, with somewhat equivocal findings. The first study shows a risk rather than a protective role for the TAT haplotype, as carrying a copy of the TAT haplotype was related to neuroticism and diurnal cortisol dysregulation in maltreatment-exposed children (Cicchetti, Rogosch, and Oshri, 2011). However, the second study found evidence for resilience associated with the TAT haplotype, based on a composite measure of positive functioning including social, psychological, and academic outcomes (Ciccetti and Rogosch, 2012). Specifically, although maltreated children showed lower functioning relative to non-maltreated children regardless of CRHR1 genotype, this discrepancy in functioning was lower for children carrying at least one copy of the TAT haplotype. Although differential susceptibility was not tested, results were consistent

Molecular genetics of resilience

with differential susceptibility rather than diathesis–stress, as children homozygous for the CGG haplotype showed the lowest positive functioning when maltreated but the highest positive functioning when not maltreated. Caution is merited, as findings were based on an ethnically heterogeneous sample without accounting for population stratification. Even so, the results show the utility of measuring positive functioning in GXE research, and suggest that if the TAT haplotype does play a role in adaptation to stress, it is likely more complex than a simple association with risk or resilience across all indices of functioning. In general, greater understanding of functional variation in CRHR1 at the genetic level, and a focus on more proximal endophenotypes (e.g., neural reactivity, diurnal, and reactive cortisol) may go some distance toward building a theoretical foundation on which to examine pathways linking CRHR1 variation to resilience and clarify seemingly contradictory genetic findings.

The FK506 binding protein 51 gene (FKBP5) A second gene of interest for HPA functioning is the gene coding for the FK506 binding protein 51 (FKBP51), which inhibits glucocorticoid receptor binding affinity and nuclear translocation, with implications for the termination of the acute stress response (Zannas and Binder, 2014). In a well-regulated HPA axis, initial FKBP51 levels are low enough to allow glucocorticoid binding, which in turn down-regulates the expression of the corticotropin releasing hormone (CRH) and adrenocorticotropic hormone (ACTH), and up-regulates the expression of FKBP51 in an ultrashort negative feedback loop. However, overexpression of FKBP51 results in impaired negative feedback and less efficient cessation of the stress response (Zannas and Binder, 2014). The gene coding for FKBP51 (FKBP5) contains several variants, including a functional haplotype tagged by three SNPs, rs3800373, rs9296158, and rs1360780 (Zannas and Binder, 2014). One of these SNPs, rs1360780, is associated with differential expression of FKBP51, such that individuals homozygous for the minor T variant show FKBP51 levels two times higher than C variant carriers. High-expressing T carriers of rs1360780 show differences in HPA functioning consistent with less efficient recovery from acute stress (Zannas and Binder, 2014). Adult T carriers (Buchmann et al., 2013) and T homozygotes (Ising et al., 2008) show prolonged elevation of blood cortisol following stress, as well as higher self-reported anxiety (Ising et al., 2008), and infant T carriers show heightened cortisol reactivity to stress (Luijk et al., 2010). However, rs1360780 was unrelated to cortisol response in a larger study of primarily female adults, with some evidence that differences are specific to male participants (Mahon et al., 2013). Additionally, adult C homozygotes with a history of childhood maltreatment showed a blunted cortisol response to laboratory stress (thought to reflect dysregulation; Juster et al., 2011), whereas T carriers reporting maltreatment showed no differences in cortisol reactivity relative to non-maltreated individuals (Buchman et al., 2013). The single study examining FKBP5 genotype in relation to diurnal cortisol found four FKBP5 variants, including the T variant of rs1360780, to be related to lower diurnal cortisol and higher depressive symptoms in older adults, although these results did not replicate in a second GWAS (Velders et al., 2011). Finally, the low-expressing C variant of rs1360780 and other variants in high linkage disequilibrium have been found to protect against mental health problems, including PTSD symptoms and depression (Appel et al., 2011; Binder et al., 2008; Zimmermann et al., 2011), as well as physical health problems (Lessard and Holman, 2013), in individuals retrospectively reporting maltreatment or trauma. However, the rs1360780 genotype showed no main or interactive effect on PTSD symptoms in adults following hurricane exposure (Dunn et al., 2014), and the C rather than the T variant may be related to risk of attempting suicide (e.g., Roy et al., 2010). Thus, although the GXE findings

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for FKBP5 are more consistent across studies than many candidate genes, inconsistencies do exist, and type of trauma and outcome are likely relevant. In particular, early childhood may be a sensitive period for corticolimbic stress and threat-response systems, during which severe or prolonged stress may exert a more profound impact (Juster et al., 2011). In the absence of early trauma, individuals genetically prone to high reactivity or prolonged response to stress may still be able to bounce back and maintain positive functioning in the face of later stressors.

The Neuropeptide Y gene (NPY) NPY serves a number of physiological functions, including the modulation of central and peripheral stress-response systems. NPY is involved in the initiation of the HPA stress response via the stimulation of CRH and ACTH release (Hirsch and Zukowska, 2012). Further, NPY activity in corticolimbic regions may modulate stress and threat-response, with NPY-induced anxiolytic and antidepressant effects, as well as anxiogenic and depressogenic effects, dependent on the receptor and region of the brain (Bowers, Choi, and Ressler, 2012). NPY has also been found to protect against stress-induced memory impairment and promote behavioral resilience in rats (Bowers, Choi, and Ressler, 2012), and may have neuroprotective and neuroproliferative effects (Decressac and Barker, 2012). Thus, the role of NPY in HPA axis function is complex, with NPY both promoting the activation and maintenance of the stress response via positive feedback and buffering the neural and emotional consequences of stress (Bowers, Choi, and Ressler, 2012; Hirsch and Zukowska, 2012). The gene coding for NPY contains three common variants associated with functional differences in expression in vitro and in vivo, all of which act as tagging SNPs for a set of haplotypes which can be grouped into high, moderate, and low expression genotypes (Zhou et al., 2008). Two of these variants, an insertion/deletion of two base pairs (TG/–) near the start of the promoter region (rs3037354) and a T to C SNP in the promoter region (rs16147), have together been found to explain a substantial proportion of the variance in expression, although functionality at the SNP level is inconsistent across studies (Shah et al., 2009; Sommer et al., 2010; Zhang et al., 2012; Zhou et al., 2008). The few studies focused on the investigation of Zhou and colleagues’ (2008) functional haplotypes show associations with neurophysiological endophenotypes with the potential to influence sensitivity to stressful conditions. Small sample sizes are a concern, but studies in healthy participants suggest associations between high-expressing haplotypes and lower neural reactivity to negative stimuli, more effective physiological and subjective coping with pain, and lower fear and anticipatory worry (Mickey et al., 2011; Sommer et al., 2010; Zhang et al., 2012; Zhou et al., 2008). Although lower reactivity to stress is often assumed to be beneficial, the role of NPY in HPA-axis activation suggests that genetic variation associated with higher NPY expression may lead to a more reactive HPA-axis, but also more capacity to cope with and recover from moderate stress due to the neuroprotective and anxiolytic effects of NPY. More research is necessary to test this hypothesis, but it is consistent with the idea of the HPA axis as a self-regulating system that operates most effectively under biological and environmental conditions that support both an efficient response to stress and a rapid recovery and return to homeostasis (Juster et al., 2011).

The Oxytocin Receptor gene (OXTR) The neuropeptide oxytocin is a key mediator of social affiliation and pair-bonding in humans and other mammals, and has been implicated in caregiving, attachment, prosocial behavior, and social affiliation and cognition, including the ability to read and respond to nonverbal social and

Molecular genetics of resilience

emotional cues (Neumann, 2008). Oxytocin also inhibits HPA and amygdala responses to stress, indicating a plausible role for genes related to oxytocin neurotransmission in resilience via both social and physiological pathways. Here we focus on the gene coding for the OXTR. Although gene variants in OXTR with known functional effects have yet to be determined, multiple studies have examined associations between OXTR polymorphisms and neural endophenotypes potentially underlying differences in social and emotional processing, although findings are highly mixed (Ebstein et al., 2012). Recently, one large study of adolescents found that individuals with the minor CC genotype of the SNP rs237915 showed lower ventral striatal reactivity to angry faces (Loth et al., 2014). This SNP also interacted with stressful life events, with C homozygotes reporting higher internalizing problems (in girls) and peer problems (in boys) under low stress conditions, but lower problems relative to T-carriers when they experienced high life stress. The authors interpreted this interaction as reflecting lower sensitivity to negative social cues in C homozygotes, which may be linked to social and emotional difficulties in a low-stress environment, but adaptive under adverse conditions. Cumulative genetic risk scores composed of multiple SNPs from oxytocin and vasopressin genes are also related to empathic communication and reciprocity between romantic partners (Schneiderman et al., in press), as well as resilience against and recovery from psychopathology in war-exposed children (Feldman, Vengrober, and Ebstein, 2014), although caution is merited without stronger knowledge of genetic functionality. Finally, OXTR SNPs have been associated with multiple phenotypes related to social competence, empathy, and prosocial behavior, although not always consistently (Ebstein et al., 2012). For instance, the A to G SNP rs53576 is relatively consistently associated with social and emotional functioning. Specifically, relative to A-carriers, G homozygotes have been found to be more skilled in reading social and emotional cues, based on emotion-recognition tests and ability to understand verbal communication despite background noise, as well as lower subjective stress reactivity and startle response (Ebstein et al., 2012), and lower cortisol reactivity when offered social support by a close friend or partner prior to laboratory stress (Chen et al., 2011). Further, perhaps related to sensitivity to subtle cues, G homozygotes showed more sensitive parenting of high-risk toddlers (Bakermans-Kranenburg and van Ijzendoorn, 2008). At the same time, the G/G genotype is related to lower negativity during early childhood, and children’s behavior partially mediates the relation between children’s genotype and caregivers’ parenting confidence in a pattern consistent with passive or evocative rGE (Kryski et al., 2014). Thus, in addition to direct biological influences of oxytocin activity, relations between OXTR polymorphisms and psychological health may be socially mediated. As such, genes involved in oxytocin and vasopressin systems may be promising targets for research aimed at understanding the social pathways linking genes and resilience.

Conclusion In a remarkably short period of time, molecular genetic research has gone from a simple, maineffect focused perspective (e.g., a gene “for” depression) to a more complex understanding of the role of gene–environment interplay in psychosocial risk and resilience. Molecular genetic studies provide evidence that genetic variation may contribute to differential risk for negative outcomes under conditions of adversity, although fewer address positive functioning. Recent research offers a stronger focus on endophenotypes and a theoretical basis on which to expect certain genes and physiological systems to be important for resilience. There is increasing recognition of the need to integrate research across genetic, biological, behavioral, and social levels of analysis, and to take a

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developmentally sensitive approach to studying resilience. However, molecular genetic research has not always kept pace with increasing theoretical complexity, or the advances in our understanding of the human genome, and despite progress, molecular genetic studies of resilience face both challenges and opportunities. First, many, though not all, GXE studies have either used broad or distal measures of environmental adversity or relied exclusively on retrospective self-report of childhood trauma. Retrospective self-report is vulnerable to confounding by genetic influences on memory, mooddependent reporting, or willingness to disclose trauma exposure (Uher and McGuffin, 2008). Additionally, self-report checklists of stressful life events often include a range of stressors (e.g., divorce, being burgled, death of a spouse or close relative; Gillespie et al., 2005), which are counted as equivalent but vary in their severity, chronicity, and effect on an individual’s life. Studies using objective and interview methods to assess stress exposure have typically fully or partially replicated the finding of an interaction between 5-HTTLPR and adversity, whereas non-­replications have relied on self-report checklists of stressful life events (Uher and McGuffin, 2010). Thus, a move to prospective or objective assessment and measures of focal stressors (e.g., harsh discipline and maltreatment) may improve both the replication and the interpretability of effects. Additionally, Boyce and Ellis’s (2005) evolutionary–developmental theory suggests that an environment characterized by both positive qualities (e.g., supportive parenting) and developmentally appropriate or manageable stressors is optimal, especially for those who are genetically or biologically sensitive to context. Such an environment may allow a child to derive the maximum benefit from positive environmental influences, while developing coping and self-­regulation skills necessary to overcome more severe stressors later in life. Thus, measuring positive environmental qualities in studies of stress seems prudent. Second is the level of analysis at which a hypothesized GXE occurs. There are multiple points at which the environment may moderate the pathways linking genes to physical and mental health, from a proximal cellular level (e.g., changes in genetic transcription or receptor expression) to the level of sociocultural opportunities and expectations. Some genetic variants may be more susceptible to epigenetic regulation (e.g., Kinnally et al., 2010), and some genes may have main effects on endophenotypes which may in turn influence reactivity to the environment, or be moderated by environmental factors. At a more distal level, genetically influenced traits such as reward-seeking or social reticence may be risky in some contexts but adaptive in other contexts, whether due to the evolutionary demands of the environment or to cultural values and practices. The third challenge facing molecular genetic research is the seemingly overwhelming complexity of both biology and behavior. Neurophysiological mechanisms such as the HPA axis or systems involved in monoamine neurotransmission do not operate in isolation. Candidate gene association studies focused on a single variant, or even the interaction between multiple variants, can capture at most a small portion of the whole. GWAS offer a valuable method for considering variation across the entire genome. However, GWAS are generally hypothesis free and, therefore, blind to theory, and rarely consider GXE, partially because of limitations on power and the difficulties inherent in measuring environment in a sample large enough to support GWAS (Vrieze, Iacono, and McGue, 2012). There are many tools that may be useful for integrating information from multiple biological systems and levels of analysis, and these tools have been applied to complex and polygenic phenotypes in the medical literature. However, the application of systems biology to the social sciences is challenging, especially given the limited knowledge of biological pathways and difficulties in measuring heterogeneous phenotypes. Nevertheless, progress is being made. For example, Luo and colleagues (2014) used protein–protein interaction databases and gene expression and

Molecular genetics of resilience

association data in human and animal research to construct networks of interactions among the proteins encoded by schizophrenia susceptibility genes (previously identified by GWAS). They found that these genes formed a single highly interconnected protein network, suggesting that despite genetic heterogeneity, genes identified as candidates for schizophrenia are involved in common biological pathways. Considerable phenotypic research has examined the individual and social factors supporting resilience as a multifaceted process (Luthar, 2006). Molecular genetic research has primarily focused on differential risk for psychopathology, but there is opportunity for further investigation into the role of genetic factors in resilience. Moving forward, this research requires a strong methodological basis, including adequately powered samples and reliable measurement of theoretically relevant environmental factors and outcomes, as well as greater consideration of the developmental context and attention to the greater biological and social systems in which genetic variants are situated. Nevertheless, despite the challenges, research examining the ways in which genetic, neurobiological, and social factors interact across the lifespan has great potential to inform our understanding of the processes underlying resilience.

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Chapter 12

Vantage sensitivity: genetic susceptibility to effects of positive experiences Michael Pluess and Jay Belsky

Vantage sensitivity: genetic susceptibility to effects of positive experiences: an introduction The notion that some people are more vulnerable to adversity due to their genes is widely embraced in the fields of developmental psychopathology and psychiatry (Rutter, Moffitt, and Caspi, 2006). A large body of so-called gene–environment interaction (GXE) studies provides substantial empirical evidence for such genetic differences in vulnerability to adverse experiences and environmental conditions (see Chapter 11), although it has to be acknowledged that much of this evidence is limited by underpowered samples and often characterized by inconsistent replication efforts (Duncan and Keller, 2011; Risch et al., 2009). What most of the successful GXE studies have in common, though, is that they typically find that carriers of a specific gene variant are more likely to succumb to the negative effects of environmental adversity, whereas carriers of an alternative gene variant appear to be resilient to the same adverse condition. Importantly, in most cases, the gene variant found to increase the risk for a maladaptive outcome in the presence of adversity tends to be unrelated to the negative outcome in the absence of said adversity. Such findings, then, are consistent with a diathesis–stress or dual-risk perspective of person–­environment interaction (Gottesman and Shields, 1967; Monroe and Simons, 1991; Zuckerman, 1999), according to which a negative outcome emerges only when an individual’s vulnerability—for example, a specific genetic variant—is combined with an external stress factor. This view suggests that carrying gene variants (i.e., alleles) associated with resilience to adversity should be considered advantageous, whereas “risk” alleles present a liability and consequently a disadvantage. Application of evolutionary theory to this diathesis–stress perspective on GXE findings would predict that gene variants conferring risk for maladaptive psychological development in the context of adverse environmental conditions would—in the long term—be eliminated from the gene pool by process of natural selection given empirical evidence that people with psychological disorders have on average significantly fewer children and are therefore less likely to pass their genes into future generations (Power et al., 2013). However, in contrast to this theoretical and reasonable prediction, many of the genetic risk variants examined in GXE studies have, in fact, a surprisingly high frequency in the general population. For example, the short allele of the serotonin transporter gene polymorphism (5-HTTLPR), which has been found to significantly increase the risk of depression in the context of early adversity (Karg et al., 2011), is carried by up to 70% in Caucasian samples (e.g., Lesch et al., 1996). Observations of such high-risk allele frequencies challenge the traditional diathesis–stress conceptualization of certain gene variants as having predominately a risk function.

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One reason for the surprisingly high frequencies of many genetic variants associated with increased risk of maladaptive development under adverse environmental conditions may be that such putative genetic risk variants are maintained in the population because they are also associated with other traits that confer reproductive advantage under different contextual conditions (Uher, 2009). In other words, the liability of a genetic variant associated with increased vulnerability under some conditions may be balanced by advantages associated with the same variant in alternative conditions. Such advantages could take different forms, including increased resilience to other forms of adversity. For example, individuals carrying the sickle-cell disease allele are at greater risk of developing a range of health problems, but at the same time are also more resilient to malaria which leads to a selective advantage for individuals carrying this allele in countries where malaria is prevalent and, hence, explains the relatively high population frequency of this risk allele (Luzzatto, 2012). However, evolutionary analysis of the high population frequencies of putative risk alleles in healthy non-clinical samples may also imply that genetic moderation should not only be expected regarding the negative response to adversity (i.e., diathesis–stress), but also regarding the benefit individuals derive from more positive and supportive experiences and exposures. Before discussing this intriguing possibility further, it is important to acknowledge other evolutionary explanations for the high frequency of risk alleles. For example, gene variants that are associated with risk today may have been adaptive in ancestral times and the main reason they are still common today is that natural selection is a slow process, particularly for genes with only moderate to small effects on reproductive fitness. However, studies investigating such selection processes suggest that at least some of these genes have actually increased in recent history, which points toward a positive rather than a negative selection process (e.g., DRD4 7-repeat allele, Ding et al., 2002). As it turns out, until recently, little empirical effort has been directed toward the investigation of genetic factors associated with an increased propensity to benefit from supportive, nurturing, or even just benign environmental conditions. One reason that such genetic moderation of positive aspects of the environment has not received much attention within the scientific community conducting GXE studies may be the lack of adequate frameworks that provide a solid theoretical rationale for the expectation of such variability (but see Shanahan and Hofer, 2005, for a discussion of gene–environment interaction in “enhancing” social contexts). Furthermore, while there is specific language to describe individuals who are more or less affected by adversity (i.e., “vulnerability” and “resilience,” respectively), terminology for those more or less responsive to positive aspects of the environment as a function of inherent characteristics is difficult, if not impossible, to find. These conceptual and semantic shortcomings within psychology have recently been addressed with the proposition of vantage sensitivity, a new concept with accompanying terminology for individual differences in response to positive experiences (Pluess and Belsky, 2013). In what follows, we present the theoretical background and basic properties of the vantage sensitivity framework before reviewing selected empirical evidence for vantage sensitivity featuring different genetic factors as moderators of positive experiences ranging from parental sensitivity to positive psychological intervention. In a concluding section, we will then highlight important conceptual differences between vantage sensitivity and theoretically related concepts of resilience (i.e., diathesis–stress) and differential susceptibility, before discussing potential practical ­implications.

From diathesis–stress to vantage sensitivity As described earlier, the diathesis–stress framework presumes that in GXE studies some individuals are more vulnerable to the adverse effects of negative experiences and exposures due to a

VANTAGE SENSITIVITY

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low Time

Figure 12.1 Graphical illustration of diathesis–stress: vulnerability (i.e., diathesis) describes the propensity to respond negatively to adversity, whereas resilience reflects protective resistance from the same negative influence as a function of genetic (or other inherent) factors. No differences are predicted in the absence of adversity.

genetic “vulnerability,” whereas others are protected from the same adverse influences as a function of genetic “resilience.” Even though numerous empirical findings in many areas of inquiry prove consistent with diathesis–stress thinking (e.g., Caspi et al., 2003), it must be acknowledged that this widely embraced framework makes no predictions regarding variation in response to putatively positive experiences. In fact, diathesis–stress reasoning suggests—at least implicitly— no differences between genetically vulnerable and resilient individuals in the absence of adversity (see Figure 12.1). Recently, an alternative model of environmental action has been advanced—differential susceptibility—­suggesting that some individuals are disproportionately susceptible to both negative and positive developmental experiences and environmental exposures (Belsky, 1997a, 2005; Belsky, Bakermans-Kranenburg, and van IJzendoorn, 2007; Belsky and Pluess, 2009a, 2013; Ellis et al., 2011). According to differential susceptibility reasoning, more susceptible individuals are not just especially “vulnerable” to adversity, but are also more “developmentally plastic” or “malleable” across a wide range of environmental qualities (Belsky and Pluess, 2009b; Boyce and Ellis, 2005; Ellis et al., 2011). Thus, many of those whom the diathesis–stress framework considers disproportionately likely to be adversely affected by negative experiences and exposures may also be disproportionately likely to benefit from supportive and enriching ones. In other words, differential susceptibility thinking encompasses both a “dark side” of environmental susceptibility, which refers to response to negative experiences (i.e., vulnerability), and what Bakermans-Kranenburg and van IJzendoorn (2011) have labeled the “bright side” or response to positive experiences and exposures (see also Homberg and Lesch, 2011). Applied to GXE studies, the differential susceptibility framework suggests that certain gene variants may increase an individual’s susceptibility to both negative and positive environmental influences (see Figure 12.2) rather than just to negative ones (Belsky et al., 2009). Importantly, the differential susceptibility framework has been derived theoretically from the following evolutionary reasoning (Belsky, 1997b, 2005; Belsky and Pluess, 2009a, 2013): (1) Humans are characterized by a capacity for developmental plasticity which allows them to be shaped by their early environment in ways presumed to prepare them to function well in the environment they are likely to encounter in adulthood. (2) However, because the future is inherently uncertain, there is always a risk that future environmental conditions would prove rather

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high Level of Functioning

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High Susceptibility Low Susceptibility

Individual A Individual B

Positive Exposure

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Figure 12.2 Graphical illustration of differential susceptibility: high susceptibility is characterized by increased susceptibility or plasticity as a function of genes (or other inherent characteristics) in response to both negative and positive exposures, whereas low susceptibility reflects a psychological inertia to environmental influences independent of their quality.

different from those experienced earlier in life. The resulting mismatch between the developmentally influential early environment and future environmental conditions during the reproductive years would mean that the individual would be poorly prepared to succeed, especially reproductively, later in life. (3) Hence, natural selection should have engaged in a process of “bet hedging,” with some individuals proving developmentally plastic and some less so. That way, the negative consequences of a discrepancy or mismatch between the early environment and its developmental sequelae and the actual future environment would, theoretically, undermine the reproductive success of only those individuals who are more susceptible to the formative effects of early environmental influences (i.e., those with a higher degree of developmental plasticity), but not those generally less susceptible (however, when environmental conditions remain stable, those that are more susceptible will have the advantage of being better adapted to the environment). These evolutionary considerations provide the theoretical rationale for the proposition that genetic moderation should be expected not just in response to negative but also in response to positive experiences as a function of the same gene variant. The same line of reasoning serves also as one plausible explanation for the high frequencies of putative genetic risk variants in healthy populations: carriers of such gene variants are not only more negatively affected by adverse experiences, but also benefit significantly more from positive supportive experiences, which maintains their significant representation in the general population gene pool. Recently, vantage sensitivity has been put forward as a concept and term to describe such hypothesized variability in response to positive experiences and environmental advantages (Manuck, 2011; Pluess and Belsky, 2013; Sweitzer et al., 2012), along with the following terminology: (A) vantage sensitivity refers to the general proclivity of an individual to benefit from positive and presumptively well-being- and competence-promoting features of the environment, just as vulnerability depicts the tendency to succumb to negative effects of adversity in the diathesis– stress framework. (B) The degree of vantage sensitivity is a function of the presence of vantagesensitivity factors (i.e., promotive factors), just as vulnerability/risk factors increase vulnerability to negative effects of adversity in the diathesis–stress framework. Vantage-sensitivity factors are by definition inherent characteristics of the individual including genetic, physiological, and psychological traits, even if the focus of interest in this chapter is on genetic factors. (C) Vantage resistance describes the failure to benefit from positive influences, just as resilience characterizes the

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Figure 12.3 Graphical illustration of vantage sensitivity: vantage sensitivity describes the propensity to respond favorably to positive experiences, as a function of genetic (or other inherent) characteristics, whereas vantage resistance reflects the inability to benefit from supportive influences. The vantage sensitivity framework makes no predictions about individual differences in the absence of positive exposures.

“failure” to succumb to the negative effects of adversity in the diathesis–stress framework. (D) The degree of vantage resistance is a function of the presence of vantage-resistance factors or absence of vantage-sensitivity ones, just as protective factors increase resilience to negative effects of adversity in the diathesis–stress framework. In summary, vantage-sensitivity factors increase vantage sensitivity, that is, susceptibility to the beneficial effects of positive experiences and exposures, whereas vantage-resistance factors diminish or even completely eliminate positive response to the same supportive conditions (see Figure 12.3 for graphical illustration).

Empirical evidence of vantage sensitivity Two polymorphisms identified as potential “plasticity” genes in the Belsky and Pluess (2009a) analysis of differential susceptibility have consistently emerged in more recent work as markers of vantage sensitivity: a polymorphism in the dopamine receptor D4 gene (DRD4) and a polymorphism in the serotonin transporter gene (5-HTTLPR). Before describing them, we should be clear that whether genes operate as moderators of environmental effects, resulting in diathesis–stress, vantage-sensitivity, or differential-susceptibility patterns of GXE interaction, they should be considered “plasticity” genes. And this is because they all relate to the degree to which an individual is responsive to the environment (see also, Pluess, 2015). Furthermore, it is important to clarify that the concept of vantage sensitivity applies to development across the whole life-course, even though the reviewed empirical evidence focuses predominately on early developmental periods (i.e., childhood).

The dopamine receptor D4 gene The dopaminergic system plays an important role in attentional, motivational, and reward processes and a polymorphism of the dopamine receptor D4 (DRD4) gene has been much studied in GXE research. Variants of the DRD4 differ by the number of 48-base pair tandem repeats in exon III, ranging from 2–11. The 7-repeat variant has been regarded as a vulnerability factor due to its links to ADHD (Faraone et al., 2001), high novelty-seeking behavior (Kluger, Siegfried,

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and Ebstein, 2002), and low dopamine reception efficiency (Robbins and Everitt, 1999), among other correlates. Findings of a recent meta-analysis of GXE studies involving DRD4 and other dopamine-­related genes and children under age 10 years indicate that those carrying less efficient dopamine-related genes are more vulnerable to negative environments, but, supporting a differential susceptibility model, also show greater vantage sensitivity in response to positive environments (Bakermans-Kranenburg and van IJzendoorn, 2011). Intriguingly, vantage-­sensitivity-related effects—that is, responsiveness to supportive environmental experiences—proved stronger than diathesis–stress-related effects. In other words, the apparent benefits of carrying putative “risk” alleles in the face of environmental support or enrichment were greater than the apparent costs under conditions of contextual adversity. In their pioneering experimental study evaluating genetic moderation of a psychological intervention, Bakermans-Kranenburg et al. (2008) investigated whether DRD4 interacted with a video-feedback parenting intervention in reducing children’s externalizing behavior in a sample of 157 families with 1–3-year-old children randomly assigned to treatment condition. Providing evidence for vantage sensitivity, the intervention proved effective in decreasing externalizing behavior, but only for children carrying the DRD4 7-repeat allele, while children without the DRD4 7-repeat did not benefit from the intervention at all. Follow-up analyses revealed that the only children in the experimental group who actually benefited from the intervention were those carrying the DRD4 7-repeat whose mothers’ parenting behavior improved as a result of the intervention (not all of them did). Thus, the mere presence of the DRD4 7-repeat in the absence of improvement in maternal positive discipline did not result in a decrease of externalizing behavior, thereby suggesting that it was the mother’s increase in positive discipline as a result of the intervention that children with the DRD4 7-repeat were more sensitive—and responsive—to. In a second intervention study by the Dutch investigatory team, the focus was on an indisputably positive outcome rather than reduction of a negative one. Kegel, Bus, and van IJzendoorn (2011) investigated genetic sensitivity as a function of the DRD4 7-repeat in response to a computer-­based literacy instruction program (N = 182 4–5-year-old boys and girls). Two intervention groups, one with positive feedback and one without, were compared to a control group on the development of emergent literacy skills. Only children carrying the DRD4 7-repeat increased their early literacy skills in response to the intervention. Notably, the positive effect of the intervention in children with the DRD4 7-repeat was restricted to the group that received positive feedback as part of the computer program. In the absence of positive feedback there was no difference in literacy skills between children in the intervention or control group, thereby suggesting that the presence of the DRD4 7-repeat allele predicted vantage sensitivity to the positive feedback component of the intervention. In a cross-sectional analysis of a longitudinal prospective study, Knafo, Israel, and Ebstein (2011) investigated whether DRD4 moderated the effects of mother-reported positivity in parenting on prosocial behavior in early childhood in a sample of 167 3.5-year-old boys and girls. Among children who did not carry the DRD4 7-repeat allele, there was no significant relation between positivity in parenting and prosocial behavior. Among children carrying the DRD4 7-repeat allele, however, evidence of increased vantage sensitivity emerged, as more positive parenting by the mothers proved related to more prosocial behavior by their children.

The serotonin transporter gene A large proportion of GXE studies are based on genetic variants in the serotonergic system, most prominently the serotonin-transporter-linked polymorphic region (5-HTTLPR), which

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is a VNTR polymorphic region in SLC6A4, the gene that codes for the serotonin transporter. Most research focuses on two variants, distinguishing two groups of individuals—those carrying at least one short allele (s/s, s/l) and those homozygous for the long allele (l/l)—though more variants than these have been identified (Nakamura et al., 2000). The short allele has generally been associated with reduced transcriptional efficiency of the serotonin transporter—a protein involved in the reuptake of serotonin from the synaptic cleft—and thus considered to be related to depression, either directly (Munafo et al., 2009; Sen, Burmeister, and Ghosh, 2004) or in the face of adversity (Karg et al., 2011; Risch et al., 2009). As it turns out, in a substantial proportion of relevant GXE studies results are actually more indicative of differential susceptibility than diathesis–stress, with 5-HTTLPR short allele carriers having the worst outcomes under adverse conditions as well as the best outcomes under supportive conditions (Belsky et al., 2009; Belsky and Pluess, 2009a). Here we summarize recent studies that investigated the moderating effect of 5-HTTLPR, specifically in response to positive experiential factors. Before doing so, however, we call attention to a recent meta-analysis of research on 2,276 Caucasian children under the age of 18 years which shows that those with one or two short alleles benefited more from positive environmental exposures than children without them (van IJzendoorn, Belsky, and Bakermans-Kranenburg, 2012). Results consistent with these meta-analytic findings emerged in Hankin and associates’ (2011) research on the interaction of 5-HTTLPR and positive parenting in the prediction of positive emotionality in middle childhood/adolescence using three independent samples totaling 1,874 9–15-year-old boys and girls. Results from two of the three samples were supportive of vantage sensitivity, with children carrying the 5-HTTLPR short allele showing the highest positive affect scores when positive parenting was high, suggesting that these children were particularly sensitive to the benefits of high positive parenting. Vantage sensitivity as a function of the 5-HTTLPR short allele is not restricted to positive experiences within the parenting domain, as revealed by Eley et al.’s (2012) evaluation of whether 5-HTTLPR moderated effects of cognitive behavioral therapy (CBT) for anxiety disorders in a sample of 359 6–13-year-old boys and girls. Clinical diagnoses of anxiety disorders and symptom severity were assessed before and after treatment, as well as six months after treatment ended. Although all children appeared to benefit from the treatment, the positive effect of the intervention at the follow-up assessment was particularly pronounced in the case of those children carrying the short allele. More specifically, those homozygous for the 5-HTTLPR short allele showed a significantly greater reduction in symptom severity from pre-treatment to follow-up assessment, so much so, in fact, that they proved 20% more likely than others to be free of anxiety disorder at the six-month follow-up assessment. Overcoming the limitations of correlational study designs, Drury and colleagues (2012) sought to determine whether 5-HTTLPR would moderate the effect of early rearing on indiscriminate social behavior when children were 54 months old, taking advantage of a randomized controlled trial. In the Bucharest Early Intervention Project (BEIP), 136 abandoned children between 6 and 30 months of age were randomly assigned to standard institutional care or a newly developed high-quality foster care program (Zeanah et al., 2003). Indiscriminate social behavior is regarded as a “signature consequence” of deprived, institutional care. Children homozygous for the 5-HTTLPR short allele randomly allocated to the high-quality foster care condition had the lowest indiscriminate social behavior scores of the whole sample at 54 months, whereas for children with the 5-HTTLPR long allele there was no beneficial effect of high-quality foster care. These data are the first experimental evidence highlighting the vantage-sensitivity character of 5-HTTLPR. In fact, the findings were also consistent with differential susceptibility given that

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those with 5-HTTLPR short alleles in institutionalized care had the highest scores of indiscriminate behavior.

Other candidate genes Vantage-sensitivity findings are not restricted to studies involving DRD4 and 5-HTTLPR. Several other genetic polymorphisms have been found to moderate positive environmental effects. For example, in a longitudinal study involving 502 children and their mothers, Luijk and associates (2011) investigated whether a genetic polymorphism located in the Mineralocorticoid Receptor (MR) gene moderated the effect of observer-rated maternal sensitivity on the child’s attachment security at the age of 14 months, assessed with the Strange Situation Procedure. Consistent with vantage sensitivity, children carrying one or more G alleles were more securely attached when their mothers’ sensitive responsiveness was particularly high, whereas children homozygous for the A allele were not affected by their mothers’ sensitivity regarding their attachment security. Importantly, when maternal sensitivity was low, there was no difference in attachment security between children with different genotypes, suggesting that differences only emerged at the upper end of maternal sensitivity, consistent with vantage sensitivity rather than differential ­susceptibility. A further example of vantage sensitivity emerged in a study by Felmingham et al. (2013) regarding genetic moderation of the response to exposure therapy in the treatment of post-traumatic stress disorder (PTSD) as a function of a genetic polymorphism located in the Brain-Derived Neurotrophic Factor (BDNF) gene. All of the 55 included study participants with clinically diagnosed PTSD underwent eight weeks of exposure-based cognitive behavioral therapy. Although all patients showed a significant pre-post decrease in PTSD symptoms, patients homozygous for the BDNF Val-allele showed a 62% reduction whereas patients with one or more Met-alleles showed only a 40% reduction in PTSD symptoms, suggesting that the BNDF Val/Val genotype increased vantage sensitivity to exposure therapy in PTSD patients by almost 50%. It is important to acknowledge that in many of the reviewed studies, the gene variants associated with increased response to supportive exposures have also been found to increase the response to negative exposures, suggesting that these genes reflect both the “dark” and “bright” sides of differential susceptibility. Whether there exist gene variants exclusively associated with vantage sensitivity remains to be determined by future research.

Genetic moderation of positive psychology intervention Given that vantage sensitivity is a rather new concept, most studies providing evidence for vantage sensitivity have not been specifically designed for that purpose. Consequently, most empirical evidence for vantage sensitivity focuses on the reduction of maladaptive outcomes in response to supportive influences (e.g., psychological intervention, high-quality parenting), with rare exceptions (Kegel, Bus, and van Ijzendoorn, 2011), rather than the promotion of adaptive and desired outcomes (i.e., well-being competence). Furthermore, evaluation studies in the field of positive psychology often neglect testing for individual differences based on the implicit assumption that the same “good thing” would benefit all people to the same degree (Gable and Haidt, 2005; Held, 2004; Lazarus, 2003). In order to address these limitations of the current vantage sensitivity evidence base we conducted a small study to test whether a range of genetic variants moderated the positive effects of a school-based positive psychology program on measures of PWB. As far as we are aware, this is the first study of this kind in the field of positive psychology. However, given the small sample

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size and further methodological limitations, the findings have to be considered exploratory and preliminary. Applying a growth curve model we tested whether 71 11–12-year-old children at the same public school in England differed in the benefit they derived from SPARK Resilience Programme, a universal school-based resilience-promoting intervention (Boniwell and Ryan, 2009; Pluess et al., submitted), as a function of genetic differences. Self-reported measures of psychological resilience (Wagnild and Young, 1993), self-esteem (Rosenberg, 1989), and life satisfaction (Huebner, 1991) were obtained before and after the three-month intervention, as well as at a six-month follow-up assessment. DNA was collected using cheek swabs and genotyped for a range of genetic variants hypothesized to moderate the effects of positive environmental influences (Belsky and Pluess, 2009a), including 5-HTTLPR, DRD4, a SNP in the COMT (rs4680), a SNP in the BDNF (rs6265), and a SNP in the OXTR (rs2268498). For each gene and outcome, growth curve models were estimated across the three measuring points in order to test whether the gene would predict the intercept centered at the six-month follow-up assessment as well as the change over time (i.e., slope). Evidence of vantage sensitivity emerged in two of the tested models: (1) COMT significantly predicted self-esteem at six months (B = −3.35, p = .01) as well as the change in self-esteem over the course of the study (B = −1.42, p = .04). According to Figure 12.4a, children carrying one or more copies of the COMT Met-allele did not differ from other children regarding self-esteem at the pre-assessment, but significantly increased in self-esteem over time. (2) Similarly, OXTR predicted life satisfaction at the six-month follow-up assessment (B = 3.30, p = .03) as well as the change in life satisfaction over the course of the study (B = 1.76, p = .02). Figure 12.4b shows that children homozygous for the OXTR T-allele increased significantly in their life satisfaction scores over the course of the intervention while not being significantly different from other children at the beginning of the intervention. These exploratory findings suggest that some children may benefit disproportionately from preventative psychological interventions aimed at promoting PWB as a function of genetic differences, whereas others respond less favorably or not at all. However, as mentioned earlier, these vantage sensitivity results have to be considered preliminary given that the sample was small and ethnically heterogeneous, and multiple testing was not controlled for statistically.

Conceptual considerations Before discussing potential mechanisms and practical implications of vantage sensitivity as a function of genes, it is important to highlight similarities and differences between vantage sensitivity and related concepts of differential susceptibility and diathesis–stress (i.e., resilience).

Vantage sensitivity versus differential susceptibility Although closely related to differential susceptibility, we would like to propose that vantage sensitivity represents more than just the “bright side” of susceptibility. For example, some genes may increase sensitivity to the benefits of supportive or enriching environments while not necessarily making individuals also more susceptible to the negative effects of contextual adversity. This, in fact, is what distinguishes the concept of vantage sensitivity from differential susceptibility. In some instances, then, genetic differences in response to environmental influences may emerge exclusively under supportive conditions, which would imply vantage sensitivity, rather than under both supportive and unsupportive conditions, which would imply differential susceptibility. However, as mentioned earlier, genes exclusively associated with vantage sensitivity have not yet been identified.

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Figure 12.4 (a) Vantage sensitivity as a function of COMT (rs6265): Before intervention, children carrying the COMT Met-allele do not differ from children homozygous for the COMT Val-allele. However, over the course of the intervention, those with the Met-allele increase in self-esteem, whereas the COMT Val-alleles show vantage resistance. (b) Vantage sensitivity as a function of OXTR (rs2268498): Before intervention, children homozygous for the OXTR T-allele do not differ from children carrying the OXTR C-allele. However, over the course of the intervention, those with the T-allele increase in life satisfaction, whereas those with the OXTR C-alleles show vantage resistance.

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A further distinction to be made between vantage sensitivity and differential susceptibility pertains to the empirical conditions required to evaluate each. In order to investigate differential susceptibility, contextual conditions should, ideally, range from the negative to the positive (Belsky, Bakermans-Kranenburg, and van IJzendoorn, 2007; Belsky and Pluess, 2009a), which is not essential for testing vantage sensitivity. In fact, many truly positive exposures do not range from the positive to the negative but only from the positive to the absence of the positive (e.g., psychological intervention versus no intervention). Consequently, in order to differentiate exclusive vantage sensitivity from differential susceptibility, the moderating effect of a gene variant has to be tested in response to both negative and positive exposures.

Vantage sensitivity versus diathesis–stress (resilience) Resilience, reflecting the absence of problematic functioning despite exposure to contextual adversity, is a concept central to many subfields of psychology ranging from clinical to developmental to positive psychology, to name just a few (e.g., Cicchetti and Garmezy, 1993; Masten and Obradovic, 2006; Werner, 1997). Though not completely unrelated to vantage sensitivity, it is important to highlight fundamental differences between the two concepts. This is especially important given inconsistent use of the terms “resilience” and “protection” in different psychological literatures. Specifically, some have mistakenly referred to vantage-sensitivity-like findings as evidence of “protection” (for review, see Luthar, Cicchetti, and Becker, 2000), most probably due to the lack of a conceptual framework—and related terminology—for thinking about and referring to variability in response to positive contextual conditions. Whereas resilience reflects what “protective” factors and processes achieve by preventing an individual from succumbing to or being harmed by some contextual adversity (Rutter, 1987), vantage sensitivity refers to “promotive” influences (Sameroff, 2000) that enable an individual to benefit—more than others—from a positive environmental experience or exposure. Ultimately, then, vantage sensitivity is about variation in the promotion of well-being or competent functioning when exposed to an experience presumed to have a beneficial effect, whereas resilience and protection are about not having one’s well-being or competence undermined when subjected to a negative experience. Important to mention is also that while the diathesis–stress model makes no explicit predictions about individual differences in response to positive exposures, vantage sensitivity also makes no explicit predictions about individual differences in response to adverse experiences. It has to be acknowledged, however, that in many of the reviewed studies vantage sensitivity findings tend to reflect the bright side of differential susceptibility.

Vantage-sensitivity genes According to the current body of empirical evidence for vantage sensitivity, the majority of detected vantage-sensitivity gene variants appear to be the same variants that emerged repeatedly as “risk” or “vulnerability” alleles in the psychological and psychiatric literatures. The empirical observation that many of these putative “risk genes” are also associated with increased vantage sensitivity to positive effects of supportive environments supports the claim that in many cases these genes should be re-conceptualized as “plasticity” genes (Belsky et al., 2009); recall, however, that all genes that are associated with variation in the extent to which individuals prove responsive to the environment should be regarded this way (Pluess, 2015). Nevertheless, it is important to differentiate between those genes that confer both vulnerability to adversity and vantage sensitivity—that is, differential susceptibility—from those that confer only one or the other. Although the same genes often seem to moderate effects of environmental

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influences whether they are exclusively negative (i.e., diathesis–stress), exclusively positive (i.e., vantage sensitivity), or both (i.e., differential susceptibility), it is important to caution against inferring that every risk gene will also, by default, function as a vantage sensitivity or differential susceptibility gene. There may very well be specific gene variants that play a predominant role in diathesis–stress but not in vantage sensitivity and vice versa. At this point in time, however, the identification of gene variants associated exclusively with vantage sensitivity remains an important objective for future research. In addition, given that most phenotypic traits are the function of many genes of small effect (see Chapter 15), vantage sensitivity is also most likely associated with multiple genes rather than a few candidate genes. A further and related issue is whether vantage sensitivity genes increase sensitivity to all kinds of positive exposures regarding different positive outcomes, or whether specific gene variants increase the benefit derived from specific experiences on specific outcomes. Furthermore, and in line with evolutionary reasoning discussed earlier, the same gene variant may increase vulnerability to a specific kind of environmental adversity while also increasing vantage sensitivity to a different unrelated contextual condition. In other words, some genes may not necessarily moderate susceptibility across the full range of the same environmental factor but predict different responses to different exposures (e.g., vulnerability to adverse exposure A and vantage sensitivity to positive exposure B).

Mechanisms accounting for vantage sensitivity Although explicit efforts to identify mechanisms and processes of vantage sensitivity have not yet been undertaken, a range of studies focusing on behavioral and neurological correlates of some of the genes that emerged as vantage-sensitivity factors in our review indicate that there are likely to be multiple processes involved in vantage sensitivity. However, before we discuss these in more detail, it has to be acknowledged that mechanisms of psychological phenomena can be studied at different levels of analysis (e.g., molecular, neurological, behavioral). At the molecular level, gene–environment interactions reflecting vantage sensitivity most likely involve epigenetic processes (see Chapter 13). Some vantage-sensitivity genes have been associated with attentional processes. For example, healthy adults carrying the 5-HTTLPR short allele outperformed others on the Wisconsin card sorting test, a task that requires, among other things, good functioning in attention and visual processing (Borg et al., 2009). Other work suggests that vantage sensitivity may be related to giving enhanced attention to emotionally relevant stimuli in particular. For example, healthy adults carrying 5-HTTLPR short alleles showed attentional bias to both negative and positive emotional stimuli compared to neutral stimuli (Beevers et al., 2009). Yet other research suggests that this attentional bias for emotionally relevant aspects related to the 5-HTTLPR short allele may actually be stronger for positive stimuli (Beevers et al., 2011) which could help explain why individuals with this genotype benefit more than others from positive influences. The fact, however, that other studies report a stronger bias for negative stimuli only (for meta-analysis, see PergaminHight et al., 2012) or no emotional bias at all (Fox, Ridgewell, and Ashwin, 2009) certainly invites caution before any conclusions are drawn as to why short-allele carriers seem to evince greater vantage sensitivity than do others. One potential explanation for these inconsistent findings may be that short-allele carriers are not so much inherently biased toward negative or positive stimuli but, rather, that their attentional bias is more easily influenced. Evidence consistent with this claim is found in a recent experimental study involving a standard Attention Bias Modification (ABM) procedure in which adults

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with 5-HTTLPR short alleles developed stronger biases for both negative and positive affective pictures than those with long alleles (Fox et al., 2011). Consequently, the authors concluded that individuals carrying the 5-HTTLPR short allele should gain most from therapeutic interventions such as ABM. This suggestion fits nicely with the findings of a recent study by Clarke, Chen, and Guastella (2011), in which the ability to adopt selective attentional processing was assessed with ABM before adult patients went through group CBT for social anxiety disorder. Confirming and extending Fox et al.’s (2011) proposition, those most ready to adopt selective attentional processing (toward threatening stimuli) in the ABM experiment were also those who showed the most positive change in response to treatment. Another potential mechanism involved in vantage sensitivity may be that individuals who benefit more from positive influences are especially sensitive to social forces. Based on empirical observations that individuals with the 5-HTTLPR short allele often prove more sensitive to social aspects of the environment, Way and Taylor (2010) made the case that activity within the serotonin system might be critically involved in setting sensitivity to social experiences. This is certainly intriguing given the fact that most of the earlier-reviewed evidence of vantage sensitivity as a function of 5-HTTLPR includes positive experiences and exposures predominately of a social nature (e.g., parenting, psychological therapy). Differences in reward sensitivity may be another mechanism underlying vantage sensitivity. In an experimental study by Roiser et al. (2006), individuals with 5-HTTLPR short alleles attended to differences in the probability of winning gambles more than those with long alleles, suggesting that the former have greater reward sensitivity. According to the differential susceptibility framework (Belsky and Pluess, 2009a, 2013), the primary reason why some individuals are more responsive to positive influences than others may be that they have a more sensitive central nervous system on which experiences register more easily and deeply. According to this “neurosensitivity” hypothesis (see also Aron, Aron, and Jagiellowicz, 2012), some gene variants contribute to heightened sensitivity of specific brain regions which then increase the response to environmental influences (Pluess, Stevens, and Belsky, 2013). One brain region that seems very likely to be involved in vantage sensitivity (as well as differential susceptibility) is the amygdala, which plays an important role in the processing of emotional stimuli (Sander, Grafman, and Zalla, 2003) and has been found to be more active in individuals carrying the 5-HTTLPR short allele (Munafo, Brown, and Hariri, 2008). Importantly—and contrary to the outdated view that the amygdala’s primary function is the detection and processing of fearful stimuli (Adolphs et al., 1999; Davis and Whalen, 2001)—recent research shows that it responds even more strongly to positive stimuli (for meta-analysis, see Sergerie, Chochol, and Armony, 2008). Hence, amygdala reactivity might be one possible central nervous mechanism by which vantage sensitivity operates.

Implications of vantage sensitivity It is not difficult to imagine the practical benefits that might accrue from the theoretically anticipated discovery of genes predicting variation in response to treatments intended to benefit children, parents, and others, be those treatments intended to remediate problems, prevent them from developing in the first place or becoming worse, or promote positive functioning. After all, if one could identify in advance those most likely to benefit from a treatment or intervention and the genetic resistance factors most likely to undermine service effectiveness, then that service could perhaps be provided on a more efficient basis. Consider in this regard not just the financial cost of endeavoring to enhance the functioning of someone who, as evidence indicates, is unlikely

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to benefit—or at least not to the degree of others—but also the disappointment experienced by such recipients and their service providers. If vantage sensitivity and vantage resistance genes could be identified, then it might prove possible to match individuals to particular treatments. Although it is not news to argue that different people may require different treatments to achieve the same outcome—whether that be the amelioration of some problem or the promotion of some valued outcome—we have until recently lacked theory and evidence regarding what endogenous factors might matter in this regard. Importantly, the range of treatments extends well beyond those provided in clinical settings to “patients” and “clients,” and thus includes widely utilized routine child care, and educational and other services. Finally, failure to account for vantage sensitivity will most likely lead to mis-estimation of treatment effects: for those who are especially vantage sensitive, treatment effects will be under-estimated, whereas for those who are vantage resistant they will be over-estimated.

Summary A growing body of empirical evidence supports the notion that individuals differ in their positive response to beneficial experiences and exposures: some people are more likely to benefit from positive supportive experiences than other people as a function of their individual genetic make-up. Importantly, these genes are unlikely to predict positive psychological outcomes in the absence of a supportive context, suggesting that the development of PWB requires both a genetic susceptibility that enables the individual to derive benefit from positive experiences as well as exposure to an environment that provides such experiences (see Chapter 15). In other words, PWB is most likely shaped by the complex interplay between vantage sensitivity genes and the effects of positive supportive environments rather than genes or contextual factors alone.

Acknowledgments We would like to express our gratitude to the school participating in the SPARK study, specifically the teachers involved in the intervention as well as all participating children and their parents. Further, we would like to thank research assistants Rory Corcoran, Majella Greene, Amandeep Hothi, Karen Wadey, and Livia Whyte for their valuable contribution to the evaluation of the SPARK program. This research was conducted with the support of a grant from the British Academy awarded to Michael Pluess (SG101368).

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Chapter 13

Epigenetics and well-being: optimal adaptation to the environment Moshe Szyf and Michael Pluess

Epigenetics and well-being: optimal adaptation to the environment: an introduction Whether PWB is predetermined by our genes—the legacy of inherited sequence differences between humans—or whether it is past experiences and physical and social environments that impact on our long-term well-being represents a fundamental question (see Chapter 15). The genes that we inherit from our parents certainly define to a large extent our physical and mental characteristics. However, the naive belief that all differences between humans could be explained by differences in the DNA sequence is inconsistent with our current understanding of gene function (see Chapter 2). Genes are “programmed” (i.e., switched on or off) by epigenetic mechanisms and this programming shapes the way genes function in different organs of the body at different times in life and in different contexts. The main focus of the emerging field of epigenetics so far has been on unraveling how aberrant epigenetic programming of genes could lead to disease. This has been well developed in the field of cancer research and more recently became the focus of metabolic and neurological/psychiatric disease research. As a consequence, epigenetics is starting to impact both diagnosis and treatment of cancer and it is anticipated that this will spread to other medical disciplines. However, there has been very little discussion or research regarding the potential role of epigenetic mechanisms in relation to PWB. In this chapter we will review basic epigenetic mechanisms and point to the potential impact of these mechanisms on PWB. Given the rather biological focus of this chapter, we will define well-being as optimal adaptation to the environment, associated with good physical and psychological health, reflecting optimal functioning within a specific environment and not just the absence of maladaptive development. The first challenge to genetic determinism or the idea that genotype exclusively defines phenotype came from the field of embryology. Multicellular organisms like ourselves have essentially the same DNA in all our tissues and organs, but it is clear that different genes are expressed in different organs, creating the startling phenotypic diversity seen in complex organisms. In addition to tissue specific gene expression of genetically identical cells, the roles of these different cells in the body are context dependent. For example, particular cells in the immune system like T-cells must express proteins on their surface for the recognition of specific foreign proteins or cells. However, cells that recognize particular invaders need to be activated only when the body is challenged. Consequently, each of these functions requires the expression of specific genes (e.g., those encoding the required proteins that distinguish the cells as T-cells) but also regulation of such gene expression dependent on particular contexts and particular times. How could one DNA template define such a diversity of gene expression? Almost seven decades ago this exact question was raised by Waddington (1959), who coined the term epigenetics as a synthesis of two concepts

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in embryology: genetics and epigenesis. Although no concrete mechanisms were proposed back then, the idea was that DNA undergoes transformations during embryonal development that do not involve changes in the genetic structure (i.e., DNA sequence), but instead define different trajectories for DNA in differently developing tissues and organs. In the last half-century, research in biochemistry has focused on investigating such epigenetic processes involved in marking where and when genes are to be expressed during development. One fundamental question has been whether epigenetic programming of genes is defined exclusively by innate evolutionary conserved factors—including genetic make-up—or whether these processes could be modulated in response to external environmental influences. A related question is whether epigenetic processes are limited to early developmental periods or whether they are amenable to changes throughout life. Although the main focus in the epigenetic literature has been on aberration of epigenetic marks in human disease, this chapter argues that the same processes play an important role regarding the development of well-being by adapting genome function to external environments and experiences. Importantly, while a misfit between epigenetic adaptation and environmental conditions is likely to result in maladaptation and potential pathology, it is conceivable that a good fit between adaptive epigenetic programming and the environment will result in optimal functioning, including PWB.

Epigenetic mechanisms Genes contain the information required to make RNA, which in turn contains the information to make proteins (see Chapter 2). Proteins are required for both building anatomical structures as well as performing physiological functions throughout the life-course. Epigenetic mechanisms, on the other hand, determine when and where the genes will produce mRNA which encode the proteins. Epigenetic mechanisms include several biochemical processes that act at different levels (this chapter will cover mainly chromatin modification and DNA methylation, see Figure 13.1). The DNA molecule is packaged in chromatin, which allows the large DNA molecule to be contained in the small nuclear space. The basic building block of chromatin is the nucleosome, which is composed of eight histone proteins (Finch et al., 1977); 140 base pairs of DNA are wrapped around a single unit of a nucleosome, which is then packaged in the nucleus in higher-order structures. Chromatin states define the accessibility of different genes to a machinery of proteins that activate genes to transcribe RNA as well as other proteins that maintain genome integrity, repair defective DNA, and trigger DNA synthesis. Importantly, chemical modifications of the tails of these histone proteins determine whether the DNA will be accessible for transcription (Strahl and Allis, 2000). In addition to chemical histone modification, there are other important epigenetic modifications such as noncoding RNA (Flanagan and Wild, 2007; Mohammad et al., 2012). Noncoding RNA has important regulatory functions, but in contrast to mRNA it does not get translated (i.e., coded) into proteins (see Chapter 2). Finally, the spatial positioning of nucleosomes on DNA also plays a critical role in defining where and when genes are transcribed. The most proximal epigenetic modification is the chemical coating of the DNA molecule itself by a small chemical group, a methyl moiety in a process termed DNA methylation (Hotchkiss, 1948). Recent studies suggest that the methyl coating could be further modified chemically by hydroxylation (Kriaucionis and Heintz, 2009) and carboxylation (Ito et al., 2011). Thus, the DNA chemical entity itself contains two layers of information: genetic and epigenetic. By having this dual identity, the genetic identity, which is common in all tissues, and the DNA methylation pattern, which is cell-type specific (Razin and Szyf, 1984), the DNA can exhibit genetic uniformity concurrent with phenotypic diversity.

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Figure 13.1 Graphical illustration of two important epigenetic mechanisms: DNA methylation and histone modification. Reprinted by permission from Macmillan Publishers Ltd: Nature, 441 (7090), Jane Qiu, Unfinished Symphony, pp. 143–145, doi:10.1038/441143a Copyright © 2006, Nature Publishing Group.

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The lesson from embryology is that remarkable phenotypic diversity can be achieved with the same genetic sequence. It is easy to see how stochastic drifts in the way the epigenetic patterns are generated during embryology might cause disease later in life by altering the programming of genes. It is also easy to accept that exposure to chemicals during gestation that would interfere with the biochemical processes and the enzymes that lay down these patterns could result in longterm changes in gene function. Indeed, studies using the Agouti mouse model have shown how maternal diet affects coat color of its offspring as well as other metabolic phenotypes: a methylrich diet of the mother during gestation resulted in changes in DNA methylation that altered the regulation of the Agouti gene that defines coat color in these mice (Dolinoy, Huang, and Jirtle, 2007; Waterland and Jirtle, 2003). It is conceivable that maternal diet during pregnancy and other gestational experiences are also important for the development of PWB in humans. In other words, the provision of a balanced source of methyl moieties may enable the innate epigenetic processes to operate at optimal levels. However, although interventions such as folic acid supplementation are commonly used to provide an adequate supply of methyl supply during gestation, the proper balance of methyl dietary supplementation is still unclear (Kim, 2004). A critical question for our discussion is whether there are—in addition to the innate and genetically predetermined epigenetic processes occurring during early development—similar organized processes that adapt the epigenetic profile to signals from experience and external exposures to create an “experience-dependent” epigenome that could confer multiple different phenotypes to identical genotypes based on their specific early life experience. Recently, it has been proposed that epigenetic mechanisms could serve as genome adaptation mechanisms that adapt the functioning of the genome to signals derived from experience (Szyf, 2012). This might be particularly important during early life when signals from the caregiving environment alter the epigenome to adapt to anticipated environments in adulthood. The potential mismatch between these environmentally programmed epigenomic states with the actual environment later in life has been hypothesized to be causing both physical disease and behavioral and mental disorders (Gluckman and Hanson, 2005; Szyf, 2012). However, a good match between epigenetic adaptations in early life and adult environments would result in optimal adaptation to the specific environmental conditions (Pluess and Belsky, 2011). As mentioned earlier, it remains to be determined whether these proposed adaptive epigenetic mechanisms are limited to early life only or whether they continue to adapt the genome throughout life, and whether these epigenetic alterations are reversible later in life by environmental and behavioral interventions. In any case, it is clear that if such an environmental-responsive genome-adaptation mechanism exists, it did not evolve to cause disease but rather to ensure life-long optimal functioning within a specific environment, ultimately aimed at increasing fitness (see Chapter 4). While such optimal functioning is likely to include PWB it is important to clarify that it is reproductive fitness rather than individual emotional wellbeing that is the focus of processes related to natural selection (and reproductive fitness is not necessarily contingent on PWB).

Chromatin modification Out of all the observed possible chemical modifications of the N-terminal tails of histone proteins, Histone acetylation is the most studied one since it is almost ubiquitously associated with gene activation by opening accessibility of DNA to proteins that turn on gene transcription (Lee et al., 1993; Perry and Chalkley, 1982). Histone methylation, on the other hand, could either turn genes on or turn them off. These different states of chromatin modifications are important for the proper programming of genes during development and through life (see also Chapter 2).

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The main difference between genetic alterations and epigenetic alterations is potential reversibility. Germ line changes in sequence cannot be changed short-term in a feasible way (except through rare de novo mutations) and are thus transferred from generation to generation. Given that epigenetic marks are laid down and removed by enzymatic reactions, and the state of histone modification needs to be maintained by the balance of different chromatin modification enzymes (i.e., acetyltransferase and histone deacetylase), it should be possible to alter the state of chromatin modification by altering the activity of one of these enzymes with the help of pharmacological agents. In fact, pharmacological treatments targeting histone acetylation are now tested as possible treatment of neurobehavioral disease (Abel and Zukin, 2008; Fass et al., 2013; Fischer et al., 2010). An important facet of chromatin modification is that histone modification occurs in a highly targeted manner in response to signaling pathways in the cell. These enzymes are targeted to particular sites in the genome by trans-acting factors that are activated in response to developmental and experiential signals (for a review and detailed discussion, see Szyf, 2009). Given that neuronal processes target histone modification enzymes by activation of signaling pathways (Guan et al., 2002) it is potentially possible to alter chromatin states not only by blocking the histone-­modifying enzymes, but also by tapping into the signaling pathways that guide and target these enzymes in the first place. These signaling pathways are activated by experience, providing a potential mechanism for how behavioral interventions such as exercise and diet might contribute to optimal functioning through epigenetic reprogramming. This obviously has important implications for reversal and correction of adverse epigenetic marks and promotion of adaptive epigenetic marks that contribute to optimal functioning. Consequently, it may be possible to improve and prevent challenges to well-being-related outcomes by altering epigenetic states through specific actions and exposures to supportive environments.

DNA methylation DNA methylation is a remarkable epigenetic modification given that it is part of the chemical entity of DNA (Hotchkiss, 1948). That is, the same chemical structure contains both ancestral information in the genetic sequence as well as epigenetic information in the form of distribution of methyl groups. DNA derived from different cell types has an identical sequence but different distribution of DNA methyl moieties (Razin and Szyf, 1984). Thus, DNA methylation provides a basic molecular explanation for the idea proposed by Waddington (1959) that DNA undergoes transformation during embryogenesis to gain particular epigenetic forms in different tissues. Although DNA methylation was already described more than sixty years ago (Hotchkiss, 1948), it is only recently that studies using genome-wide mapping of DNA methylation were able to confirm these early observations of tissue-specific DNA methylation patterns and their formation during cellular differentiation (Lister et al., 2009) and embryonal development (Lister et al., 2013). Early studies on the role of DNA methylation in regulating genome function suggested that methyl marks in critical command posts of genes (5’ regulatory regions) can silence gene expression (Razin and Riggs, 1980). In other words, DNA methylation can mark which segments of the genome are expressed in particular cell types during cellular differentiation. Later studies provided mechanisms for such silencing of gene function by DNA methylation. A methyl group at a critical position of a gene can directly interfere with binding of protein factors that normally interact with this position in order to turn the gene on (transcription factors). By disrupting such interaction, DNA methylation silences the gene (Comb and Goodman, 1990). An alternative mechanism was proposed a decade later, which involves recruitment of proteins that recognize

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methylated DNA in regulatory regions of gene-termed methylated DNA binding proteins (MBD) (Nan et al., 1998). These proteins recruit chromatin-modifying complexes that modify chromatin and silence gene function (Nan et al., 1998), illustrating the tight relationship between the two epigenetic levels of regulation of genes: DNA methylation and chromatin (histone) modification (D’Alessio and Szyf, 2006).

The reversible DNA methylation reaction DNA methylation—as well as chromatin modification—is defined by reversible enzymatic reactions and is thus potentially manipulatable throughout life by social experience. The main interest in DNA methylation has been in explaining the process of cellular differentiation during development and addressing the enigma of how one genome could express a diverse set of phenotypes in a multicellular organism like humans. The idea that DNA methylation is responsible for cellular identity seemingly implies that DNA methylation patterns must remain fixed after completion of cellular differentiation, since any change in DNA methylation might threaten the cellular identity of adult tissues. The concept of maintenance DNA methylation in palindromic CG dinucleotide sequences through cell division was consistent with this idea (Razin and Riggs, 1980). The transfer of methyl groups to DNA is catalyzed by enzymes DNA methyltransferases (DNMT) that use the ubiquitous methyl donor S-adenosyl-methionine (Adams et al., 1975; Drahovsky and Morris, 1971; Gold et al., 1966). DNMT1 is a DNA methyltransferase enzyme that is proficient in methylating the daughter strand across a methyl group in the parental strand, thus allowing the faithful copying of a DNA methylation pattern during cell division ensuring the maintenance of the DNA methylation pattern (Gruenbaum et al., 1982). This mechanism is extremely important in cell division and implies that once a change in DNA methylation is introduced into the DNA it will be maintained through subsequent cell divisions of the cell lineage. However, in postmitotic cells that do not divide, such as neurons in the brain, this feature (not mechanism) is irrelevant, suggesting that DNA methylation could play an important role in further modifying the phenotype in adult tissue in response to experiential signals. Support for the proposed ability to form new DNA methylation states in the adult brain, well after completion of brain maturation, is provided by the fact that reduction in DNMT3A in the aging brain is implicated in reduced cognition and that cognition could be augmented in an aging mouse by reintroduction of DNMT3A (Oliveira, Hemstedt, and Bading, 2012). If DNA methylation were static in adult brains, these manipulations of an enzyme involved in DNA methylation would not be effective. If indeed DNA methylation is dynamic in response to signals in mature cells that do not divide any further (i.e., postmitotic), then the DNA methylation reaction has to be reversible. In other words, there must be mechanisms that can remove methyl groups from DNA in the absence of cell division. Two principal reasons account for the initial resistance to accept the idea that DNA methylation is a reversible biochemical reaction. First, if DNA methylation is indeed guarding the differentiated phenotype of a cell, it has to be persistently and strictly maintained. Second, the bond between a methyl group and a carbon ring is considered an extremely strong chemical bond. Notwithstanding these objections, more than a decade ago we proposed that DNA methylation is a reversible signal like other physiological signals (Ramchandani et al., 1999) and reported the existence of a protein with an activity that could directly remove methyl moieties from cytosine (Bhattacharya et al., 1999). Although the activity of this specific protein was contested (Ng et al., 1999), other activities that reverse DNA methylation were proposed and demonstrated (Barreto et al., 2007; Guo et al., 2013; Jost, 1993; Rai et al., 2008; Razin et al., 1986). Thus, although the biochemical mechanism responsible for DNA methylation is still being explored, it is nevertheless

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Figure 13.2 DNA methylation reactions. DNA methylation and other chromatin modifications provide different functional states to identical genes. An active gene (on the left) is characterized by an open chromatin and acetylated histones with no methylation in transcription start sites and critical transcription factor binding sites. The gene is transcribed as indicated by the dark grey horizontal arrow encoding a protein that contributes to a phenotype a. If the gene is methylated (CH3) (right) either in response to developmental innate triggers or exogenous environmental triggers, DNA methylation prevents binding of either transcription factors or the transcription machinery or both. The histones are modified as well by lack of acetylation (Ac) or methylation (CH3) in particular residues, such as lysine 9 on histone 3. The gene is not transcribed (X) and a protein is not produced contributing to a different phenotype B. The state of methylation is defined by a balance of enzymatic reactions, methylation by DNA methyltransferases (DNMT), and demethylation by demethylases.

clear that DNA methylation is a reversible reaction. Therefore, DNA methylation is a candidate mechanism to dynamically alter and adapt gene programs in response to experiential signals, as will be discussed in Figure 13.2.

Epigenetic adaptation to the environment Adaptation of genome function to changing, life-long environmental conditions is critical for optimal functioning of the organism. Natural selection, which acts through genetic sequence selection, is a slow and inefficient mechanism and cannot account for the dynamic changes in the environment that occur through a single life-course or a few generations. Optimal functioning—­ reflected to some extent in PWB—will obviously be compromised without adjustment of genome functions to changing environments. Thus, predetermined genetic and conserved epigenetic signals generated during development cannot by themselves delineate successful adaptation across life. It stands to reason that maintenance of optimal functioning will require genome adaptation at multiple timescales from a transgenerational timescale where critical experiential information is passed from one generation to the other, a life-long timescale that ensures a stable phenotype is well adapted to anticipated environments, up to proximal timescales that require

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further adjustment in response to fast-changing environments (Szyf, 2012). First, evidence for the hypothesis that the biochemical epigenetic mechanisms discussed embed environmental information in the genome came with experiments that examined the impact of experiences in early life on behavioral outcomes in rats.

First evidence: epigenetic changes in response to maternal care It has been suggested that the developing child receives cues from the immediate environment regarding the quality of future environments and that adaptation of the phenotype in response to these cues would prepare the developing child for the anticipated world that she/he is going to live in (Barker, 1998; Gluckman and Hanson, 2005). Most of the epidemiological and animal research that examines the impact of early experiences focuses on how adversity early in life is associated with mental and physical disorders later in life. However, it stands to reason that the same processes that operate in early developmental periods to translate early life experience into life-long phenotypes are fundamentally adaptive processes that prepare the organism for optimal functioning in a specific context and that adult disease represents a maladaptation of this inherently adaptive response (Gluckman and Hanson, 2005; Szyf, 2012). Understanding these processes is therefore critical in order to elucidate how early experiences can shape adult well-being (see Figure 13.3). The first evidence for epigenetic processes mediating the impact of early experience on lifelong behavioral phenotypes came from studies in rats. A long line of work has demonstrated that the natural variation in maternal care is associated with phenotypic differences in their adult offspring, particularly in behaviors related to stress responsivity (Francis et al., 1999; Liu et al.,

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Figure 13.3 Epigenetic adaptation to the environment. Epigenetic adaptation of genome function to innate developmental signals and exogenous environmental signals from three spheres: the biosphere, the physical sphere, and the social sphere. These act on signaling pathways that sculpt the DNA methylation to adjust genome function to developmental and environmental signals and define a phenotype that promotes optimal functioning.

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1997). Adult offspring of rat mothers that provided low-quality parenting (i.e., low licking and grooming) showed heightened stress responsivity in comparison with offspring of mothers that provided high-quality parenting (i.e., high licking and grooming). Cross-fostering experiments revealed that these differences in offspring behavior were neither genetic nor germ-line mediated (Francis et al., 1999). The first line of studies demonstrating epigenetic programming by early life experience focused on candidate genes. The glucocorticoid receptor (GR) in the hippocampus acts in a negative feedback of the hypothalamic-pituitary-adrenal axis (HPA) by glucocorticoid (GC). Weaver et al. (2004) showed that differences in maternal licking and grooming resulted in differences in DNA methylation, histone acetylation, and expression of the NR3C1 GR exon 17 promoter in the hippocampus. These differences emerged in the pups soon after birth in response to maternal care and remained stable into adulthood, suggesting that reduced expression of GR in the hippocampus in offspring of low licking and grooming mothers—programmed by epigenetic silencing— leads to heightened stress responsivity. What is the mechanism that translates maternal care into epigenetic changes in a particular address in the genome? First, empirical work suggests that the release of neurotransmitters in response to behavioral signals activates particular neuronal receptors in specific anatomic positions and circuitries in the brain, which in turn trigger a cascade of intracellular signaling proteins that activate transcription factors that target chromatin-modifying enzymes to the genome (Weaver et al., 2007). Neuronal activation was shown to alter DNA methylation in the brain, most probably through a pathway that involves signaling and targeting by chromatin and DNA sequence specific factors (Chen et al., 2003; Zhou et al., 2006). For example, changes in the state of phosphorylation of MeCP2—a protein that recognizes methylated DNA in response to neuronal activation—were shown to target demethylation to the BDNF sequence in neuronal cultures. MeCP2 was proposed to mediate demethylation of the Arginine Vasopressin (AVP) gene in neurons of the hypothalamic paraventricular nucleus in response to early life stress (Murgatroyd et al., 2009). These results provide biochemical plausibility for the notion that social exposures can alter DNA methylation in the brain. Importantly, in non-neuronal tissues, activation of hormones such as glucocorticoids in response to stress as well as other candidate cytokines might be operating through similar mechanisms. The fact that these DNA methylation changes occur through organized physiological pathways rather than chaotic responses to adversity suggests that the underlying mechanisms are adaptive and evolved to promote optimal functioning by altering genome programming to cope with anticipated challenges. In other words, the main implication of a dynamic epigenome is the potential to adapt to the environment by shaping the phenotype to the anticipated environmental conditions in order to secure optimal functioning through successful adaptation. An important question is whether these adaptive dynamic epigenetic responses in early childhood are fixed thereafter or whether they are reversible in later life in order to adapt to potential environmental changes. To test the plausibility of reversing early life epigenetic programming in adulthood we used pharmacological epigenetic modifiers that were injected into the brain of adult rats who were either reared by high- or low-licking and grooming mothers. We used either trichostatin A—an inhibitor of histone deacetylase—to increase histone acetylation and reduce DNA methylation and activate the NR3C1 gene in the offspring of low-licking and grooming mothers, or methionine—a donor of methyl groups—to increase DNA methylation and silence the NR3C1 gene in offspring of high-licking and grooming mothers (Weaver et al., 2004; Weaver et al., 2005). We showed that we could reverse the hyper-activated stress responsivity in the offspring of low-licking and grooming rats and enhance stress responsivity in offspring of

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high-licking and grooming rats, demonstrating that in spite of the long-term stability of early life epigenetic programming it could be reversed in adulthood using epigenetic pharmacological modifiers (Weaver et al., 2004; Weaver et al., 2005). The immediate follow-up question is whether reversal of epigenetic programming is limited to pharmacological agents or whether behavioral interventions later in life could act through the same pathways, and reverse early life epigenetic programming and readjust the epigenome to the current adult environment. The observation that social environments and other experiences activate signaling pathways that could trigger changes in DNA methylation certainly suggests that this might be a possibility (Weaver et al., 2004). Importantly, the basic concepts revealed in the Weaver et al. (2004) study were supported by several other studies examining candidate genes in models of early life social adversity. For example, DNA methylation of the BDNF gene promoter in the adult prefrontal cortex in rat pups was altered in response to exposure to stressed abusive fostering mothers (Roth et al., 2009), and the AVP gene was shown to be hypo-methylated in the hypothalamus paraventricular nucleus in mice exposed to early life stress (Murgatroyd et al., 2009). Furthermore, maternal separation resulted in altered DNA methylation in the hippocampus in the promoters of the NR3C1, AVP, and Nuclear Receptor Subfamily 4, Group A, (Nr4a1) genes (Kember et al., 2012). An interesting experiment in mice provides support for the idea presented here that the DNA methylation alterations seen in response to social stress mediate phenotypic adaptation, and that this mechanism persists even in adults and is not limited to early life exclusively: Elliott et al. (2010) showed that chronic social stress in adult mice triggered demethylation of the regulatory region of the CRH gene, but only in a subset of mice that displayed social avoidance.

Early life adversity and DNA methylation changes in humans The effects of early life adversity on changes in DNA methylation of the NR3C1 gene in the hippocampus have been replicated in a sample of human brains of people that committed suicide and control subjects. Consistent with animal studies, the NR3C1 promoter (1f) was hyper-methylated at particular sites in the hippocampus of those who were exposed to child abuse as compared with those who were not (McGowan et al., 2009). In addition, NR3C1 was examined in several studies regarding the impact of early life adversity and stress on children. For example, Nr3c1(1f ) exon was found to be slightly differentially methylated in genomic DNA from cord blood mononuclear cells from neonates who were exposed to maternal depression in utero (Oberlander et al., 2008). Similarly, a correlation was observed between stress in pregnant women, their newborn birth weight, and hyper-methylation of the NR3C1 promoter in cord blood (Mulligan et al., 2012). Other studies have shown increased NR3C1 methylation in people who experienced early parental death (Melas et al., 2013) and in relation to severity and the number of type of maltreatments during childhood (Perroud et al., 2011). Although these studies are intriguing, we are not aware of any published study that investigated whether positive supportive environmental influences have similar effects on DNA methylation. However, given that epigenetic mechanisms aim at adaptation to the environment, it can be expected that the epigenome would be shaped equally by positive exposures.

Broad response of the epigenome to early life adversity DNA methylation and other epigenetic processes are highly tissue specific and this has been known for three decades (Razin and Szyf, 1984). Recent studies recapitulated these conclusions that many differentially methylated regions exist between different tissues, although consistency

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in inter-individual DNA methylation between tissues has been shown (Slieker et al., 2013). It is therefore plausible that any change in DNA methylation that is relevant for behavior will only be detected in the brain. If this were true, it would make the study of epigenetics almost impossible in living humans. However, the assumption behind such an argument is that the brain and body and the different physiological systems are independent entities. We will argue here on the basis of accumulating data that the changes in DNA methylation that are detected in response to environmental influences are not limited to the brain and involve other tissues as well in an integrated body-wide response. In other words, the changes in DNA methylation in the periphery (e.g., blood, saliva) are not a “surrogate” marker of changes in the brain, but instead reflect the crosstalk between the brain and the periphery that is normally needed to integrate physiological and mental processes required for maintaining optimal functioning. Another related issue is the focus on candidate genes. A narrow interpretation of early life adversity would focus on stress, and therefore, most work to date has focused on the glucocorticoid receptors and other stress-related genes, as described in previous sections. However, genes act in networks (Tuch, Li, and Johnson, 2008), and it is therefore plausible that early life environment will reorganize the epigenetic states of complex networks of genes rather than a few genes associated with the immediate stress response. There is now emerging evidence that the changes in DNA methylation in response to early life stress involve many genes and that they are not limited to the brain, as we will discuss. DNA methylation in blood. Changes in DNA methylation associated with early life social adversity are not limited to the brain. When we examined adult rhesus monkeys that were exposed to differential rearing during early life with their biological mother or in a nursery, differences in methylation were observed in hundreds of genes in both prefrontal cortex and T cells from blood samples (Provencal et al., 2012). Although there was a small overlap between blood and brain, most of the differences were tissue specific, supporting the hypothesis that T cells are not merely surrogates to the brain, but rather play a particular role in the response to early life maternal deprivation. The question whether DNA methylation changes relevant to behavior are detectable in blood cells has important practical implications for the study of behavioral epigenetics in living humans, but also for the future development of DNA methylation probes as tools for risk prediction and follow-up of response to interventions or therapeutics. Several studies have provided preliminary evidence for DNA methylation differences in blood cells that are reflective of social exposures. For example, Uddin et al. (2010) reported differential DNA methylation of immune-related genes in blood samples of people suffering from PTSD. Similar differences in blood DNA methylation of individuals with PTSD have been associated with child maltreatment (Mehta et al., 2013). Furthermore, changes in blood DNA methylation of the FKBP5 gene were detected as a function of early life maltreatment (Klengel et al., 2013) and in the NR3C1 gene of adults with history of child maltreatment (Perroud et al., 2011). Finally, genome-wide changes in blood DNA methylation in adults were found to be associated with early life poverty (Borghol et al., 2012). These studies point to the prospect that it might be possible to use noninvasive DNA methylation markers of both health and disease and to utilize them for following up interventions that aim at increasing physical well-being and PWB. Gene-specificity of DNA methylation. Examination of the differences in the maternal transcriptome between offspring of high- and low-licking and grooming rat mothers revealed differences in hundreds of genes. For example, the changes in DNA methylation, transcription, and histone acetylation in response to maternal care in the rat hippocampus were not limited to the NR3C1 gene, but involved large spans of the genome (Kohlmann et al., 2011). Furthermore, epigenetic modulations with trichostatin A and methionine that reversed the impact of maternal care on stress responsivity also impacted hundreds of genes (Weaver, Meaney, and Szyf, 2006). Similar

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findings emerged in brains of human suicide victims who were exposed to child abuse as children: differences in DNA methylation were found not only in the NR3C1 gene, but also in the promoters of rRNA genes which are involved in the ubiquitous protein synthesis machinery and are unrelated to the stress response (McGowan et al., 2008). Hence, as in rats, there is a broad DNA methylation response to child adversity in the human hippocampus (Labonte et al., 2013; Suderman et al., 2012). In summary, although data are still sparse and most studies suffer from limitations, they nevertheless all point to the fact that changes in DNA methylation associated with the social environment are not limited to the brain and are not limited to a small subset of candidate genes. Together, these data support the idea that changes in DNA methylation in response to the early environment do not just reflect stochastic noise like random genetic differences. On the contrary, DNA methylation changes are highly organized, affecting multiple gene pathways and multiple body systems. They point to the possibility that there is a physiological system that senses early life physical and social environments and programs the genome in response. If indeed such a system exists, it stands to reason that it was selected in evolution not to cause adult disease, but to prepare the individual for a biologically optimal life by coordinating genome function with anticipated environments.

Epigenetic change as mediator of gene–environment interaction Extreme genetic determinism argues that genetic variation explains both vulnerabilities of humans as well as well-being outcomes. Examples supporting this view are found in rare Mendelian mutations with extremely high penetrance, such as genes associated with Alzheimer’s disease (Piaceri, Nacmias, and Sorbi, 2013; Tanzi, 2013) or breast cancer (Bowcock, 1993). However, in most other cases the relationship is more complex and single genetic changes in common alleles are associated with only a small effect, whereas the combination of multiple genetic differences explains larger proportions of the variance (see Chapter 15). In contrast to this extreme view of genetic determinism the dominant idea in the field to date is that, although genetic variation plays a critical role, genetic effects can be influenced by environmental exposure including both chemical–physical and social environments (see Chapters 11 and 12). For a long time such gene–environment interactions have been a predominately statistical construct describing situations where the statistical probability for a genetic variant to confer either risk or protection from a disease was influenced by environmental conditions. However, in absence of a biological mechanism for gene–environment interaction it is difficult to either understand or take full advantage of this concept for intervention and prevention. One of the most cited documents of such a statistical interaction is the report that the short allele of the serotonin transporter gene (5-HTTLPR) is associated with high risk of depression when the carriers of this allele were exposed to stressful environments early in life (Caspi et al., 2003). Since this pioneering study, many other reports have described gene–environment interactions (see Chapter 11). The epigenetic studies described here are beginning to provide a general mechanism for the way in which exposures and experiences can stably program genes and alter their long-term function. The studies on the pathways leading from maternal care to NR3C1 epigenetic programming in the rat (Weaver et al., 2004, 2007)—discussed earlier—provide a physiological route for how early life social conditions alter the epigenetic programming of the gene. Changes in NR3C1 DNA methylation in response to child abuse that are detected during adulthood probably involve similar interactions. Thus, it is suggested here that gene–environment interactions are the normal process by which external environments modulate the genome to ensure adequate adaptation of genome function to these environments, and that genetic variation tweaks these adaptations further.

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There is empirical evidence that well-defined gene–environment interactions involve alterations in DNA methylation which are triggered by the early environment and modulated by genetic differences. For example, in rhesus macaques the short allele of the 5-HTTLPR is targeted for DNA methylation when the monkeys experience early life maternal deprivation leading to enhanced risk of depression (Barr et al., 2004). Similarly, the 5-HTTLPR short allele was more methylated in Rhesus monkeys that were maternally deprived, and such higher methylation was associated with behavioral stress reactivity in infants (Kinnally et al., 2010). An allele of the FKBP5 gene, encoding a proximal negative regulator of response to glucocorticoids, has been found to confer risk for PTSD upon individuals if they experienced early life adversity (Zimmermann et al., 2011). Recent studies are consistent with the idea that the mechanisms that mediate this environmental effect on this gene are epigenetic, suggesting that the genetic sequence alters the probability of a site becoming methylated in response to adverse early life conditions. For example, Klengel et al. (2013) demonstrated that a distant sequence at intron 7, which also contains a glucocorticoid response element (GRE), is differentially demethylated in carriers of the FKBP5 risk allele who were exposed to adversity early in life in comparison with either risk allele carriers not exposed to early life adversity or those exposed to adversity but not carriers of the risk allele. Thus, activation of GR by glucocorticoids—which are elevated in response to early life distress—may recruit demethylases to a GRE in intron 7 of the FKBP5 gene, resulting in demethylation of this polymorphic sequence (Klengel et al., 2013). In summary, we propose that gene–environment interactions evolved to guarantee successful adaptation to the environment (i.e., optimal functioning) so that changes in the environment as interpreted at critical points in life, particularly early life, are registered as changes in gene function.

Implications of epigenetics for understanding and enhancing psychological well-being Although we are very early on in our understanding of how epigenetic processes respond to environmental cues, it is important to consider the impact that the concepts emerging from epigenetic research might have on our understanding of PWB and the promotion thereof. The main focus of most genetic and epigenetic research has been on the identification of genetic and epigenetic factors that can either predict or associate with disease. The underlying hypothesis in such work has been that genetic and epigenetic variation occurs randomly. If indeed DNA methylation and other epigenetic changes that are associated with disease are stochastically generated, then the implications for well-being promotion are limited. However, the hypothesis proposed here suggests that this might not be the case. DNA methylation changes that occur during early life are proposed to be a mechanism for promoting optimal functioning through successful adaptation by adjusting the genome to environmental signals, thus preparing the organism to function well in anticipated environments. If the data indeed point to an organized process that responds to environmental signals, it is highly unlikely that it evolved exclusively in relation to adverse experiences. Understanding how these epigenetic processes function in response to supportive environmental influences and in the prediction of positive outcomes might provide new insights into strategies that will promote epigenomic-driven well-being. One of the fundamental properties of epigenetic processes is that they are reversible. This bears the optimistic message that it might be possible to alter the course of an epigenetic trajectory aimed toward vulnerability, unhealthy growth, and aging through pharmacological treatment. For example, it was possible to reverse a cancer state by inhibiting the DNA methylation enzyme DNMT1 (Ramchandani et al., 1997), and the anxious and stressful behavior of adult offspring of a

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low-licking and grooming rat could be reversed by treatment with the histone deacetylase inhibitor trichostatin A (Weaver et al., 2004). Such epigenetic interventions with drugs or nutritional supplements that either promote or inhibit DNA methylation such as the methyl donor SAM might be used to promote well-being. However, even more interesting are the possibilities that certain behavioral interventions might alter trajectories of DNA methylation and reverse adverse DNA methylation signatures and promoter healthier programs. One example is the role of physical exercise beyond its immediate proximal effects on metabolism and muscle function: recent evidence suggests that exercise in both animals (Tajerian et al., 2013) and humans could alter DNA methylation profiles (Barres et al., 2012; Nitert et al., 2012; Ronn and Ling, 2013) and, thus, account for the long-term and broad positive effect that exercise has on health and well-being. It stands to reason that exercise is an example of an intervention that can tap into the physiological processes that control DNA methylation states associated with the regulation of gene networks involved in metabolism. Hence, it seems likely that pharmacological, nutritional, and behavioral interventions that tap into the DNA methylation and epigenetic regulatory mechanisms might be able to protect and promote physical well-being and PWB. Although we have almost no information on DNA methylation alterations at later points in life, it stands to reason that DNA methylation continues to evolve and respond to environmental signals throughout the life-course. Given that DNA methylation change has been associated with aging (Issa, 2014), it is tempting to speculate that healthy aging involves proper progression of the DNA methylation profile and that unhealthy aging will involve disruption of this process. It will be important to understand how the evolving DNA methylation profiles during aging predict healthy aging, what the processes are that might disrupt the physiological trajectory of DNA methylation, how they are influenced by environments and exposure to create resilience, and under what conditions this process becomes maladaptive. Such knowledge might open up possibilities to utilize DNA methylation markers in order to differentiate healthy and unhealthy aging and design interventions that will reverse the DNA methylation profiles of unhealthy aging to profiles associated with healthy aging. In summary, we propose that epigenetic processes are fundamental for understanding PWB, particularly regarding how environmental and genetic factors interact in the development of positive outcomes. In order to advance knowledge pertaining to epigenetics and well-being, future research efforts should move the focus from identifying markers for disease toward how epigenetic processes coordinate and respond to environmental signals throughout life in the prediction of well-being. Delineating healthy trajectories of DNA methylation and understanding how they relate to specific environmental exposures and experiences will provide important tools and strategies to understand, monitor, and promote the development of PWB.

Acknowledgment Work from MS lab described in this study was supported by a grant from the Canadian Institue of Health Research MOP-42411.

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Chapter 14

Imaging genetics of positive psychology Hans Melo and Adam Anderson

Imaging genetics of positive psychology: an introduction Molecular genetics has made significant advances in understanding the structure and function of a variety of genes, including specific gene mutations, expression, and frequency. Perhaps the most prominent indicator of the success in this field is the completion of the human genome project (Consortium, 2004), which, alongside decoding the entire human DNA sequence, has fuelled a major effort to identify common genetic variants that impact gene expression and function (Naidoo et al., 2011). With recent estimates suggesting that 82% of all genes are expressed in the brain (Hawrylycz et al., 2014), the impact and importance of genetic factors for brain function in health and disease cannot be overestimated. Hence, it should not be surprising that results from twin studies typically reveal 40% to 70% heritability for a number of aspects of cognition, emotion, and personality (Plomin, Owen, and McGuffin, 1994). Although heritability accounts for the majority of susceptibility for many mental disorders (Bigos and Weinberger, 2010; Plomin, Owen, and McGuffin, 1994), positive mental qualities such as SWB have also demonstrated a strong heritable component, with a current mean heritability estimate of 40% (see Chapters 5 and 10). In order to probe into the genetic basis of brain function and behavior, researchers have devised a number of techniques (Burmeister, McInnis, and Zöllner, 2008). Two distinctive approaches can be identified from the literature; (1) GWAS that look across the entire genome to identify so-called risk genetic markers for a particular disease or condition, and (2) candidate gene studies that focus on the association between pre-specified genes of interest and traits and diseases (see Chapter 3). It is important to note that these two approaches are not necessarily exclusive, since genes identified from GWAS may be used in candidate gene studies to probe deeper into genotype–phenotype associations. While early GWAS suffered from several complications which precluded the identification of risk genes (Malhotra and Goldman, 1999; Newton-Cheh and Hirschhorn, 2005), the field has matured substantially and has now been reasonably successful at identifying risk genes for a number of well-defined medical conditions (McCarthy et al., 2008) and is beginning to show progress in more complex heterogeneous psychiatric disorders such as schizophrenia and bipolar disorder (O’Donovan et al., 2008; Riley et al., 2010; Sleiman et al., 2013). On the other hand, early candidate gene association studies suffered from inconsistent results, failure to replicate, and small effect sizes (Barnett, Scoriels, and Munafò, 2008; Malhotra and Goldman, 1999; Munafò, 2010), but have since begun to adopt more rigorous testing yielding promising results (Bogdan, Hyde, and Hariri, 2013). Researchers generally attributed such issues to lack of power due to inadequate sample size, and due to large “noise” from intervening factors in the path from genetic mechanisms to observable behaviors (Munafò, 2010).

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An important intervening factor is the multiple interactions between genes and the environment, which will be discussed later in this chapter (see also Chapters 11, 12, and 15), as well as the fact that clinical evaluations, personality questionnaires, and psychological test batteries carry a large degree of individual variability and subjectivity which may obscure the genetic bases associated with the constructs of interest such as personality traits. In the case of psychological and cognitive tests, potential genetic effects might also be obscured by the use of alternative task strategies whereby mechanically distinct cognitive processes might result in equivalent behavior (Hariri et al., 2005). These observations would also help to explain the need for very large sample sizes, generally in the hundreds or even thousands, necessary to identify even small genetic effects (Glatt and Freimer, 2002). In sum, there seems to be an explanatory gap between genes and behavior and subjective experience. In order to overcome some of these issues, an increasing number of researchers have sought to incorporate neuroimaging as a way to strengthen their approach by identifying intermediate phenotypes—so-called endophenotypes—between genes and behavior (Bigos and Weinberger, 2010; Bogdan, Hyde, and Hariri, 2013; Meyer-Lindenberg and Weinberger, 2006). Endophenotypes in this context may be seen as common neurobiological denominators of behavioral phenotypes across affected individuals (Gottesman and Gould, 2003; Sanislow et al., 2010). For example, a specific pattern of brain activation associated with a genetic variant might serve as an endophenotype for a particular disorder (Hariri et al., 2005). The advantage of this method is that it opens up the possibility of redefining complex conditions in terms of a few more stable phenotypes, which have a clear genetic component and whose neural correlates are well defined. Specific genetic polymorphisms may directly influence the dynamics of key neuromodulators affecting neural mechanisms that, in turn, mediate and moderate behavior. For this reason, genetic effects may have a stronger impact at the level of brain mechanisms than at the level of behavior or subjective experience, thus increasing the probability of observing a true association between phenotype and genotype, a property called penetrance (Burmeister, McInnis, and Zöllner, 2008). Modern functional neuroimaging techniques allow researchers to measure brain activity associated with specific cognitive and affective processes in real time, collecting thousands of data points in a matter of minutes. Not only are these data more objectively measured than subjective inventories, but they may also be closer to the genetic mechanisms at play. Hence, neuroimaging has allowed researchers to identify endophenotypes in the form of patterns of neural activation associated with genetic variants and distinct cognitive or affective processes (Hariri et al., 2005). The large number of data points also means that statistical power may be improved, requiring fewer subjects, in the tens rather than hundreds, in order to successfully detect significant gene effects on neural processes related to behavior. Furthermore, in the case of candidate gene studies, this allows for assessment of the impact of specific genetic variants on functional pathways of interest associated with cognitive or emotional processes. While still in its infancy, this approach has already helped to elucidate some of the underlying mechanisms in the path between genes and behavior and pathology (Bigos and Weinberger, 2010), and future developments may hold the promise of developing a more fine-grained taxonomy of the biological mechanisms contributing to PWB. In this chapter we introduce the methodology for imaging genetics involving candidate genes, and discuss relevant examples. We present research looking at the interaction between positive affect and cognition and its neurological underpinnings, as well as how they may relate to other topics in positive psychology. We follow with a discussion of two of the most widely studied genetic polymorphisms: the VNTR of the serotonin transporter gene (5-HTTLPR) and an SNP of the cathecol-O-methyltransferase gene (COMT Val-158-Met). These examples are meant as

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representative illustrations of the field, and not as comprehensive reviews. We also provide a short discussion of gene–environment interactions within the context presented. Finally, we provide considerations and directions for future research in this field.

The imaging genetics approach Selection of candidate genes The first step toward successfully implementing this method is to carefully select a candidate genetic variation based on what is currently known about its dynamics in local neural circuits and large networks as they relate to the phenotype of interest. The idea is simple: the more we know about the specific physiological mechanisms of a certain genetic variation and its effects on brain function, the stronger and more specific hypotheses we can formulate, and the more likely we are to identify patterns of brain activation associated with the genetic variation and the phenotype of interest. For this reason, it is generally recommended to use well-defined functional polymorphisms for which much is known at the cellular and systems levels (Bigos and Weinberger, 2010). Examples of such well-defined polymorphisms include variations in the APOE, COMT, and 5-HTT genes (Hariri et al., 2005). However, there may be other, less well-understood genetic variations such as newly identified polymorphisms from GWAS with possible functional implications for phenotypes of interest (Consortium, 2011). Researchers have pointed out that studies involving novel genes must be approached with greater caution, especially when interpreting results (Abbott, 2008; Hariri, 2009). Finally, another consideration for selecting genes and variants of interest is their frequency in the population (Hariri, 2009; Meyer-Lindenberg and Weinberger, 2006). Common genetic variations are easier to find in the general population, and typically yield stronger effects and are better for inferring about the greater demographic (Bogdan, Hyde, and Hariri, 2013). This is of importance for research programs studying general cognitive or emotional processes aimed at understanding common phenotypes affecting a larger demographic. Studies using rare genetic variants present in only a few individuals will likely lack power to detect effects on the phenotype of interest. For this reason it is recommended to constrain the imaging genetics method to common genetic variants that are also well defined in terms of cellular and brain dynamics, unless dealing with a specific condition in a reduced population (i.e., disease population) (Hariri et al., 2005).

Selection of task and imaging method Equally important is the adequate selection of a behavioral task (most imaging studies require participants to perform a task that activates the region of interest) and imaging technique. The first distinction is between functional and structural imaging modalities. Functional imaging refers to the recording of neural activity by measuring changes in metabolism, blood flow, or electrical potentials; such methods include functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) (Grady, 2002). Contrast structural imaging refers to the recording of anatomical measures of different tissue types such as gray and white matter, independent of neural activity; these methods include structural MRI and Diffusion Tensor Imaging (DTI) (Ashburner and Friston, 2000). For the purposes of this chapter we will constrain the discussion to functional neuroimaging and fMRI in particular, but we will allude to other modalities in the conclusion as future directions. Task selection and neuroimaging technique go hand in hand, since experimental tasks are specifically designed with an imaging method in mind in order to probe a theoretical question. Deciding which imaging technique to use, of course, depends on

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the question at hand, and the usual trade-off between spatial vs. temporal specificity also applies. For example, if a researcher is interested in the genetic effects on temporal dynamics of a specific cognitive process (i.e., attention) at the millisecond level, a neuroimaging technique such as EEG might be most appropriate. On the other hand, if a researcher is mostly interested in the impact of a polymorphism on brain function in a specific brain region associated with a behavioral phenotype, it might be best to use an imaging technique with good spatial resolution such as fMRI. It is also important to consider that not all imaging methods have the same probability of identifying a genotype–phenotype association. For example, some studies suggest that fMRI as a technique might have an advantage at identifying associations between genes and neural mechanisms compared to other imaging methods (Meyer-Lindenberg, 2012). With regard to experimental tasks, researchers must bear in mind the relatively small effects of single genetic variants and aim to use efficient designs to maximize power and sensitivity. For this reason it is recommended that researchers make use of widely tested paradigms that are known to engage desired brain areas with robust and reliable effects. Fortunately, thanks to the exponential growth of neuroimaging studies over the last decade there is now a wide literature on classical behavioral tasks designed to investigate specific neural mechanisms relating to a wide range of cognitive and emotional processes. Finally, another alternative that has recently gained interest is to use the patterns of neural activation when participants are not performing a specific task but rather just resting passively inside the scanner (resting state fMRI) to identify associations between genetic variants and traits of interest (Jamadar et al., 2013; Kruschwitz et al., 2014).

Genes, brains, and emotions Positive affect and cognition A body of work indicates that affective states have a measurable impact on cognition, behavior, and general life outcomes including financial success, mental health, and socioeconomic status (Côté, Gyurak, and Levenson, 2010; Isen, 2001; Kuhnen and Knutson, 2011). Over the years several models of affect and emotion have been proposed, and while they often disagree in their approach, most propose a central role for valence and arousal (Knutson, Katovich, and Suri, 2014). Valence refers to the positive (attractive) or negative (aversive) quality of an event or object, as it is perceived. For example, for most people a sunny day would carry positive valence, whereas a cold rainy day would carry negative valence. Arousal, on the other hand, is related to the subjective intensity of an emotion, where high arousal is associated with high-intensity emotions such as excitement or anger, and low arousal is associated with low-intensity emotions such as contentment or sadness. Interestingly, research has shown valence-specific effects on cognition, such that positive and negative affective states impact cognition differently. For example, a vastly confirmed finding is the narrowing of attention during negative affective states, sometimes referred to as “weapon focus,” where the breadth of attention is constricted to a smaller focal region and thus peripheral details are not encoded (Christianson and Fällman, 1990; Derryberry and Reed, 1998; Easterbrook, 1959). In case of a life-threatening situation, this narrowing of attention may facilitate quick and decisive action trajectories that would improve chances of survival. Research has suggested that this interaction between negative affect and attention may underlie psychiatric disorders such as anxiety and depression (Oaksford et al., 1996). By contrast, another line of work suggests that positive emotions have an opposite effect on selective attention by promoting a broadening of attention (Fredrickson, 2001). Work in our lab

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has identified robust behavioral effects and neural mechanisms associated with positive affect across cognitive domains. For example, we have previously shown how positive affect may result in relaxation of attentional filters across cognitive domains (Rowe, Hirsh, and Anderson, 2007). In the visuospatial domain, positive affect results in a “leaky” attentional filter impairing spatial selective attention performance and allowing peripheral irrelevant information to be more deeply encoded. In the conceptual domain, positive affect is associated with increased capacity for association of disparate words (e.g., mower, atomic, foreign = POWER), such that participants under positive mood are able to solve significantly more problems than when under negative mood. With regard to brain mechanisms, a further study examined whether positive and negative affect influenced perceptual encoding in the visual cortices (Schmitz, Rosa, and Anderson, 2009). Using a face/place paradigm, the study found positive affective states enhanced activity in the parahippocampal place area (PPA) related to novel places as well as adaptation to repeated places. These findings indicate that affective valence differentially biases gating of early visual inputs, fundamentally altering the scope of perceptual encoding. Thus, we have found that positive affect can literally alter the spotlight of visual attention, making it more diffuse and encompassing. Connecting these findings with the greater context of positive psychology, we propose that this shift in information processing may facilitate more open, flexible, and exploratory cognition. This assertion resonates strongly with the Broaden-and-Build theory, which proposes that positive emotions serve to adaptively broaden awareness and thought–action repertoires, which in turn build enduring personal resources, ranging from physical and intellectual resources to social and psychological resources (Fredrickson, 2001). For example, participants experiencing positive mood are more readily able to solve the Duncker candle task (Isen, Daubman, and Nowicki, 1987), a classic problem-solving task which asks participants to fix a lit candle on the wall using only a candle, a book of matches, and a box of thumbtacks. This task requires participants to overcome functional fixedness and use the materials in unconventional ways in order to solve the problem. Similarly, people are more likely to solve unusual word associations when they are in a positive mood, compared with negative or neutral mood (Bolte, Goschke, and Kuhl, 2003; Isen, Daubman, and Nowicki, 1987). As converging evidence for this theory accrues, a picture has begun to emerge in which positive affect engenders a creative and more generative mindset that results in greater cognitive flexibility across diverse situations, including medical diagnosis (Estrada, Isen, and Young, 1994), industrial negotiations (Carnevale and Isen, 1986), intuitive judgments (Bolte, Goschke, and Kuhl, 2003), decision making (Isen, 2001), and creative problemsolving tasks (Isen, Daubman, and Nowicki, 1987). This evidence strongly links positive affect with an increased capacity for creativity and novel thinking. Neuroimaging studies have made great advances in identifying the associated neural correlates, including several frontal and inferior parietal and posterior temporal regions (Gonen-Yaacovi et al., 2013). While these studies provide evidence of fundamental changes in information processing related to positive affective states, they are not able to elucidate the direction of the relationship between attention and affect. Does broadened attention result in positive affect? Or does positive affect result in broadened attention? Furthermore, they are unable to elaborate on the neurobiological mechanisms underpinning individual differences in affective style. Are there people with a biological predisposition for experiencing positive or negative emotions? Importantly, such biological predispositions may be genetic in nature. We propose that if affective states are able to influence such a wide range of processes, then genes acting on these affective networks may also have an impact across cognitive and affective domains. Identifying related genetic variants may thus help elucidate not only the relation between affect and genes, but also the relationship between affect and attention, as well as other cognitive

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faculties. Likewise, related endophenotypes may have wide implications for higher cognitive and emotional processes such as emotion regulation, resilience, judgment and decision-making, personality, and well-being. In what follows, we extend our discussion to examine research relating to two such genetic variants of interest.

Imaging genetics and the serotonin transporter gene One of the most extensively studied functional genetic variations is the length-repeat polymorphism in the promoter region of the serotonin transporter gene (5-HTTLPR, SLC6A4). Physiologically, this polymorphism affects serotonin transporter transcription as well as function, with the long allele exhibiting higher concentrations of 5-HTT mRNA and nearly two-fold greater serotonin reuptake as compared to short allele carriers (Hariri et al., 2002; Lesch et al., 1996). This means not only that for long allele carriers more serotonin is recycled, but also that its concentration in the synapse is potentially regulated more efficiently. Thus, researchers often refer to the long allele as the more efficient variant and to the short allele as the less efficient variant (Frank and Fossella, 2011). Behaviorally, early studies linked the low-expressing short allele to neuroticism, depression, and anxiety-related traits (Caspi et al., 2003; Lesch et al., 1996; Lesch and Mössner, 1998). Subsequent work has served to further elucidate the role of 5-HTTLPR in sensibility to stress vulnerability to mental disorders (Caspi et al., 2010). This investigation also sparked a number of studies linking this genetic variant with a wide range of phenomena in health and disease, ranging from alcoholism (Kweon et al., 2005) to romantic love (Marazziti et al., 1999; Reiner and Spangler, 2010). With regard to affective and cognitive neuroscience, a body of work has shown that this variation in the serotonin transporter gene modulates activity in key brain regions in response to emotional stimuli. Early studies using fMRI reported enhanced amygdala reactivity to negative stimuli in 5-HTTLPR short allele carriers (Hariri et al., 2002; Hariri et al., 2005). Other studies showed modulation of neural responses to positive stimuli, suggesting a wider role of this genetic variant in emotional processing (Canli et al., 2005). For example, a study using a classic attention paradigm to examine the role of 5-HTTLPR in processing affective stimuli found increased activation in nine brain regions including bilateral superior frontal gyrus and posterior cingulate for short allele carriers when processing positive stimuli (Canli et al., 2005). Critically, eight of those clusters (or 91% of voxels) were lateralized to the left hemisphere, which is consistent with evidence of left-hemispheric processing of positive stimuli (Davidson, 1992). Interestingly, whereas this and previous studies reported short allele modulation of amygdala activation in response to negative stimuli, there were no group differences in amygdala activation associated with processing positive stimuli. Moreover, morphological analyses identified differences in brain structure between 5-HTTLPR variants, with the short allele associated with reduced gray matter volume in brain areas critical for emotional processing, including the perigenual anterior cingulate and amygdala (Pezawas et al., 2005). From another perspective, studies have shown that negative stimuli draw greater attentional resources than do positive stimuli, and thus attentional biases to negative items are generally stronger than biases to positive ones when compared to a neutral baseline (Ito et al., 1998). For this reason, genetic influences on selective attention to emotional stimuli would be most pronounced on biases to negative rather than positive information. This fact could also potentially explain why early studies reported an effect on negative stimuli, but not a potentially weaker effect on positive stimuli even if present. Relating back to our proposition that certain people might be biologically predisposed to experience positive or negative emotions and the related effects on attention, a number of studies

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point to a role of 5-HTTLPR in biasing attention to emotional stimuli. A recent meta-analysis (Pergamin-Hight et al., 2012) found that the short allele exhibits enhanced attentional vigilance for negative stimuli, an effect not found in the long allele. However, the study pointed out that findings have been inconsistent at times, making it unclear as to whether 5-HTTLPR is associated with a negative attentional bias alone, or whether it impacts processing of affective information more generally, including positive stimuli. For example, it is possible that long-allele carriers could show an attentional bias for positive stimuli or no affective bias at all. This is further obscured by the fact that most studies are only interested in findings with potential implications for mental disorders such as anxiety and depression and, hence, tend to focus on the processing of negative stimuli. A number of studies have indirectly addressed this concern. For example, a study reported an attentional bias for positive stimuli associated with the long allele (Fox, Ridgewell, and Ashwin, 2009). The researchers proposed that this apparent predisposition for attending to positive stimuli and avoiding negative stimuli might act as a resilience mechanism shielding against potential life stressors. However, given the fact that processing of positive and negative information has been shown to rely on overlapping brain networks that are influenced by serotonergic and dopaminergic dynamics, our contention is that genetic variants, such as 5-HTTLPR, are more likely to impact the processing of emotional information as a whole rather than specifically to negative or positive stimuli. In support of this hypothesis, more recent studies report effects of 5-HTTLPR biasing attention to both negative and positive information (Beevers et al., 2009; Fox et al., 2011). For example, Fox and colleagues (2011) used an attentional bias modification technique to show that participants with the short allele developed stronger biases for both negative and positive pictures when compared to participants with the long allele (see also Chapter 8). Consequently, the authors argued that this genetic polymorphism might best be seen as a plasticity gene rather than a vulnerability gene, such that short allele individuals are more responsive to the environmental cues whether positive or negative, suggesting that 5-HTTLPR may impact emotional processing as a whole rather than just the processing of negative stimuli (see Chapter 12). While most of this work has investigated implications for mental disorder specifically, a new line of research is emerging which aims to examine implications for optimal human functioning as well. Most notably, an association study reported that long-allele carriers were more likely to report higher levels of life satisfaction when compared to carriers of the short allele (De Neve, 2011), but these results were not replicated in a follow-up study with a different sample (De Neve et al., 2012). The authors point to limitations such as population stratification, small effect sizes, and gene–environment interactions that could have precluded a positive finding. Given the wealth of knowledge regarding 5-HTTLPR, future work should aim to test wellinformed hypotheses to elucidate the underlying neurobiological mechanisms and potential variables mediating the observed phenomena.

Catechol-O-methyltransferase (COMT) Another genetic variant that has also been studied extensively is the COMT polymorphism, which codes for an enzyme that breaks down extracellular dopamine (DA). In the prefrontal cortex (PFC), where dopamine transporters are scarce, COMT catabolism plays a major role in dopamine dynamics, greatly impacting extracellular dopamine levels and affecting PFC-dependent cognitive processes such as executive function. Critically, a functional polymorphism resulting from valine to methionine substitution (COMT Val158Met) has been shown to greatly influence activity of the enzyme with the Val-allele exhibiting a three- to four-fold enhanced degradation

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of DA as compared to the Met-allele (Lachman et al., 1996). This means that Met-carriers exhibit higher levels of DA in the PFC than Val/Val homozygous individuals (Chen et al., 2004). Several studies link COMT metabolism in PFC to a range of phenomena in healthy and ill populations. With regard to mental disorder, the Met-allele has been associated with several conditions including alcoholism (Tiihonen et al., 1999), major depression (Ohara et al., 1998), bipolar disorder (Mynett-Johnson et al., 1998), and panic disorder (Woo et al., 2004), and it has also been proposed as a susceptibility gene for schizophrenia, anxiety, and other disorders (Stefansson et al., 2008; Zammit et al., 2004). On the other hand, its implications for optimal functioning and PWB have not been studied in as much depth, but we will discuss relevant studies and theoretical implications for the development of the field later in this chapter. A substantial body of research has helped to elucidate COMT’s impact on cognition and in particular, on frontally dependent cognitive processes such as executive control (Colzato, van den Wildenberg, and Hommel, 2014; Colzato et al., 2010). Converging evidence suggests that this functional polymorphism impacts tasks involving cognitive flexibility and cognitive stability. In general, it has been found that Met-carriers exhibit an advantage on frontally mediated cognitive tasks. For example, Met-allele has been associated with enhanced working memory (Goldberg et al., 2003) and decreased error rate in an executive function task (Egan et al., 2001). Moreover, evidence from several neuroimaging studies shows reliable genotypic differences in activation in the PFC (Bertolino et al., 2006; Caldú et al., 2007; Meyer-Lindenberg et al., 2005). Researchers have proposed that the Met-allele is related to increased tonic dopamine transmission subcortically, but increased phasic activity in the PFC (Bilder et al., 2004). This is hypothesized to allow Met-carriers to maintain information on working memory relevant to their present task, and thus to benefit cognitive stability. On the other hand, low levels of tonic DA exhibited by Val/Val homozygotes may be beneficial for tasks requiring rapid adjustments in behavior to keep up with a rapidly changing environment, and thus enhance cognitive flexibility. Consistent with this picture, research indicates that Met/Met carriers do indeed show more stable prefrontal activation states, and enhanced performance in tasks relying on working memory, at the cost of impoverished cognitive flexibility (Nolan et al., 2004). Val/Val participants show the opposite tendency of enhanced cognitive flexibility, i.e., impoverished cognitive stability (Colzato et al., 2010; Egan et al., 2001; Goldberg et al., 2003). With regard to how COMT may impact affective processing, a number of studies have identified gene–dose effects related to reactivity to unpleasant stimuli. Specifically, studies have found that the number of Met-alleles is positively correlated with reactivity to negative stimuli (Drabant et al., 2006; Smolka et al., 2005; Williams et al., 2010). fMRI studies reveal that Met-carriers elicit a stronger response to negative stimuli in critical brain regions, including the amygdala, left hippocampus, as well as frontal areas such as the ventrolateral (vl-PFC) and dorsolateral prefrontal cortex (dl-PFC) (Drabant et al., 2006; Smolka et al., 2005). This suggests that response to negative stimuli may be related to increased levels of dopamine in those brain areas. Furthermore, the effects across these brain regions suggest a role of COMT in connectivity dynamics in cortico-limbic circuitry. Indeed, a connectivity analysis found increased coupling between right vl-PFC and parahippocampal gyrus, as well as amygdala and orbitofrontal cortex in Met-homozygous individuals (Drabant et al., 2006). Interestingly, none of these studies found an effect of COMT on processing positive stimuli, even when using highly arousing positive images. This suggests that the Met-allele may react specifically to stressful information, which may increase demand of prefrontal resources for regulation of negative emotional states. Moreover, this increased recruitment of frontal regions for processing negative information may translate into impoverished emotion regulation for Metcarriers. On the other hand, Val-carriers who do not exhibit increased reactivity for negative stimuli

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also do not show increased reactivity for positive stimuli (Williams et al., 2010). This suggests that Val-carriers might have a more even-handed reactivity to positive and negative stimuli. This also means that in the case of Val-carriers, neither positive nor negative information requires increased recruitment of frontal regions for emotional processing and emotion regulation, which may translate into enhanced affective processing and emotional regulation compared to Met-carriers. In support of this, a study looking at the association between social acceptance and COMT found that Val-homozygous individuals reported greater perceived social acceptance and greater maintenance of positive emotions during stress than Met-carriers (Waugh et al., 2009). The authors suggested that COMT affect social functioning by impacting emotion regulatory circuitry. While previous studies elucidate the effect of COMT on processing of emotional stimuli, they do not directly investigate differences in emotional flexibility. However, given that previous research indicates an important role for the orbitofrontal cortex in processing affective salience when integrating and updating reinforcement contingencies, it is possible that COMT may also play a role in adapting to a changing emotional environment. The evidence linking the Met-allele to increased connectivity between OFC and amygdala during the processing of emotional stimuli also points to a possible role in emotion regulation. Indeed, previous work shows that COMT may influence emotional behavior by altering the functional coupling between amygdala and hippocampus with the OFC (Bertolino et al., 2006). Furthermore, researchers have previously proposed that Met-carriers may be more easily locked into an affective state by reverberating emotional information via this network (Drabant et al., 2006). However, such claims have not been directly tested in fMRI using an affective manipulation task. Ongoing studies in our lab are testing whether Val-carriers exhibit faster adaptation to emotional challenge as compared to their Met-counterparts. Future work should aim to elucidate the contribution of COMT to emotional processing and more specifically to emotional flexibility.

Gene–environment interactions Another important line of research has emphasized the role of the environment and its interactions with genetic variants in shaping behavior and psychopathology (see Chapters 11 and 12), that is, the fact that in some cases gene expression is highly dependent on the environment, and so genes may act differently according to environmental demands. For example, a well-known longitudinal study found that individuals with the short allele of the 5-HTTLPR were more likely to exhibit depressive symptoms than those with long alleles, but only in relation to stressful life events (Caspi et al., 2003). In other words, it was the combination of the short allele and a stressful environment that led to depression, and not an individual’s genetic make-up alone. A recent meta-analysis showed strong evidence for the role of the serotonin transporter gene in moderating the relationship between early life stress and depression, with the short allele exhibiting increased stress sensitivity (Karg et al., 2011). In a related study, individuals with the short allele of the 5-HTTLPR were found to be more susceptible to their environment, becoming more easily biased to attend to positive or negative images (Fox et al., 2011). In this case, the authors hypothesized that while these individuals might be more vulnerable to a negative environment, they may also benefit most from a positive environment as well as from clinical interventions aimed at reducing negative biases (see Chapter 11). While an early study reported an inferior response to clinical interventions for short-allele carriers (Bryant et al., 2010), a more recent clinical study with a larger sample found that short-allele carriers were 20% more likely to respond to cognitive behavioral therapy for treating anxiety (Eley et al., 2012).

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More generally, these studies highlight the importance of the environment such that genes should not necessarily be considered vulnerability or risk genes in and of themselves, but perhaps “adaptability” or “plasticity” genes, whereby environmental contingencies determine whether people develop a beneficial or prejudicial impact (Belsky and Pluess, 2009; Belsky et al., 2009). This is in contrast to the diathesis–stress model that proposes that individuals with a certain genetic profile are at a higher risk of developing psychopathology when encountering adversity than individuals who lack the genetic vulnerability (Hankin and Abela, 2005). The alternative framework, differential susceptibility, proposes that it is not so much that individuals vary in their susceptibility to psychopathology itself, but rather that these individuals are more susceptible (and responsive) to the environment, whether positive or negative (Belsky et al., 2009). Thus, the same genetic factors that may put an individual at risk of psychopathology may also confer an advantage for benefiting from a supporting environment. By the same token, recent work has put forward the notion of vantage sensitivity, the possibility that certain genetic factors interact exclusively with positive environments and may predispose individuals to behavioral advantages (Pluess and Belsky, 2013). A more detailed discussion of this work is presented in Chapters 11 and 12. Hence, when considering genetic influences on cognitive and affective processes as well as more complex constructs such as resilience and PWB, we must also consider interactions with the environment (see Chapter 15). The study of genetic influences on positive traits and PWB does not imply a deterministic view, but on the contrary highlights the interaction between genes and the environment that ultimately shapes our experience and behavior (see Chapter 16). Thus, becoming aware of our genetic predispositions may inform us and allow us to make better decisions with regard to our environment, which may in turn positively impact our mental health.

Conclusions and future directions Imaging genetics has emerged as a powerful technique that, by leveraging molecular genetics and cognitive neuroscience, may allow us to develop a more fine-grained biological characterization of optimal human functioning, including PWB. Despite its youth, imaging genetics has already significantly advanced our understanding of how genetic variation affects brain function and, together with the environment, acts to guide behavior in health and disorder. In this chapter we presented evidence for two genetic variants that have been widely studied. The connection between 5-HTTLPR and risk for anxiety and depression is now well established, despite varying effects and controversial findings (Munafò, 2010), and this line of research has also emphasized the importance of the environment in mediating such genetic susceptibilities (Caspi et al., 2010). Imaging genetics has served not only to strengthen these associations, but also to elucidate their neural mechanisms, such as amygdala reactivity to negative stimuli (Caspi et al., 2010; Pezawas et al., 2005). It is also important to highlight that reported gene effects have been greater in the brain than at the behavioral level, supporting the idea that imaging genetics offers greater penetrance than behavioral methods (Caspi et al., 2010). Likewise, the role of the COMT polymorphism in mediating frontally dependent cognitive tasks has been established, and its role in emotion processing and its related neural mechanisms are beginning to become clearer (Mier, Kirsch, and Meyer-Lindenberg, 2010). There is growing evidence that Met-carriers may exhibit increased reactivity to negative stimuli which might be related to risk for emotional disorders (Williams et al., 2010). On the other hand, Val-carriers might exhibit an advantage for emotion regulation and maintenance of positive emotion during stress (Waugh et al., 2009). However, future work should consider in more detail the role of the environment in conferring susceptibility or

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advantage in relation to the COMT genotype. This work should thus consider gene–environment interactions from the point of view of differential susceptibility and vantage sensibility rather than relying exclusively on the diathesis–stress model. A promising avenue for future work is to further examine the interaction between positive affect and attention, and to test whether direct genetic influences and gene–environment interactions are at play. This is of particular interest because previous research suggests a global shift in information processing associated with positive affect and may thus carry implications for other cognitive functions, including flexibility, creativity, resilience, and more generally for PWB. For example, we have discussed how our ability to recover from emotional challenges might be directly related to emotional and cognitive flexibility. Impoverished resilience may be associated with sticky cognitive states, whereas high resilience may be characterized by more flexible and adaptive cognitive states. Future research could look at whether sticky and flexible states may be associated with 5-HTTLPR and COMT as well as other genetic polymorphisms of interest such as MAOA (Buckholtz and Meyer-Lindenberg, 2008), TPH2 (Waider et al., 2011), and BDNF (van Winkel et al., 2014), while also taking into consideration interactions with the environment. Specifically, studies could directly test the hypothesis that the long allele of the 5-HTTLPR and the Val-allele of the COMT gene might be related to flexible emotion regulation. This work could make use of different modalities from functional and structural neuroimaging such as gray and white matter segmentation (Winkler et al., 2010) to offer stronger results from converging lines of evidence. Future research should also aim to examine the interaction between different genes, so called gene–gene interactions or genetic epistasis, that have been shown to impact brain function and behavior. Just as it is clear that genes interact with the environment to guide behavior in health and disease, it is also clear that genes do not act independently of one another, but rather act in concert to confer susceptibility for health and disorder (Zuk et al., 2012). Furthermore, researchers have repeatedly noted that single genes typically account for only a small fraction of the population variance for a given phenotype, but that by taking into account the interaction among genes we might be able to explain a much greater proportion of the missing variance (Manolio et al., 2009; Moskvina et al., 2011). While testing for gene–gene interactions represents a serious computational challenge for GWAS (Moskvina et al., 2011), imaging genetics can exploit what has been learnt from previous work about specific genes, especially those that may be functionally related, such as 5-HTTLPR and COMT, to constrain the analysis to subsets of genes with higher probabilities of interaction. This line of work may further be extended to include environmental factors, thus effectively assessing gene–gene–environment interactions and potentially capturing an even greater proportion of the variance (Ressler et al., 2010). Finally, another exciting avenue for future research is to combine imaging genetics with the emerging field of epigenetics (see Chapter 13). This field aims to identify factors that alter gene expression without changing the underlying DNA sequence, including DNA methylation and histone modification (Bird, 2007). Most relevant to our discussion is the fact that the biological mechanisms through which gene–environment interactions are enacted are not well understood, but recent developments in the field of epigenetics suggest that DNA methylation may play an important role (Kinnally et al., 2011; Ouellet-Morin et al., 2013; Vijayendran et al., 2012). For example, a landmark study linked maternal behavior in rodents to changes in DNA methylation in offspring that influenced response to stress (Weaver et al., 2004). More recent studies begin to show that changes in DNA methylation during critical development stages are associated with increased risk of stress-related conditions (Kinnally et al., 2011; Ouellet-Morin et al., 2013; Vijayendran et al., 2012). Future studies combining imaging genetics and epigenetics may provide

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much insight into how these changes in DNA methylation affect brain function and structure, and may contribute to behavioral advantages and disadvantages in the path from genes to PWB.

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Application and implication

Chapter 15

Genes, environment, and psychological well-being Michael Pluess and Michael J. Meaney

Genes, environment, and psychological well-being: an introduction Whether individual characteristics are primarily inherited as a function of genes or rather acquired over time in response to environmental conditions is probably one of the longest-­standing questions regarding the origins of human nature. Although fundamentally an ancient philosophical question that has been discussed at least since the days of Greek philosophers Plato and Aristotle more than two thousand years ago, it remains an important and often fiercely debated issue to this very day, usually referred to as the nature versus nurture debate. Whereas nature stands for the notion that individual traits are predominately determined by heritable, sequence-based genetic variation, nurture refers to the view that it is primarily environmental influences, particularly those occurring early in life, that shape developmental outcomes. Looking back on history, the pendulum has swung back and forth between extreme views of nature versus nurture (Rutter, Moffitt, and Caspi, 2006). For example, Watson’s (1930) famous claim that he could raise any child as either lawyer or thief regardless of their dispositions is a manifestation of the prevailing extreme environmentalism associated with the heydays of behaviorism in the 1950s and 1960s. This view was replaced toward the end of the twentieth century with a perspective more consistent with genetic determinism, encouraged by the emerging field of behavior genetics and significant progress in molecular genetics at that time, particularly the prospect of sequencing and mapping the complete human genome for the first time as part of the Human Genome Project, which started in 1990 and was completed in 2003. Although such extreme and conflicting views may still be held by a small minority, most academics would agree today that both nature and nurture (i.e., genes and environment) play an important role in human development. Moreover, advances in molecular biology over the past 30 years reveal that environmental signals and genomic function are completely interdependent of one another, such that additive approaches are biologically unrealistic. Consequently, the discussion is no longer whether development is determined either genetically or environmentally, but rather what the processes are by which genes and environment interact to produce phenotypic variation. In this chapter we will discuss the contribution of genes and environment, and their interaction, to PWB. After identifying the basic question underlying most empirical efforts aimed at investigating the relative contribution of genetic and environmental factors to development, we will discuss to what degree current methodological approaches in behavior and molecular genetics are positioned to address this question. We will then proceed by emphasizing the fundamental importance of considering gene–environment interplay and highlight the need for more biologically based concepts of development, before concluding that future work should focus on

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investigating biological pathways that include the dynamic interplay between genes and environment rather than simply attempting to quantify the unique contributions of heritable and environmental factors to PWB.

The basic question In contrast to the classic nature–nurture debate, which was driven by conflicting and irreconcilable extreme views of complete genetic versus complete environmental determinism, the current discussion is much more balanced and appreciates that both genetic and environmental factors play an important role in the development of psychological outcomes, including those related to well-being. Consequently, a large proportion of research in behavior genetics aims at investigating the relative contribution of genetic and environmental factors pertaining to the development of specific outcomes. Most of this work addresses the following question in one way or another: To what degree is a psychological trait determined by a genetic predisposition versus shaped by environmental factors? There are at least two important yet implicit assumptions underlying this question. First, it is assumed that genetic and environmental factors both play a causal role in the development of psychological outcomes and, second, the question implies that the relative contribution of genes and environment may be largely independent and, therefore, distinguishable and discretely quantifiable. While the first assumption is supported by a large body of empirical evidence, the second assumption is fundamentally irreconcilable with a biologically informed perspective and therefore, highly problematic (Lerner, 1993; Lewontin, 2006; Meaney, 2001, 2010), as we will discuss later in more detail. The causal role of genetic factors is supported by the widely observable fact that the more genetically similar people are to each other (i.e., degree of relatedness), the more similar they tend to be regarding their phenotype. For example, in an unrelated population the risk of developing schizophrenia stands at 1%, but this increases significantly the greater the degree of relatedness to a person with schizophrenia, with a risk of 2% for third-degree relatives (i.e., cousins, uncles, aunts), 5% for second-degree relatives (i.e., nieces, grandchildren, half siblings), up to 17% for first-degree relatives (i.e., parents, children, and siblings), and 48% for identical twins (Gottesman, 2001). The causal role of genes is further and perhaps more strongly supported by the existence of so-called single-gene hereditary disorders such as cystic fibrosis, sickle-cell anemia and hemophilia A, where the presence of a single-gene variant can be reliably linked to the disorder (i.e., all individuals carrying the gene variant usually develop the disorder, whereas those without the gene variant do not). However, opposite to single-gene disorders, complex traits like those related to PWB will most likely be influenced by a large number of different variants across various genes rather than single variants. Furthermore, the majority of these variants will influence each other’s effects in complex and intricate gene–gene interaction effects, so-called Epistasis (Mackay, 2014), in addition to gene–environment interaction and direct genetic effects. Before causal effects of environmental factors are discussed, it is important to define what “environment” refers to. According to Boardman, Daw, and Freese (2013), environment may be conceptualized broadly as an “external, multilevel, and multidimensional feature that determines an individual’s exposure to risks and resources and constrains or enables people to engage in healthy lifestyles at different stages of the life course.” In other words, environmental influences originate from multiple settings (e.g., family, school or work, community, etc.), across multiple domains (e.g., social, economic, physical, etc.), and are longitudinal in nature (e.g., environments change over time). Furthermore, the majority of the different environmental levels and domains are likely associated with each other, with earlier environmental characteristics influencing later

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ones, including environment–environment interactions (i.e., one environmental factor moderating the effect of another environmental factor). Given this complexity, specific environmental influences may be difficult to identify and distinguish from more stochastic factors (see section on stochastic processes). Nevertheless, a large number of intervention studies featuring randomized controlled designs, as well as research on the impact of catastrophic events (e.g., earthquakes), provide evidence that changes in the environment lead to changes in psychological outcomes. For example, in a unique randomized controlled trial in which institutionalized Romanian children were allocated to high-quality foster care in early childhood, significant improvement in attachment quality was observed under the foster care condition, but not for the control children that remained in institutionalized care, providing evidence that the early care environment causally influences social–emotional development (Zeanah et al., 2005). Given such empirical evidence suggesting that both genetic and environmental factors can causally influence human development, it is not surprising that many studies aim to delineate the relative importance of both influences regarding particular outcomes. Importantly, whereas some traits may be predominately a function of a genetic predisposition, other characteristics may be more influenced by environmental factors. For example, whether someone’s eyes are blue or brown is determined predominately by genes, regardless of environmental influences. On the other hand, more complex traits, such as the acquisition of advanced mathematical skills, require a particular environmental context (i.e., school) and will not develop as a function of a genetic predisposition alone. Consequently, the relative contribution of genetic and environmental variations varies for different characteristics: whereas some traits will be more closely linked to genetic influences, other traits, particularly the more complex ones, may be more likely to reflect environmental influences. Consequently, in order to understand human function and the relevant predictors thereof, it is important to know to what degree a specific psychological trait is potentially subject to environmental factors. This consideration has important implications. For example, given recent interests of the British government (CMEPSP, 2009), and others, to assess PWB as a measure of how well the country is doing, it may appear crucial to understand whether well-being is primarily a function of genes or rather shaped by the environment. If PWB is largely a reflection of environmental conditions, it may indeed be useful to assess the status quo of a country regarding well-being and to develop policies and interventions aimed at changing the environment in order to improve PWB. On the other hand, if well-being were primarily a genetically predetermined stable trait, it would not be a useful indicator of how well people are doing in response to social policy. Similarly, psychological, social, and educational interventions and services may be able to cause substantial changes in traits that are malleable in response to environmental influences, but less so in traits that are primarily determined by the genetic make-up. Consequently, knowing to what degree a psychological trait is genetically predetermined (and therefore less likely to change in response to the environment), or shaped by the environment (and therefore more likely to change in response to environmental influences), is of fundamental importance. However, although the basic question regarding the relative contribution of genes and environment in the development of psychological outcomes is a valid and important one, it is less clear whether it is actually possible to answer this question with the available methods given the dynamic and complex interplay between genes and environment (see Chapters 11 and 12). Over the last 50 years and perhaps particularly over the last decade, a substantial number of methods have been developed in the fields of behavioral and molecular genetics aimed at investigating the contribution of heritable genetic factors in psychological and psychiatric outcomes (see Chapter 3 for a detailed presentation of these methods). In what follows we will discuss to what degree these approaches allow us to determine how genes and environment contribute to positive psychological states.

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Quantitative behavioral genetics Traditional quantitative behavioral genetics methods generally partition the variance of specific phenotypic traits within a group of people that differ in their degree of relatedness into “genetic” and “environmental” components by applying statistical models (Plomin, DeFries, and McClearn, 1990). Importantly, these studies examine so-called genetic effects without directly examining the genome (Sokolowski and Wahlsten, 2001), which means that the issue directly under study in quantitative behavioral genetics is “heritability” rather than genetics.1 This statistical approach, developed by Fisher and Haldane at the turn of the last century, has actually a long history in agriculture and animal sciences as the basis for selective breeding (Griffiths and Tabery, 2008; Plomin et al., 2013; Wahlsten, 2003), where the ability to estimate the passage of traits from parent to offspring is more relevant than understanding the actual mechanism of inheritance. Consequently, the methods of quantitative behavioral genetics are informed by a statistical, rather than a biological perspective (Meaney, 2010). The most common research approach in quantitative behavioral genetics is the twin study, which relies on comparing the level of similarity in the outcome of interest between identical (i.e., monozygotic) and fraternal (i.e., dizygotic) twin pairs to derive estimates of heritability. If the phenotypic similarity is greater between identical compared to fraternal twins, it can be concluded that the investigated trait has a heritable component (given that identical twins are more similar to each other genetically than fraternal twins while sharing a similar environment). Whatever proportion of the phenotypic similarity is not explained by this heritability component (A) is then attributed to environmental influences, whereby these are further divided into shared (C) and non-shared components (E). In this chapter we will not further differentiate between the two environmental components, but rather will stick to the dichotomy of genes and environment as the primary focus of the discussion (see Chapter 3 for a detailed description of the different components). Important to mention is that current statistical methods of quantitative behavioral genetics are much more sophisticated than the traditional comparison of concordance rates between identical and fraternal twins, allowing the complex simultaneous modeling of genetic and environmental contribution across several different traits, changes over time, as well as gene– environment interactions (e.g., Purcell, 2002). In this volume, authors report, review, and discuss twin study findings of the most common measures of PWB. To summarize, Nes and Røysamb (see Chapter 5) report results of a new meta-analysis suggesting that the weighted average heritability of SWB across 13 studies was 40%, which means that environmental factors (or purely random or stochastic events) explained the remaining 60% of the variability (see also Chapter 10 for a similar meta-analysis). Cloninger and Garcia refer in their chapter (see Chapter 6) to a twin study on positive affect (Bouchard, 2004), reporting a heritability of 50% (which means the environment accounts for the remaining 50% of variance). In Chapter 7, Ebstein et al. mention a twin study on ten different values according to which nine had heritability estimates ranging from 11% to 38% (Schermer et al., 2008). The highest heritability for optimism, according to the review by Fox and Booth (see Chapter 8) was estimated at 36% (Mosing et al., 2009). At first sight these findings seem to suggest that phenotypic traits associated with PWB have a substantial genetic basis, but that environmental influences play a more significant role. However, 1

It is important to note that while sequence-based variation in DNA is inevitably a major contribution to heritable influences, other molecules such as RNAs and certain structurally stable forms of proteins (e.g., prions) are also “inherited,” particularly from the mother (Jablonka and Lamb, 2005).

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before using these behavioral genetics findings to address the basic question outlined earlier regarding the relative contribution of genes and environment in the determination of PWB, we have to consider several critical issues inherent to the methodology of quantitative behavioral genetics (in addition to those raised in Chapter 3).2 First, quantitative behavioral genetics methods and results imply a strict dichotomy between genetic and environmental factors and their effects on a particular outcome. However, the notion that genes and environment would act independently does not make much sense in light of biological findings that point to a highly dynamic and extremely complex interplay between multiple genetic and environmental factors (see Chapters 2, 11, 12, and 13). The main reason for the biologically untenable proposition of environmentally independent genetic effects is that within quantitative behavioral genetics analyses, “genetic” influences are defined statistically rather than biologically (and the same is true for the definition of environmental influences). However, it is important to acknowledge that although the classic twin study does not provide estimates for variance accounted for by gene–environment interplay, the relevance of both gene–environment correlation and interaction has been considered and investigated in the quantitative behavioral genetics literature for many years. Second, twin studies provide estimates for heritable and environmental components regarding the difference between people within a particular population rather than the presence of the particular trait. Heritability estimates derived from quantitative behavioral genetics provide insight into why some people score higher or lower on a particular trait than other people, but not whether the trait itself is genetically or environmentally determined. Hence, these estimates are inherently unable to address the basic question raised earlier whether people are born happy or made happy due to environmental factors. Statistically informed methods of quantitative behavioral genetics are only able to address heritability of the variability of a trait, but not the composition of the actual trait itself. Third, twin studies provide heritability estimates for differences within a particular population rather than for an individual person (Visscher, Hill, and Wray, 2008). This is one reason why heritability estimates tend to vary between samples and across time (given that variability of a trait varies between samples and over time). Interpreting these population estimates on the level of an individual person is a serious, but very common error. Fourth, quantitative behavioral genetic findings do not inform us about the causal effects of genes and environment in the prediction of an outcome. The finding that 40% of SWB is explained by a heritable component (see Chapter 5) does not mean that 40% of the happiness of an individual person is determined by genes, but that the difference in happiness between all people included in the study has a heritable component of 40% (i.e., the difference in happiness scores is generally smaller between people that are more closely related). This common misunderstanding has led many people interested in positive psychology to misinterpret twin study findings in the following way: 50% of their individual happiness is determined by genes and therefore unchangeable, but the remaining 50% is influenced by the environment and therefore malleable (Lyubomirsky, Sheldon, and Schkade, 2005). 2

Twin studies are performed under conditions where environmental variation, at least at the level of the family, is minimized, which would tend to inflate the estimates of the relative contribution of heritable factors. Moreover, gene–environment interactions preclude the strict interpretation of genetic or environmental “main effects.” However, heritability estimates, if not strictly used as an indication of contribution, can nevertheless reveal interesting differences between specific traits of interest in the degree to which they may, depending upon environmental context, reflect heritable influences.

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In summary, quantitative behavioral genetics findings do not allow us to determine whether people are happy because they are born that way or because they are shaped that way due to the quality of their environment, given that such results should not be interpreted causally and not on an individual level. So what, then, do estimates of heritable and environmental influence tell us about PWB? Quantitative behavioral genetics methods statistically divide the observed variance of a trait into different components and tell us to what degree this variability within a particular population is associated with genetic similarity between included individuals (measured by their degree of relatedness). If the heritability estimate is very small, this suggests that differences in the population are not related to heritable factors. If heritability estimates are large, it can be concluded that differences between people are associated with heritable factors. Taking the aforementioned disputed assumptions of twin study designs into account (see also Chapter 3), these estimates give us a rough understanding whether and to what degree heritable factors may play a role regarding inter-individual differences of a specific trait. Consequently, the behavioral genetics findings regarding PWB, as presented in this book, suggest that while a significant amount of variation in PWB between people may have a genetic basis, the nature of that influence and the degree to which it varies as a function of the environmental context remains at issue.

Molecular genetics The field of molecular genetics has been making huge and continuous progress over the last decade, as reflected in the constant development and improvement of analytical methods driven by significant technological advances. While it took 13 years, $3 billion, and a global network of geneticists to sequence the complete human genome for the first time, it is now possible to do the same in 24 hours for about $1,000 and almost certainly far less in the near future. Rather than focusing on single genetic variants in small samples—as was the norm in so-called candidate gene studies not too long ago—it is now possible and custom to simultaneously and routinely genotype for hundreds of thousands of genetic variants in large samples, applying sophisticated DNA microarray technology. The anticipation of such exciting possibilities toward the end of the millennium shortly before completion of the Human Genome Project generated considerable optimism regarding the identification of the genetic structure underlying psychological traits and disorders (see, for example, Plomin and Rutter, 1998). Given the substantial heritability estimates of many psychological outcomes (according to quantitative behavioral genetics), it was assumed that once genetic differences could be measured on the molecular level and across the whole genome, it would just be a matter of time until the specific genetic variants associated with phenotypic traits were identified. In other words, it was expected that these new molecular genetics methods would overcome one of the main limitations of quantitative behavioral genetics by measuring actual genetic variation associated with psychological outcomes, rather than simply inferring genetic contribution statistically. However, more than ten years after the completion of the Human Genome Project, much of this enthusiastic optimism has faded, at least in the disciplines of psychology and psychiatry (Plomin, 2013). The sobering fact is that the identification of genetic variants underlying significant heritability estimates has not even met the most cautious of forecasts. For example, according to twin studies, depression has a substantial heritability of approximately 40% (Sullivan, Neale, and Kendler, 2000). But even studies based on samples as large as 50,000 individuals failed to identify gene variants underlying the heritability of depression (Hek et al., 2013; Ripke et al., 2013). And in the case of other traits, where gene variants have been successfully identified by molecular methods, they usually explain only a fraction of the heritability estimates (for example, less than

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1% of educational achievement in a sample involving >120,000 individuals; Rietveld, Medland, et al., 2013). This discrepancy between heritability estimates derived from quantitative behavioral genetics and variance explained by molecular findings has been labeled “missing heritability.” Although there may be many reasons that account for the phenomenon of missing heritability (Crow, 2011; Manolio et al., 2009), one potential conclusion is that the dichotomy between genes and environment—as implied by some of these methods investigating direct links between genes and phenotypes—does not capture the biological reality. There are three molecular genetics methods that are of particular interest regarding the quantification of genetic influences on psychological outcomes, all of which focus on the direct association between genetic variation and phenotypic outcomes (see Chapters 3 and 10 for a more detailed presentation). Candidate gene studies investigate whether a single-gene variant—selected based on its hypothesized specific role in the biological pathway from gene to behavior—predicts a particular outcome, whereas GWAS test whether any of usually .5 to 2 million common gene variants across the whole genome are associated with the outcome of interest, statistically controlling for multiple testing. Both methods basically investigate whether and to what degree genetic variation on the molecular level explains phenotypic variation. Generally, such association studies have not been very successful in studies on complex psychological outcomes and when they were, they usually explained only small fractions of the phenotypic variance. In addition, these findings often do not replicate across samples. More recently, new methodology has been developed that uses GWAS data to estimate genetic heritability rather than identifying specific gene variants. Genomic-Relatedness-Matrix Restricted Maximum Likelihood (GREML), for example, as implemented in the software Genome-Wide Complex Trait Analysis (GCTA) (Yang, Lee et al., 2011), estimates the proportion of variance represented by common genetic variants across the genome, assessed by associating genetic similarity between unrelated individuals to phenotypic similarity between the same individuals (Yang, Manolio et al., 2011). While the identification of specific gene variants with GWAS has not been as successful as expected, GREML findings are encouraging. For example, according to a recent GREML analysis for depression based on a sample of almost twenty thousand individuals, 21% of the similarity between people regarding depression could be explained by their genetic similarity based on almost 1 million gene variants (Lee et al., 2013). This estimate is in line with other studies reporting GREML heritability estimates that tend to account for usually about half of the heritability estimates derived from twin studies. Consequently, such findings suggest that heritability estimates of quantitative behavioral genetics studies may indeed have a genetic basis, but, importantly, rather than being a function of a few genetic variants, it is more likely a multitude of genes with very small effects that make up genetic heritability (and identification of these small effects requires very large samples). These three molecular genetics methods have not yet been frequently applied in relation to PWB. However, the existing studies reviewed in this volume (see Chapters 8 and 10) show a pattern comparable to what tends to emerge in psychiatric genetics studies. For example, although a candidate gene study found a significant association between the long allele of the serotonin transporter polymorphism (5-HTTLPR) and life satisfaction (DeNeve, 2011), this association did not replicate in an independent sample (DeNeve et al., 2012). Regarding GWAS, few have been published so far, although a large-scale GWAS meta-analysis on PWB is currently being conducted (see Chapter 10). However, given the generally limited success of GWAS in relation to other related psychological outcomes (e.g., the personality trait extraversion; de Moor et al., 2012), it is rather unlikely that GWAS will identify specific genes for PWB, at least not gene variants of large effect. That said, reliable identification of gene variants with small effect may still contribute significantly to our knowledge regarding the genetic architecture of well-being and point to

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involved biological systems (but will require GWAS with very large samples; see the current study on well-being by the Social Science Genetic Association Consortium, Chapter 10). GREML analyses, on the other hand, may be more promising. For example, the first and only GREML study that has been conducted so far related to PWB found that genetic variants accounted for up to 18% of the variance in SWB (Rietveld, Cesarini et al., 2013). These findings suggest that while there probably will not be single-gene variants that account for significant proportions of the variance of well-being measures, a large number of gene variants with very small effects together may explain up to 18% of the difference in PWB between people, which is about half of the heritability estimates reported by twin studies (40%; see Chapter 5). However, most of the critical issues mentioned in relation to quantitative behavioral genetics findings also apply to these molecular genetics findings. In more detail, but without reiterating the previous section, genetic association and GREML studies focus on direct effects of genetic factors on phenotypic outcomes and do not account for the dynamic interplay between genes and environment. Consequently, even though these studies are measuring genetic differences on the molecular level, the analyses and resulting findings are still more informed by a statistical rather than a dynamic biological perspective, not taking into account the complex interplay between genetic and environmental factors in the development of a complex trait. In addition, these studies generally estimate whether gene variants predict differences in PWB between people of a particular population rather than the degree to which genetic factors determine an individual’s level of PWB (although this is theoretically possible given that genetic information is measured on an individual level). Furthermore, all these findings are based exclusively on associations and do not allow for causal interpretation. Are these molecular genetics studies allowing us to conclude whether and to what degree people are feeling happy as a function of their genetic make-up? Unfortunately, the answer is no, given that these results cannot be interpreted causally and not on an individual level. However, these studies tell us that although there may not be specific gene variants with large effects, a genetic component underlying the variation of PWB between people does exist and most likely reflects the combined influence of many genetic variants of small effect. Nevertheless, whether this genetic component is specific to differences in PWB as opposed to other phenotypic traits (e.g., personality traits) is unclear given that these findings are inherently correlational. While the GREML findings validate—at least partially—that heritability estimates of twin studies reflect genetic factors, the absence of replicable genetic associations with PWB suggest that the assumption of large direct genetic effects on well-being is most probably misguided, at least in a simplistic “gene-to-well-being” fashion.

Gene–environment interplay Although some genetic and environmental factors may have a direct causal influence on PWB, as discussed at the beginning of this chapter, it is untenable to assume that these contributions portray independent forces of development. Current understanding of developmental psychology as well as molecular biology strongly suggests that genes and environment influence each other in the development of psychological traits, which is usually referred to as gene–environment interplay. While there may be multiple ways in which genetic and environmental factors impinge on each other, two kinds of interplay have gained particular attention in the research community: gene–environment correlation (rGE) and gene–environment interaction (GXE). rGE refers to the idea that genetic factors increase the probability of exposure to specific environmental conditions (Plomin, DeFries, and Loehlin, 1977). For example, a person with a genetic

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disposition for an outgoing personality is more likely to appreciate social situations and therefore also more likely to seek out socially stimulating environments. Hence, in such a case, exposure to a more social environment is not independent of genetic disposition. Given that most of us are able to select and shape our immediate environment based on our personal preferences—which are influenced by our genes—rGE is the norm rather than the exception. This is confirmed empirically by behavioral genetics studies suggesting that up to 40% of the variance in experienced life events can be explained by heritable factors (Plomin et al., 1990). In short, genes may not only have a causal effect on psychological outcomes, but may also influence the environmental conditions to which an individual is exposed. This raises the possibility that the presumed direct effects of genetic factors on particular outcomes may, in fact, be mediated by environmental exposures. For example, genes may turn out to be associated with PWB simply because they increase the likelihood of people to seek out environments that promote their well-being. Importantly, in quantitative behavioral genetics studies which divide population variance into heritable (A) and environmental components (C and E), rGE between heritability and the non-shared environment (E) will be captured in—and hence inflate—the heritability estimate, while rGE between heritability and the shared environment (C) will contribute to—and inflate—the shared environment estimate (Rijsdijk and Sham, 2002). The notion of gene–environment interaction suggests that genetic and environmental factors are intertwined in an even more complex and dynamic way by moderating each other’s effects. Evidence that environmental factors may affect the magnitude of heritable influences on phenotypic outcomes emerged consistently in twin studies on the heritability of cognitive ability (IQ): heritability estimates for IQ tend to be significantly higher in children growing up in relatively affluent environments compared to heritability estimates of children reared in poverty (Harden, Turkheimer, and Loehlin, 2007; Rowe, Jacobson, and Van den Oord, 1999; Turkheimer et al., 2003). Hence, whether and to what degree an influence of heritable factors emerges may depend on the conditions and variability of environmental factors. Such heritability–environment interactions (HXE) highlight that estimates derived from quantitative behavioral studies are always influenced by the characteristics of the particular sample. Importantly, in quantitative behavioral genetics studies that do not account for such gene–environment interplay, HXE between heritability and the non-shared environment will inflate estimates of the non-shared environment, while HXE between heritability and the shared environment will be captured in the heritability estimate (Rijsdijk and Sham, 2002). The specific investigation of GXE is of particular interest in the field of molecular genetics. In molecular genetics studies, GXE can be described from either a more genetic or a more environmental perspective, but both views describe the same fundamental idea: environmental (or genetic) effects are moderated by genetic (or environmental) factors. For example, whether an environmental factor has an impact on PWB may depend on an individual’s genetic make-up. Some people are more affected by well-being-promoting environmental factors and some people less due to genetic differences between them, as captured in the differential susceptibility framework (Belsky and Pluess, 2009, 2013) and more recently in the concept of vantage sensitivity (Pluess and Belsky, 2013; see also Chapter 12 in this book). Likewise, genetic effects may differ depending on environmental exposure (similar to HXE): in one particular context a genetic factor may predict PWB, but less so in another context. Over the last ten years a large number of studies have emerged providing empirical evidence for such gene–environment interaction on the basis of candidate genes (see, for example, Caspi et al., 2002, 2003). Although the majority of these studies focus on the interplay between candidate genes and adverse environmental factors in the prediction of psychological problems (see Chapter 11), they nevertheless support the notion that

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Genes 1

2

Environment

3

Phenotype

1

Figure 15.1 Graphical illustration of the complex interplay between genes and environment in the development of a phenotype: (1) main effects of genes and environment; (2) gene–environment correlation; (3) gene–environment interaction.

environmental and genetic factors influence each other in a highly dynamic and bidirectional manner rather than having independent effects (Rutter, Moffitt, and Caspi, 2006). Important to mention, however, is that gene–environment interaction studies have been found to suffer from similar difficulties as candidate gene studies and GWAS, including underpowered samples, lack of replications, and small effect sizes (Duncan and Keller, 2011). Nevertheless, these studies provide strong evidence that genotype–phenotype relations are contextually dependent. The complex interplay between genetic and environmental factors regarding their contribution to a phenotypic outcome is illustrated in Figure 15.1.

The role of epigenetic mechanisms in gene–environment interplay The significant role of gene–environment interplay pertaining to psychological development is further supported by important recent discoveries in the field of Epigenetics (see Chapters 2 and 13), which focuses on the study of molecular mechanisms involved in the modification of gene expression without changing the DNA structure (Szyf and Bick, 2013; Zhang and Meaney, 2010). It is important to recall here what genes do and what they do not do. Genes are defined as sections of DNA that contain the biological code for the production of specific proteins.3 These proteins are involved in biochemical processes on a fairly basic cellular level rather than directly impacting highly complex psychological states and behaviors. In other words, genes are first expressed into proteins that then function on a cellular level, and, together with many other proteins and biological processes, affect the function of higher organs including the brain, which, eventually, directs behavior. Most importantly, a gene must be expressed before it can have an effect on the organism. If a gene is not expressed, it may as well not exist from a functional, biological 3

It is now apparent that large segments of the genome are transcribed, resulting in the production of RNA species that influence cellular function without being translated into proteins. Nevertheless, the same caveats apply to RNA functions. Genes code for RNAs and proteins rather than complex behaviors.

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perspective. Interestingly, gene expression is regulated by cellular transcription factors, which are, in turn, regulated by both internal and external environmental conditions. The very process of gene expression thus involves an interaction between environmentally regulated transcription factors and their direct interaction with the DNA. The ability of transcription factors to access DNA-binding sites and thus regulate gene expression is directly influenced by a range of so-called epigenetic modifications (see Chapters 2 and 13 for a more detailed introduction). Epigenetic mechanisms include biochemical modifications to either the DNA or to its histone protein partners that, collectively, form the chromatin environment within which the genome operates. These modifications determine the opening and closing of chromatin: transcription factors readily gain access to DNA in open chromatin environments, much less so when chromatin is closed. Epigenetic modifications thus promote or inhibit the expression of a gene and therefore its activity. The most intensively studied epigenetic modifications are those that involve methylation of either the DNA or the histone proteins. Since these modifications are chemically more stable, they provide a candidate mechanism for the stable changes in gene expression. Recent studies provide empirical evidence that such epigenetic modifications can be altered by environmental factors (see Chapter 13). The environment can thus stably influence the degree to which a gene is expressed. For example, it has been shown that experiences in early childhood can lead to epigenetic modifications which affect gene expression of the glucocorticoid receptor in the adult brain (McGowan et al., 2009). In summary, while genes are coding for proteins that may affect brain function and hence behavior, environmental factors play an import role regarding whether these genes are expressed in the first place or not. The fascinating field of epigenetics not only provides insight into the biological mechanisms involved in gene–environment interplay, but also emphasizes how fundamental the complex bidirectional links between genes and environment are in development. More recent studies reveal that like any aspect of the phenotype, epigenetic signals are a product of gene–environment interactions. Genome-wide variation in DNA methylation at birth, for example, although largely defined by maternal environmental conditions, has been shown to be moderated by fetal genotype (Teh et al., 2014). Furthermore, the impact of maternal anxiety during pregnancy on genome-wide DNA methylation in the offspring has been found to be moderated by the same BDNF Val66Met polymorphism that moderates environmental influences on multiple features of brain function (Chen et al., 2015). Similarly, Binder and colleagues (Klengel et al., 2013) reported that the impact of childhood adversity on the methylation state of the FKBP5 gene—the product of which regulates glucocorticoid signaling—is moderated by genetic variation of the FKBP5 gene. Such findings confirm a biological basis for the gene–environment interactions revealed in studies of child development (Meaney, 2010). In conclusion, the majority of quantitative behavioral and molecular genetics methods aimed at quantifying and identifying the relative contribution of genes and environment to PWB fail to account for the complex bidirectional relationship between genes and environment (i.e., rGE and GXE). However, from a biological perspective, it is exactly this interplay between genetic and environmental factors that is deemed central to the development of PWB rather than just the independent effects of either.

Stochastic processes Although this chapter is primarily about the roles of genes and environment in the prediction of PWB, it has to be acknowledged that genetic and environmental factors may not be the only players involved in shaping psychological outcomes. So-called stochastic (i.e., random) factors are likely to play an important role too, above and beyond genetic and contextual influences.

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Stochastic factors include any processes that appear to be varying at random and cannot be predicted or attributed to specific mechanisms or biological processes. Although a rather fuzzy concept that has not yet been investigated widely, there are empirical studies that emphasize the relevance of such—apparently random—factors. For example, in a recent study, a group of genetically identical mice were all housed in the same enclosure—an enriched environment with multiple levels, bridges, tunnels, and toys (Freund et al., 2013). Over time these genetically identical mice developed inter-individual differences in their behavior, even though they shared both the exact same genotype and environment. Although de novo somatic mutations or factors such as dominance hierarchies cannot be excluded as an explanation, the authors concluded that the different choices these mice made regarding how they interacted with their environment led over time to different experiences, consequently shaping individual development in different directions. Given the study design, these different choices could not have been a function of genetic or environmental factors and are therefore considered stochastic.4 Applied to the development of PWB in humans, the study by Freund et al. (2013) suggests that genes and environment alone are unlikely to account fully for an individual’s level of well-being. People make choices based on different factors, some of which are fundamentally random (i.e., independent of genetic and environmental components), and these choices may co-influence both the selection of the environment as well as gene expression through epigenetic mechanisms and, hence, contribute substantially to the development of individual differences in PWB. In other words, we are not slaves of either genetic or environmental factors. Rather, both genes and environment provide important developmental influences whose effects are further modified and redirected by a number of stochastic processes.

Conclusion One of the most basic questions in psychology aims at identifying to what degree psychological traits are determined by genetic predispositions versus environmental factors. This question implies that genes and environment have independent and distinctly quantifiable effects. Quantitative behavioral as well as molecular genetics studies appear to confirm such independent effects, suggesting that both heritable genetic as well as environmental factors play an important role regarding individual differences in PWB. However, although these findings seem to suggest that genes and environment may indeed have independent effects and that environmental factors may be slightly more influential than genetic ones, it is important to acknowledge that such an interpretation is informed by a statistical rather than a biological perspective. The latter clearly implies complex gene–environment interplay that precludes an interpretation of main effects. The dynamic and intricate interplay between genes and environment may, in fact, be the central and determining factor regarding an individual’s well-being rather than the statistically derived individual contribution of each. In other words, genes and environment are both important players in development, but it is the interplay between them that may be the defining element. Like different football players that pass the ball between them while moving it forward, development of PWB is not just the function of one player, but the resulting combined additive and multiplicative effect of multiple players, including genes, environment, their interaction (including epigenetic processes; see Figure 15.1), as well as stochastic processes. Indeed, the critical question is not one of relative importance, but rather how genetic, environmental, and stochastic factors influence brain development and function, and thus determine PWB.

4

Stochastic processes have also been implicated in the establishment of epigenetic signals across the genome (Labrie, Pai, and Petronis, 2012).

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Given the complex relationship between genes and environment, it is unlikely that current methods and approaches of quantitative behavioral and molecular genetics will be able to precisely and meaningfully disentangle genetic and environmental contributions to PWB. Consequently, in addition to addressing limitations of current studies, future work should aim at developing and applying methodological approaches that are able to model the complex interplay between genes and environment over time in order to identify the biological pathways that lead to PWB on an individual level.

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Genetics of psychological well-being: current state and future directions Michael Pluess

Genetics of psychological well-being: current state and future directions: an introduction Until the 1950s only a few empirical studies examined questions regarding genetic influences on psychological traits. Significant advances in research methods since then, however, have enabled not only the statistical partitioning of population variance into heritable and environmental components, but more recently also the investigation of psychological outcomes on the molecular genetic level. Although research on PWB has generally seen an impressive growth since the “launch” of positive psychology toward the end of the last century (Linley et al., 2006; Seligman et al., 2005), work focused on the investigation of genetic factors is still sparse in this relatively new field of inquiry. Nevertheless—and as evidenced in this book—the field is making significant progress and we know a lot more today about the genetics of PWB than we did only a few years ago. In what follows I will first summarize where we are today regarding our knowledge of the role of heritability and genetics in positive psychology, before making several suggestions for future research. I will then discuss the relevance of such work given the potential for important practical application, before closing this edited book with the conclusion that more advanced and more sophisticated research is needed, not only regarding genetic aspects, but also regarding environmental factors, and—most importantly—regarding the interplay between both.

Current state of knowledge Evidence that heritable factors play a significant role regarding individual differences in PWB is provided by a respectable number of quantitative behavioral genetics studies. The first twin study that included a measure of PWB reported a heritability estimate of 40% for positive emotionality (Tellegen et al., 1988) and this initial result has now been replicated multiple times, as summarized in two recent independent meta-analyses on SWB and life satisfaction involving at least 12 different studies (see Chapters 5 and 10; Bartels, 2015). In fact, the work summarized in this book suggests that all discussed measures of PWB or positive functioning are characterized by a substantial heritable component, usually in the range of 20–50% (see Chapters 1, 5, 6, 7, 8, 9, and 10). In cases where it was possible to derive measures reflecting stable dispositional rather than momentary well-being, heritability estimates reached up to 80% (see Chapters 5 and 6; Lykken and Tellegen, 1996; Nes et al., 2006). Furthermore, quantitative behavioral genetics studies inform us that the heritability components of different well-being domains—including positive emotionality, vitality, and sociability—overlap to a large degree (i.e., 25–99%; Bartels and Boomsma,

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2009; Eid et al., 2003; Keyes, Myers, and Kendler, 2010), suggesting that the heritable variance of these different positive outcomes reflects a general heritable factor of well-being. Importantly, only 41–50% of this general heritable well-being factor overlaps with the heritable components of psychopathology (i.e., depression and anxiety), which means a substantial proportion of the heritability underlying PWB is independent of heritable factors associated with psychological problems (Kendler et al., 2011). Furthermore, even though the presence of PWB is generally correlated with the absence of psychological problems (and vice versa), the correlation between heritability estimates of both tends to range between r = .30–.60 depending on gender and the specific psychological problem (Bartels et al., 2013; Nes et al., 2008). This provides a strong basis for the assumption that PWB—or positive mental health—is not just the absence of mental illness and the genetic risk thereof, but also—at least to some degree—a reflection of the presence of heritable factors associated explicitly with PWB (see Chapter 1). The existence of a genetic component specific to positive traits is further supported by findings that SWB is relatively constant over time and highly correlated with stable personality traits, including high extraversion and low neuroticism (Diener et al., 1999). Moreover, heritability of these personality traits overlaps to a large degree with the heritable component of well-being, suggesting that the same genetic factors may account for both PWB and positive personality (see Chapter 9; Hahn, Johnson, and Spinath, 2013; Weiss, Bates, and Luciano, 2008). Molecular genetics studies aimed at the identification of the specific gene variants that make up the reported heritability of PWB are still rare (see Chapter 10 for a detailed summary). However, according to a first attempt at estimating the total genetic contribution to well-being, 12–18% of the variance could be accounted for by the combined effect of more than five hundred thousand common gene variants (Rietveld et al., 2013). The few existing candidate gene studies provide some evidence for direct associations between individual gene variants and well-being outcomes. For example, significant associations emerged between life satisfaction and 5-HTTLPR (DeNeve, 2011); job satisfaction and 5-HTTLPR, as well as DRD4 (Song, Li, and Arvey, 2011); happiness and MAOA (Chen et al., 2013); prosocial behavior and AVPR1 (Knafo et al., 2008), OXTR (Israel et al., 2009), DRD4 (Anacker et al., 2012), and DRD2 (Reuter et al., 2013); optimism, self-esteem, self-efficacy, and OXTR (Saphire-Bernstein et al., 2011); optimism and MR (Klok et al., 2011); self-transcendence/spirituality and 5-HT1A (Lorenzi et al., 2005), as well as DRD4 (Comings et al., 2000). However, many of these candidate gene studies suffer from substantial methodological limitations, including underpowered samples. In addition, they often fail to replicate (for example, 5-HTTLPR and life satisfaction; DeNeve et al., 2012). In contrast to research efforts focused on genetic main effects, results from so-called GXE studies, investigating the dynamic interplay between candidate genes and environmental factors, tend to be somewhat more consistent, although many GXE studies suffer from similar methodological problems as candidate gene association studies (see Chapters 11 and 12; Duncan and Keller, 2011; Manuck and McCaffery, 2014). Even though the majority of existing GXE studies focus on psychiatric outcomes, the discovery that certain gene variants predict resilience in response to adverse experiences is certainly relevant to the study of PWB. For example, the now wellvalidated observation that the long allele of the 5-HTTLPR protects individuals with a history of childhood maltreatment from the development of depression (Borg et al., 2009; Caspi et al., 2003) suggests that psychological resilience, an important feature of PWB, has a genetic component. This has been confirmed in a recent twin study, according to which emotional resilience to stressful life events has a heritable component of up to 50% (Amstadter, Myers, and Kendler, 2014). Importantly, according to the differential susceptibility framework (Belsky and Pluess, 2009), gene–environment interaction should also be observable in response to positive experiences, with

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some people benefiting more than others from supportive exposures as a function of genetic factors (see Chapter 12). Such differences in vantage sensitivity (Pluess and Belsky, 2013) emerged, for example, in response to a parenting program, with children carrying the DRD4 7-repeat allele being more positively affected by the intervention than children with other genotypes (Bakermans-Kranenburg et al., 2008). Importantly, this moderating effect of DRD4 and other dopaminergic genes has recently been confirmed in a meta-analysis (Bakermans-Kranenburg and van IJzendoorn, 2011). Effects of gene–environment interplay are likely to involve epigenetic mechanisms (see Chapters 2 and 13). Although studies specifically investigating epigenetics pertaining to wellbeing outcomes have not yet been published, work in the field of psychopathology suggests that specific environmental conditions and experiences can affect gene expression (i.e., determine whether a gene is active or not) through epigenetic modification of DNA (Borghol et al., 2012; McGowan et al., 2009) and that the degree of such environmentally induced epigenetic modification (e.g., methylation) is further moderated by genetic differences (Beach et al., 2014; Klengel et al., 2013). In other words, environmental conditions co-influence whether a gene is active (i.e., expressed into RNA which is then translated into protein) or not. Such complex dynamics between environmental factors and gene expression are likely to play an important role in the development of PWB. A recent study (Fredrickson et al., 2013)—though not undisputed (Brown et al., 2014; Cole and Fredrickson, 2014)—provided first evidence for significant differences between hedonic and eudaimonic well-being regarding the expression of a genetic profile associated with inflammation and immune response, suggesting that different kinds of PWB may be associated with specific gene expression patterns (see Chapter 10). Given that genes do not code directly for psychological outcomes but for proteins which affect behavior indirectly through their effects on the central nervous system, imaging genetics studies, which aim at investigating associations between gene variants, brain characteristics, and behavior, are of great relevance in research on the genetics of psychological outcomes. Although brain imaging studies suffer similar methodological limitations as candidate genes studies (i.e., small samples, lack of replication) and are relatively scarce in the field of positive psychology, studies focused on psychopathology provide some suggestions for mechanisms involved in observed associations between genetic polymorphisms and PWB (see Chapter 14). For example, 5-HTTLPR has been associated with individual differences in amygdala reactivity, a brain region involved in the processing of emotional information (for a meta-analysis based on 34 studies with a total sample size of N = 1,157, see Murphy et al., 2013), suggesting that carriers of the 5-HTTLPR long allele may be more resilient in the face of adversity because their amygdala is less responsive to negative experience (Canli and Lesch, 2007). On the other hand, the short allele of the 5-HTTLPR, while not associated with resilience, has been found to promote an increased positive response to supportive environmental influences, probably as a function of a more reactive amygdala (see Chapter 12; Eley et al., 2012; Pluess and Belsky, 2013). In conclusion, current knowledge regarding the genetics of PWB is primarily based on quantitative behavior genetics and few emerging molecular genetics studies. Although gene–­ environment interplay, epigenetics, and genetic influences on brain function are likely to play a central role in the development of PWB, this has not yet been investigated in great detail. This is certainly not surprising given that positive psychology is a relatively new field of inquiry and more sophisticated research methods have become available only fairly recently. However, there are several important issues inherent to the study of heritability and genetics of psychological traits that have to be considered before turning our attention to suggestions for future research.

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Important considerations in research on heritability and genetics First, and as discussed in detail in Chapter 15, quantitative behavioral genetics as well as genetic association studies provide estimates regarding the extent to which heritable or genetic factors explain differences in well-being between people of a specific population, rather than the genetic contribution to an individual’s well-being. In other words, heritability estimates expressed in percentage terms refer to the proportion of the population variance explained by heritable factors rather than the proportion of an individual’s total well-being. Furthermore, these estimates of genetic and environmental contribution are based on a statistical rather than a biological perspective. Given the highly complex and bidirectional interplay between genetic and environmental factors, it is biologically implausible to distinguish between exclusively genetic and exclusively environmental effects on PWB (Lerner, 1993; Meaney, 2001). Second, almost all of the genetic polymorphisms found to be associated with well-being have previously been identified as risk factors for pathological outcomes. Hence, even if the detected associations were not false positive findings, the involved gene variants are certainly not specific to well-being. In other words, gene variants exclusively associated with PWB have not yet been detected. Given that individual genes code for proteins that affect cellular function rather than complex behavior, it is unlikely that there are gene variants directly and exclusively associated with PWB. It is much more likely that certain gene variants influence “endophenotypic” neurobiological features (e.g., neurotransmitter systems in the brain) that are involved in a wide range of behavioral outcomes, including PWB. Furthermore, whether such neurobiological characteristics and associated genes predict well-being is likely to be dependent on the environment. For example, as mentioned earlier, the 5-HTTLPR long allele promotes resilience in the face of adversity (Karg et al., 2011), but the 5-HTTLPR short allele increases the response to positive experiences (Pluess and Belsky, 2013). This suggests that neither the long nor the short allele of the 5-HTTLPR are “well-being genes” per se, but that either has positive (i.e., resilience and vantage sensitivity for long and short alleles, respectively)—as well as negative (i.e., vantage resistance and vulnerability for long and short alleles, respectively)—effects depending on the environmental context and the specific outcome of interest. Third, although some candidate genes for well-being outcomes have been detected, the few existing replications tend to be negative. Given the rather disappointing findings in molecular psychiatry, where substantial resources have been devoted to the identification of disorder-­ specific genes with limited success so far, it seems unlikely that there are specific well-being genes with large effect. Much more likely is that well-being is a function of many thousand—probably not highly specific—gene variants of small effect, as suggested by GREML findings on well-being (see Chapter 10) and recent large-scale psychiatric GWAS (Wray et al., 2014). Taking these important considerations into account, there are several areas future research should focus on in order to advance our knowledge regarding the role of heritable and genetic factors related to PWB.

Future directions for research The fact that research on genetics of PWB has a relatively short history compared to psychiatric genetics is not necessarily a disadvantage for the field. In fact, future research on genetics of wellbeing is likely to benefit from the often painful and sobering lessons learned in the field of psychiatric genetics, with access to an ever-growing multitude of increasingly sophisticated methods and statistical tools originally developed in the search for genetic markers of psychopathology.

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Given that the same methodological and biological issues arise in genetic research whether the focus is on genes associated with psychological problems or PWB, future research on genetics of PWB can and should learn from psychiatric genetics by applying the most current knowledge as well as the most advanced methodological tools. In what follows I will highlight a number of selected and central issues that future research should prioritize in order to advance and solidify knowledge regarding the genetics of PWB. 1 Theoretical concepts of well-being and their measurement. Positive psychology features a myriad of well-being constructs and associated measures. Many of these measures overlap substantially with each other as well as with measures of psychopathology. This lack of specificity is highly problematic for genetic research. Hence, future work should aim at identifying distinct components of well-being, including behavioral, physiological, and neurological endophenotypes (i.e., intermediate phenotypes) of PWB (Gottesman and Gould, 2003). More specific measures of well-being-related traits are a prerequisite for successful research, particularly for molecular genetics studies. 2 Study design. Future work on genetics of PWB should focus on overcoming basic limitations of current studies. This includes recruitment of large but phenotypically well-characterized samples (i.e., up to 100,000 or more participants for GWAS, see Chapter 10), replication of significant findings in independent samples, as well as moving from correlational to experimental and from cross-sectional to longitudinal studies. Important to mention, several international consortia have recently been formed in order to combine existing data for large-scale GWAS, including such focused on well-being (e.g., the Social Science Genetic Association Consortium). 3 Molecular genetics methods. The field of molecular genetics is progressing extremely fast. New methodology is now available that allows for whole-genome sequencing of large samples at moderate cost, bivariate estimation of heritability based on genome-wide data (GREML) which allows testing for genetic overlap between different variables, as well as multi-arrays for genome-wide measurement of epigenetic markers (i.e., methylation), to name some of the most significant recent methodological advances. Future studies should apply these new methods in adequately powered samples that feature high-quality measures of well-being. 4 Molecular mechanisms. Given that current quantitative behavioral genetics methods as well as gene association studies tend to be informed by statistical rather than biological considerations, future studies should make a clear and decisive move toward a more biologically based approach. Rather than simply identifying individual gene variants associated with well-being, studies should aim at understanding the specific molecular mechanisms underlying well-being and associated endophenotypes. This includes investigating biologically meaningful networks of multiple genes rather than single-gene variants, measuring gene expression in addition to DNA structure, and examining the role of inherited and environmentally acquired epigenetic markers and related processes. Ultimately, a focus on these mechanisms will require experimental studies on the molecular level in both animal and human models. For example, the investigation of intra-individual epigenetic changes over time in response to an intervention aimed at promoting well-being and consequent identification of the affected gene networks and associated higher-level traits. 5 Multi-level approach. Rather than focusing exclusively on the molecular level by identifying gene variants associated with well-being, future studies should also adopt methodological approaches that allow for the integration of different levels of analysis (i.e., molecular to ­cellular to physiological/neurological to behavior). Genetic effects (i.e., molecular level) on well-being

CURRENT STATE AND FUTURE DIRECTIONS

(i.e., behavioral level) will always be mediated by cellular, neuroendocrine, and other biological processes. In other words, biological pathways associated with “well-being” genes will generally stretch across multiple levels of analysis. The combination of genetics, epigenetics, and gene expression with brain imaging and psychoneuroendocrinology in experimental and longitudinal studies will be fundamental to the study of biological pathways and processes involved in PWB. Such multi-level approaches will also be crucial for the identification of well-being endophenotypes. Furthermore, a thorough understanding of these pathways will be crucial in order to apply knowledge regarding heritability and molecular genetics of PWB in practice. 6 Gene–environment interplay. As discussed in detail in Chapter 15, it is impossible to understand the role and function of genes without taking into account the role and function of environmental factors. Hence, future studies should focus on the complex and bidirectional interplay between genes and the environment and investigate how these two forces shape PWB across development. This should include studies testing whether and to what degree specific environmental factors contribute to the biology of well-being through, for example, epigenetic mechanisms and the interplay with genetic factors. Progress in the study of gene–­ environment interplay also requires moving on from single-candidate gene studies to the exploratory investigation of gene–environment interaction on the basis of genome-wide data, as well as biologically defined gene networks. Such approaches will more likely lead to the successful identification of genes specifically involved in PWB, compared to current studies which tend to focus on the usual candidate gene suspects that, as discussed earlier, do not appear to be specific to well-being. Besides these more methodological and technical issues, perhaps the more important question regarding future research on genetics and PWB is whether a better understanding of genetics related to well-being has any potential to improve people’s lives if applied practically.

Potential for practical application The enthusiasm for genetic research in the fields of psychology and psychiatry is deeply rooted in the hope that better genetic knowledge would lead to the development of new and more effective treatments. However, even after many years of extensive and costly research, it has not (yet) been possible to put this hope into practice. In fact, the discovery that psychological outcomes are the function of many genes of very small effect rather than of a few genes with large effect suggests that it will be very difficult to identify specific biological targets for treatment on the basis of genetic association findings alone. The same challenge applies to the study of genetics related to PWB: identification of gene variants that explain miniscule fractions of the variance of well-being is unlikely to inform psychological practice in a meaningful way (although such findings may nevertheless lead to the successful discovery of biological systems involved in processes related to well-being). Hence, what is needed is a better understanding of the biological pathways between genes and well-being, including the interplay between genetic and environmental factors. Once we understand the biological mechanisms of how these genes—in combination with environmental forces—affect well-being, practical application will be much more realistic. Future applications on the basis of such advanced biological knowledge may include the development of psychological as well as pharmaceutical treatments aimed at promoting well-being, personalized lifestyle suggestions aimed at maximizing well-being (e.g., nutrition, physical exercise, social environment, etc.) based on an individual’s genotype, as well as applying vantage sensitivity (Pluess and Belsky, 2013)

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by taking an individual’s genetic sensitivity to specific environmental influences or particular forms of psychological intervention into account, to name just a few possibilities. Although these potential applications are promising and exciting, it will probably take many additional years of high-quality research before our knowledge can be translated into effective practice. However, current theoretical and empirical knowledge regarding the heritability and molecular genetics of PWB is already important and relevant now, even in the absence of the proposed practical applications. For example, the understanding that PWB has a substantial genetic component—and is therefore a relatively stable trait—is important for anyone interested in well-being and its promotion, particularly governments planning to use well-being outcomes as measures of how well a country and its policies are doing.

Conclusion In summary, research on the heritability and genetics of PWB is still in a relatively early stage. However, given the rapid progress in the field of molecular genetics, it is not unlikely that our knowledge will increase substantially in the coming years, especially if future research focuses on the investigation of biological pathways across multiple levels of analysis. Significant advances in our understanding of biological mechanisms related to genetics and well-being will also be fundamental for the practical application of such knowledge in intervention and genetic counseling. Most importantly, given the intricate interplay between genetic and environmental factors, one cannot be understood without the other. Future research will only be of relevance if it takes both genes and environment and their dynamic and complex interplay into account.

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Saphire-Bernstein, S., Way, B. M., Kim, H. S., Sherman, D. K., and Taylor, S. E. 2011. Oxytocin receptor gene (OXTR) is related to psychological resources. Proceedings of the National Academy of Sciences of the United States of America, 108(37), 15118–15122. Seligman, M. E., Steen, T. A., Park, N., and Peterson, C. 2005. Positive psychology progress: empirical validation of interventions. American Psychologist, 60(5), 410–421, doi: 10.1037/0003–066x.60.5.410. Song, Z., Li, W., and Arvey, R. D. 2011. Associations between dopamine and serotonin genes and job satisfaction: preliminary evidence from the Add Health Study. Journal of Applied Psychology, 96(6), 1223. Tellegen, A., Lykken, D. T., Bouchard, T. J., Wilcox, K. J., Segal, N. L., and Rich, S. 1988. Personality similarity in twins reared apart and together. Journal of Personality and Social Psychology, 54(6), 1031. Weiss, A., Bates, T. C., and Luciano, M. 2008. Happiness is a personal(ity) thing: the genetics of personality and well-being in a representative sample. Psychological Science, 19(3), 205–210, doi: 10.1111/j.1467– 9280.2008.02068.x. Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., and Middeldorp, C. M. 2014. Research review: polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry and Allied Disciplines, 55(10), 1068–1087. doi: 10.1111/jcpp.12295.

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Author Index

A

Abbott, A.  232 Abel, T.  215 Abela, J. R. Z.  239 Adams, M. J.  152, 153, 155 Adams, R. L.  216 Adolphs, R.  205 Adrianson, L.  101 Aghajanian, G. K.  140 Aked, J.  67 Aknin, L. B.  121 Alarcon, G. M.  133 Albert, D. J.  140 Alessandri, G.  136, 137, 138 Ali, F. R.  167 Alliey-Rodriguez, N.  141 Allis, C. D.  212 Allman, J. M.  155 Allport, G. W.  5, 146 Almal, S. H.  28 Alter, M. D.  35 Altmüller, J.  172 Amstadter, A. B.  267 Anacker, K.  267 An Choi, J.  116 Anderson, A. K.  234 Ando, J.  105 Andrews, F. M.  6, 77 Angner, E.  76 Annas, J.  3, 17, 114 Antonovsky, A.  4 Antypa, N.  122 Apicella, C. L.  118 Appel, K.  183 Aravind, L.  32 Archer, T.  100, 101, 106 Archontaki, D.  16 Arenander, A.  124 Argyle, M.  114, 117, 121 Aristotle  75, 117, 119 Armony, J. L.  205 Arnedo, J.  107 Arnett, J. J.  85 Aron, A.  205 Aron, E. N.  205 Arthaud-Day, M. L.  77 Arvey, R. D.  117, 118, 267 Asbury, K.  48 Ashburner, J.  232 Ashton, M. C.  147 Ashwin, C.  140, 167, 204, 236 Ask, H.  89 Atienza, A. A.  133 Ayers, A. C.  4

B

Bacon, S.  79 Bading, H.  216 Baker, J. H.  47 Baker, L. A.  84, 102–3, 107 Bakermans-Kranenburg, M. J.  185, 195, 198, 199, 203, 268 Baltes, P. B.  5 Bann, C. M.  104 Bardi, A.  119 Bargmann, C. I.  58 Barker, D. J.  218 Barker, R.  184 Barnett, J. H.  230 Barr, C. S.  223 Barraclough, B.  148 Barres, R.  224 Barreto, G.  216 Barrett, H. C.  60 Barrett, L. F.  102 Bartels, M.  16, 79, 81, 83, 87, 88, 90, 149, 155, 163, 164, 166, 169, 171, 266, 267 Barter, J.  102 Bates, T. C.  16, 77, 107, 153, 155, 267 Batty, G. D.  147, 148, 154 Beach, S. R. H.  268 Beaudet, A. L.  32 Becker, B.  203 Beevers, C. G.  204, 236 Beja-Pereira, A.  116 Belsky, J.  91, 169, 179, 180, 194, 195, 196, 197, 199, 201, 203, 205, 214, 239, 259, 267, 268, 269, 271 Benet-Martínez, V.  147 Benjamin, J.  106 Bentham, J.  76, 114 Bentler, P. M.  135 Bergeman, C. S.  80 Berridge, K. C.  61 Bertolino, A.  237, 238 Bhattacharya, S. K.  216 Bick, J.  260 Bigos, K. L.  230, 231, 232 Bilder, R. M.  237 Binder, E. B.  180, 183, 261 Binmore, K.  119 Bird, A.  32, 33, 240 Biswas-Diener, R.  12 Blasi, G.  122 Blokland, G. A. M.  38 Blonigen, D. M.  149 Blum, M. G.  59 Boardman, J. D.  252 Bogdan, R.  230, 231, 232

278

AUTHOR INDEX

Bolte, A.  234 Boniwell, I.  4, 201 Bood, S.-Å.  100 Boomsma, D. I.  16, 79, 83, 90, 138, 149, 155, 169, 266 Borg, J.  140, 204, 267 Borghol, N.  221, 268 Bouchard, T. J., Jr.  104, 117, 147, 149, 254 Bowcock, A. M.  222 Bowers, M.  184 Bowling, N. A.  133 Boyce, W. T.  186, 195 Boyd, R.  116 Bradburn, N. M.  6, 14, 77, 102 Bradley, R. T.  106, 182 Bratko, D.  89, 149 Bremne, J. D.  65 Bressler, J.  32 Bridges, M. W.  133 Brown, N. J. L.  172, 268 Brown, S. M.  180, 181, 205 Brownbridge, G.  102 Browning, B. L.  51 Browning, S. R.  51 Bryant, F. B.  6 Bryant, R. A.  238 Bubar, M. J.  122 Buchanan, J. P.  49 Buchmann, A.  183 Buckholtz, J. W.  240 Burger, J.  116 Burmeister, M.  106, 199, 230, 231 Burt, S. A.  46 Bus, A. G.  198, 200 Butkovic, A.  89, 149 Butler, J.  8

C

Cabanac, A. J.  58 Cabanac, M.  58, 60 Cacciopo, J. T.  98 Cadoret, R. J.  47, 48 Caldji, C.  124 Caldú, X.  237 Camerer, C. F.  119, 120 Campbell, A.  104 Campbell, D. T.  153 Campbell-Sills, L.  181 Canella, D.  28 Canli, T.  118, 168, 235, 268 Cantril, H.  6, 79, 82 Caprara, G. V.  79, 81, 87, 136, 137, 138, 139 Cardon, L. R.  166 Carey, G.  44, 152 Carlson, L. E.  124 Carnevale, P. J. D.  234 Carroll, S. B.  58 Carver, C. S.  132, 133, 148 Caspi, A.  47, 52, 140, 148, 168, 169, 178, 179, 180, 181, 193, 195, 222, 235, 238, 239, 251, 259, 260, 267

Catalino, L. I.  15 Cattell, R. B.  146, 147 Cesarini, D.  121, 169, 258 Chabris, C. F.  169 Chalkley, R.  214 Chamberlain, K.  134 Chan, M. Y.  148 Chapman, B. P.  154 Charney, D.  177, 178, 180, 181 Chase, D. L.  61 Chauhan, S. P.  41 Chen, F.  185 Chen, H.  165, 168, 267 Chen, J.  237 Chen, K.  168 Chen, N. T.  205 Chen, S.  178 Chen, W. G.  219 Chew, S. H.  120 Chiao, J. Y.  115, 118 Chiesa, A.  124 Chimpanzee Sequencing and Analysis Consortium 151 Chipman, P.  181 Cho, R. J.  171 Chochol, C.  205 Choi, D.  184 Choi, I.  116 Chomsky, N.  121 Christal, R. E.  146 Christianson, S.-Å.  233 Cicchetti, D.  107, 180, 182, 203 Cieciuch, J.  14 Cieri, R. L.  155 Clark, L. A.  14, 90, 97, 100 Clarke, P. J.  205 Cloninger, C. R.  97, 98, 99, 100, 103, 104, 105, 106, 107, 166 Cloninger, K. M.  106 Cloninger, R. C.  135, 136 Clough, P.  138 CMEPSP 253 Cole, S. W.  171, 172, 268 Colzato, L. S.  237 Comb, M.  215 Comings, D. E.  106, 141, 267 Conner, T. S.  97, 102 Consortium, I. H. G. S.  230 Consortium, T. S. P. G.  232 Cook, T. D.  153 Cooper, H.  147, 148, 151, 154 Cornelis, M. C.  139 Cornelissen, G.  121 Corr, P. J.  100 Costa, P. T., Jr.  97, 102, 133, 147 Côté, S.  233 Coyne, J. C.  172 Crawford, C.  62 Croson, R.  119 Crow, T. J.  257 Cruz-Gordillo, P.  69 Csikszentmihalyi, M.  3, 5

AUTHOR INDEX

Cunningham, K. A.  122 Czajkowski, N.  79, 83, 84, 155

D

Da Cunha-Bang, S.  122 D’Alessio, A. C.  216 Darwin, C.  23, 115 Daubman, K. A.  234 David, S. A.  4 Davidson, R. J.  64, 235 Davies, G.  169 Davis, D. M.  123 Davis, M.  205 Daw, J.  252 Deary, I. J.  147, 148, 154 Debats, D. L.  134 Deci, E. L.  114, 171 Decressac, M.  184 DeFries, J. C.  254, 258 de Gelder, B.  64 Degnan, K.  178 De Hert, M.  148 de Moor, M. H. M.  50, 257 Dempster, E.  181 De Neve, J. E.  81, 87, 164, 167, 236, 257, 267 De Neve, K. M.  147, 148, 151, 154 Denissen, J. J. A.  152, 154 De Pascalis, V.  65 Derksen, J.  181 Derryberry, D.  233 De Waal, F. B. M.  115, 154 Dewitte, S.  121 Dhingra, S. S.  15 Didonna, F.  123 Dienberg Love, G.  171 Diener, C.  64, 107 Diener, E.  5, 6, 9–11, 12, 64, 76, 77, 79, 82, 100, 102, 107, 117, 118, 146, 148, 171, 267 Digman, J. M.  147 Ding, Yl  194 Djebali, S.  28 Dobzhansky, T.  56 Dodds, C.  181 Dolinoy, D. C.  214 Donaldson, Z. R.  118 Donnellan, M. B.  83 D’Onofrio, B. M.  46, 137, 138 Doya, K.  122 Drabant, E. M.  237, 238 Drahovsky, D.  216 Dreber, A.  118 Drury, S. S.  199 Duberstein, P.  154 Dubois, D.  124 Dudbridge, F.  51, 173 Duncan, L. E.  193, 260, 267 Dunn, E. W.  121, 183 Dunn, J.  48 Durham, W. H.  116 Durr, A.  30 Dyrbye, L. N.  15 Dyrdal, G. M.  77

E

Earle, K.  138 Easterbrook, J. A.  233 Eaton, S. B.  62 Eaves, L. J.  44, 137, 138, 139, 140 Ebdrup, B. H.  122 Eber, H. W.  147 Ebstein, R. P.  52, 106, 118, 120, 178, 179, 185, 198 Eckel, C. C.  119 Egan, M. F.  181, 237 Ehli, E. A.  167 Eid, M.  80, 87, 88, 103, 104, 267 Eisenberger, N. I.  62 Eley, D. S.  104 Eley, T. C.  199, 238, 268 Ellickson-Larew, S.  148 Elliott, E.  220 Ellis, B. J.  186, 195 Ellison, C. W.  135 Emmons, R. A.  102 Enns, R. M.  147, 151, 153, 154 Eriksson, N.  141 Erlandsson, A.  107 Esteller, M.  34 Estrada, C. A.  234 Everitt, B. J.  198 Eysenck, H. J.  147 Eysenck, S. B. G.  147

F

Fällman, L.  233 Fan, C.  121 Faraone, S. V.  197 Fass, D. M.  215 Feder, A.  177, 178, 180, 181 Fehr, E.  121 Feld, S.  5 Feldman, F.  76 Feldman, R.  185 Feldman Barrett, L.  100 Felmingham, K. L.  200 Figueredo, A. J.  152, 153, 154 Finch, J. T.  212 Finkel, D.  79, 80, 150 Fischer, A.  215 Fisher, P. M.  122 Fiske, D. W.  153 Flanagan, J. M.  212 Floel, A.  178 Folkman, S.  5 Foote, W. E.  140 Fossati, A.  136 Fossella, J. A.  235 Fox, E.  134, 140, 167, 204, 205, 236, 238 Fox, N.  178 Fox, S. H.  122 Francis, D.  218, 219 Frank, M. J.  235 Franz, C. E.  16, 81, 87 Fredrickson, B. L.  15, 99, 103, 165, 171, 172, 233, 234, 268 Freese, J.  252

279

280

AUTHOR INDEX

Freimer, N. B.  231 Freund, J.  262 Frimer, J. A.  115 Friston, K. J.  232 Frokjaer, V. G.  123 Funder, D. C.  146 Fung, H. H.  102 Furlong, M. J.  148

G

Gable, S. L.  200 Gachter, S.  119 Gaines, J. F.  146 Gale, C. R.  148 Gallagher, M. W.  13 Galton, F.  146 Garcia, D.  101, 103, 105, 106, 107 Garmezy, N.  203 Garrigan, D.  57 Gartner, M. C.  149 Gelernter, J.  106, 181 Gerstein, M. B.  28 Geyer, P. K.  34 Ghosh, D.  106, 199 Gigantesco, A.  16, 163 Gilbert, D. T.  60 Gillespie, N. A.  44, 104, 105, 186 Gillham, J. E.  132 Gintis, H.  121 Glatt, C. E.  231 Glimcher, P. W.  119 Gluckman, P.  214, 218 Gogtay, N.  115 Gold, M.  216 Goldberg, L. R.  147 Goldberg, T. E.  237 Goldin, P. R.  124 Goldman, D.  230 Gonen-Yaacovi, G.  234 Goodall, J.  155 Goodman, H. M.  215 Goodman, N.  140 Gordon, J.-S.  114 Goschke, T.  234 Gosling, S. D.  149 Gottesman, I. I.  48, 78, 180, 193, 231, 252, 270 Gottfredson, L. S.  148 Gould, T. D.  180, 231, 270 Grady, C. L.  232 Grafman, J.  205 Graham, J.  114–15, 116 Grant, F.  15 Gratten, J.  170 Gray, J. A.  99, 100 Greenhouse, J. B.  148 Greenspoon, P. J.  14 Griffiths, P. E.  254 Grinde, B.  58, 59, 60, 62, 63, 64, 65, 66, 68 Grossman, P. J.  119 Groves, J.  181 Gruenbaum, Y.  216 Gu, L.  122

Guan, W.  50 Guan, Z.  215 Guastella, A. J.  205 Guille, C.  15 Gunderson, J. G.  100 Guo, J. U.  216 Gurin, G.  5 Gusella, J. F.  166 Güth, W.  120 Gyurak, A.  233

H

Hahn, E.  77, 81, 87, 152, 153, 155, 267 Haidt, J.  104, 114–15, 200 Hajek, T.  181 Halberstadt, L. J.  154 Ham, B. J.  123 Hamer, D.  168 Hammock, E. A.  114, 118, 119 Han, S.  115, 116 Hancks, D. C.  29 Hankin, B. L.  199, 239 Hanson, M.  214, 218 Harden, K. P.  259 Hare, B.  155 Hariri, A. R.  123, 167, 168, 180, 181, 205, 230, 231, 232, 235 Harlow, L. L.  135 Harris 163 Harris, E. C.  148 Harris, J. R.  80, 87, 88 Hart, G. D.  3 Hatemi, P. K.  49 Hauger, R.  182 Haworth, C. M. A.  90 Hawrylycz, M.  230 Haybron, D.  75 Hayes, J. A.  123 Headey, B.  14 Heath, A. C.  42, 44, 153 Heils, A.  167 Heinrich, M.  104 Heintz, N.  212 Hek, K.  256 Held, B. S.  200 Helliwell, J. F.  66 Hemstedt, T. J.  216 Hen, R.  35 Henrich, J.  116 Hensch, T. K.  64 Herbst, J. H.  106 Hettema, J. M.  148 Higgins, J. P. T.  86 Hill, W. G.  255 Hills, P.  121 Hirsch, D.  184 Hirschhorn, J. N.  230 Hirsh, J. B.  234 Hodgson, G. M.  115, 116 Hofer, S. M.  178, 194 Hoffman, E.  119 Holman, E.  183

AUTHOR INDEX

Homberg, J. R.  195 Hommel, B.  237 Hone, L. C.  12 Hopwood, C. J.  45, 85 Horsburgh, V. A.  137, 138 Hosang, G.  181 Hoschi, C.  181 Hotchkiss, R. D.  212, 215 Hu, S.  168 Huang, D.  214 Huebner, E. S.  201 Huedo-Medina, T. B.  86 Huppert, F. A.  8, 9–11, 12, 14 Huta, V.  6 Hyde, L. W.  230, 231, 232

I

Iacono, W.  88, 186 Ingram, C. J.  35, 116 Insel, T. R.  15 Iran-Nejad, A.  107 Irwin, M. R.  171 Isen, A. M.  233, 234 Ising, M.  183 Israel, S.  119, 198, 267 Issa, J. P.  224 Ito, S.  212 Ito, T. A.  98, 235 Izuma, K.  62

J

Jablonka, E.  254 Jacobson, Kr. C.  259 Jaffee, S. R.  47 Jagiellowicz, J.  205 Jahoda, M.  5, 6, 76 Jakobsson, M.  59 Jamadar, S.  233 Janss, L.  51 Jazaieri, H.  124 Jiang, Y. H.  32 Jimmefors, A.  101 Jirtle, R. L.  214 John, O. P.  147 Johnson, A. D.  221 Johnson, W.  41, 77, 78, 79, 80, 83, 84, 90, 153, 155, 267 Jokela, M.  107 Jonassen, R.  180, 181 Jones, C. N.  148 Josefsson, K.  98, 102, 103, 104, 105, 106 Joseph, C.  114–15 Joseph, S.  168 Joshanloo, M.  14 Jost, J. P.  216 Judge, T. A.  118 Juster, R.  177, 182, 183, 184

K

Kabat-Zinn, J.  124 Kahneman, D.  64, 67, 102 Kaliman, P.  124

Kandler, C.  153 Kant, I.  114 Karas, D.  14 Karg, K.  181, 193, 199, 238, 269 Karnik, M.  181 Katovich, K.  233 Katz, J.  102 Kazazian, H. H., Jr.  29 Kegel, C. A. T.  198, 200 Keith, D.  136 Keller, L. M.  117 Keller, M. C.  48, 193, 260, 267 Kelley, J.  14 Keltikangas-Järvinen, L.  107 Kember, R. L.  220 Kendler, K. S.  3, 16, 17, 41, 43, 47, 76, 81, 90, 118, 148, 149, 154, 256, 267 Kenrick, D. T.  146 Kern, M. L.  8 Kessler, R. C.  148, 151 Ketelaar, T.  100 Keyes, C. L. M.  3, 6, 7, 9–11, 12, 13, 14, 15, 16, 17, 43, 76, 81, 132, 152, 153, 171, 267 Khan, A. A.  148 Khazon, S.  133 Kim, H. S.  116 Kim, Y. I.  214 King, J. E.  82, 147, 149, 151, 153, 154, 155 King, L.  148 Kinnally, E. L.  186, 223, 240 Kirk, K. M.  137, 138, 139, 140 Kirsch, P.  239 Klengel, T.  221, 223, 261, 268 Klok, M. D.  139, 267 Kluger, A. N.  197 Knafo, A.  117, 118, 119, 198, 267 Knäuper, B.  97 Knutson, B.  102, 233 Kobasa, S. C.  138 Kocher, M. G.  120 Koelle, M. R.  61 Koenig, H. G.  135, 137, 139 Kohlberg, L.  155 Kohlmann, A.  221 Kolarz, C. M.  82 Kometer, M.  122 Konečná, M.  155 Konner, M.  62 Koo, M.  116 Kopecek, M.  181 Krebs, D.  62 Kriaucionis, S.  212 Kringelbach, M. L.  61 Kristjánsson, K.  114 Krueger, A. B.  64, 67, 102 Krueger, R. F.  47, 48, 79, 83, 84, 90 Kruschwitz, J. D.  233 Kryski, K.  185 Kuhl, J.  234 Kuhnen, C. M.  118, 233 Kurzban, R.  60 Kweon, Y. S.  235

281

282

AUTHOR INDEX

L

Labonte, B.  222 Labrie, V.  262 Lachman, H. M.  237 Laeng, B.  65 Laity, J. H.  166 Lamb, M. J.  254 Lamers, S. M. A.  13 Landau, V. I.  82, 149, 153 Lander, E. S.  170 Landrø, N.  180, 181 Larsen, R. J.  100, 102, 118 Laucht, M.  181 Lawton, M. P.  102 Layard, R.  17, 66, 67 Lazarus, R. S.  5, 200 Lee, B. M.  166 Lee, B. T.  123 Lee, D. Y.  214 Lee, K.  147 Lee, S. H.  257 Leknes, S.  61 Lepper, H. S.  79, 82, 166, 168 Lerner, R. M.  252, 269 Lesch, K. P.  118, 168, 180, 193, 195, 235, 268 Lessard, J.  183 Leve, L. D.  48 Levenson, R. W.  233 Levinson, D. F.  28 Levy, E. R.  168 Levy, Y.  179 Lewis, G. J.  16 Lewontin, R. C.  252 Li, C.  50 Li, H.  221 Li, M.  50 Li, W.  118, 267 Lichtenstein, P.  90 Lieberman, M. D.  62 Lim, Y. J.  14 Ling, C.  224 Linley, P. A.  266 Lister, R.  215 Liu, D.  218 Loehlin, J. C.  41, 117, 147, 149, 258, 259 Loewenstein, G.  119 London School of Economics and Political Science Centre for Economic Performance Mental Health Policy Group  15 Long, J.  44 Lopez, S. J.  4, 13 Lopez-Figueroa, A. L.  122 Lorenzi, C.  141, 267 Loth, E.  185 Lu, H.  58 Lu, W.  28 Lubke, G. H.  169 Lucas, R. E.  77, 83 Lucht, M. J.  104 Luciano, M.  77, 107, 153, 155, 267 Ludwig, D. S.  124 Luhmann, M.  77

Luijk, M. P. C. M.  183, 200 Luo, X.  186 Luthar, S. S.  177, 178, 180, 187, 203 Lutz, A.  64 Luzzatto, L.  194 Lykken, D. T.  16, 41, 64, 79, 82, 83, 84, 87, 88, 90, 103, 108, 149, 155, 266 Lynch, M.  150 Lyubomirsky, S.  79, 82, 121, 148, 166, 168, 255

M

MacDonald, D. A.  135 MacFarlane, K. G.  104 Mackay, T. F.  252 MacLeod, A.  98 MacLeod, C.  134 Maguire, E. A.  64 Mahon, P.  182, 183 Main, H.  178 Malhotra, A. K.  230 Manolio, T. A.  141, 240, 257 Manuck, S. B.  91, 196, 267 Marazziti, D.  235 Marcus, G.  116 Martin, J.  167 Martin, M.  114, 117 Martin, N. G.  48, 137, 138, 139, 140 Martínez-Fundichely, A.  29 Mascagni, F.  123 Maslow, A. H.  5, 155 Masse, R.  14 Masten, A. S.  177, 203 Mathews, A.  134 Matteson, L. K.  81, 87, 88 Mattson, M.  181 McArdle, J. J.  43, 45 McCabe, K. A.  119, 121 McCaffery, J. M.  267 McCarthy, M. I.  230 McClearn, G. E.  254 McCrae, R. R.  97, 102, 133, 146, 147 McCraty, R.  103, 106 McDermott, R.  168, 169 McDonald, A. J.  123 McGowan, P. O.  124, 220, 222, 261, 268 Mcgreal, R. I. T. A.  168 Mcgue, M. K.  79, 82, 88, 150, 186 McGuffin, P.  179, 186, 230 McInnis, M. G.  230, 231 McMahon, D.  75 Meaney, M. J.  124, 221, 252, 254, 260, 261, 269 Medland, S. E.  49, 257 Mehl, M. R.  97 Mehta, D.  221 Meier, M. H.  42 Meissner, A.  32 Melas, P. A.  220 Meltzer, H. Y.  122 Menne-Lothmann, C.  80, 83 Merjonen, P.  106 Mestre, T. A.  122 Meyer, M.  58, 59

AUTHOR INDEX

Meyer-Lindenberg, A.  231, 232, 233, 237, 239, 240 Miao, F. F.  102 Mickey, B.  184 Middeldorp, C. M.  149 Mier, D.  239 Mikhail, J.  121 Mill, J. S.  114 Miller, D. E.  114 Miller, G. F.  115, 152, 154, 155 Miller, R.  181 Mischel, W.  146 Moffitt, T. E.  63, 66, 178, 179, 193, 251, 260 Mohammad, F.  212 Monroe, S. M.  193 Moore, R.  98 Morris, N. R.  216 Mosing, M. A.  136, 137, 138, 139, 254 Moskvina, V.  240 Mössner, R.  235 Moum, T.  82 Mrozek, D. K.  82 Muguruza, C.  122 Mulligan, C. J.  220 Munafò, M. R.  180, 181, 199, 204, 230 Murgatroyd, C.  219, 220 Murphy, S.  180, 181, 268 Myer, V. E.  26 Myers, D. G.  107, 118 Myers, J. M.  3, 16, 17, 43, 76, 118, 148, 267 Myers, R. M.  172 Myerson, A.  76 Mynett-Johnson, L. A.  237

N

Nacmias, B.  222 Naidoo, N.  230 Nakamura, M.  199 Nan, X.  216 Naragon-Gainey, K.  67, 148 Naumann, L. P.  147 Neale, M. C.  43, 45, 86, 90, 256 Nes, L. S.  133 Nes, R. B.  16, 67, 77, 79, 80, 81, 82, 83, 84, 89, 91, 149, 155, 266, 267 Nesse, R. M.  60 Nestler, E. J.  61, 177, 178, 180, 181 Neumann, I.  185 Newcomb, M. D.  135 Newton-Cheh, C.  230 Ng, H. H.  216 Nicholls, A. R.  138 Nichols, R. C.  41 Nidich, S. I.  124 Nieto-Fernandez, F.  61 Nilsson, K. W.  141 Nima, A.  101 Ninan, I.  181 Nitert, M. D.  224 Noble, E. P.  106 Nolan, K. A.  237 Norlander, T.  100

Norton, M. I.  121 Nowicki, G. P.  234

O

Oaksford, M.  233 Oberlander, T. F.  220 Obradovic, J.  203 O’Connor, M. F.  62 Odbert, H. S.  146 Oden, M. L.  148 O’Donovan, M. C.  230 Ohara, K.  237 Ohlsson, R.  26 Oishi, S.  77 Oliveira, A. M.  216 Ono, Y.  106 Oshri, A.  182 Ouellet-Morin, I.  240 Owen, M. J.  230 Ozer, D. J.  147

P

Padh, H.  28 Pai, S.  262 Paloutzian, R. F.  135 Palumbo, G.  65 Panksepp, J.  61, 68 Parent, A.  58 Pascual-Leone, A.  64 Pastor, W. A.  32 Paunio, T.  81, 83, 87 Pavot, W.  107 Penke, L.  152, 154 Pergamin-Hight, L.  204, 236 Perkins, L.  149, 153 Perou, C. M.  121 Perroud, N.  220, 221 Perry, M.  214 Peterson, C.  114, 124, 132, 138 Petrillo, G.  14 Petronis, A.  262 Pettersson, E.  107 Pezawas, L.  167, 168, 235, 239 Philibert, R. A.  29 Philipson, L.  60 Phillips, D. A.  65 Piaceri, I.  222 Plomin, R.  48, 49, 136, 137, 138, 139, 230, 254, 256, 258, 259 Plotkin, H.  56 Pluess, M.  91, 169, 179, 180, 194, 195, 196, 197, 199, 201, 203, 205, 214, 239, 259, 267, 268, 269, 271 Polanczyk, G.  182 Portela, A.  34 Posner, M. I.  124 Power, R. A.  193 Preacher, K. J.  13 Prescott, C. A.  148 Preuss, U. W.  122 Price, T. S.  47 Provencal, N.  221 Prüfer, K.  58, 59, 155

283

284

AUTHOR INDEX

Pryzbeck, T. R.  98, 135, 136 Purcell, S.  47, 90, 254 Pusey, A. E.  155

R

Radloff, L. S.  169 Rai, K.  216 Raine, A.  90 Rakyan, V. K.  51 Ramchandani, S.  216, 223 Rao, A.  32 Rasmussen, H. N.  148 Ray, L.  182 Razin,  212, 215, 216, 220 Redelmeier, D. A.  102 Reed, M. A.  233 Refojo, D.  182 Reich, D. E.  170 Reiner, I.  235 Rejko, K.  170 Ressler, K. J.  184, 240 Rest, J. R.  124 Reuter, M.  120, 267 Rhee, S. H.  86 Richerson, P. J.  116 Ridd, M. J.  168 Ridgewell, A.  140, 167, 204, 236 Riemann, R.  80, 82, 83, 84, 153 Rietveld, C. A.  51, 79, 118, 165, 169, 170, 257, 258, 267 Rigdon, M. L.  121 Riggs, A. D.  215, 216 Rijsdijk, F. V.  259 Riley, B.  230 Ripke, S.  118, 256 Risch, N. J.  52, 172, 193, 199 Robbins, T. W.  198 Roberts, B. W.  147, 154 Robitschek, C.  13 Rodrigues, S. M.  139 Rogosch, F. A.  107, 180, 182 Roiser, J. P.  205 Ronchitelli, V.  65 Ronn, T.  224 Rosa, E. D.  234 Rosenberg, M.  201 Roth, T. L.  220 Rothman, S.  181 Rowe, D. C.  259 Rowe, G.  234 Roy, A.  183 Røysamb, E.  16, 79, 80, 83, 87, 90, 91, 149, 154 Rujescu, D.  122 Russell, J.  27, 100 Russo, S. J.  61 Rustichini, A.  119 Rutter, M.  177, 178, 179, 193, 203, 251, 256, 260 Ryan, J. A.  124 Ryan, L.  201 Ryan, R. M.  114, 171 Rybakowski, F.  106 Ryff, C. D.  5, 6, 11, 13, 76, 171

S

Saatcioglu, F.  124 Sabeti, P. C.  36 Sabol, S.  168 Sachs, J.  66 Sadato, N.  62 Sagiv, L.  117 Saito, D. N.  62 Saklofske, D. H.  14 Sameroff, A. J.  203 Samochowiec, J.  106 Samuel, D. B.  148 Sander, D.  205 Sandvik, E.  107 Sanislow, C. A.  231 Saphire-Bernstein, S.  139, 267 Satpute, A. B.  62 Scheier, M. F.  132, 133, 148 Schermer, J. A.  117, 254 Schimmack, U.  77, 83, 107 Schkade, D.  121, 255 Schmidt, J.  78, 147, 148, 151, 154 Schmitt, D. P.  147 Schmitt, J.  179 Schmittberger, R.  120 Schmitz, T. W.  234 Schneewind, J.  114 Schneiderman, I.  185 Schnittker, J.  79, 80, 87, 88, 90 Schofield, P.  181 Schulman, P.  136 Schulz-Heik, R. J.  47 Schupp, J.  77 Schütz, E.  100, 101, 106 Schwartz, S. H.  116, 117, 119 Schwarz, N.  97 Schwarze, B.  120 Scollon, C. N.  102 Scolnick, E. M.  15 Scoriels, L.  230 Segerstrom, S. C.  132, 133 Seligman, M. E. P.  3, 8, 9–11, 12, 107, 114, 124, 136, 138, 148, 266 Sen, S.  15, 106, 199 Sephton, S. E.  133 Seppala, E.  102 Sergerie, K.  205 Serretti, A.  122, 124 Severin, F. T.  5 Sewell, D.  138 Shaffer, E. J.  14 Shah, S.  184 Shaked, A.  119 Sham, P. C.  167, 259 Shanahan, M. J.  178, 194 Shapiro, S. L.  123, 124 Sharma, M.  170 Sheard, M. H.  140 Sheen, T. A.  132 Sheikh, H.  182 Sheldon, K. M.  121, 255 Shields, J.  193

AUTHOR INDEX

Shih, J. C.  168 Shmotkin, D.  11, 171 Shonkoff, J. P.  65 Shostak, M.  62 Shultz, J.  78, 147, 148, 151, 154 Siegfried, Z.  197 Sigirest, H. E.  4 Sikström, S.  103 Simoes, E. J.  3, 15, 17 Simons, A. D.  193 Singer, B. H.  171 Skowronski, J. J.  103 Slagter, H. A.  64 Sleiman, P.  230 Slieker, R. C.  221 Smith, K. S.  61 Smith, R.  5 Smith, V. L.  119, 121 Smith, Z. D.  32 Smolka, M. N.  237 Snyder, C. R.  4 So, T. T.  12 Sokolowski, M. B.  254 Sommer, W.  184 Song, Z.  118, 267 Sorbi, S.  222 Soto, C. J.  147 South, S. C.  47 Spangler, G.  235 Spellman, P. T.  171 Spinath, F. M.  77, 117, 153, 155, 267 Spotts, E. L.  87 Steel, P.  78, 147, 148, 151, 154 Stefansson, H.  237 Steger, M. F.  137, 138, 139, 140, 152, 153 Stein, M.  181 Stephens, M. A. P.  133 Strahl, B. D.  212 Stubbe, J. H.  16, 79, 80, 87, 88, 90 Suderman, M.  222 Suh, E.  77 Suldo, S. M.  14 Sullivan, P. F.  90, 121, 256 Sulutvedt, U.  65 Suri, G.  233 Surtees, P.  181 Sutin, A. R.  118 Suzuki, M. M.  32, 33 Svrakic, D. M.  98, 106, 107, 135, 136 Svrakic, N. M.  106, 107 Sweitzer, M. M.  196 Szyf, M.  124, 212, 214, 215, 216, 218, 221, 260

T

Tabery, J.  254 Tabibnia, G.  62 Tabor, H. K.  172 Tajerian, M.  224 Takahashi, H.  62 Takkinen  80, 87 Tambs, K.  79, 83, 84, 155 Tamietto, M.  64

Tang, Y.-Y.  124 Tanzi, R. E.  222 Tatsuoka, M. M.  147 Tay, L.  118 Taylor, S. E.  205 Taylor, W.  182 Tellegen, A.  16, 79, 80, 82, 83, 84, 87, 88, 90, 100, 103, 104, 108, 149, 152, 155, 266 Terman, L. M.  148 Thomas, G.  170 Thompson, C. P.  103 Thompson, S. G.  86 Tiihonen, J.  237 Tikkanen, R.  168, 169 Townsend, A. L.  133 Tracey, I.  61 Travis, F.  124 Tsai, J. L.  102 Tuch, B. B.  221 Tugade, M. M.  103 Tupes, E. C.  146 Turkheimer, E.  78, 83–4, 107, 259 Turner, R. A.  104 Tyrka, A.  182

U

Uddin, M.  221 Uher, R.  178, 179, 186, 194

V

Van den Oord, E. J. C. G.  259 van den Wildenberg, W. P. M.  237 van der Aa, N.  81, 83 Van der Lubbe, P. M.  134 van IJzendoorn, M. H.  185, 195, 198, 199, 200, 203, 268 Van Lawick-Goodall, J.  154 van Winkel, M.  240 Veenhoven, R.  76 Veit, C. T.  14 Velders, F.  183 Vengrober, A.  185 Verma, M.  51 Vermetten, E.  65 Veroff, J.  5, 6 Verweij, K. J. H.  107, 152 Veselka, L.  115 Viding, E.  51 Vijayendran, M.  29, 240 Viken, R. J.  149 Vinkhuyzen, A. A. E.  169 Visscher, P. M.  255 Vitalini, M. W.  34 Vitterso, J.  91 Vrieze, S.  186 Vukasovic, T.  89

W

Waddington, C. H.  211, 215 Wagner, G. G.  77 Wagnild, G. M.  201 Wahlsten, D.  254

285

286

AUTHOR INDEX

Waider, J.  240 Waldman, I. D.  86 Walker, W. R.  103 Wallace, B.  120 Waller, N. G.  117, 152 Wallrath, L. L.  34 Walsh, J. B.  150 Walsh, M. L.  140 Wang, D.  27 Ward, M. E.  51 Ware, J. E.  14 Warlop, L.  121 Warr, P.  102 Waterland, R. A.  214 Waterman, A. S.  6, 171 Watson, D.  14, 67, 90, 97, 99, 100, 148 Watson, J. B.  251 Waugh, C. E.  238, 239 Way, B. M.  205 Wearing, A.  14 Weaver, I. C. G.  219, 220, 222, 224, 240 Weinberger, D. R.  123, 230, 231, 232 Weischenfeldt, J.  29 Weiss, A.  77, 80, 104, 107, 147, 148, 149, 151, 152, 153, 154, 155, 267 Weisstaub, N. V.  123 Wellcome Trust Case Control Consortium 50 Wellington, M. A.  146 Wells, A.  66 Wendland, J. R.  167 Werner, E. E.  5, 203 Westerhof, G. J.  14 Wezeman, F. R.  134 Whalen, P. J.  205 Whittington, J. E.  14 Wild, L.  212 William, J.  124 Williams, J.  155 Williams, K. D.  62 Williams, L. M.  237, 238, 239 Wilson, A. J.  150 Wilson, T. D.  60 Wilson, W. R.  76 Winkielman, P.  97

Winkler, A. M.  240 Withey, S. B.  6, 77 Wittchen, H. U.  63, 66 Witte, A.  178 Wobber, V.  155 Woo, J.-M.  237 World Health Organization  4 Wrangham, R.  155 Wray, N. R.  170, 255, 269 Wright, P. E.  166 Wroblewski, E. E.  155

Y

Yang, J.  51, 52, 169, 257 Yildrim, B.  181 You, S.  148 Young, H. M.  201 Young, L. J.  118 Young, M. J.  234 Young, R. A.  26 Yu, J.  27

Z

Zalla, T.  205 Zammit, S.  237 Zannas, A.  180, 183 Zeanah, C. H.  199, 253 Zhang, K.  184 Zhang, T. Y.  260 Zhang, Y.  58 Zhong, S.  120 Zhou, Q.  178, 184 Zhou, Z.  219 Zika, S.  134 Zimmerman, M.  177 Zimmermann, P.  183, 223 Zinnbauer, B. J.  134, 135 Zohar, A. H.  97, 103, 104, 106, 107 Zöllner, S.  230, 231 Zomerdijk, J. C.  27 Zuckerman, M.  104, 118, 193 Zuk, O.  240 Zukin, R. S.  215 Zukowska, Z.  184 Zurowski, M.  122

Subject Index

A

acetylation xv adaptation  56, 58 adoption studies  48–9 adrenocorticotrophic hormone (ACTH)  183, 184 affective profiles model  100–1 age factors  98, 103–4, 105, 108 aging 223 agreeableness  147, 148, 152 allele  xv, 78 association studies  166–9, 172 candidate gene studies  257 epigenetics 222–3 gene–environment interaction  267 GWAS 170 imaging genetics  235–7, 238, 240, 268 optimism and spirituality  139, 140, 141 QTL linkage  166 research considerations  269 vantage sensitivity  193–4, 198, 199, 200, 201, 202, 203, 204–5, 268 virtue and values  120 alu repeats  29 amygdala  xv, 122–3 COMT  237, 238 imaging genetics  235, 237, 238, 239, 268 resilience  180, 181, 182, 185 vantage sensitivity  205 5-HTTLPR  235, 268 animal personality  149, 150–2, 154–5 animal studies covitality 151 epigenetics  218–20, 221, 223, 224 stochastic processes  262 anxiety 100 attention 233 behavior genetics  267 imaging genetics  235, 236, 238, 239 meaning in life  134 5-HT2A binding  123 5-HTTLPR  199, 235, 236, 238, 239 arousal 233 association studies see candidate gene association studies assortative mating  xv, 41, 89 attention  233–4, 235–6, 240 Attentional Bias Modification (ABM)  204–5 Attributional Style Questionnaire (ASQ)  136 AURKC 166 autonomy  7, 8, 10 autosome  xv, 24, 25 AVPR1  xv, 118, 267

B

Behavioral Activation System (BAS)  78, 99 Behavioral Inhibition System (BIS)  78, 99 behavior economics  119–23 behavior genetics  147, 171, 266–7, 268 covitality 153 environment  252, 254–6, 257, 259, 261, 262–3 “first law”  78 future research directions  270 genetic determinism  251 methodology  38–49, 52 optimism and spirituality  136 positive affect and emotionality  105–6 research considerations  269 salutogenetics  3, 5, 16, 17 “second law”  83–4 subjective well-being  79 virtue and values  121 Big Five  147–9, 151–2 biometric models  39–48 bivariate heritability  150–1 blood genomics  121–3 brain-derived neurotropic factor (BDNF)  xv epigenetics  219, 220, 261 imaging genetics  240 resilience 181–2 vantage sensitivity  200, 201 Broaden-and-Build Theory  99–100, 234 broad-sense heritability (H2)  xv, 39, 78 Bucharest Early Intervention Project (BEIP)  199

C

Caenorhabditis elegans  57, 58 candidate gene  xv epigenetics  217, 219, 220, 221 see also candidate gene association studies candidate gene association studies  50, 136, 166–9, 172, 256, 257, 258, 267 future research directions  270 research considerations  269 gene–environment interaction  259–60 imaging genetics  230, 231–2 optimism 139–40 resilience 186–7 spirituality 140–1 catechol-O-methyltransferase (COMT)  xv imaging genetics  231, 236–8, 239–40 resilience  178, 181 vantage sensitivity  201, 202 Center for Epidemiology Studies Depression Scale (CES-D) 169 Children of Twins (COT) design  46 Cholesky decomposition  42–3, 44

288

SUBJECT INDEX

chromatin xv epigenetics  212, 214–15, 216, 261 chromosome  xv, 24 DNA methylation  33 inversion 29 nuclear position  35 stability 30 telomeres 28 circumplex model  100, 117 cognition  233–5, 237, 240 common-disease common-variants (CDCV) hypothesis  xv, 170 common pathway model  43, 45 comorbidity  xv, 63, 147–9 conscientiousness  147, 148, 151–2 consequentialism 114 conserved transcriptional response to adversity (CTRA) 171–2 cooperation, origin of  116 copy number variation (CNV)  xv, 28–9 corticotropin-releasing hormone (CRH)  xv, 183, 184, 220 co-twin control method  46–7 covitality  xv, 146–56 CPG islands  33 CRHR1  xv, 182–3 cultural neuroscience  115–16 cytokine  xv, 171–2, 219

D

Darwinian Happiness  66–7, 68 default contentment  64, 68 deoxyribonucleic acid see DNA depression  100, 149 attention 233 BDNF 181 behavior genetics  267 CRHR1 182 environmental factors  154 FKBP5 183 imaging genetics  235, 236, 238, 239 meaning in life  134 molecular genetics  256, 257 5-HT2A binding  123 5-HTTLPR  140, 168, 193, 199, 222–3, 235, 236, 238, 239, 267 developmental plasticity  xv, 195–6 diathesis-stress  xv, 91 vs imaging genetics  239, 240 resilience  178, 179, 183 vs vantage sensitivity  193–5, 196–7, 198, 199, 203, 204 Dictator Game (DG)  119 differential susceptibility  xvi, 91, 259, 267–8 vs imaging genetics  239, 240 resilience  178–9, 183 vs vantage sensitivity  195–6, 200, 201–4, 205 Diffusion Tensor Imaging (DTI)  232 discords  63, 66 dizygotic twins (DZ)  xvi, 39–48, 254 covitality 150 optimism 136–8

DNA  xvi, 24, 25, 26 association studies  168 behavior genetics  171 epigenetics  32–3, 211–14, 260, 261, 268 gene expression  171 genotype 56 GWAS 50 heritability 254 Human Genome Project  230 linkage analysis  49, 163–6, 172 methylation see DNA methylation methyltransferases see DNMT microarray technology  256 mitochondrial (mDNA)  xviii, 31–2 Pol II transcribed genes  26 structure 270 telomeres 28 vantage sensitivity  201 variation 28–31 DNA methylation  xvi, 32–3 epigenetics  212–14, 215–17, 219–24, 240–1, 261, 268 epigenome-wide association studies  51 future research directions  270 imaging genetics  240–1 reversible  216–17, 223 DNMT (DNA methyltransferases)  xvi, 32 epigenetics  216, 224 DRD2  xvi, 267 DRD4  xvi, 29, 267 gene–gene interactions  52 spirituality 141 vantage sensitivity  197–8, 201, 268 virtue and values  118, 120 DUXA 166 dystrophan 27

E

EEG (electroencephalography)  xvi, 232, 233 embryology  211–12, 214, 215 emotional well-being  5, 6, 13, 16 endophenotype xvi future research directions  270, 271 imaging genetics  231, 235 research considerations  269 resilience  179–80, 181, 184, 185, 186 environment 251–63 covitality  149, 153 epigenetic adaptation to the  217–22 evolution 62–3 future research directions  271 resilience 186 salutogenetics  16, 17 subjective well-being  83–5, 89, 90 values 117 environmental determinism  251, 252 environmental mastery  7 enzyme xvi chromatin modification  124, 215, 219 COMT  178, 236 DNA methyltransferases  32, 216, 223 embryology 214 lactase 35

SUBJECT INDEX

MAOA  167, 168 PLCE1 gene  141 epigenetic mechanisms  xvi, 32–6, 51, 211, 212–17, 218, 220, 262 gene–environment interplay  260–1, 268, 271 epigenetics  xvi, 23, 211–24, 268 environment  217–23, 260–1, 263 future research directions  270, 271 imaging genetics  240–1 implications for PWB  223–4 mechanisms see epigenetic mechanisms mindfulness 124 salutogenetics 18 epigenome  xvi, 220–2 epigenome-wide association studies (EWAS)  51 epistasis  xvi, 52, 78, 252 imaging genetics  240 Equal Environments Assumption (EEA)  41, 48 etiology  xvi, 3, 4 of positive affect and emotionality  102, 107 eudaimonic well-being  xvi, 75, 76, 268 evolution 67 gene expression  171–2 salutogenetics  3, 5, 6–7, 8, 9, 12, 17 virtue 114 eukaryote  xvi, 26 European Social Survey (ESS)  12 evolution 56–69 covitality  152, 154–5 differential susceptibility  195–6 of morality and values  115 resilience 186 vantage sensitivity  193–4, 204 executive functioning  178, 181, 237 exon xvi Pol II transcribed genes  26, 27 experimental economics  119–23 explanatory style  132–3 Expressions of Spirituality Inventory (ESI)  135, 139 extended-family studies  49 extraversion affect  100, 102, 103, 104, 107 covitality  147, 148–9, 151–2 SWB 267

F

family studies  48 fitness  xvi, 56, 68 animal personality  155 epigenetics 214 grief 62 Five-Factor Model  147–9, 151–2 FKBP5 xvi epigenetics  221, 223, 261 resilience  182, 183–4 functional imaging  232 functional magnetic resonance imaging (fMRI)  xvi, 232, 233, 235, 237, 238

G

gender see sex differences gene  xvi, 25

gene–culture coevolution  116 gene–environment correlation (rGE)  xvi, 173, 255, 258–9, 260, 261 adoption studies  48 resilience  178, 182, 185 subjective well-being  90, 91 twin studies  47 gene–environment interaction (GXE)  xvii, 173, 195, 255, 258, 259–60, 261, 267–8 adoption studies  48–9 epigenetics 222–3 future research directions  271 imaging genetics  238–40 molecular genetics  52 resilience  178–9, 183–4, 186 subjective well-being  91 twin studies  47–8 vantage sensitivity  193–4, 197, 198–9, 204 gene expression  xvi, 24, 25, 28, 29, 34, 171–2, 269 AVPR1a 118 blood genomics  121–2, 123 COMT 181 CRHR1 182 epigenetics  211–12, 215–16, 219, 260–1, 268 epigenome-wide association studies  51 FKBP5 183 future research directions  270, 271 gene–environment interaction  47, 52 Human Genome Project  23 imaging genetics  230, 235, 238, 240 mindfulness 124 molecular genetics  171–2, 186–7, 270 NPY 184 stochastic processes  262 SWB 90 5-HTTLPR 180 gene–gene interactions  52, 240 genetic determinism  211, 222, 251, 252 genetic variation  28–31, 35–6 gene transcription  xvi, 24, 25, 26 epigenesis  32, 33, 34, 35 Pol I transcribed genes  27–8 Pol II transcribed genes  25–7 Pol III transcribed genes  27–8 gene translation  xvi, 26 genome xvii association studies  167–8 epigenetics  214, 215, 217, 221, 223 gene–culture coevolution  116 Human Genome Project  28, 30, 230, 251, 256 linkage analysis  49, 166 molecular genetics  186, 257 noncoding regions  28 genome-wide association studies (GWAS)  xvii, 35, 50–1, 136, 170–1, 172–3, 257–8, 260 covitality  153–4, 155 future research directions  270 imaging genetics  230, 232, 240 optimism and spirituality  141, 142 research considerations  269 SNPs  30, 136, 170, 173

289

290

SUBJECT INDEX

genome-wide complex trait analysis (GCTA)  51–2, 169, 257 covitality 155 genomic-relatedness-matrix restricted maximum likelihood (GREML)  xvii, 257, 258, 269 future research directions  270 genomic tone  xvii, 35 genotype  xvii, 78 association studies  167, 168–9 covitality 155 cultural neuroscience  116 epigenetics  214, 261 evolution  56, 58 gene–environment interaction  260 GWAS and candidate gene studies  230 imaging genetics  231, 233, 240 practical application of research findings  271 resilience 181–2 subjective well-being  79, 90 vantage sensitivity  200, 204, 268 virtue and values  120 gestation  xvii, 214 glucocorticoids  xvii, 183, 219, 221, 223, 261

resilience  181, 182 histone  xvii, 24 histone modification  xvii, 32, 33–4, 212, 213, 214–15, 219, 221, 224, 261 histone octamer  xvii, 34 hominins  xvii, 58–60 hominoids xvii homozygous xvii AVPR1a 118 BDNF 200 COMT  202, 237–8 CRHR1 183 FKBP5 183 MR 200 OXTR 201 5-HT1A 141 5-HTTLPR  140, 199 human genome  24–5 Human Genome Project  28, 30, 230, 251, 256 hydroxylation  xvii, 212 hypothalamic–pituitary–adrenal (HPA) axis epigenetics 219 resilience  177, 182–4, 185, 186

H

I

haploid  xvii, 24 haplotype xvii optimism 140 resilience  182–3, 184 virtue and values  120 hedonic well-being  xvii, 75, 76, 268 evolution 67 gene expression  171–2 salutogenetics  3, 6, 8, 9, 11, 12, 13, 17 virtue 114 heritability  xvii, 272 of anxiety  267 behavior genetics  254–6, 257, 266–7 bivariate 150–1 broad-sense (H2)  xv, 39, 78 of covitality  152–3 of depression  267 evolution 56 future research directions  271 gene–environment correlation  259 of IQ  259 of mental health  16–17 missing  xviii, 257 molecular genetics  51, 256–8, 267 narrow-sense (h2)  xvi, 39, 78 of optimism, spirituality, and meaning in life  132–42, 254 of positive affect and emotionality  102–8 practical application of research findings  272 research considerations  269 of SWB  78–91, 149–51, 163, 230, 254, 255 twin studies  39–41, 42, 45, 47 of virtue and values  117–18, 121 heritability–environment interactions (HXE)  259 hippocampus xvii COMT  237, 238 epigenetics  219, 220, 221

ideal affect  102 identity-by-descend (IBD)  166 imaging genetics  xvii, 230–41, 268 approach 232–3 future research directions  271 independent pathways model  43, 44 interest (engagement with) in life  8, 9 interpersonal relationships  8–11 intron  xvii, 26 epigenetics 223 resilience 182

J

job satisfaction molecular genetics  267 values 118

L

languishing  13, 15 latent growth curve modeling  44–6 life history strategy  154 Life Orientation Test (LOT) and revision (LOT-R)  133, 136, 138 life satisfaction  107 behavior genetics  266 candidate gene studies  257 covitality 152 heritability  79, 83 meaning in life  134 molecular genetics  267 values 118 5-HTTLPR  167–8, 236 linkage analysis  xvii, 49–50, 163–6, 172 complex traits  166 linkage disequilibrium  xvii locus xviii candidate gene association studies  50

SUBJECT INDEX

GWAS 170 human genome  24 linkage analysis  163, 166 SWB 78 long interspersed nuclear elements (LINES)  29 longitudinal designs, twin studies  43–6

M

magnetic resonance imaging (MRI)  232 MAOA  xviii, 267 association studies  168–9, 172 imaging genetics  240 promoter 29 mastery (competence)  8, 9 environmental 7 maternal care, epigenetic changes in response to  218–20, 221–2 meaning in life  132, 134–5, 137, 138–9, 142 Meaning in Life Questionnaire (MLQ)  139 Mediator complex  26 meditation 64 Mental Health Continuum long form (MHC-LF)  13, 14, 16 short form (MHC-SF)  12, 13–14 mental health/illness  13, 15–16, 17–18 mental toughness  138 messenger RNA (mRNA)  xviii, 26, 171 blood genomics  121–2 epigenetics 212 imaging genetics  235 Pol II transcribed genes  26, 27 meta-analysis  xviii, 257 BDNF 181 DRD4  198, 268 HTR2A 122 molecular genetics  163, 170, 173 SWB  75, 85–9, 91, 170, 173, 254 5-HTTLPR  199, 236, 238, 268 methylation xviii DNA see DNA methylation micro-array  xviii, 171 microRNA 28 microsatellite  xviii, 29–30, 31 Midlife Development in the United States (MIDUS)  13, 14, 16 covitality  151, 152 mindfulness 123–4 minisatellite  xviii, 29, 31 Minnesota Multiphasic Personality Inventory (MMPI) 148 mismatches 62–3 missing heritability  xviii, 257 mitochondrial DNA (mDNA)  xviii, 31–2 molecular genetics  78, 163–73, 267, 268, 272 environment  256–8, 259, 261, 262–3 epigenetics 211–24 future research directions  270, 271 genetic determinism  251 imaging genetics  230–41 methodology  38, 49–52 optimism and spirituality  136 positive affect and emotionality  106–7

practical application of research findings  272 of resilience  177–87 vantage sensitivity  193–206 virtue and values  116, 118–19, 120 molecular mechanisms  270 mononuclear cells  xviii cord blood  220 peripheral blood (PBMCs 171 monozygotic twins (MZ)  xviii, 39–48 behavior genetics  254 covitality  150, 154 optimism 136–8 mood modules, evolution  60–2, 63, 64–6, 67–8 moral foundations theory  114–15 morality 114–24 MR  xviii, 114–24, 267 optimism 139–40 vantage sensitivity  200 Multidimensional Personality Questionnaire (MPQ)  79, 152 multivariate twin modeling  42–6 covitality 150 mutation  xviii, 31, 56 de novo  215, 262 Mendelian 222 molecular genetics  230 PLCE1 141 SWB  152, 154

N

narrow-sense heritability (h2)  xviii, 39, 78 natural selection  xviii, 23, 56 differential susceptibility  196 epigenetics  214, 217 vantage sensitivity  193, 194 nature–nurture debate  78, 251, 252 negative affect  98–103, 107 nervous systems, evolution of  57–8 neurosensitivity hypothesis  205 neuroticism affect  100, 102, 107 covitality  147, 148–9, 151–2 SWB 267 5-HT2A binding  123 5-HTTLPR 235 neurotransmitters  xviii, 168 dopamine 141 endophenotypes 269 epigenetics 219 monoamine 140 mood modules  61 opioid 69 oxytocin 104 serotonin 167 spirituality 141 noncoding RNA  xviii, 25, 212 nonshared environment  xviii, 39–43, 45–6, 47–8, 259 covitality  149–51, 153 optimism and spirituality  136–9 positive affect and emotionality  104, 105 SWB  83–4, 88–9 virtue and values  117

291

292

SUBJECT INDEX

NPY xviii resilience  182, 184 NR3C1 xviii epigenetics  219–20, 221–3 nuclear position  32, 34–5 nucleosome  xviii, 34 epigenetics 212 nucleotide  xviii, 24, 29–31, 120, 216 see also single nucleotide polymorphism nucleus  xviii, 31, 35, 212

O

openness to experience  147, 148, 151, 152 optimism  8, 11, 12, 132–4, 136–8, 139–40, 141–2, 254, 267 covitality 148 default contentment  64 Oxford Happiness Questionnaire  121 OXTR  xviii, 104, 267 optimism 139 resilience  178, 184–5 vantage sensitivity  201, 202 virtue and values  116, 119 oxytocin  104, 139, 184–5

P

pathogenesis  xviii, 3–4 pedigrees 151 penetrance xviii imaging genetics  231, 239 PERA scales  8, 11 PERMA model  8 PERMA-Profiler (PERMA-P)  8 personal growth  7, 8, 10 personality psychology  76 pessimism  133, 136 phenotype  xix, 78 adoption studies  48 behavior genetics  254–5 covitality  151, 152, 153–4, 155 environment  251, 252, 259, 260 epigenetics  212–14, 216, 217, 218, 261 evolution  56–7, 58 family studies  48 future research directions  270 gene–environment interaction  259, 260 genome complex trait analysis  169 GWAS and candidate gene studies  136, 168, 170, 230 imaging genetics  231–2, 233, 240 molecular genetics  50, 51, 256, 257, 258 resilience  179–80, 185, 186 salutogenetics 17 twin studies  39–47 vantage sensitivity  204 phosphorylation  xix, 34, 219 pleiotropy xix covitality  152, 153 Pol I transcribed genes  27–8 Pol II transcribed genes  25–7 Pol III transcribed genes  27–8 polygenic risk scores  50–1, 173

polymorphism xix association studies  168 cultural neuroscience  116 epigenetics 261 imaging genetics  231–2, 233, 235, 236, 237, 239, 268 molecular genetics  50, 52 research considerations  269 resilience  178, 181, 185 spirituality 141 subjective well-being  79 vantage sensitivity  197, 200 virtue and values  118, 120 Portrait Values Questionnaire (PVQ)  117, 119 positional candidates  50 positive affect  8, 9, 13, 97–108 imaging genetics  233–5, 240 positive emotionality  97–8, 102–8 behavior genetics  266 positive psychology  xix, 266 future research directions  270 imaging genetics  230–41, 268 salutogenetics  3, 4–5, 17, 18 twin studies  255 vantage sensitivity  200–1, 202 positive relations  7 practical application of research findings  271–2 prefrontal cortex  xix, 122 dopamine degradation  178 epigenetics 220 imaging genetics  236–7 promotive factor  xix, 178, 196 protective factor  xix, 4, 46, 178, 197, 203 psychological well-being  xix Public Goods Game (PGG)  121 purpose in life  7, 8, 10

Q

quality of life evolution  66, 68 heritability 79 quantitative semantics  103 quantitative trait loci (QTL)  xix, 166

R

rare variation  31 religiosity  117–18, 134, 138–9 see also spirituality resilience  xix, 4, 5, 8, 10 epigenetics 224 gene–environment interaction  267 imaging genetics  239, 240, 268 molecular genetics  177–87 research considerations  269 vs vantage sensitivity  193–5, 196–7, 201, 203 retroposon xix replication 32 variation 29 ribonucleic acid see RNA ribosome  xix, 26 Pol I transcribed genes  27 Pol III transcribed genes  27 risk factors  xix, 16, 269

SUBJECT INDEX

environmental 90 neuroticism 148 resilience 5 twin studies  46 vantage sensitivity  196 r/K-selection xv RNA xix epigenetics  212, 222, 260 heritability 254 micro-arrays 171 microsatellites 30 noncoding  xviii, 25, 212 Pol I transcribed genes  27 Pol II transcribed genes  25–7 Pol III transcribed genes  27–8 transcription  25, 33

S

salutogenesis  xix, 3–18 self-acceptance (self-respect, self-esteem)  7, 8, 9 Self-Anchoring Scale  6 sense of coherence (SOC)  4 setpoint of happiness evolution 64 SWB 83 sex chromosomes  24, 25 sex differences heritability of mental health  16 MAOA 168–9 optimism  137, 140 resilience  179, 185 subjective well-being  79 twin studies  41–2 virtue and values  117, 120 shared environment  xix, 39–43, 46–9, 259 covitality 150 optimism and spirituality  136–9 positive affect and emotionality  102, 104, 105 SWB  83–4, 86, 90 virtue and values  117–18 shelterin 28 short tandem repeats see microsatellite sibling studies  48 simplex models  43–4 single nucleotide polymorphism (SNP)  xix, 30–1 covitality 153 GCTA 169 GWAS  30, 136, 170, 173 imaging genetics  231 molecular genetics  50–1 optimism and spirituality  139, 141 resilience  180, 182, 183, 184, 185 SLC6A4  xix, 180, 199, 235 SNP see single nucleotide polymorphism social acceptance  7, 8, 11 social actualization (social growth)  7 social coherence  7, 8, 11 social contribution  7, 8, 11 social integration  7, 11 social push hypothesis  90 Social Science Genetic Association Consortium (SSGAC)  170, 258, 270

social support and affiliation  178, 181 social well-being  7 somatic cell  xix, 24 spirituality  132, 134–6, 137, 138–9, 140–2 see also religiosity stochastic factors  xix, 253, 261–2, 263 stress 5 resilience 177–8 see also diathesis-stress structural equation models (SEM) extended-family studies  49 twin studies  39, 42 structural imaging  232 Subjective Happiness Scale  168 subjective well-being (SWB)  xix, 75–91, 104, 107, 230 behavior genetics  254, 255, 266–7 covitality  146, 147, 148–55 Darwinian Happiness  66, 67 default contentment  64 determinants 77–8 GWAS 172–3 molecular genetics  163, 164–5, 167, 171, 172–3 salutogenetics  5, 13, 14, 16, 17 values  117, 118

T

telomere  xix, 28 TTAGGG units  30 Temperament and Character Inventory (TCI)  98, 99, 100 positive affect and emotionality  97–8, 102, 103–4, 105–6, 107, 108 spirituality  135, 138, 141 TPH2 240 trait theories of personality  146–54 transcription factor  xix, 28 epigenetics 261 transcriptome  xx, 171 epigenetics 221 transfer RNA (tRNA) mitochondrial DNA  32 Pol III transcribed genes  27 trichostatin A  xx Trust Game (TG)  120–1 TTN 27 twin studies  38–48 behavior genetics  254–6 covitality  150, 152–3, 154 optimism 136–8 salutogenetics 16 spirituality and meaning in life  137, 138–9 two-continua model  3, 4, 14–18

U

ubiquitination xx U-index 67 Ultimatum Game (UG)  120 univariate twin model  39–42 USP29 166 utilitarianism 114

293

294

SUBJECT INDEX

V

valence 233–4 values 115–24 Values In Action (VIA) scale  138, 139 vantage sensitivity  xx, 91, 193–206, 259, 268 genes 203–4 vs imaging genetics  239, 240 implications 205–6 mechanisms 204–5 practical application of research findings  271–2 research considerations  269 variable number tandem repeat (VNTR)  xx see also minisatellite vasopressin 185 virtue 114–24 covitality 155 vitality  8, 9

W

weapon focus  233 winners-curse 169

Z

zinc finger protein genes  166 5-HT1A  xx, 267 spirituality 140–1 5-HTR2A 122–3 5-HTTLPR  xxx, 29, 267 candidate gene studies  167–8, 172, 257 epigenetics 222–3 gene–environment interactions  52 imaging genetics  231, 235–6, 238, 239, 240, 268 optimism and spirituality  140, 141 positive affect and emotionality  106 research considerations  269 resilience  180–1, 186 values 118 vantage sensitivity  193, 197, 198–200, 201, 204–5