Molecular Marker Techniques: A Potential Approach of Crop Improvement 9819916119, 9789819916115

Table of contents :
Preface
Acknowledgment
Contents
About the Editor
1: Molecular Markers for Harnessing Heterosis
1.1 Introduction
1.2 Unveiling the Genetics of Heterosis via Molecular Markers
1.3 Genetic Diversity Assessment and Heterotic Grouping Using Molecular Markers
1.4 Prediction of Heterosis Using Molecular Markers
1.5 Current Challenges and Future Prospects
1.6 Conclusions
References
2: Kompetitive Allele-Specific PCR (KASP): An Efficient High-Throughput Genotyping Platform and Its Applications in Crop Varie...
2.1 Introduction
2.2 Evolution of Plant Molecular Markers
2.3 Kompetitive Allele-Specific PCR (KASP)
2.3.1 Principles of the KASP Technique
2.3.2 Requirements of KASP Assay
2.3.2.1 Prerequisites of KASP Assay
2.3.2.2 Thermal Cycle Reaction Components of KASP Assay
2.3.2.3 Standard Thermal Cycle Reaction Conditions for KASP Assay
2.3.3 Operational Steps of KASP Genotyping Technique
2.3.4 Why Is KASP Technique Being Used?
2.3.5 Application Fields of KASP Technology in Crop Improvement
2.3.6 Present Status of Utilization of the KASP Technique in Crop Improvement
2.3.6.1 Wheat
2.3.6.2 Maize
2.3.6.3 Rice
2.3.6.4 Legume Crops
2.3.6.5 Horticultural Crops
2.3.7 Advantages of KASP Genotyping Technique
2.3.8 Bottleneck of KASP Genotyping Technique
2.3.9 Prospects of KASP Technology
2.4 Conclusions
References
3: Marker Assisted Recurrent Selection for Crop Improvement
3.1 Introduction
3.2 Principle of MARS
3.3 General Outline of Mars
3.3.1 Parental Choice
3.3.2 Population Development
3.3.3 Genotyping
3.3.4 Phenotyping
3.3.5 QTL Analysis
3.3.6 Recombination Cycles
3.4 Strategies of Marker-Assisted Recurrent Selection (MARS)
3.5 Cost Efficiency of Marker-Assisted Recurrent Selection (MARS)
3.5.1 Factors that Control MARS Efficiency
3.5.1.1 Population Size Submitted to Selection at Each Cycle
3.5.1.2 Use of Single Versus Flanking Markers
3.5.1.3 Pre-Flowering/Early Selection
3.5.1.4 Number of Generations per Year
3.5.1.5 Price of Marker Data Points
3.6 Software Package for MARS Analysis
3.7 Application of MARS in Different Crops
3.8 Conclusion
References
4: Concepts and Employment of Molecular Markers in Crop Breeding
4.1 Introduction
4.2 Molecular Markers
4.2.1 Classification of Molecular Markers
4.2.1.1 Restriction Fragment Length Polymorphism (RFLP)
4.2.1.2 Randomly Amplified Polymorphic DNA (RAPD)
4.2.1.3 Simple Sequence Repeats (SSRs)
4.2.1.4 Amplified Fragment Length Polymorphism (AFLP)
4.2.1.5 Single-Nucleotide Polymorphism (SNP)
4.3 Employment of Molecular Markers in Crop Breeding
4.3.1 Determination of Genetic Diversity
4.3.2 Linkage Map Construction
4.3.3 QTL Mapping
4.3.4 Marker-Assisted Selection (MAS)
4.3.5 Association Mapping
4.3.6 Evolution and Phylogenic Study
4.3.7 Detection of Heterosis
4.3.8 Identification of Haploids
4.3.9 Genome-Wide Association Study (GWAS)
4.3.10 Marker-Assisted Backcrossing (MABC)
4.3.11 Marker-Assisted Gene Pyramiding
4.3.12 Targeting Induced Local Lesions in Genome (TILLING)
4.3.13 Genome Editing (CRISPR-Cas)
4.4 Conclusions
References
Untitled
5: Microsatellites as Potential Molecular Markers for Genetic Diversity Analysis in Plants
5.1 Introduction
5.2 Biodiversity
5.3 Analysis of Genetic Diversity
5.4 Molecular Markers
5.5 SSR (Simple Sequence Repeat) Markers
5.6 Types of SSR Markers
5.6.1 Genomic SSR Markers
5.6.2 EST-SSR Markers
5.7 ISSR (Inter-Simple Sequence Repeat) Markers
5.8 miRNA-Derived SSRs
5.9 Analysis Using SSR Markers
5.10 Use of Next-Generation Sequencing in Developing SSR Markers
5.11 SSR Markers in Characterization of Genetic Diversity
5.12 SSR Markers and Their Cross-Species Transferability
5.13 Conclusion
References
6: Application of Molecular Markers in Assessment of Genetic Diversity of Medicinal Plants
6.1 Introduction
6.2 Genetic Diversity
6.3 Methods for Assessing Genetic Diversity in Medicinal Plants
6.3.1 Morphological Markers
6.3.2 Cytological Markers
6.3.3 Biochemical Markers
6.4 Molecular Markers
6.4.1 Hybridization-Based Markers
6.4.2 PCR-Based Markers
6.4.2.1 Randomly Amplified Polymorphic DNA
6.4.2.2 Amplified Fragment Length Polymorphism
6.4.2.3 Microsatellite Markers
6.4.2.4 Inter Simple Sequence Repeats
6.4.2.5 Sequence-Related Amplified Polymorphism
6.4.2.6 Cleaved Amplified Polymorphic Sequences
6.4.2.7 Single Nucleotide Polymorphism
6.4.2.8 Diversity Arrays Technology
6.4.2.9 Sequence-Characterized Amplified Region
6.4.2.10 Start Codon Targeted Markers
6.4.2.11 Random Amplified Microsatellite Polymorphisms
6.4.2.12 Selective Amplification of Microsatellite Polymorphic Loci
6.4.2.13 DNA Amplification Fingerprinting
6.4.2.14 Directed Amplification of Minisatellite Region DNA
6.5 Diversity Assessment Works in Medicinal Plants
6.6 Conclusion
References
7: Non-coding RNA Based Marker: A New Weapon in Armory of Molecular Markers
7.1 Introduction
7.1.1 Detection of LncSSRs
7.1.2 Distribution in Genome
7.1.3 Development of LncRNA-SSRs Markers
7.1.4 Databases/Tools for LncRNA
7.2 Challenges and Future Prospects
7.3 Conclusion
References
8: Molecular Marker Techniques in Niger Crop Improvement
8.1 Introduction
8.2 Botanical Description
8.3 Genetic Problems in Niger Crop Improvement
8.4 Molecular Markers
8.4.1 Characteristics of a Good Molecular Marker
8.4.2 Types of Molecular Markers
8.4.3 Applications of Molecular Markers
8.5 Application of Molecular Markers in Niger Crop Improvement
8.5.1 Genetic Diversity
8.5.1.1 Random Amplified Polymorphic DNA Marker (RAPD)
8.5.1.2 ISSR Markers
8.5.1.3 Amplified Fragment Length Polymorphism (AFLP)
8.5.1.4 RAPD and AFLP
8.5.1.5 Simple Sequence Repeat (SSR) or Microsatellite Markers
8.5.1.6 Single Nucleotide Polymorphism (SNP)
8.5.1.7 Simple Sequence Repeats (SSR) and Single Nucleotide Polymorphism (SNP)
8.5.1.8 Expressed Sequence Tags (ESTs)
8.5.1.9 Internal Transcribed Spacers (ITS)
8.5.2 Biological Processes and Abiotic Stresses
8.5.2.1 Micro RNAs
8.6 Conclusion
References
9: Applicability of Molecular Markers in Ascertaining Genetic Diversity and Relationship Between Five Edible Bamboos of North-...
9.1 Introduction
9.2 Materials and Methods
9.2.1 Sample Collection and DNA Extraction
9.2.2 PCR Amplification Reaction with ISSR, SCoT, CBDP, and iPBS Primers
9.2.3 Data Analysis
9.3 Results and Discussion
9.3.1 Polymorphism and Markers Efficacy
9.3.2 Correlation Analysis
9.3.3 Genetic Distance and Cluster Analysis by UPGMA
9.3.4 Principal Coordinate Analysis (PCoA)
9.4 Conclusion
References
10: DNA Markers-Assisted Crop Improvement for Biotic and Abiotic Stresses in Legumes
10.1 Introduction
10.2 DNA Markers
10.2.1 RFLP (Restriction Fragment Length Polymorphism)
10.2.2 RAPD (Randomly Amplified Polymorphic DNA)
10.2.3 AFLP (Amplified Fragment Length Polymorphism)
10.2.4 SSR (Simple Sequence Repeats)
10.2.5 ISSR (Intersimple Sequence Repeat)
10.2.6 STS (Sequence-Tagged Site)
10.2.7 CAPS (Cleaved Amplified Polymorphic Sequences)
10.2.8 SCARs (Sequence-Characterized Amplified Regions)
10.2.9 SNP (Single-Nucleotide Polymorphism)
10.2.10 GBS (Genotyping by Sequencing)
10.2.11 DArT (Diversity Array Technology)
10.3 DNA Markers-Assisted Characterization of Biotic Stress in Legumes
10.4 DNA Markers-Assisted Characterization of Abiotic Stress in Legumes
10.5 DNA Markers-Assisted Characterization of Stress-Related QTLs and Genes in Legumes
10.6 DNA Markers-Assisted Breeding and Development of Biotic Stress-Tolerant Lines in Legumes
10.7 Improvement of Legume Crops Via MAS
10.8 Amalgamation of Technologies with the Latest OMICS Approaches
10.9 Conclusion
References
11: Molecular Marker-Assisted Crop Improvement in Pulses
11.1 Introduction
11.2 Molecular Markers
11.3 Molecular Markers in Chickpea
11.4 Molecular Markers in Lentil
11.5 Molecular Markers in Fieldpea
11.6 Molecular Markers in Grass pea
11.7 Molecular Markers in Pigeonpea
11.8 Molecular Markers in Mungbean
11.9 Molecular Markers in Urdbean
11.10 Conclusion
References
12: Application of Molecular Markers for the Assessment of Genetic Fidelity of In Vitro Raised Plants: Current Status and Futu...
12.1 Introduction
12.2 Somaclonal Variation
12.2.1 Causes of Somaclonal Variation
12.2.2 Disadvantages of Somaclonal Variation
12.3 Detection of Somaclonal Variation Through Molecular Markers
12.3.1 Hybridization-Based Molecular Markers
12.3.1.1 RFLP
12.3.2 PCR-Based Molecular Markers
12.3.2.1 RAPD
12.3.2.2 AFLP
12.3.2.3 SCAR
12.3.2.4 ISSR
12.3.2.5 SSR
12.4 The Influence of Molecular Marker Techniques
12.5 Molecular Markers in the Age of Omics
12.6 Next-Generation Sequencing (NGS) for the Detection of Somaclonal Variation
12.7 Conclusion
References
13: Marker-Assisted Breeding in Vegetable Crops
13.1 Introduction
13.2 Classification of Markers
13.2.1 Morphological Markers
13.2.2 Biochemical Markers
13.2.3 Isozyme Markers
13.2.4 Seed Storage Proteins
13.2.5 Cytological Markers
13.2.6 Molecular Markers
13.3 Prerequisite of Marker-Assisted Selection (MAS)
13.3.1 Appropriate Marker Systems and Reliable Markers
13.3.2 Rapid DNA Extraction and High-Throughput Marker Detection
13.3.3 Genetic Map
13.3.4 Association Between Marker and Trait
13.4 Major Thrust Areas of Marker-Assisted Breeding
13.4.1 Marker-Assisted Backcrossing (MABC)
13.4.2 Marker Assisted Gene Pyramiding
13.4.3 Marker-Assisted Recurrent Selection (MARS)
13.4.4 Genomic Selection
13.5 Mapping Population
13.5.1 F2 Population
13.5.2 F2-Derived F3 Population
13.5.3 Backcross Population
13.5.4 Doubled Haploids
13.5.5 Recombinant Inbred Lines
13.5.6 Near-Isogenic Lines
13.6 Applications of Molecular Markers
13.6.1 Marker-Assisted Evaluation of Breeding Material
13.6.2 Marker-Assisted Backcrossing
13.6.3 Evolution and Phylogeny
13.6.4 Multitrait Introgression
13.6.5 Genetic Mapping
13.7 Genetic Enhancement of Vegetable Crops Using MAS
13.7.1 Tomato (Solanum lycopersicum L.)
13.7.2 Genetic and Genomic Resources
13.7.3 Mapped Genes and QTLs
13.7.4 Genomic and Marker-Assisted Selection
13.7.5 Pepper
13.7.6 Genetic and Genomic Resources
13.7.7 Mapped Genes and QTLs
13.7.8 Genomic and Marker-Assisted Selection
13.7.9 Brinjal
13.7.10 Genetic Resources
13.7.11 Mapped Genes and QTLs
13.7.12 Genomic and Marker-Assisted Selection
13.7.13 Pea
13.7.14 Genetic Resources
13.7.15 Mapped Genes and QTLs
13.7.16 Genomic and Marker-Assisted Selection
13.7.17 French Bean
13.7.18 Genetic Resources
13.7.19 Mapped Genes and QTLs
13.7.20 Genomic and Marker-Assisted Selection
13.7.21 Carrot
13.7.22 Genetic Resources
13.7.23 Mapped Genes/QTLs
13.7.24 Genomic and Marker-Assisted Selection
13.7.25 Cucumber
13.7.26 Genetic Resources
13.7.27 Mapped Genes and QTLs and Marker-Assisted Selection
13.7.28 Spinach
13.7.29 Genetic Resources
13.7.30 Mapped Genes and QTLs
13.7.31 Genomic and Marker-Assisted Selection
13.7.32 Okra
13.7.33 Genetic Resources
13.7.34 Mapped Genes/QTLs and Marker-Assisted Selection
13.8 Conclusion
References
14: Marker-Assisted Breeding for Soybean Mosaic Virus Resistance in Soybean (Glycine max)
14.1 Introduction
14.2 A Brief Summary of Soybean Mosaic Virus and Its Management
14.3 Nature of SMV Resistance
14.4 Marker-Assisted Breeding (MAB)
14.5 Synopsis of DNA Markers in Soybean
14.6 MAB for SMV Resistance
14.6.1 Mapping of SMV Resistance Genes
14.6.2 Genomic Regions and Candidate Genes Linked to SMV Resistance
14.6.2.1 Rsv Series
14.6.2.2 Rsc Series
14.6.2.3 QTLs and SNPs Associated with Resistance to SMV
14.6.3 Gene Pyramiding for SMV Resistance
14.7 Conclusion and Perspectives
References
15: Recent Advancements in Molecular Marker Technologies and Their Applications in Crop Improvement
15.1 Introduction
15.2 Molecular Markers
15.3 Molecular Markers for Crop Improvement
15.3.1 Restriction Fragment Length Polymorphism (RFLP)
15.3.2 Randomly Amplified Polymorphic DNA (RAPD)
15.3.3 Inter Simple Sequence Repeats (ISSR)
15.3.4 SSR
15.3.5 AFLP
15.3.6 SCAR
15.3.7 STS
15.3.8 CAPS
15.3.9 EST
15.3.10 SNP
15.3.11 DArT
15.3.12 SCoT
15.3.13 GBS
15.3.14 WGRS
15.4 Applications of Molecular Markers in Crop Improvement
15.4.1 Marker Assisted Selection (MAS)
15.4.1.1 Limitations of MAS
15.4.1.2 Marker-assisted Back-Cross Breeding
15.4.2 QTL Mapping
15.4.3 Gene Tagging
15.4.3.1 Tagging of Disease Resistance Genes
15.4.3.2 Tagging of Male Sterility Genes and Heterosis
15.4.4 Diversity Evaluation
15.4.5 Testing of Seed Purity
15.4.6 Gene Pyramiding
15.4.7 Map-Based Cloning of Genes
15.5 Conclusion
References
16: Recent Advances in the Use of Molecular Markers for Fruit Crop Improvement
16.1 Introduction
16.2 Types of Markers
16.2.1 Classical Markers
16.2.1.1 Morphological Markers
16.2.1.2 Cytological Markers
16.2.1.3 Biochemical Markers
16.2.1.4 DNA Markers
Restriction Fragment Length Polymorphism (RFLP)
PCR-Based Methods
16.3 Use of Molecular Markers in Fruit Crops
16.3.1 Estimation of Genetic Diversity
16.3.2 DNA Fingerprinting
16.3.3 Diagnosis of Diseases
16.3.4 Selection of Seedless Progenies
16.3.5 Determination of Sex in Fruit Crops
16.3.6 Linkage Maps and QTL Mapping in Fruit Crops
16.3.6.1 Association Mapping
16.3.7 Marker-Assisted Selection
16.4 Conclusion
References
17: Genomics-Assisted Breeding for Climate-Resilient Crops
17.1 Introduction
17.2 Genomics-Assisted Breeding for Climate-Resilient Crops
17.3 Genomics-Assisted Breeding: A Way Forward
17.3.1 Utilization of Crop Germplasm Resources (Genetic Resources) for Crop Genetic Improvement
17.3.2 Genome Sequencing and Sequence-Based Markers
17.3.3 High-Throughput Phenotyping
17.3.4 Marker-Trait Association for Genomics Assisted Breeding
17.3.5 Marker-Trait Association for Genomics-Assisted Breeding
17.4 Genomics Assisted Breeding for Stress Tolerance
17.4.1 Biotic Stress
17.4.2 Abiotic Stress
17.5 Genomics Assisted Breeding for Designing Future Crops
17.5.1 Haplotype-Based Breeding (HBB)
17.5.2 Accelerating Crop Breeding Development Through the Integration of High Throughput Phenotyping and Genomic Assisted Bree...
17.6 Conclusion
References

Citation preview

Nitish Kumar   Editor

Molecular Marker Techniques A Potential Approach of Crop Improvement

Molecular Marker Techniques

Nitish Kumar Editor

Molecular Marker Techniques A Potential Approach of Crop Improvement

Editor Nitish Kumar Department of Biotechnology Central University of South Bihar Gaya, Bihar, India

ISBN 978-981-99-1611-5 ISBN 978-981-99-1612-2 https://doi.org/10.1007/978-981-99-1612-2

(eBook)

# The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Plant breeding is the combination of the principles and techniques of changing the genetic make-up of a plant to make it better suited for human needs. The selection process has expedited and the scope of traditional plant breeding has multiplied in recent years with the introduction of morphological and biochemical markers. Yet, the discovery that natural populations exhibit broad polymorphism, the degree of which can be determined by DNA-based molecular markers, was the event that truly changed plant breeding in the twentieth century. The invention of the polymerase chain reaction (PCR) in 1990 was another innovation. With opportunities like map-based cloning and marker-assisted selection (MAS) breeding, the idea of DNA-based markers has greatly improved our ability to follow small areas of the chromosome. Before using them in actual breeding programmes to improve the resilience of crops to climate change, new theories and concepts in MAS breeding must be well understood. Molecular markers provide plant breeding with an important and valuable new source of information. Linkage between molecular markers can be translated to genetic linkage maps, which have become an important tool in plant genetics. Linkage between (quantitative) trait data and occurrences of marker alleles allows the identification of important genetic factors underlying observable traits. Knowledge that results from such analyses, that is, the location on the genome of important genetic factors (quantitative trait loci or QTLs) can and should be applied when making selection and breeding decisions. In this chapter, concepts of MAS breeding and the type and characteristics of DNA markers are presented so that choice of the marker(s) can be made rational and for the defined purposes. To learn how MAS works, basic molecular biology principles need to be understood. This book Molecular Marker Techniques—A Potential Approach of Crop Improvement has been designed to provide a basic understanding with regard to use of molecular markers in crop improvement. Gaya, India

Nitish Kumar

v

Acknowledgment

Thanks to all the authors of the various chapters for their contributions. It had been a bit of a long process from the initial outlines to developing the full chapters and then revising them in the light of reviewer’s comments. We sincerely acknowledge the author’s willingness to go through this process. I also acknowledge the work and knowledge of the members of our review panels, many of which had to be done at short notice. Thanks to all the people at Springer Nature, India especially Ms. Aakanksha Tyagi and Ms. Muthuneela Muthukumar with whom we corresponded for their advice and facilitation in the production of this book. I am grateful to my family members Mrs. Kiran (Wife), Miss Kartika Sharma, and Laavanya Sharma (Daughters) and parents for their incredible and selfless support all the time.

vii

Contents

1

Molecular Markers for Harnessing Heterosis . . . . . . . . . . . . . . . . . Jyotsna Baby, Toji Thomas, and T. Dennis Thomas

2

Kompetitive Allele-Specific PCR (KASP): An Efficient High-Throughput Genotyping Platform and Its Applications in Crop Variety Development . . . . . . . . . . . . . . . . . . . Md. Zahidur Rahman, Md. Tasnimul Hasan, and Jamilur Rahman

3

Marker Assisted Recurrent Selection for Crop Improvement . . . . . Suvarna, K. Ashwini, and R. Yashaswini

4

Concepts and Employment of Molecular Markers in Crop Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Varsha Kumari, S. B. Yeri, Priyanka Kumawat, Sharda Choudhary, Shyam Singh Rajput, Ashok Kumar Meena, Ram Kishor Meena, Raj Kumar Meena, and Poonam Kumari

5

Microsatellites as Potential Molecular Markers for Genetic Diversity Analysis in Plants . . . . . . . . . . . . . . . . . . . . . . . . Tania Sagar, Nisha Kapoor, and Ritu Mahajan

1

25 55

69

81

6

Application of Molecular Markers in Assessment of Genetic Diversity of Medicinal Plants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 R. S. Sharma, Nairita Vaidya, S. R. Maloo, Ashish Kumar, Stuti Sharma, R. Shiv Ramkrishnan, and Varsha Kumari

7

Non-coding RNA Based Marker: A New Weapon in Armory of Molecular Markers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Ravi S. Singh, Prakash Singh, Sweta Sinha, Ujjwal Kumar, Ruchi Kumari, and Sanjeev Kumar

8

Molecular Marker Techniques in Niger Crop Improvement . . . . . . 127 Suvarna

ix

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Contents

9

Applicability of Molecular Markers in Ascertaining Genetic Diversity and Relationship Between Five Edible Bamboos of North-East India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 Leimapokpam Tikendra, Hamidur Rahaman, Abhijit Dey, Manas Ranjan Sahoo, and Potshangbam Nongdam

10

DNA Markers-Assisted Crop Improvement for Biotic and Abiotic Stresses in Legumes . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 Vasudha Maurya, Narayan Singh, Ashutosh Sharma, and Rahul Kumar

11

Molecular Marker-Assisted Crop Improvement in Pulses . . . . . . . . 199 Diptadeep Basak, Ankita Chakraborty, Arpita Das, and Joydeep Banerjee

12

Application of Molecular Markers for the Assessment of Genetic Fidelity of In Vitro Raised Plants: Current Status and Future Prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 Pritom Biswas and Nitish Kumar

13

Marker-Assisted Breeding in Vegetable Crops . . . . . . . . . . . . . . . . 257 Anirban Maji, Shouvik Gorai, Soham Hazra, Wahidul Hasan, G. Parimala, and Pritam Roy

14

Marker-Assisted Breeding for Soybean Mosaic Virus Resistance in Soybean (Glycine max) . . . . . . . . . . . . . . . . . . . . . . . . 303 Adhimoolam Karthikeyan, Manickam Dhasarathan, Pukalenthy Bharathi, Mayalagu Kanimoli Mathivathana, Santhi Madhavan Samyuktha, and Natesan Senthil

15

Recent Advancements in Molecular Marker Technologies and Their Applications in Crop Improvement . . . . . . . . . . . . . . . . . 319 Sweta Sinha, Shaurya Singh, Mankesh Kumar, Ravi Shankar Singh, Satyendra, and Dharamsheela Thakur

16

Recent Advances in the Use of Molecular Markers for Fruit Crop Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Lalrinmawii, Hidayatullah Mir, and Nusrat Perveen

17

Genomics-Assisted Breeding for Climate-Resilient Crops . . . . . . . . 357 Sudha Manickam, Veera Ranjani Rajagopalan, Bharani Manoharan, Senthil Natesan, and Raveendran Muthurajan

About the Editor

Nitish Kumar is Senior Assistant Professor at the Department of Biotechnology, Central University of South Bihar, Gaya, Bihar, India, for the last 12 years. Dr. Kumar completed his doctoral research at the Council of Scientific and Industrial Research—Central Salt and Marine Chemicals Research Institute, Bhavnagar, Gujarat, India. He has a wide area of research experience in the field of crop improvement using plant biotechnology techniques. He has published more than 70 research articles in leading international and national journals, more than 20 book chapters, and 7 books. Dr. Kumar is a recipient of the Young Scientist Award from the Science and Engineering Research Board (SERB) in 2014. He has received many awards/fellowships/projects from various prestigious Indian government organizations like CSIR, DBT, ICAR, SERB-DST, and BRNS-BARC, among others. He is a reviewer for various international journals.

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1

Molecular Markers for Harnessing Heterosis Jyotsna Baby, Toji Thomas, and T. Dennis Thomas

1.1

Introduction

Breeding for heterosis or hybrid vigour is a major step forward in crop breeding, evidenced by the rapid rise in the productiveness of several crops (Wang et al. 2018; Kumar et al. 2020). Heterosis is the term used to describe the phenomenon in which the offspring resulting from the cross of genetically distinct parents show superiority above its parents in terms of biomass, growth rate or any other features. The phrase superiority does not imply positive or higher value; instead, it relies on the trait’s economic value or the breeding goal. Crosses can be made between distinct varieties, species or even genera. However, genetic divergence between parents plays significant roles in the heterotic levels of progeny (East 1936). Even though considerable knowledge has been contributed by heterosis in several crops, the genetic mechanisms underlying heterosis remain elusive. Several research studies have been put forth in this direction to solve this mystery (Fujimoto et al. 2018). The advent of molecular marker-based technologies has shed light on the intricate genetic mechanisms underlying heterosis to a great extent (Lippman and Zamir 2007). The classical genetic explanations underlying heterosis are mainly based on mechanisms such as dominance, overdominance and epistasis. Quantitative trait locus (QTL) mapping has been performed in all major crops as part of dissecting the genetic background of heterosis. QTL has benefitted to a large extent in crop breeding (Li et al. 2018). Apart from unveiling the genetic background of heterosis, the avenues of molecular marker-based technologies are extended up to the prediction of heterosis in hybrids. The genetic distance (GD) determined using

J. Baby · T. Thomas Department of Botany, St. Thomas College Palai, Kottayam, Kerala, India T. Dennis Thomas (✉) Department of Plant Science, Central University of Kerala, Kasaragod, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_1

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molecular markers is proposed as an effective approach for the prediction of heterosis and forming heterotic groups of germplasm (Geng et al. 2021). Recently, several studies in rice, maize, cotton, etc., communicated the prospects of employing molecular markers such as random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), simple sequence repeat (SSR) and single nucleotide polymorphism (SNP) in parental selection for heterosis breeding (EL-Refaee et al. 2016; Tomkowiak et al. 2020; Geng et al. 2021). Nevertheless, the inferences obtained from different studies employing molecular marker-based GD in heterosis prediction were inconsistent. While certain markers were found to be effective predictors in some crops, the same marker fails to provide accurate results in some other crops (Marcón et al. 2019; Patil et al. 2020; Salem et al. 2022). Efforts are in progress to resolve this bottleneck through a clever selection of molecular markers (Rajendrakumar et al. 2015) and also complementing with ‘omics’-based strategies (McKeown et al. 2013). In essence, breeding for heterosis in crop plants has progressed to a great extent with the prominence of molecular markers. This chapter considers the various aspects of molecular markers as promising tools for harnessing heterosis.

1.2

Unveiling the Genetics of Heterosis via Molecular Markers

Efforts to solve the puzzle of heterosis have been touched over a century; however, its mechanism remains obscure (Schnable and Springer 2013). Molecular markers are much helpful tools for unveiling the genetic basis of heterosis in various crop plants, and many such studies have revealed a wide range of genetic pathways underlying heterotic effects (Krishnan et al. 2013). They enabled to execute quantitative trait locus (QTL) evaluations, a feasible practice for mapping and thereby locating genes concerned with complex traits (Meyer et al. 2010). Molecular marker technology along with next-generation sequencing made a viable study of genomic-scale mapping in all notable crops. Dissecting the underlying genetics of heterosis, especially the genetic architecture behind the heterotic vigour, resulted in increased grain yield and has been greatly advantaged by these advancements (Huang et al. 2016; Xiao et al. 2021). The contribution of overdominance effects behind the genetics of heterosis has been demonstrated in Solanum lycopersicum by Semel et al. (2006). The study comprised 76 introgression lines, and each of these plants has a single chromosomal segment from Solanum pennellii (wild species) that replaces the homologous segment of S. lycopersicum. Eight hundred and forty-one QTLs for 35 traits were evaluated. The genomic regions with overdominance effects were found to be related to yield and yield-associated phenotypes such as plant weight, fruit weight, seed morphology and brix. Therefore, they inferred the prevalence of overdominant QTLs as the reason for increased reproductive fitness in Solanum lycopersicum. Another study was performed in Gossypium hirsutum to understand the genetics underlying heterosis. F2:3 and F2:4 generations were considered for examining QTL effects at the levels of single locus and two locus. Thirty-eight QTLs in F2:3 and 49 QTLs in F2:4 were identified related

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to yield and yield-associated traits. The results obtained from QTL analyses suggested overdominance and partial dominance effects at the level of single locus and epistasis effect at the level of two locus (Liang et al. 2015). In line with this, Shang et al. (2016) communicated the cumulative effects of overdominance, partial dominance, epistasis and environmental effects of QTLs as the decisive mechanisms behind heterosis for yield in upland cotton. These results were unanimous with the inferences obtained by Li et al. (2018). They also confirmed overdominance, additive effects and epistasis as the major mechanisms behind heterosis and stated the environmental effects in the heterotic expression of G. hirsutum. Dissecting the genetic mechanisms of heterosis in Brassica napus through the analysis of 28 QTLs for four yield traits has been performed. Fifty-five per cent of QTLs exhibited dominance, suggesting the importance of dominance in heterotic behaviour. Digenic interactions such as additive X additive (AA), additive X dominance (AD/DA) and dominance X dominance (DD) are also found to be widely occurring in the population. These results concluded that the prominence of epistasis together with dominance is the reason for heterosis in B. napus (Li et al. 2012). Lu et al. (2003) explored the genetic background of heterosis using SSR markers in the Zea mays population. One hundred and sixty SSR markers were employed to form 10 linkage groups, which represented 10 chromosomes of maize. Twenty-eight QTLs were identified for grain yield, and all 10 chromosomes were found to have grain yield QTLs. Most of the QTLs for grain yield (86%) revealed overdominance, while QTLs for stalk lodging, plant height and grain moisture displayed partial dominance to complete dominance. The prevalence of dominance and epistatic effects behind the genetics of heterosis in maize has been conveyed by Tang et al. (2010). One hundred and forty-three digenic interactions such as AD, DD and AA were ascertained for the characters including ear length, grain yield and ear row number and 100-kernel weight. The accessibility of high-density markers across the genome made it easier to explore the genetic mechanisms behind complex traits (Krieger et al. 2010). The combined effects of overdominance, dominance and epistasis as the mechanism behind heterosis for grain yield in maize were established by Guo et al. (2014) by using recombinant inbred line and ‘immortalized F2’ (IF2) populations of hybrid, Yuyu22. SNP bin map formed of 3184 bins were used in this study, and bin maps were found to perform better than SSR linkage map with the identification of more QTLs with good LOD scores and greater accuracy for QTL detection. Similar analyses of the IF2 population using an SNP bin map (1619 bins) were reported in rice hybrid, Shanyou 63 as part of enquiring about the heterosis of yield and associated traits. The results suggested trait-dependent variation of the genetic mechanism behind heterosis (Zhou et al. 2012).

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Genetic Diversity Assessment and Heterotic Grouping Using Molecular Markers

One of the limiting factors of every breeding programme targeting crop improvement is the meagre genetic base. Identifying the genetic diversity and heterotic groups is therefore vital in developing inbreds and efficiently managing their germplasm for enhancing the success rate of breeding programmes. In other words, allocating germplasm towards various heterotic pools is a fundamental approach in crop breeding because the large number of potential combinations can be realized by eliminating the unnecessary combinations of these heterotic groups (Meena et al. 2017). Various methods such as pedigree analysis, specific combining ability (SCA) effects, grain yield and molecular marker-based methods are used in the process of heterotic grouping (Fan et al. 2003; Reid et al. 2011; Xie et al. 2014). These methods can be categorized as quantitative and molecular-based (Oyetunde et al. 2020). Quantitative methods such as heterotic group’s specific and general combining ability (HSGCA), SCA and heterotic group’s general combining ability of multiple traits (HGCAMT) identify heterotic groups on the basis of combining ability of inbreds and field data (Badu-Apraku et al. 2013). On the contrary, molecular markerbased methods rely on genetic similarity or GD for heterotic grouping (Wegary et al. 2013). Molecular marker-based evaluation of genetic variation and heterotic grouping of inbreds has now emerged as a promising approach (Younas et al. 2012) in comparison with morphological and pedigree analysis in certain crops (Bashir et al. 2015). Swift advancements in the field of molecular markers are bringing about a shift in the preferred marker type for grouping almost every 2 or 3 years. In the initial phase (around 1990), RFLP markers were the method of choice, which was followed by AFLP markers (Suwarno 2014). Recently, SSR or microsatellite markers are used in many heterotic grouping studies (Vittorazzi et al. 2018; Yingheng et al. 2018; Havrlentová et al. 2021; Mahato et al. 2021). Most recently, SNP markers are also gaining ground (Dari et al. 2018; Adu et al. 2019; Gupta et al. 2020) because of their various advantages such as much abundance in genome, dimorphism, high reproducibility, low mutation and genotyping error rates (Li et al. 2020). The conversion of SNP markers into Kompetitive allele-specific PCR (KASP) markers has been communicated by Li et al. (2020). SNPs with elite qualities such as even distribution, stability and polymorphism in the range of 40–60% and faults detected in lower than 20 genotypes were selected for KASP assays. In addition to these established markers, Jin et al. (2019) demonstrated the practical applications of InDel (insertion–deletion) markers in heterotic grouping of Solanum lycopersicum inbreds. The co-dominance, reproducibility and multiallelic nature exhibited by these markers in this crop are the prime aspects of the marker selection. A similar approach has been employed earlier by Kladmook et al. (2012) in the clustering of Oryza sativa landraces from Thailand. InDel markers clearly differentiated the landraces into indica and japonica subspecies. Together with data from ISSR and SSR markers, the immense genetic diversity among the

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cultivars was established. Furthermore, the fruitfulness of employing DArT (diversity array technology) markers in heterotic grouping of wheat has also been reported (Boeven et al. 2016). Heterotic grouping employing various molecular markers is now a common practice in crop breeding studies. Some of the important studies in molecular marker-based heterotic grouping and genetic diversity analysis (2010 onwards) are listed in Table 1.1. Of the various crops studied and grouped, maize stands as one among the most studied crops. Maize is a prime food crop for humans and also in terms of the livestock industry (Undie et al. 2012). These studies in maize have made it clear that grouping of maize genotypes based on molecular markers into heterotic pools is a convincing approach (Badu-Apraku et al. 2015; Richard et al. 2016; Dari et al. 2018; Sharma and Kumar 2018; Adu et al. 2019). Also, the positive correlation of molecular marker-assisted heterotic grouping with SCA and F1 grain yield is denoted (Akinwale et al. 2014; Badu-Apraku et al. 2015; Richard et al. 2016). In addition, it has been demonstrated that incorporating reference inbred lines (inbreds having known heterotic relationship and genetic constitution) can improve the confidence of molecular marker-based heterotic grouping (Richard et al. 2016). Similar investigations are of dire need in rice breeding also, as productivity has to be increased to sustain the growing population with the shrinking agricultural land (Huang et al. 2015). The study performed in Oryza sativa by Huang et al. (2015) grouped F1 hybrids as homozygous (HO) or heterozygous (HE) based on their parental genotype at a certain locus. Each locus was evaluated and considered as a positive locus (PL) if there occurred a significant difference between HE and HO. Further grouping of PL into effect-increasing loci (IL) and effect-decreasing loci (DL) was based on whether HE performed better than HO or not, respectively. Results of the research were suggestive of the significant correlation of DLs and ILs to heterosis with the exception of a few traits. Four groups were identified for the 12 inbred lines considered for the study. Studies have suggested that genetic diversity analysis by employing molecular markers along with agronomical traits is much feasible (Dias et al. 2008; BarroKondombo et al. 2010). Heterotic grouping of Sorghum bicolor was performed based on agronomical characters and molecular markers. Five groups were identified by agronomical traits, while seven heterotic groups were recognized by molecular marker-based GD. Clusters formed by SSR markers were not in accordance with the analysis results of agronomical traits. In addition, cluster analysis by both of these methods failed to distinguish between maintainer lines and restorer lines. The study suggested the simultaneous use of both clusters for breeding programmes in Sorghum (Wang et al. 2013). A similar investigation in Solanum lycopersicum has been carried out by Jin et al. (2019) to compare molecular and morphological characterizations of genetic diversity. The molecular characterization [InDel (insertion–deletion) marker]-based subgroups were found to have greater genetic differentiation and gene diversity than groups based on agronomic characters. Also, the coefficient of variation (CV) of the InDel assay-based five subgroups was greater than the four groups classified by morphological traits. A definite association of GD based on molecular markers with morphological distance is observed as opposed to

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Table 1.1 List of studies in a heterotic grouping in various crops using molecular markers (2010 onwards) Crop Avena sativa L.

Marker Microsatellite

Brassica napus L.

SSR

Brassica oleracea L. var. capitata

SSR

65 inbred lines

SNP markers converted Kompetitive allele-specific PCR (KASP) markers SSR

244 inbred lines

SSR

16 parental genotypes (3 maintainer and 13 restorer)

ISSR, SSR, InDel

126 rice accessions

SSR, intron length polymorphism (ILP)

12 rice accessions from 60 IRRI inbred lines

Oryza sativa (Indica hybrid)

Oryza sativa L.

Plant material White, brown, yellow seeded oats and a naked oat (Avena sativa var. nuda Koern) subgroup 86 modern and conventional cultivars

18 parental lines developed at IRRI

Major results/ findings 7 heterotic groups were formed on the basis of genetic differentiation

Assessed the genetic diversity of both cultivars and grouped them (preliminary heterotic groups) 21 inbreds of flatheaded morphotype were included under 3 heterotic groups and 42 inbreds of round-headed morphotype under 5 heterotic groups Classified spring cabbage into 7, autumn cabbage into 6 and winter cabbage into 5 heterotic groups 2 heterotic groups and 4 heterotic patterns have been identified 16 parental genotypes were included under 7 subgroups (6 restorer and 1 maintainer) On combining the data of 3 markers, five clusters were identified Based on GD, 12 inbreds were included in 4 groups

References Havrlentová et al. (2021)

Younas et al. (2012)

Xing et al. (2018)

Li et al. (2020)

Xie et al. (2014)

Yingheng et al. (2018)

Kladmook et al. (2012)

Huang et al. (2015)

(continued)

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Table 1.1 (continued) Crop Pennisetum glaucum (L.) R. Br.

Marker SNP

Plant material 580 hybrid parents

SSR

225 accessions including 214 landrace

SSR

342 hybrid parental lines

Secale cereale L.

SSR

5 eastern European varieties and 2 central-eastern (Carsten and Petkus) heterotic pools

Solanum lycopersicum L.

InDel (insertion– deletion)

324 inbred lines

Sorghum bicolor (L.) Moench

SSR

142 parental lines

Triticum aestivum L.

DArT

110 varieties of winter wheat

Zea mays L.

SNP

26 CIMMYT inbred lines

Major results/ findings Molecular markers can assign the new and existing germplasm into heterotic groups Heterotic groups can be developed since large divergence was observed among various landraces 7 putative heterotic pools were identified The genetic base of the Carsten pool is found to be narrow and it has to be a primary target for broadening InDel-based GD is more efficient than morphological marker-based GD in heterotic grouping 7 heterotic groups were identified by molecular markerbased GD Heterotic groups can be formed by making use of the global genetic diversity of wheat by employing a unified framework Positive correlation between markerbased GD and heterosis was observed, and hence, heterotic grouping and heterosis prediction are possible

References Gupta et al. 2020

Bashir et al. (2015)

Ramya et al. (2018) Fischer et al. (2010)

Jin et al. (2019)

Wang et al. (2013)

Boeven et al. (2016)

Dari et al. (2018)

(continued)

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Table 1.1 (continued) Crop

Marker SNP

Plant material 45 inbreds from South Africa and the USA

SSR

28 inbreds crossed in diallel design to produce 378 hybrids 18 inbred lines

Satellite

Zea mays L. var. sachharata Zea mays L. var. everta

SNP

94 yellow and white early maturing inbred lines

SNP

91 diallel crosses from 14 quality protein maize (QPM) inbreds 12 inbred lines

SSR

Satellite

38 popcorn genotypes

Major results/ findings 4 heterotic groups (BSS, N, SC and Lancaster) were identified from inbred lines 4 heterotic groups were identified Microsatellite markers can be employed for the heterotic grouping of maize inbred lines 3 heterotic groups were identified, and SNP markerbased grouping was found to be much reliable SNP markers are effective in a grouping of QPM inbred lines 3 heterotic groups were identified Sufficient genetic variability was found, and heterotic groups were recognized

References Richard et al. (2016)

Akinwale et al. (2014) Sharma and Kumar (2018)

Adu et al. (2019)

BaduApraku et al. (2015) Mahato et al. (2021) Vittorazzi et al. (2018)

the nonsignificant correlations obtained by Acosta-Quezada et al. (2012) in Solanum betaceum. It is also suggested that InDel GD is more preferred over the morphological distance in the parental selection for hybrid breeding because of its greater genetic differentiation potential and correlations with pedigree and origin (Jin et al. 2019).

1.4

Prediction of Heterosis Using Molecular Markers

Enormous combinations are made and evaluated through hybrid breeding annually. This demands intense labour and time, still having poor selection efficiency (Soni et al. 2017). The introduction of genomic tools like molecular markers paved the

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way towards heterosis prediction. If useful molecular markers are pointed out considering heterotic prediction, feasible crosses can be proposed, thereby speeding up the breeding programme (Rajendrakumar 2015). In the initial stage, molecular marker-based prediction of heterosis was mainly based on markers like RAPDs, RFLPs and ISSRs. The achieved success rate was low because of the poor association of heterosis with the genetic heterogeneity of these markers. Later, there was a change in focus from these random or anonymous markers to informative or specific markers, which had specific effects on heterosis. Specific markers may have positive or negative effects on heterosis; however, they are connected to heterotic effects (Rajendrakumar et al. 2015). The idea of effectincreasing loci for predicting heterosis in Oryza sativa (indica rice) has been demonstrated by Renming et al. (2008). They have shown the superiority of effect-increasing loci over total loci and positive loci in predicting F1 traits for heterosis. In a similar manner, the prediction of heterosis for yield has been reported in Oryza sativa. The study compared the ability of expressed sequence tag-derived simple sequence repeats (EST-SSRs) for heterotic prediction with that of genomic SSRs. EST-SSRs proved to have a better correlation in comparison with genomic SSRs. Ten prime EST-SSR markers, which are informative in nature, have been suggested, based on higher values of correlation with yield heterosis (Jaikishan et al. 2010). Estimation of genetic divergence by the combined use of EST-SSRs and morphological markers has also been reported by Pavani et al. (2018). Correlating coefficient of marker polymorphism (CMP) has been obtained by dividing the number of polymorphic markers by the total number of markers. Then, the correlation between standard heterosis and CMP values was also calculated and used for the prediction of heterosis in rice of 40 F1s. The traits employed include panicle weight, productive tillers and significant grain yield per plant. Multi-allelic markers carry greater information per locus in comparison with biallelic markers. Thus, multi-allelic markers are expected to enhance the chance of differentiating various functional alleles for determining marker effects. However, in hybrids, observations are reduced per allelic configuration. This raises a question of whether multi-allelic markers such as SSR markers are much efficient than biallelic markers (Schrag et al. 2010). A brief overview of the nature and types of molecular markers employed in heterosis prediction is depicted in Fig. 1.1. According to the theory of quantitative genetics, heterosis observed in a hybrid offspring depicts a linear association with parental genetic distances (Falconer and Mackay 1996). Hence, heterosis prediction based on parental genetic distance and mid-parent performance is feasible (Reif et al. 2012). Table 1.2 provides a list of heterosis prediction studies in various crops employing molecular markers and their conclusions (2010 onwards). Several molecular markers were used to avail the association of GD of parents with the hybrid vigour of their offspring. Nevertheless, contradicting results have been reported for different crops, different lines, different markers and different traits considered (Buti et al. 2013; Wegary et al. 2013; Tian et al. 2017; Pandey et al. 2018). Geng et al. (2021) conducted a study on Gossypium hirsutum to assess the efficiency of heterosis prediction on the basis of parental GD, assessed by SSR and

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Fig. 1.1 A brief overview of the types and nature of molecular markers employed in heterosis prediction

SNP markers. The GD estimated using these markers showed a positive correlation. Thus, the use of molecular markers to assess parental GD thereby assisting in the selection of parents in cotton breeding has been revealed. In Brassica napus, a significant positive correlation between GD and heterosis has been conveyed by various researchers (Sang et al. 2015; Tian et al. 2017), while others failed to find any such correlations (Qian et al. 2007, 2009; Luo et al. 2016). A study conducted by Tian et al. (2017) in B. napus evaluated the relationship among combining ability, GD and heterosis using inbred lines. No significant association of hybrid vigour with GD was observed when the total molecular markers were used. However, there was a moderate enhancement in the accuracy of heterosis prediction by employing favouring (effect-increasing) markers. They suggested the possibility of using selected effect-increasing markers for further studies concerning heterosis prediction of yield and related traits. In line with this, the molecular markers (SSR markers) linked with major agronomic traits (yield-related traits) have been identified (Wolko et al. 2022). Polymorphic information content (PIC) values, which describe the quality of marker by identifying its capability for detecting the polymorphism among different genotypes, have been employed. PIC values of selected markers (ranging from 0.602 to 1.000) indicated that they were helpful for analysing variations among the individuals under study. SNPs are also an effective tool for characterizing germplasm and estimating genetic distances since they are highly polymorphic, biallelic and reproducible. Also, they have an advantage over SSR markers as they are found to a great extent in the genetic material (van Inghelandt et al. 2010). Recently, the prediction of combining ability and heterosis based on GD among maize inbred has been carried out using SNP markers. A good correlation coefficient value of 0.91 was calculated

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Table 1.2 List of studies in heterosis prediction in various crops employing molecular markers (2010 onwards) Crop Brassica carinata A. Braun

Marker RAPD

Plant material 7 inbred lines and their 21 F1 s in a half-diallel design

Brassica napus L.

SNP

155 parental lines and 218 F1 (153 crossed with the remaining 2)

SSR and SNP

9 elite inbreds crossed in halfdiallel design to produce 36 F1s

Brassica rapa L. ssp. pekinensis

Total SNPs and homozygous SNPs

14 parental lines in a half-diallel cross to obtain 91 hybrids

Carica papaya L.

SSR

8 parents crossed in a diallel design to produce 56 F1 s

Cucumis sativus L.

AFLP

Daucus carota L.

RAPD and AFLP

6 cucumber genotypes crossed in partial diallel design to produce 15 F1s 7 CMS lines, their maintainer lines and 2 fertile lines (used as father lines)

Major results/ findings RAPD markerbased GD is not reliable for heterosis prediction Yield-related traits are much complex and the efficiency of heterosis prediction using SNP-based GD is limited GD based on favouring markers are useful for heterosis prediction, while GD based on total markers failed to show a significant association with heterosis The selected markers are effective in assessing GD in B. rapa and can be employed for parental selection for breeding programmes Poor correlation between SSR-based GD and heterosis is observed Poor correlation between GD and heterosis was observed Significant correlation is observed between RAPD markerbased GD and

References Mohammed et al. (2014)

Luo et al. (2016)

Tian et al. (2017)

Yue et al. (2022)

Vivas et al. (2018)

Olfati et al. (2012)

Jagosz (2011)

(continued)

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Table 1.2 (continued) Crop

Gossypium hirsutum L.

Marker

Plant material

SSR and SNP

4 male and 282 female parents

QTL targeted SSR

11 inbred lines of cotton and 30 intraspecific hybrids were obtained by crossing them

Helianthus annuus L.

LTRretrotransposons and SNP

6 inbreds crossed in half-diallel design

Oryza sativa L.

SSR

9 parental genotypes were used to produce 36 F1 in partial diallel crosses

Microsatellite

8 parents and 28 F1 s in a halfdiallel design of mating under drought condition

Major results/ findings total yield heterosis. Similar significant association was observed among AFLP markerbased GD marketable yield heterosis Parental GD can be employed for predicting heterosis related to certain traits Linear relationship is exhibited by lint yield and seed cotton yield with heterosis. The other traits do not show correlation with GD probably because of environmental factors Changes in retrotransposon composition of the genome can generate heterosis Significant correlation between GD and grain yield heterosis was observed, while some other traits failed to show such a correlation GD deciphered by microsatellite marker can be used for predicting grain yield and sterility in rice

References

Geng et al. (2021)

Li et al. (2019)

Buti et al. (2013)

EL-Refaee et al. (2016)

Salem et al. (2022)

(continued)

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Table 1.2 (continued) Crop

Marker SSR

Plant material 1 CMS line from IRRI crossed with 19 Lao PDR germplasm to produce hybrids

SSR

40 autotetraploid lines (for study) and 40 diploid lines (as control)

RAPD

21 F1 hybrids

Paspalum notatum Flüggé

SSR and ISSR

24 sexual tetraploids and 24 apomictic tetraploids

Pennisetum glaucum (L.) R. Br.

SSR

45 genotypes of African and Asian origin

SSR and SNP

150 hybrid parents with 75 B-lines (seed parents) and 75 R-lines (restorer)

Major results/ findings Significant positive association between markerbased GD and yield heterosis was obtained Marker-based GD failed to predict heterosis in diploid rice, while it exhibited a significant association with grain length, yield, etc., in autotetraploid rice Study of the specific genomic regions of candidate genes that encode the respective traits is successful in heterotic prediction Molecular marker can be employed for heterosis prediction especially forage yield No significant correlation between markerbased GD and grain yield heterosis was found. Hence, GD failed to accurately predict heterosis Prediction of grain yield heterosis is valid only when genetically related parents are used

References Xangsayasane et al. (2010)

Wu et al. (2013)

Rahman et al. (2022)

Marcón et al. (2019)

Patil et al. (2020)

Singh and Gupta (2019)

(continued)

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Table 1.2 (continued) Crop Sesamum indicum L.

Marker SSR

Plant material 7 parents and 21 crosses in a 7 × 7 half-diallel design

Solanum lycopersicum L.

ISSR

10 tomato lines crossed to obtain 45 hybrids

Triticum aestivum L.

SNP

20 winter elite cultivar and their 100 hybrids by crossing them

Vigna unguiculata L. Walp

ISSR and SRAP

Zea mays L.

SSR

6 cowpea genotypes crossed in halfdiallel design to form 15 F1s 11 parental lines (3 males and 8 females) crossed to generate 24 hybrids 13 hybrids and 19 inbred lines

AFLP, RAPD, SSR

SSR

SSR

15 quality protein Zea mays L. inbreds and 105 hybrids by their diallel crossing 2 open-pollinated and 35 S6 inbreds

Major results/ findings SSR-based GD are inefficient in heterosis prediction in sesame ISSR markers efficiently estimated the genetic dissimilarity that aided in the identification of best hybrids No clear association between SNP marker-based GD and heterosis was found Marker-based GD of parents can be employed for heterosis prediction Poor prediction by SSR-based GD was observed

SSR markers are useful than AFLP markers in selecting parental components. RAPD markers failed to prove a clear association between GD and heterosis. No significant correlation between SSR marker-based GD and heterosis was observed Significant association

References Pandey et al. (2018)

Figueiredo et al. (2016)

Nie et al. (2019)

Abd El-Fattah et al. (2019)

Dermail et al. (2020)

Tomkowiak et al. (2020)

Wegary et al. (2013)

Kustanto et al. (2012) (continued)

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Table 1.2 (continued) Crop

Marker

Plant material and for molecular study; 28 inbreds, 5 testers and 140 F1 hybrids for field study

SNP

SNP

Microsatellite

Zea mays L. var. everta (popcorn)

RAPD

10 inbred lines crossed with 13 diseaseresistant and drought-tolerant lines and obtained 130 hybrids 7 inbred lines crossed in a diallel fashion

48 endogamic lines and 224 hybrids from their crosses 14 popcorn populations

Major results/ findings between GD and grain yield was observed. The higher the GD in hybrid, the higher is the heterosis on the particular character. GD exhibited poor correlation with heterosis under the situation of drought

Determination of GD among parental inbreds of Zea mays is a reliable method for heterosis prediction Molecular marker form effective tools for heterosis prediction RAPD markers failed to accurately estimate heterosis in popcorn

References

Ndhlela et al. (2015)

Perić et al. (2022)

Fernandes et al. (2015)

de Souza et al. (2012)

for high-parent heterosis and GD (Perić et al. 2022). A similar positive association between GD and heterosis in maize by using various molecular markers has been demonstrated earlier also (Kustanto et al. 2012; Fernandes et al. 2015; Tomkowiak et al. 2020). Nevertheless, in certain studies, molecular marker-based GD failed to prove a significant predictive value possibly due to the type of markers employed and poor genetic variability of the lines used for the study (Wegary et al. 2013; Ndhlela et al. 2015). Prediction of heterosis by means of GD is less commonly found in vegetables than in other economically viable crops (Jagosz 2011). A positive association of hybrid heterosis with GD between parents in Capsicum annuum by employing AFLP markers (Geleta et al. 2004) and a similar significant association between fruit-shaped heterosis and GD in Cucumis melo making use of microsatellites have been reported (José et al. 2005). However, in Daucus carota, the prospects of

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availing molecular markers and thereby GD for heterosis prediction are limited to the extent of the initial selection of useful factors for crosses (Jagosz 2011). Espósito et al. (2014) investigated the prediction of hybrid performance for yield in Pisum sativum. They concluded that the genetic distances inferred from sequence-related amplified polymorphism (SRAP)’s markers could be employed for the prediction of heterotic combinations in Pisum genotypes considered and also agree with the perception that the degree of correlation of genetic variation with heterotic vigour depends on the parental genetic material selected. However, the prediction involving numerous crosses requires including lots of SRAP combinations. The investigations on whether the variability exhibited by retrotransposon elements in the genome has any connection to heterosis have been carried out in Helianthus annuus. Inter-retrotransposon-amplified polymorphism (IRAP) protocol was used to infer the GD between six inbred lines, and a significant correlation between IRAP-based GD and heterosis has been obtained. The observed polymorphisms are attributed to long terminal repeat (LTR)-retrotransposon activity due to which new insertions occur in the genome. Insertions in the intergenic segments may alter the regulatory mechanisms of adjoining genes (Buti et al. 2013). Helianthus is particularly prone to retrotransposon variability as demonstrated earlier in cultivated and wild genotypes (Vukich et al. 2009).

1.5

Current Challenges and Future Prospects

We have come a long way in our understanding of the genetics behind heterosis along with its prediction with the availability of molecular marker-based technologies. Recently, many refinements have been achieved in molecular marker-based technologies, increasing the accuracy and efficiency of heterosis breeding procedures (Rajendrakumar et al. 2015). Despite these advancements made, accurate and reliable prediction of heterosis for all crops is yet to be realized. Varying levels of dominance effects, random dispersion of the chosen molecular marker, improper genome coverage and high GD between the parental lines are suggested to be the possible reasons for the poor predictions observed (Mohammed et al. 2014; Dermail et al. 2020). In many crops, heterotic grouping by using molecular markers is a successful approach. However, the poor association of marker-based GD with heterosis has been reported in some crops, affecting the prediction of best parental combinations for hybrids. The only viable option is to assign the germplasm into heterotic groups using molecular marker-based GD. This can increase the opportunities for developing good hybrids by selecting parents from elite heterotic groups (Xie et al. 2014; Gupta et al. 2020). In comparison with the heterosis prediction by employing RFLP, RAPD, ISSR and AFLP, significant progress has been achieved by the use of SSR and SNPs (Rajendrakumar et al. 2015). Future investigations will have to aim at mapping the heterotic loci employing genomic markers such as SNPs, which could unveil the genetics of heterosis, thereby making accurate predictions (Luo et al. 2016). Also, prediction accuracy can be improved by complementing molecular markers with transcriptomic

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and metabolomic data than by employing genomic markers alone (Knoch et al. 2021). Recently, notable progress has been made in understanding the genetics of heterosis through the use of high-density SNP bin maps (Zhou et al. 2012; Guo et al. 2014). However, some constraints are there, which reduce the accuracy of the obtained results. In certain cases, overdominance is unable to distinguish from pseudo-overdominance, thus leading to faulty conclusions. Employing high-density markers will allow the positional cloning of genes that underlie QTLs, which will figure out whether the quantitative traits are governed by overdominance or pseudooverdominance and also aid to have a better understanding of the genetics of heterosis (Guo et al. 2014). Rapid advancements in sequencing methods now make possible more accurate characterization of the entire array of SNPs that are present in various parental lineages, thereby facilitating the mapping of causal regions of heterosis within overdominant loci to single SNP resolution. In the near future, it is likely to point out single nucleotide polymorphisms that bestow heterosis via single-gene overdominance, when present in heterozygous conditions (termed as quantitative trait nucleotides, QTNs). Potentially, SNPs recognized as QTNs could become more accurate and efficient markers for heterosis prediction of various parental lines (McKeown et al. 2013).

1.6

Conclusions

Breeding for heterosis is a prime goal of crop improvement programmes. A thorough knowledge of the genetic background of heterosis has been realized to a great extent with the advent of molecular marker technology together with next-generation sequencing. It helps in detecting the genes underlying heterosis and also locating their genomic positions. Furthermore, the grouping of the germplasm into heterotic groups, thereby facilitating the selection of feasible parental combinations for developing the best hybrids, is also benefitted by molecular marker technologies. Heterosis prediction, which aims to predict potential combinations of breeding, advantaged by the knowledge of the markers and genetic loci linked to hybrid vigour can be regarded as one of the ultimate targets of molecular marker-based approach in crop breeding. Even though a positive association of heterosis with GD has been observed in many crops, the predictive value of molecular markers is still inconclusive. A dearth of irrefutable evidence connecting marker-based GD and heterosis is a bottleneck in completely relying on molecular markers for heterotic prediction, thus skipping field trials. Recent studies reviewed here suggested that a judicious choice of molecular markers is crucial for accurate heterotic prediction and no particular marker category can be stated to be precise. Employing high-throughput markers and complementing marker-based approach with transcriptomic and molecular data have the prospects of further refining the prediction potential.

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Acknowledgement The authors are thankful to the Council of Scientific and Industrial Research for bestowing CSIR JRF to conduct the research work of Jyotsna Baby, and it helped to prepare and submit the book chapter.

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Tian HY, Channa SA, Hu SW (2017) Relationships between genetic distance, combining ability and heterosis in rapeseed (Brassica napus L.). Euphytica 213:1. https://doi.org/10.1007/s10681016-1788-x Tomkowiak A, Bocianowski J, Kwiatek M, Kowalczewski PL (2020) Dependence of the heterosis effect on genetic distance, determined using various molecular markers. Open Life Sci 15:1–11. https://doi.org/10.1515/biol-2020-0001 Undie UL, Uwah DF, Attoe EE (2012) Effect of intercropping and crop arrangement on yield and productivity of late season maize/soybean mixtures in the humid environment of South Southern Nigeria. J Agric Sci 4:37–50. https://doi.org/10.5539/jas.v4n4p37 van Inghelandt D, Melchinger AE, Lebreton C, Stich B (2010) Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers. Theor Appl Genet 120(7):1289–1299. https://doi.org/10.1007/s00122-009-1256-2 Vittorazzi C, Júnior ATA, Guimarães AG, Silva FHL, Pena GF, Daher RF, Gerhardt IFS, Oliveira GHF, Santos PHAD, Souza YP, Kamphorst SH, Lima VJ (2018) Evaluation of genetic variability to form heterotic groups in popcorn. Genet Mol Res 17(3):1–17. https://doi.org/10. 4238/gmr18083 Vivas M, Cardoso DL, Christine H, Ramos C, Henrique P, Felipe S, De Moraes R, Pereira MG, Estadual U, Darcy F, Goytacazes C (2018) Genetic diversity between papaya lines and their correlation with heterosis in hybrids for disease resistance and morpho-agronomic traits. Summa Phytopathol 44(2):110–115. https://doi.org/10.1590/0100-5405/176828 Vukich M, Schulman AH, Giordani T, Natali L, Kalendar R, Cavallini A (2009) Genetic variability in sunflower (Helianthus annuus L.) and in the Helianthus genus as assessed by retrotransposon-based molecular markers. Theor Appl Genet 119(6):1027–1038. https://doi. org/10.1007/s00122-009-1106-2 Wang L, Jiao S, Jiang Y, Yan H, Su D, Sun G, Yan X, Sun L (2013) Genetic diversity in parent lines of sweet sorghum based on agronomical traits and SSR markers. Field Crop Res 149:11–19. https://doi.org/10.1016/j.fcr.2013.04.013 Wang Y, Zhang X, Shi X, Sun C, Jin J, Tian R, Wei X, Xie H, Guo Z, Tang J (2018) Heterotic loci identified for maize kernel traits in two chromosome segment substitution line test populations. Sci Rep 8(1):1–15. https://doi.org/10.1038/s41598-018-29338-1 Wegary D, Vivek B, Labuschagne M (2013) Association of parental genetic distance with heterosis and specific combining ability in quality protein maize. Euphytica 191(2):205–216. https://doi. org/10.1007/s10681-012-0757-2 Wolko J, Łopatynska A, Wolko Ł, Bocianowski J, Mikołajczyk K, Liersch A (2022) Identification of SSR markers associated with yield-related traits and heterosis effect in winter oilseed rape (Brassica napus L.). Agronomy 12:1544. https://doi.org/10.3390/agronomy12071544 Wu JW, Hu CY, Shahid MQ, Bin GH, Zeng YX, Liu XD, Lu YG (2013) Analysis on genetic diversification and heterosis in autotetraploid rice. SpringerPlus 2(1):1–12. https://doi.org/10. 1186/2193-1801-2-439 Xangsayasane P, Xie F, Hernandez JE, Boirromeo TH (2010) Hybrid rice heterosis and genetic diversity of IRRI and Lao rice. Field Crop Res 117(1):18–23. https://doi.org/10.1016/j.fcr.2010. 01.012 Xiao Y, Jiang S, Cheng Q, Wang X, Yan J, Zhang R, Qiao F, Ma C, Luo J, Li W, Liu H, Yang W, Song W, Meng Y, Warburton ML, Zhao J, Wang X, Yan J (2021) The genetic mechanism of heterosis utilization in maize improvement. Genome Biol 22(1):1–29. https://doi.org/10.1186/ s13059-021-02370-7 Xie FM, He ZZ, Esguerra MQ, Qiu FL, Ramanathan V (2014) Determination of heterotic groups for tropical Indica hybrid rice germplasm. Theor Appl Genet 127(2):407–417. https://doi.org/10. 1007/s00122-013-2227-1 Xing LI, Hailong YU, Zhiyuan LI, Xiaoping LIU, Zhiyuan FANG, Yumei LIU, Limei YANG, Zhuang M, Honghao LV, Zhang Y (2018) Heterotic group classification of 63 inbred lines and hybrid purity identification by using SSR markers in winter cabbage (Brassica oleracea L. var. capitata). Hortic Plant J 4(4):158–164. https://doi.org/10.1016/j.hpj.2018.03.010

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Kompetitive Allele-Specific PCR (KASP): An Efficient High-Throughput Genotyping Platform and Its Applications in Crop Variety Development Md. Zahidur Rahman, Md. Tasnimul Hasan, and Jamilur Rahman

2.1

Introduction

Agriculture is the largest or may be the only source of food for mankind on this earth. Agriculture farming is one of the oldest histories of mankind, which had been started 11,500 years ago with the domestication of grass crops. In the long history of agriculture, it faced many changes and went through several phases to ensure abundant supply of food, cloths, timber, medicines, and numerous industrial raw materials for human being. The world is changing every day, and agriculture is getting affected mostly by the changes in this world. Furthermore, the world population is increasing, arable land is decreasing at a drastic rate, the soil is losing fertility, and above all climate is getting unfavorable for usual farming practice. Due to these reasons, the stable supply of agricultural products has become the top most challenge to the farmers and scientists and other stakeholders of the sector. According to the United Nations (UN), the current total population of the world is about 7.98 billion and it would reach 8.5 billion by 2030 (UN DESA Report 2022). To feed the vast amount of population, the agriculture sector needs the adoption of modern technologies to attain sustainable crop production. For sustainable crop production, varietal improvement is an essential platform to meet this demand and face the challenges. Crop improvement has been an essential part of agriculture since its beginning. In previous times, people used to identify and select plant species with good quantity and quality of products and used them for cultivation. Thus, people performed crop improvement without knowing what they were doing. Some improvements also took place in nature for the survival of plant species through natural selection, but with the M. Z. Rahman · M. T. Hasan · J. Rahman (✉) Department of Genetics and Plant Breeding, Sher-e-Bangla Agricultural University, Dhaka, Bangladesh e-mail: [email protected] # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_2

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advancement of time, crop improvement has been a part of human practice in agriculture. People started to use different breeding methods to improve crop species as per their needs and thus started to get benefitted from it. One of the most visible examples of these benefits of crop improvement is the “green revolution,” which started from the hands of famous American agronomist Norman Borlaug. The green revolution averted the hunger of millions of people around the world. To recognize his contribution to food production, he was awarded the Nobel Prize in Peace in 1970. Another example of these benefits of crop improvement is golden rice, which helped in reducing vitamin A deficiency in African children (Dawe et al. 2002; Zimmermann and Qaim 2004). The hybrid varieties, high-yielding varieties, and resistant, tolerant, and transgenic varieties are the outcomes of modern crop improvement practices. Nevertheless, the modern crop improvement programs were not like this in the past. In the very beginning, it was a kind of traditional selection practice, and with time and scientific advancement in crop, improvement has come to the present position. Crop breeding aims to produce and make the best use of genetic variations because genetic diversity is the principal foundation of varietal development. In the long track record of crop improvement, breeding techniques have evolved through three eras, viz. primitive breeding, traditional breeding, and molecular breeding, depending on the variety of technological versions used in the era (Lin 2021; Delannay et al. 2011). The three primary crop breeding techniques used nowadays are conventional breeding, selection breeding using molecular markers, and transgenic breeding based on genetic transformation. Among these, conventional breeding has low effectiveness since it depends on breeders’ skill to arbitrarily recombine the desirable genes of the breeding components based on personal experience. Transgenic breeding and molecular marker-assisted selection breeding are typically intended to improve certain individual features, and their efficacy is higher than conventional breeding. The molecular markers are mostly employed in agricultural improvement programs for the commercialization of the products (Leng et al. 2017). The molecular markers basically work at the gene level—identification of gene (s) behind the trait of interest. In fact, the genetic regulation of biological traits is a complex regulatory network involving many genes (Chambers et al. 2014). Hence, the use of several functional genes is required to enhance the crop varieties’ potentials. The high-throughput gene-specific molecular marker platform effectively promotes the directional improvement of crop varieties (Agarwal et al. 2008). At present, DNA molecular markers have reached its third generation through the continuous advancement in high-throughput sequencing technology. The firstgeneration markers were grounded on molecular hybridization technology, e.g., restriction fragment length polymorphism (RFLP); the second-generation marker was mainly based on PCR technology, e.g., sequence tagged sites (STS), simple sequence repeat (SSR), and amplified fragment length polymorphism (AFLP). Single nucleotide polymorphism (SNP) is the nucleus of third-generation molecular marker technology, which is developed on the basis of next-generation sequencing DNA chip. SNP features include a high distribution density, robust genetic stability,

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and diallelic genotypes as compared to first- and second-generation molecular markers. Therefore, the third-generation molecular marker technology is rapid and easy to attain high-throughput and automated detections. In recent years, the development of next-generation sequencing (NGS) has promoted the development and improvement of chip-based labeling platforms, and numerous high-throughput genotyping technologies have also been designed and used (Rasheed et al. 2017). This includes the KASP (Kompetitive allele-specific PCR) high-throughput SNP detection system designed by the British Government Chemist Laboratory (Laboratory of the Government Chemist, LGC) based on the concept of competitive allele-specific PCR (Semagn et al. 2014). The another is Fluidigm genotyping platform, based on a microfluidic chip-based system developed by Fluidigm in the United States (Seo et al. 2020). Due to the numerous advantages, viz. high throughput, cheaper costing, and excellent operability, KASP technology has drawn much attention in the field of molecular breeding and crop improvement, and the technology has already established itself as a global benchmark technology in this field. More than 2000 reported that references on this genotyping technology are available in disciplines like medicine, disease diagnosis, and agricultural research (Majeed et al. 2018). This chapter primarily reviews the fundamentals of KASP technology, the working principles, its usages in the molecular breeding of various important crops, and the advantages and disadvantages of the technology in crop variety development.

2.2

Evolution of Plant Molecular Markers

Understanding the KASP technology needs to go one step behind. Modern plant biotechnology has progressed tremendously due to the rapid advancement of plant genomics during the past few decades. One of the most important advancements of plant genomics is the emergence of molecular markers for the identification and manipulation of DNA polymorphism and variations present in the genomes. Molecular marker technology is nowadays a key component of crop development due to its virtuous ability to identify polymorphisms. Selection from various types of molecular makers requires proper attention and knowledge due to the presence of numerous types of molecular markers varied in their concepts, approaches, and applications (Kesawat and Das Kumar 2009). Markers can be classified into three broad headings namely morphological, biochemical, and molecular markers (Fig. 2.1). Molecular markers differentiate the genotypes; hence, the technology is highly desirable in the selection of targeted genotypes at early segregating generation with high precision. In the early days, different biochemical markers, viz. anthocyanins, phenolics, and other secondary metabolites, were among the first biochemical compounds utilized as markers to identify various plant strains. However, a number of issues including instability and inadequacy in supply hindered the wider adoption of these biochemical markers. Moreover, prior to the development of DNA markers, enzyme markers (allozymes and isozymes) also had drawn some attention for a limited

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Fig. 2.1 Classification of different markers

period of time. Linkage maps were created using morphological and allozyme markers in the 1980s. Allozyme markers were frequently employed in population genetics till the early 2000s (Liu & Furnier 1993). Over the past 40 years, progress in the field of molecular biology has supported the development of a variety of DNA-based molecular markers. The development of nucleic acid hybridization, polymerase chain reaction, and more recently DNA sequencing techniques led to a significant improvement in the resolution and utility

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Fig. 2.2 Popularity trend of different commonly used markers in the last four decades. Popularity has been considered based on the total number of citations (left panel) for each marker on Google Scholar and Internet

of DNA marker systems. The popularity of RNA-based (or cDNA-based, transcriptomic, or functional) markers is also increased due to the development of gene-based molecular markers (Grover and Sharma 2014). A popularity trend of different molecular markers is illustrated in Fig. 2.2 based on their citation number found online. Along with plant phenotyping, genotyping is an important step in finding agronomically desirable genes and studying population dynamics. The advances in PCR technology in the late 1990s and early 2000s prompted the development of DNA marker systems (Elshire et al. 2011). With the development of PCR techniques, the uses of molecular markers also evolved and that is why from the view of PCR involvement, molecular markers now can be classified under two headings, viz. PCR-based molecular marker and non-PCR-based molecular marker. Among the PCR-based markers, RAPDs, AFLPs, ISSRs, SSRs, and SNPs have gained broad acceptance. While non-PCR-based molecular markers are few, among them restriction fragment length polymorphism (RFLP) and minisatellites are popular. High-throughput and ultrahigh-throughput levels of molecular marker development and applications have been attained due to the recent breakthrough in NGS sequencing technology. Though microsatellites, SNPs, and genotyping by sequencing (GBS) substantially satisfy most user requirements, however, the selection of markers depends entirely on the purpose and research objectives. An evolutionary pathway of different markers is illustrated in Fig. 2.3.

30 Fig. 2.3 Evolution in developing of plant molecular markers

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PCR-based markers are the latest addition of molecular marker technology, and a variety of marker types are available under this category. The most widely used PCR-based marker is randomly amplified polymorphic DNA (RAPD). Besides RAPD, several others, e.g., inter-simple sequence repeat (ISSR), amplified fragment length polymorphism (AFLP), resistance gene analog polymorphism (RGAP), indels, sequence-characterized amplified region (SCAR), and single nucleotide polymorphism (SNP), are under this group. Each of them is highly specific and effective. The latest addition to this technology is Kompetitive allele-specific PCR also known as KASP, which is not a marker itself, but it is a cheap and highthroughput technique to detect precise markers like SNP marker.

2.3

Kompetitive Allele-Specific PCR (KASP)

In the last two decades, gene sequencing technology has developed immensely and plant genome data have become more available. Hence, single nucleotide polymorphism (SNP) has become very popular. SNPs are found in high density in the genome and are high throughput and also provide easy and automated analysis. A single nucleotide polymorphism (SNP) occurs when a single nucleotide in a DNA sequence varies between the members of the same species or a chromosome pair. SNPs work as a molecular marker and can be sued to identify the genes associated with traits (Chen & Sullivan 2003). Now, several high-throughput genotyping platforms have been developed by using SNPs; KASP is one of them. KASP is a high-throughput genotyping technology, which works by using SNPs and became very popular in recent years because of its high throughput, low costing, and strong usability (Thomson 2014). The technology has a great possibility in fields of crop improvement (Qingqing et al. 2022). KASP is a homogenous fluorescentbased genotyping type of polymerase chain reaction designed and developed by KBioscience, UK. KBioscience has almost 10 years of experience in the construction of related technologies for KASP and optimized the genotyping analysis using KASP. KBioscience not only created its own series of SNP genotyping tools, but they also have a stock of over 500,000 KASP assays that have undergone validation (Qingqing et al. 2022). Although there are several high-throughput genotyping techniques have been developed using SNPs, the use of these techniques highly depends on their costing, requirements, sensitivity, reliability, reproducibility, accuracy, and flexibility. KASP platform meets all these criteria and makes it more adoptable to its users. It is accurate, cost-efficient, reliable, and flexible. Multiplex technologies generate anywhere from 100 to over a million SNPs per run and are not economical to use for small-to-moderate numbers of SNPs (Semagn et al. 2014). For a smaller number of SNPs, KASP can be used easily.

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Principles of the KASP Technique

KASP is a fluorescence-based qPCR technology in which fluorescence-labeled primers are utilized to identify the targeted allele after an RT-PCR, and the result can be seen directly on the computer in the form of a fluorescence signal. Here, the creation of signal is based on the fluorescence resonance energy transfer and allelespecific oligo extension. Therefore, no gel electrophoresis is needed to be run for the detection and analysis of the results. The KASP genotyping assay uses Kompetitive allele-specific PCR to detect two alleles of an individual SNP (He et al. 2014). In this process, two allele-specific forward primers and a common reverse primer are used for the PCR where each of the forward primers has a specific tail at one end (Fig. 2.3). These primers are specific to alleles, and their tails are specific to fluorescent dye (FAM/HEX), which means which tail binds FAM (fluorescein amidites)-labeled FRET (fluorescence resonance energy transfer) cassette will not bind HEX (hexachlorofluorescein)-labeled FRET cassette and vice versa. Besides this, universal FRET cassette used in the PCR mix is remained quenched in the initial stage. During the thermal cycling, the allele-specific primers bind to their respective sites and elongate, and with this, the tail sequence also gets attached to the newly synthesized DNA strand. After subsequent rounds of PCR, complement for the allele-specific primer tail is also generated, hence enabling the FRET cassette to bind to the DNA. When the FRET cassette binds to the specific tail of the primer, the quencher particle got released and the dye emits the signal. Kompetitive allele-specific PCR achieves bi-allelic discrimination through the competitive binding of the two allele-specific forward primers. Only one of the two possible fluorescent signals may be generated, if the genotype at a given SNP is homozygous. If the genotype is heterozygous, a mixed fluorescent signal will be generated (LGC Genomics Application Guideline). The basic principle of KASP genotyping has been illustrated in detail in Fig. 2.4.

2.3.2

Requirements of KASP Assay

KASP is an easy and handy method of PCR operation. This method can be easily performed in an ordinary laboratory having a regular qPCR setup. Additionally, it does not require any gel electrophoresis instrument setup for analysis of the results. Every PCR method, viz. KASP, TaqMan, and Golden Gate, is unique because of their ingredients and reagents used, and the operating conditions and the operating setups. Performing KASP in the laboratory also demands certain prerequisites and conditions. The technology utilizes some particular assay mixture and reagents, maintains some specific thermocyclic conditions, and follows some specific steps. Combining these requirements properly provides a successful operation and an accurate result.

Kompetitive Allele-Specific PCR (KASP): An Efficient High-Throughput. . .

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Fig. 2.4 Basic principles of KASP technique

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2.3.2.1 Prerequisites of KASP Assay Being a PCR-based technology, KASP is very sensitive and specific. The success of the assay is largely depending on the quality and quantity of the DNA used as template. DNA that has been isolated from seed, leaf, root, stem, and flower can be used as templates. The amount of DNA needed varies depending on the size of the genome, where larger genome-sized species require more DNA to ensure sufficient copies of the genome present during the initiation of KASP. The KASP requires roughly 5–10 ng DNA templates for every SNP analysis. Here, the most crucial factor is the purity of DNA because contaminations might make PCR less effective. The most frequent contaminants that can impair PCR are polyphenols and polysaccharides. By using 10-phenanthroline, polyvinylpyrrolidone-40 (PVP-40), diethyldithiocarbamic acid, sarcosyl, and sorbitol, the impurities of DNA can be eliminated (Zhang and Stewart 2000). DNA can be extracted from a single plant or a large number of different plants depending on the research aims. DNA from a single plant per entry is preferred in marker-assisted recurrent selection. A large number of studies have employed the bulked technique, since genotyping of each and every individual plant per accession is costly and time-consuming (Reif et al. 2005; Warburton et al. 2010). For KASP genotyping, the bulk approach or DNA pooling (10–15 plants per sample before DNA extraction) is advised for single cross-hybrid, inbred lines, and bi-parental mapping populations. KASP typically performs well for both bulk method and single plant method; however, genotype plots are not usually sharper for the bulk approaches as in single plants. However, as KASP is not a quantitative assay, it is ineffective for crops, which are open-pollinating. To overcome this issue, DNA should be extracted from a single plant, or a minimum of three to four repeats of bulk DNA is advised (Semagn et al. 2014). 2.3.2.2 Thermal Cycle Reaction Components of KASP Assay In a thermal cycle reaction mixture of SNP-specific KASP assay, the universal KASP-TF Master Mix (Bioresearch Technologies) is used. The mixture is added to the DNA samples, and a thermal cycling reaction is performed, followed by an endpoint fluorescent read. The KASP-TF assay mix includes three assay-specific non-labeled oligos: two allele-specific forward primers and a common reverse primer. Each of the allele-specific primers carries a distinctive tail sequence that matches a universal FRET cassette, one labeled with FAM dye and the other with HEX dye. The KASP-TF Master Mix includes free nucleotides, MgCl2, Taq polymerase, and ROX passive reference dye, in an optimized buffer solution (KASP genotyping manual by LGC). Fig. 2.5 illustrates the thermal cycle reaction components of KASP assay. 2.3.2.3 Standard Thermal Cycle Reaction Conditions for KASP Assay Standard thermocycler suitable for quantitative PCR (qPCR) can be used to perform KASP assay. In most of the literature, we found similar PCR conditions for KASP assay. KASP chemistry utilizes a two-step touchdown PCR method, with the elongation and annealing steps programmed into a single step. A 15-min activation

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Fig. 2.5 Thermal cycle reaction components of KASP assay Table 2.1 Standard KASP thermal cycle reaction conditions Step Description 1 Activation 2 Denaturation Annealing/elongation 3 Denaturation Annealing/elongation

Temperature (°C) 94 94 61–55 94 55

Time Number of cycles in each step 15 min 1 20 s 10 1 min (drop 0.6 °C per cycle) 20 s 26 60 s

is necessary (94 °C) initially, followed by 10 cycles of touchdown PCR and 26 cycles of standard 2-step PCR (LGC standard KASP thermal cycle program guide). If the fragment length is below 120 bp, no additional extension step is necessary (Graves et al. 2016; Rasheed et al. 2016; Zhao et al. 2017; Kusza et al. 2018). The standard KASP thermal cycle conditions are briefed in Table 2.1.

2.3.3

Operational Steps of KASP Genotyping Technique

In principle, KASP technology is like a qPCR technology. KASP assay can be operated with a regular qPCR setup laboratory. The assay includes activation, denaturation, and annealing steps just like the other PCR steps. However, the

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Fig. 2.6 Operational steps of KASP genotyping technology

laboratory operation of KASP technology can be described in three steps. The operational steps of KASP genotyping are shown in Fig. 2.6, and a brief description of these steps is as follows.

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• The primers and probe designing are the very first step toward KASP assay. First, two forward primers are designed for the particular SNP. Each primer’s 3′ is modified in correspondence with the SNP allele (Rosas et al. 2014). For example, in Fig. 2.5, the 3′ end of the primer for allele 1 corresponds to A, and the 3′ end of the primer for allele 2 corresponds to C. In the next, a tag sequence is added to the 5′ end of each forward primer like the tag sequences 1 and 2 in Fig. 2.5. Fluorescent probes corresponding to the tag sequences such as probe 1 and probe 2 are designed in the subsequent task. A FAM fluorophore is added to the 5′ end of probe 1, and a HEX fluorophore is added to the 5′ end of probe 2 (Patterson et al. 2017). A quenching probe with a quenching group at the 3′ end was designed simultaneously, corresponding to the two probes. In actual practice, only common primers with 5′-terminal tag sequences are required to be designed for particular SNPs, and the probes are typically included in the KASP kit, e.g., LGC KASP-TF kit (Qingqing et al. 2022). • The second step is PCR amplification. In the first round of PCR amplification, allele-specific primer, which pairs to the 3′ end, identifies the specific allele template and completes allele identification (Fig. 2.5). From the second round of PCR amplification, the template carrying the universal tag sequence appears in the product, and this step completes the introduction of the universal tag sequence into the PCR product corresponding to the SNP. Then, in the PCR amplification process, the fluorescent probes are added to the PCR product by binding to the DNA strand complementary to the universal sequence. After multiple rounds of PCR amplification, more fluorescent probes anneal to the newly synthesized, unquenched on the complementary chain of the quenching group, and the fluorescence intensity of the PCR product is gradually enhanced. • The third step is fluorescence detection and analysis. A dedicated fluorescence signal detector or a common fluorescence quantitative PCR instrument (equipped with FAM and HEX detection channels) is used to read signals and uses computer software to collect signals and analyze allele types.

2.3.4

Why Is KASP Technique Being Used?

KASP is an adaptable, affordable, and precise technology based on qPCR and fluorescence detection methods. It meets the requirements of low-, medium-, and high-throughput genotyping using the regular laboratory facility. The costeffectiveness of the KASP approach is higher than that of any other multiplex methods, e.g., TaqMan resulting in $15 vs. $50 per assay (Semagn et al. 2014). Apart from the low costing, KASP is a rapid technique as compared to other multiplexed methods. Additionally, there is a lower genotyping error rate of KASP (0.7–1.6%) as compared to other multiplex methods, viz., TaqMan (Semagn et al. 2014). In KASP, both high throughput and automated detection can be obtained with flexibility. When compared with the TaqMan method, KASP is similar to TaqMan in concept as it is based on terminal fluorescence reading. However, KASP differs from

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Table 2.3 Comparison between KASP and TaqMan assays Point of comparison Costing (per SNP per sample)a Design success rate (%)b Detection failure rate (%)b DNA template needed (per 5 μl sample)b a b

KASP Generally, stays in the range of US$ 0.16–0.21 depending on the number of samples 49

TaqMan Generally, costs around US$ 0.68 per SNP per sample 39

6.5

7.0

20 ng

5 ng

Semagn et al. (2014) Ayalew et al. (2019)

the TaqMan method in that it utilizes a universal probe that is compatible with a variety of gene-specific primers rather than requiring the designing of probes for each individual site. Utilization of this universal probe in KASP significantly lowers the cost of reagents (Majeed et al. 2018). According to various findings, using KASP technology in commercial services is roughly 80% less expensive than those that use the TaqMan probe approach (Broccanello et al. 2018). Again, genotyping with KASP technology is simple and does not require any gel electrophoresis detection, hence does not need specialized equipment. Researchers may successfully do genotyping using standard qPCR equipment following the KASP technique. In addition, KASP outperformed TaqMan in terms of assay design, and the success rates were recorded in KASP 98–100% against TaqMan’s 72% (Semagn et al. 2014). Again, the assay translation into effective functioning assays of KASP ranged from 93% to 94% against TaqMan’s 61%. From the viewpoint of development trend, high throughput, lower expense, and easy handling are the main directions of molecular marker technology development, and the KASP detection technique meets these three requirements simultaneously. A comparison between KASP and TaqMan method is portrayed in Table 2.3. At present, KASP technology has been used broadly in genetic research and population of plants, animals, and humans (Hiremath et al. 2012; Chang et al. 2021; Landoulsi et al. 2017).

2.3.5

Application Fields of KASP Technology in Crop Improvement

KASP can be used to genotype a wide range of species for different purposes. CIMMYT frequently uses KASP for QC (quality control) analysis, QTL mapping, MAS, and allele mining applications that require SNP data ranging from a few to several hundred data points per sample. At CIMMYT, the systematic mining of vast germplasm collections for particular functional polymorphisms has been a key application of the KASP platform. In order to identify the accessions having

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favorable alleles at the targeted locus or loci, a large germplasm collection can be examined by the KASP platform using SNPs or small indels, which are either diagnostic or closely linked to such polymorphisms. This strategy is particularly helpful when phenotyping is laborious or hugely expensive due to a large number of individuals frequently screened in applied breeding programs. KASP has numerous uses in allele mining, marker-assisted selection (MAS), quantitative trait locus (QTL) mapping, and quality control (QC) genotyping. For instance, KASP has been utilized in qualitative control evaluations for maize at CIMMYT (Semagn et al. 2014). At present, KASP marker technology has also been used frequently in the identification of seed purity. Traditionally, the identification of seed purity, which mainly relies on phenotypic identification, is an inefficient method. With the continuous development of DNA molecular marker technology, scientists have begun to use different types of molecular markers to identify the authenticity and purity of seeds (Zhang et al. 2011). The KASP-based genotyping platform has made this process more accurate and more reliable.

2.3.6

Present Status of Utilization of the KASP Technique in Crop Improvement

Kompetitive allele-specific PCR (KASP) is a high-throughput genotyping technology developed on the basis of single-nucleotide polymorphism (SNP). Because of its high flux, low cost, and strong operability, this technology has great application potential in the field of crop improvement. KASP assay has been used in an increasing number of studies in recent years because of their high-throughput and low-cost properties to conduct out identification and application of germplasm resources, marker-assisted selection breeding, genetic map construction, and genome mapping (Qingqing et al. 2022). Some examples of applications of KASP in crop improvement are discussed herewith.

2.3.6.1 Wheat To examine the genetic diversity, Guo et al. (2020) collected 438 wheat accessions from ten agro zones. They developed 52 KASP markers and specify 47 genes subjected to grain yield, quality, adaptation, and stress resistance. The analysis revealed clear differences in winter and spring growth habits. Zhang et al. (2021) also identified 44 diverged genes using 44 KASP markers from 207 wheat lines associated with wheat grain yield, quality, adaptability, and stress tolerance. Roncallo et al. (2019) calculated the genetic diversity in 168 durum wheat genotypes collected from Argentina and other countries using 85 pairs of KASP markers. KASP technology has been used to identify the genes that control abiotic stress tolerance and disease resistance in wheat. Rasheed et al. (2016) developed and identified 70 KASP markers linked to common wheat adaptability, yield, quality, and resistance to biotic and tolerance to abiotic stress. They found that all KASP markers were significantly linked to related phenotypes in wheat cultivars and in bi-parental segregated populations. Their study also discovered that KASP markers

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had a detection efficiency that was 45 times more than that of conventional PCR markers based on gel electrophoresis. Later, Ur Rehman et al. 2021 developed 11 KASP markers and found 16 QTLs associated with drought tolerance in 153 wheat cultivars. In addition, Wang et al. (2020a) used three KASP markers for drought-tolerant genes and three KASP markers for preharvest sprouting (PHS) resistance genes in bread wheat. All these six pairs of markers effectively distinguished respective varieties that were resistant to germinate at the ear stage and easy to germinate at the ear stage. It also distinguished between the drought-tolerant and drought-susceptible varieties. Rust resistance is always wanted to wheat improvement. Qureshi et al. (2018) showed that Yr34 and Yr48 were the same genes for wheat stripe rust resistance by using 20 pairs of KASP markers. Fang et al. (2020) developed two pairs of KASP markers, viz. Lr34-E11-KASP and Lr34-E22-KASP, for the wheat leaf rust resistance gene Lr34. Wu et al. (2017) and Wu et al. (2017a) identified three QTLs related to stripe rust resistance on chromosomes 2BS, 3BS, and 6BL using 17 and 18 pairs of KASP markers, respectively. The QTL of chromosome 6BL was located between KASP markers, IWB71602 and IWB55937 within 1.4 cm interval. Fusarium head blight is a serious disease of wheat. Su et al. (2018) developed the TaHRC-KASP marker for Fusarium head blight (FHB) in wheat. They screened several Fusarium-resistant landraces using the marker and found resistant the Fhb1 gene of FHB. Zhan et al. (2021) used 20 KASP markers to find the wheat powdery mildew resistance gene PmCH7087 in the F2 segregated populations. They found the target resistance gene in the 9.68 Mb range, and the resistance is controlled by a single dominant gene. Khalid et al. (2019) used 124 pairs of KASP markers to analyze the allelic variation of 87 functional genes and found the target genes regulating the expression of important agronomic traits in wheat. Anuarbek et al. (2019) used 32 pairs of KASP markers to analyze tetraploid durum wheat germplasm and found that eight pairs of KASP markers were significantly related to five agronomic traits. Liu et al. (2021a) used the KASP marker for QTL mapping in F2 populations developed from the awnless cultivar ZLWM and “double-awn” wheat line 4045. Xiong et al. (2021) developed 25 KASP markers of 37 QTLs related to agronomic traits such as heading date, plant height, 1000-grain weight, and ear length. These KASP markers were used for MAS in wheat breeding.

2.3.6.2 Maize Using whole-genome resequencing data of maize inbred lines from various sources, Lu et al. (2019) identified a number of SNPs and developed 700 KASP molecular markers. From these, 202 KASP markers were chosen for further phylogenetic tree construction and population structure analysis and showed that 202 KASP markers play an important role in the analysis of germplasm resource, construction of genetic map, and division of heterotic group in maize. For quality control in maize, Ertiro et al. (2015) evaluated the level of genetic purity and identity among two to nine seed sources of 16 inbred lines using 191 KASP and 257,268 GBS markers and found that the correlation between the 191 KASP and 257,268 GBS markers was 0.88 for purity and 0.93 for identity.

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Jagtap et al. (2020) used RNA-sequenced data to find heat stress response (HSR) genes in maize and created 100 KASP markers. They found that 71% of the markers were polymorphic and 21% of the markers only produced one gene. Jagtap et al. (2020) used RNA-sequenced data to find heat stress response (HSR) genes in maize and developed 100 KASP markers. They found that 71% of the markers were polymorphic and 21% of the markers only produced one gene. The remaining 8% of the genes, or solely heterozygous genes, were unable to produce any effective amplification signals. Nair et al. (2015) precisely mapped the maize stripe resistance gene Msv1 by using 156 KASP markers in the F2 population of CML206 and CML312 cultivars. In Africa, maize lethal necrosis (MLN) is a significant disease that can cause yield losses of up to 100% (Awata et al. 2021). In this context, Awata et al. (2019) created 500 KASP markers that could influence the MLN phenotype and discovered at least seven anti-MLN QTLs by examining the F3 populations. Stalk fracture caused by strong wind severely reduces yields in maize. Wang et al. (2020b) discovered two candidate genes Zm00001d039769 and Zm00001d039913 on chromosome three related to high stem breakage angle affecting stem flexibility and lodging resistance. Two KASP markers were developed for two candidate genes found to be significantly related to the culm breaking angle.

2.3.6.3 Rice Shao et al. 2020 find out an allelic variation on the Wx gene that regulates the amylose content of rice using the resequencing data of the Rice Genome Project (RGP) and successfully developed six KASP markers. These markers were capable of distinguishing all existing Wx allelic material. With the help of these markers, they carried out Wx genotyping on the hybrid parental varieties. Additionally, Yang et al. (2019a) genotyped 38 indica cultivars, nine japonica cultivars, and one Javanica cultivar using three KASP markers to identify the genes, Wx, BADH2, and ALK. Addison et al. (2020) created nine KASP markers to study SNPs for aroma gene Badh2, in 2932 rice varieties, and also tested various American rice materials to identify Hap6 in American aromatic rice germplasm. In the same year, Li et al. (2020) created two pairs of allelic variants linked to Badh2-E2 and Badh2-E7 and used KASP markers to identify fragrant allele in rice germplasm. They showed that the majority of the aromatic rice contained the Badh2-E2 and Badh2-E7 alleles. Pariasca-Tanaka et al. (2014) developed 1225 polymorphic KASP markers based on SNPs for 12 Korean temperate japonica rice varieties. These 1225 KASP markers were evenly distributed across the rice genome, with an average marker density of 3.3 KASP markers per Mbp. Cheon et al. (2020) created 771 KASP markers and used them to construct genetic maps and analyze QTLs for disease resistance and panicle germination resistance in Korean temperate japonica rice varieties. Anaerobic germination (AG) is an important trait for direct-seeded rice (DSR). Ghosal et al. (2019) found 170 and 179 polymorphic SNPs from two F2 populations and develop KASP marker to identify SNPs and QTL related to anaerobic germination. They found that five SNPs and four QTLs were co-related with anaerobic germination in rice. Again, aerobic rice production (AP) is an efficient method of reducing water use in rice cultivation, and the narrow root cone angle (RCA) gene is

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regarded as a key component of aerobic rice production. qRCA4 plays an important role in RCA variants (Vinarao et al. 2021). Vinarao et al. (2021a) used nine pairs of KASP markers to construct three F2 populations with different genetic backgrounds. Lei et al. (2020) detected a major QTL associated with relative aerial length (RSL) on chromosome 7: qRSL7 and developed 25 pairs of KASP markers near QRSL7 based on SNPs.

2.3.6.4 Legume Crops Ongom et al. (2021) carried out molecular fingerprinting and heterozygosity identification on 1436 F1 population obtained from 225 cross combinations using 17 KASP markers in cowpea. Wang et al. (2020a) evaluated polymorphism in 94 lentil germplasm by using 78 KASP markers. 2.3.6.5 Horticultural Crops Yang et al. (2020) developed 53 KASP markers to genotype 34 samples of Chinese cabbage to categorize them into three classes based on the heading type. By whole-genome sequencing analysis, Shen et al. (2021) identified 100 SNPs for KASP marker development and examined 372 broccoli germplasm using these markers. Wang et al. (2022) screened out 46 KASP markers from a collection of grapes. From the markers, 25 pairs successfully used to distinguish this desired germplasm of grapes. Fleming et al. (2022) used KASP technology to identify bacterial spot-resistant allele in peach fruit and found two suitable alleles Ppe. XapF1-1 and Ppe.XapF6-2, respectively. Winfield et al. (2020) used 350 F2 population of Honeycrisp and Qinguan varieties of apple to find out drought stress QTL and developed three KASP markers. They successfully found 28 candidate genes, which regulate water usage efficiency in apples. Peng et al. (2021) designed KASP markers for tomato yellow mosaic virus disease-resistant gene Ty-1 and root-knot nematoderesistant gene Mi-1, which were combined with the previously reported tomato mosaic virus disease-resistant gene Tm-22, tomato spotted wilt virus gene Sw-5 (Devran and Kahveci 2019) and Fusarium neck rot resistance gene Frl. Jingjing et al. (2019) studied 130 watermelon materials and developed three KASP markers for resistance to Fusarium wilt, anthracnose, and powdery mildew. Through whole-genome resequencing of 106 diverse soybean lines, Cheng et al. (2017) identified three resistance candidate genes related to soybean on chromosome 6. They developed KASP markers for genes Glyma.06g206900 found in different genetic populations. Scholten et al. (2016) genotyped interspecific three-way crosspopulations in onion and developed 1100 KASP markers. They found Botrytis squamosa resistance locus on chromosome 6 using these KASP markers. Burow et al. (2019) developed 15 pairs of KASP markers to study the allelic variation of bmr6 and bmr12 in the seedlings of sorghum. Kante et al. (2018) bred three F2 segregating populations of West African sorghum POP CD (CK60A DT-298), POP FD (FambeA DT-298), and POP FL (FambeA Lata). They developed 11 KASP markers and found seven QTLs associated with fertility restorer gene Rf. Ma et al. (2021) developed KASP markers for the identification of persimmon germplasm.

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Table 2.2 List of KASP applications in the improvement of different crops Crop Wheat (Triticum aestivum)

Field of application Identification of germplasm, markerassisted selection, and construction of genetic map

Maize (Zea mays)

Identification of germplasm, markerassisted breeding, construction of phylogenetic tree, and gene mapping

Rice (Oryza sativa)

Identification of germplasm, markerassisted selection, QTL analysis, and construction of gene map

Cowpea (Vigna unguiculata) Lentil (Lens culinaris) Chinese cabbage (Brassica rapa) Broccoli (Brassica oleracea) Grape (Vitis vinifera) Peach (Amygdalus persica) Apple (Malus pumila) Tomato (Lycopersicon esculentum) Watermelon (Citrullus lanatus) Soy bean (Glycine max) Onion (Allium cepa)

Molecular marker-assisted breeding

References Guo et al. (2020), Rasheed et al. (2016), Qureshi et al. (2018), Fang et al. (2020), Liu et al. (2021b), Zhan et al. (2021), Xiong et al. (2021), Babiker et al. (2016), Neelam et al. (2012) and Wu et al. (2017) Yan et al. (2019), Jagtap et al. (2020), Nair et al. (2015), Heckenberger et al. (2005), Awata et al. (2021), Awata et al. (2019) and Wang et al. (2020a) Shao et al. (2020), Yang et al. (2019b), Addison et al. (2020), Li et al. (2020), Cheon et al. (2020), Ghosal et al. (2019), Vinarao et al. (2021), Lei et al. (2020), Kang et al. (2019), Steele et al. (2018), Yu et al. (2019) and Pariasca-Tanaka et al. (2014) Ongom et al. (2021) and Benoit et al. (2016)

Germplasm identification and marker-assisted selection Germplasm identification and gene map construction

Yang et al. (2020), Shuangjuan et al. (2021) and Shuangjuan et al. (2018)

Germplasm identification and DNA fingerprinting

Shen et al. (2021) and Shen et al. (2020)

Germplasm identification

Wang et al. (2020a)

Molecular marker-assisted breeding

Fleming et al. (2022)

Marker-assisted breeding

Winfield et al. (2020)

Marker-assisted breeding

Devran and Kahveci (2019) and Peng et al. (2021)

Germplasm identification and marker-assisted breeding

Cao et al. (2021) and Jingjing et al. (2019)

Gene mapping

Cheng et al. (2017), Yuan et al. (2017) and Wang et al. (2018) Scholten et al. (2016)

Genetic map construction and gene mapping

Wang et al. (2020b)

(continued)

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Table 2.2 (continued) Crop Sorghum (Sorghum bicolor) Persimmon (Diospyros kaki) Potato (Solanum tuberosum) Pepper (Capsicum annuum) Sunflower (Helianthus annuus) Peanut (Arachis hypogaea) Coffee (Coffea arabica) Gourd (Lagenaria siceraria) Cotton (Gossypium hirsutum) Barley (Hordeum vulgare) Radish (Raphanus sativus) Chickpea (Cicer arietinum)

Field of application Gene mapping and marker-assisted breeding

References Burow et al. (2019) and Kante et al. (2018)

Germplasm identification

Ma et al. (2021)

Molecular marker-assisted breeding

Kaiser et al. (2021) and Kante et al. (2021)

Molecular marker-assisted breeding

Holdsworth and Mazourek (2015)

Molecular marker-assisted breeding

Gascuel et al. (2016) and Radanović et al. (2022)

Germplasm identification and marker-assisted breeding

Khera et al. (2013) and Leal-Bertioli et al. (2015)

Germplasm identification

Akpertey et al. (2021)

Germplasm identification and marker-assisted breeding

Wang et al. (2021)

Marker-assisted breeding

Zhao et al. (2021)

Marker-assisted breeding

Genievskaya et al. (2022) and Poland et al. (2012)

Marker-assisted breeding

Kim et al. (2019)

Genetic map construction

Hiremath et al. (2012)

KASP technology has been used in breeding programs, identification of germplasm resources, genetic map construction, and gene mapping in potato, pepper, sunflower, peanut, coffee, and many other crops. The application of KASP in different crops is listed in Table 2.2.

2.3.7

Advantages of KASP Genotyping Technique

Kompetitive allele-specific PCR (KASP) is a homogenous, fluorescence-based gel-free high-throughput single-plex SNP genotyping platform. It is flexible,

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cheap, and accurate. Additionally, it is based on conventional PCR and fluorescence detection method and can meet the requirements of low-, medium-, and highthroughput genotyping on the basis of ordinary laboratory operations. With certain flexibility, it is an easier method to achieve high-throughput and automated detection (Semagn et al. 2014). On the other hand, it can be also used as a substitute for TaqMan and Golden Gate methods. It provides several privileges to the users for a wide range of works. The main advantages of KASP genotyping technology are mentioned as follows. • The best feature of KASP technology is that it is a gel-free system, that is why its result can be seen directly via computer software. It eliminates the hassle of gel electrophoresis and saves time. • Another notable point of KASP genotyping is the low costing. It has been found that KASP gives high-throughput results in 30–45% cheaper budget compared to other techniques, i.e., TaqMan and Golden Gate. The average cost for any other multiplex chip-based methods is around $50 per assay, whereas the KASP costs around $15 per assay with the same output results. In commercial practice, the KASP technique is found to cost 80% lesser in comparison with that of the TaqMan probe technique (Semagn et al. 2014). • The KASP technique is also time-saving. Nearly, all multiplex methods take around 6–7 weeks to deliver the results, whereas the KASP gives the results just after 2 weeks. As an example, KASP assay costs about $15 per assay and the results of the assays can be delivered within 2–3 weeks, while the Golden Gate assay costs about $42 and takes about 6 weeks to provide the results (Kumpatla et al. 2012). • Despite being cheaper and less time-consuming, the accuracy and reliability of KASP are relatively higher than the other methods. It has a lower genotyping error rate of 0.7–1.6%, which is lower than other commonly used techniques. Comparing KASP with the chip-based Illumina Golden Gate platform, genotyping error of KASP in positive control DNA samples was 0.7–1.6% in most cases, which was lower than the results found with Golden Gate (2.0–2.4%), with a higher high accuracy (Semagn et al. 2014). • Another advantage of KASP is that it is found with higher assay design success rates (98–100%) and translation into successful working assays (93–94%) compared with that of TaqMan’s 72% and Golden Gate’s 61%, respectively (LGC Genomics Application Note). • The KASP method is more flexible than other methods for which it can be used easily, when there are many SNPs in a lesser sample or when there are lesser SNPs in large samples. Multiplex technologies generate roughly from 100 to over a million SNPs per run, which are not suitable in economic point to use for smallto-moderate numbers of SNPs. For a smaller number of SNPs, KASP can be used optimally. • Another flexibility of the KASP technique is that it can be performed with an ordinary qPCR setup, which frees it from excessive cost involvement and the need for unique laboratory setup.

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Bottleneck of KASP Genotyping Technique

KASP is a cheap and rapid approach compared to other PCR techniques; however, the system has some flaws as well. Like other methods, KASP also has some bottlenecks in practical use. • When it comes to the question of the usage of molecular markers, conventional markers like SSR are still the most popular labeling approaches employed by most researchers (Miah et al. 2013). Although the available SNP data are incredibly rich, it also includes a great deal of redundant data. On the other hand, KASP is the best suited for either SNP or InDel markers, which makes it ineffective when there is no information about the presence of any SNP or InDel markers. • Secondly, as far as the technology itself is concerned, due to the restriction of the SNP site, the primer designing for KASP will be restricted by the sequence near the SNP, so that the designed primers cannot be effectively used for PCR amplification (Rosas et al. 2014). • Despite being a high-throughput genotyping assay, KASP has an error rate of between 0.7% and 1.6% (Semagn et al. 2014). When the mutant allele is present in very small amounts, KASP is probably not appropriate. It is challenging to distinguish between an individual or sample, carrying mutant or wild-type DNA, when there is less than 5% of the mutant allele relative to the wild-type allele (Majeed et al. 2018). • Another limitation of the KASP method is that only two alternative alleles at any specific site can be identified due to the use of individual quencher oligos for each allele-specific primer at a FRET cassette. However, planning KASP for the detection of three or more alleles at the same site and allele identification at different polymorphic sites is possible only in multiplexed reaction.

2.3.9

Prospects of KASP Technology

KASP is a promising technology in modern biological research. It has been used in crop improving, viz. rice, wheat, maize, cowpea, lentil, soybean, apple, watermelon, peanut, and coffee, and is also used in improving other different agronomical and horticultural crops. CIMMYT is using this technology on regular basis for wheat and maize improvement. Scientists are using the technology in improving rice especially to develop resistant and tolerant varieties at IRRI. It is also being used in gene mapping, germplasm identification, and for marker-assisted breeding. The popularity of this technology is increasing with the abundance of more accurate SNP data. Today, several research institutes, e.g., CIMMYT and BRRI, routinely use the KASP platform, producing in excess of humungous data points yearly for breeding cultivars and for medical and commercial purposes. Its uses will be increased in the future as it offers a cost-effective and high-throughput molecular marker development platform. Apart from the agricultural sector, several other sectors like medical and animal health sectors are also using this technology. KASP is being used in

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commercial egg-laying chicken breeding programs. Researchers have also looked for polymorphisms in the exons 2–6 of the gene encoding the eggshell protein ovocalyxin-32 (OCX32), which is linked to the firmness and thickness of the eggshell. KASP assay was recently utilized to isolate SNPs in several neurological disorders like Alzheimer’s disease (rs75932628 of TREM2) (Benitez et al. 2014), rs1476679 of ZCWPW1 gene (Allen et al. 2015), and both rs7412 and rs429358 for apolipoprotein E genotyping (Keogh et al. 2017), Parkinson disease (Landoulsi et al. 2017), amyotrophic lateral sclerosis (Fogh et al. 2013), polyglutamine diseases (Bettencourt et al. 2016), and genetic generalized epilepsy (VRK2 rs2947349 (“Genetic determinants of common epilepsies: a meta-analysis of genome-wide association studies” 2014). Thus, it is evident and quite clear that this platform will expand more in the medical field too. In the future, KASP would be a very useful platform among researches in the molecular biology field.

2.4

Conclusions

At present, molecular breeding has been proposed and implemented as an efficient and precise breeding technology and KASP genotyping platform has been successfully implemented for this purpose. This technology has prospective application potentials in the field of crop improvement. The attractive features of KASP already had drawn the attention of researches, and researchers around the world have started to use this technology in various fields. With good abundant gene sequence information and increase in the number of KASP molecular markers, this technology would be an important auxiliary method for research studies in germplasm resource identification, QTL analysis, population structure analysis, and marker-assisted breeding programs.

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Marker Assisted Recurrent Selection for Crop Improvement Suvarna, K. Ashwini, and R. Yashaswini

3.1

Introduction

For any crop improvement programme, the first requirement is the genetic variation existing in that crop germplasm. So, exploitation of variation present or if not variation available then creation of genetic variation is the prime most importance, since it paves the way for selection and other breeding activities, those improves or generates new cultivars. Selection is defined as a process by which some individual plants possessing certain characteristics are chosen at the expense of others to become the parents in the subsequent generation. Selection is effective only for the variation which is heritable. No amount of selection can lead to improvement, if the preferred individuals look to be superior to unselected individuals only on account of the environmental effects and not due to their genetic constitution. Selection affects the genotypes rather than the genes directly by altering their relative contribution to the progeny as a result of which frequency is changed under the influence of selection. Selection itself does not create a new gene, but produces new gene combinations that may occur as a result of recombination through cross-pollination either artificial or natural and/or natural and induced mutations which can be picked up and maintained by selection. So, the presence of variability in general and its extent is necessary for selection to succeed. Recurrent selection aims to improve quantitatively inherited qualities by steadily enhancing the population’s frequency of beneficial alleles. Several experimental results show that recurrent selection extends the response to selection and successfully improves breeding populations (Sprague and Eberhart 1977; Hallauer and Miranda 1988; Hallauer 1992). Indirect selection for potential QTL alleles can be achieved by utilizing the relationship among alleles present at quantitative trait loci Suvarna (✉) · K. Ashwini · R. Yashaswini Department of Genetics and Plant Breeding, College of Agriculture, Raichur, University of Agricultural Sciences, Raichur, Karnataka, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_3

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(QTL) and alleles of molecular marker loci. There has been considerable interest in both plants and animals in the possibility of using DNA markers in individual or mass selection (Smith 1967; Edwards et al. 1987; Stuber et al. 1987; Zehr et al. 1992; Edwards and Johnson 1994). Some reports (Edwards et al. 1987; Stuber et al. 1987; Lande and Thompson 1990; Edwards and Johnson 1994; Knapp 1998) supported that marker-assisted selection might significantly improve the breeding effectiveness based on their research results. Improving inbred lines instead of populations was the primary focus of previous marker-assisted breeding efforts in agricultural plants. Consequently, most QTL mapping research in plants begins with crossings between inbred lines, from which marker-QTL connections in backcross, F2, or in other populations are identified (Edwards et al. 1987; Stuber et al. 1987). However, F2 individuals’ marker genotypes are determined, whereas F3 (S1) progenies or test-crossed offspring of F2 individuals together with an inbred line will be used for recording phenotypic characteristics (Stromberg et al. 1994; Ribaut et al. 1997). Evaluations of genic markers are deferred until the F2 in some designs: F2:S4 or S3 lines (which are the results of F2 plants continuously selfing until S3 or S4 generation), whereas phenotypic characters are measured on F2 or F2:S3: progeny from S4 testcross (Zehr et al. 1992; Stromberg et al. 1994; Eathington et al. 1997a, b). By choosing various combinations of favourable alleles, pedigree selection and marker-assisted backcrossing could be an efficient strategy to boost the breeding value of lines with a comparatively smaller number of genes from a few excellent parental lines. (Ragot et al. 1995; Hospital and Charcosset 1997). However, this shortly becomes impossible when the number of QTL alleles increases. Most of the time, there will be a lot of factors that control quantitative traits, especially in studies to advance multiple characters. Consequently, marker-assisted recurrent selection is an effective strategy for enhancing the frequency of desirable alleles in the population. The recent advancements in statistical tools for outbred populations, QTL mapping, is now possible in any population (Xu and Atchley 1995; Knott et al. 1996; Hoeschele et al. 1997). For breeding programmes involving markers, this includes the choices available to breeders when selecting parents. Heritability of the attributes, number of families, marker coverage in the genome, established markerQTL connections, family sizes, type of population, and choice of various markerassisted schemes all affect how effective MARS is in comparison to traditional phenotypic recurrent selection (PRS). (Lande and Thompson 1990; Edwards and Page 1994; Gimelfarb and Lande 1994; Knapp 1998 and Xie and Xu 1998a). A theoretical contrast may offer breeders with some sort of guidelines for selecting a selection strategy, despite the difficulty of experimentally comparing these factors. In the 1990s, MARS was established (Edwards and Johnson 1994; Stam 1995), which takes into account markers at every generation to target all features that are crucial and for which genetic information can be gathered. Usually, experimental populations’ QTL analyses provide genetic data, including the locations and effects of QTL. While QTL mapping is done using a biparental population, both parents frequently contribute to advantageous alleles. As a result, the ideal genotype is a mosaic of chromosomal fragments from both parents. Obtaining people with the

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most accumulated desirable alleles is the goal of MARS. The ideal genotype, which is the mosaic of beneficial chromosomal segments from two parents, will, however, typically never occur in any Fn population of realistic size (Stam 1995). As it was previously mentioned, a breeding technique that uses individuals from the experimental population to produce or come close to this ideal genotype could involve several successive generations of crossing individuals (Stam 1995; Peleman and Van Der Voort 2003). This technique is known as MARS or genotype construction. This concept can be extended to circumstances in which advantageous alleles are inherited from more than two parents. Please remember that MARS can be initiated even in the absence of QTL data, with selection being based on significant marker– trait associations discovered throughout the MARS procedure. In general, MARS outperforms phenotypic selection in the accumulation of favourable alleles in a single individual, as shown by all simulation studies (Van Berloo and Stam, 1998 and 2001), and MARS is superior to phenotypic selection by 3–20% (Van Berloo and Stam 2001). MARS is preferable to phenotypic selection when the population under selection is larger or more heterozygous, such as the BC1 and F2 populations. MARS can be used to select particular characteristics, such as yield under water stress, but it should also consider a number of other characteristics that the breeder may be interested in (like yield under ideal conditions, resistance to disease, standability, and maturity). In order to find characters at a certain locus that are negatively correlated, and so that all of these variables can be taken into account when choosing the final alleles to recombine. Breeders use MARS to create elite lines with the appropriate mix of favourable alleles originating from both the parents by utilizing QTL data obtained from their populations of interest. Breeding populations contain the QTL alleles that breeders are interested in, and these alleles are gained through successive intercrossing with just genotypic selection. The best varieties are chosen for release after a final phenotypic screen on the recombined lines. This enables the generation of children with the best possible combination of essential alleles from both parents, which is not feasible by recombination alone.

3.2

Principle of MARS

The foundation of MARS is the simple notion that by recombining quantitative features with QTL effects that are specific to that population, superior progenies for varietal development can be produced. The superior lines are then recombined to produce the genotypes that perform better than their parents at the end of the selection process, once the de novo QTL identification is completed in each population of interest. The desirable alleles can be fixed at an initial stage of recombination, if one out of the two parents has a significant QTL (historical information or de novo identification), the same as for quality traits or biotic stress resistance. The accumulation of this type of information across a several backgrounds (and haplotypes for given loci) is one way to associate breeding values with individual haplotypes, eventually leading to breeding-by-design strategies, despite the fact that the genetic effects of minor QTL may be more specific to a particular genetic background.

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General Outline of Mars

The MARS steps are explained as follows.

3.3.1

Parental Choice

MARS performs best in populations that result from best-to-best crossings, which are between best parental lines which will be successful in a conventional breeding programme. To limit variability in yield testing, use parents with comparable maturities. Segregation based on the characteristics like height or maturity should not be considered for the evaluation of yield. To concentrate the MARS project on the best populations, more crosses between various parents would probably be a good idea. To detect sets of polymorphic markers spaced on average every 10–20 cm, fingerprint each parent.

3.3.2

Population Development

The desire to advance as quickly as possible to the phenotyping stage for QTL analysis and the desire to acquire relatively fixed progenies are often well-balanced in F3-derived populations. MARS does not require particularly mature populations. In this instance, progenies are advanced to the F3 generation using the single-seed descent method, and single F3 plants are selfed to produce F3:4 or F3:5 progenies, depending on how many seeds are required for multi-location yield testing. The breeder’s preference for precision in QTL mapping will determine the population’s size, which can range from 200 to 400, with 300 serving as a good starting point until more information is available about the ideal population size for the breeder’s requirements. Generally, the population size is made to fit a 96-well plate design, so it would be numerous of a given number (92, 94, or other) fitting in that configuration. Before being evaluated in the field, each F3-derived progeny will need to be test-crossed to a common tester in hybrids like maize.

3.3.3

Genotyping

MARS does not require a more marker density, as less recombination occurs during the derivation of the F3 population. In most cases, it should be sufficient to have markers that cover the whole genome and have about 10 cm mean distance between them. SNPs or SSRs can be used, but the extension to multiple MARS projects will be greatly facilitated by SNPs. In order to quickly select a specific group of SNPs that are polymorphic for a provided MARS population, it is best to fingerprint the parental breeding germplasm with a reasonably high density of SNPs (1000–2000) prior to using MARS on a large scale. DNA material is taken directly from the F3 plants (just a few leaf punches for SNPs) or from F4 progenies from each F3, if

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additional leaf sample is desired (for instance for SSRs) or if sampling could not be completed at the F3. The samples are then genotyped at the polymorphic loci identified from screening of parents.

3.3.4

Phenotyping

Then, replicated designs are used to conduct multi-location field experiments in order to better evaluate the desired features. The effectiveness of MARS depends on accurate plant phenotyping. Possibilities for negative correlations with goal traits can emerge from the opportunistic analysis of non-target characters that are segregating within the population.

3.3.5

QTL Analysis

There are numerous QTL analysis techniques available for identifying QTL for our target attributes. Final QTL selection frequently benefits from the use of a selection index where different essential features are given varying weights. The breeder will ideally examine many models to compare the outcomes and select the QTLs to recombine.

3.3.6

Recombination Cycles

A few sets of F3-derived progenies choose to participate once a few important QTLs have been identified in the view of their complementarity for the existence of advantageous alleles and their phenotypic performance. A number of individual plants (F4 or F5, depending on which is best for that crop) are grown and genotyped from each progeny in order to choose the best ones to be used in recombination crosses (nearest marker to the QTL peak or flanking markers). Crossing four pairs of offspring (eight lines) first, then the two pairs of F1s that will appear in the second cycle, and lastly the two F1s in the final cycle would be a typical situation (Fig. 3.1). The F1s are genotyped at each stage, and the best ones are used for the subsequent recombination cycle. In the end, the lines that come out are selfed a few times so that they will be stable. In order to maintain variability at the unselected loci for the final phenotypic assessment prior to variety release, a few different independent sets of parental progenies should be used, as should the retention of several progenies from the last recombination cycles. Each intermediate recombination phase can likewise be used to create new lines. Depending on the crop (ease of crossing, number of offspring produced each cross, cycle duration, etc.), the number of loci to recombine, and the breeder’s preference, several recombination procedures are employed. So, it is hard to give a one-size-fits-all recommendation. Through the molecular breeding

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Parent A x Parent B Population Development

F1 F2

Single seed descent

F3

200-300 F3 progenies

F3:4

200-300 progenies

Recobmination

F3:5 (if needed) Multilocation phenotyping 8 plants/family (A-H), 4 sets of 8 families/cross

1st Recombination cycle 2nd Recombination cycle rd

Population Development

3 Recombination cycle

A xB F1

C xD

Ex F

x F1

F1

F1

x

GxH x

F1

F1

F1 F2 F3

F3:4 Multilocation phenotyping The best lines release as varieties

Fig. 3.1 The mechanism of MARS scheme. (Source: Eathington et al. (2007))

platform, software that will assist breeders in this process is being developed. They should be ready for our users by the time it is needed.

3.4

Strategies of Marker-Assisted Recurrent Selection (MARS)

There are different strategies for MARS. The choice of strategy depends on various factors. Theoretical comparisons of factors give some guidelines as experimental comparison becomes costly. Xie and Xu (1998a) researched the genetic features of expected responses from several marker-assisted recurrent selection approaches and provided a detailed explanation of the MARS tactics. They contrasted the effectiveness of MARS with that of marker-aided mass selection (MS) and phenotypic recurrent selection (PRS). Marker-assisted selection (MS), full-sib families (FSF), half-sib families (HSF), test cross progeny (TC), St families (S˷F) and combined HSF selection with within-family MS (CSHS) and combined FSF selection with within-family MS (CSFS) were the strategies used in their investigation.

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Cost Efficiency of Marker-Assisted Recurrent Selection (MARS)

1. Selection for single trait: Regardless of the heritability of the variable, MARS was always more cost-effective than phenotypic selection if the cost of genotyping was lower than the cost of evaluating one individual in one plot (Moreau et al. 2004). 2. Simulating simultaneous selection for multiple traits: MARS would be more costeffective than phenotypic selection if the cost of genotyping was lower than the cost of phenotyping one individual for all characteristics (Xie and Xu 1998b).

3.5.1

Factors that Control MARS Efficiency

3.5.1.1 Population Size Submitted to Selection at Each Cycle Expanding the population size should lead to a higher genetic gain through MARS (Van Berloo and Stam 2001). Populations subjected to selection in private programmes are presumably far bigger than the 160 and 300 individuals reported by Openshaw and Frascaroli (1997) and Moreau et al., respectively (2004) . 3.5.1.2 Use of Single Versus Flanking Markers Using flanking markers for QTL under selection improves genotype prediction at the QTL in comparison to using single markers. The predictive value of a single marker decreases when a single marker is used because recombination events between the marker and the QTL cause the marker and the QTL to lose linkage much more quickly than when flanking markers are used. 3.5.1.3 Pre-Flowering/Early Selection Choosing plants before they flower ensures the best mating techniques, because the genotypes of the plants being selfed or intercrossed are totally known. This is not the case, however, when selection is impossible before flowering and requires intercrossing the genotypes of selected plants’ selfed progeny, whose genotypes may have significantly deviated from those of their genotyped parents. 3.5.1.4 Number of Generations per Year Despite having a direct correlation with the rate of genetic gain, cycle length has not been taken into account in any simulations or experimental studies of MARS. When adopting marker-assisted recurrent selection instead of phenotypic recurrent selection in maize, cycle length can be shortened by a factor of three to six. So until the genetic gain from one cycle of MARS is larger than a third or a sixth of the genetic gain from one cycle of phenotypic selection, respectively, marker-assisted recurrent selection will be preferred to phenotypic selection. Nurseries are accessible to private maize breeding programmes during the off-season. Furthermore, they often constructed productive continuous nurseries where three to four generations of maize could be grown each year. In contrast to phenotypic recurrent selection,

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which can only carry out a maximum of one cycle each year, the usage of such nurseries enables them to carry out MARS continually. Even if MARS does not offer any advantages over phenotypic selection on a per-cycle basis, its effect on the pace of genetic gain could still be quite favourable.

3.5.1.5 Price of Marker Data Points Big commercial companies have worked hard to lower the price of experimental field plots as well as marker data points. Large private breeding programmes typically have a lower ratio of experimental field plot cost to marker data point than the majority of governmental research institutions or small private programmes, which could influence opinions on the economic effectiveness of MARS.

3.6

Software Package for MARS Analysis

The software OptiMAS (GUI) is used for MARS analysis (Fig. 3.2). OptiMAS Mainly Deals with Three Steps of MARS • Estimation of genetic values by computation of genotypic probabilities. • Individuals will be selected, and crosses will be identified for making among the selected individuals. • Decision support tool is an important tool that will be used to conduct markerassisted selection programmes. – Estimation of genetic values by computation of genotypic probabilities. This tool offers each candidate the probabilities of being homozygous or heterozygous for individual founder alleles as well as the favourable and unfavourable alleles for each QTL, depending on the categorization of founder alleles into favourable and unfavourable categories. Genetic value/molecular score will be predicted for each individual for each QTL. For each QTL, different weights can be assigned. The average molecular scores for each cycle of selection are automatically plotted on a graph to indicate their evolution. – To select candidates, the simple options are available. The molecular score is used to make truncation selection. Weighted molecular score offering different QTLs of more or less importance. A utility criterion that selects candidates based on the likelihood that their progeny would have a better genotype. A manual selection. Histograms are used to compare lists of individuals. Selected plant pedigree can also be checked by an imagining tool facility. – Implemented schemes for the detection of crosses between selected individuals.

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Fig. 3.2 Software OptiMAS (GUI) for MARS analysis

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Complete: all possible combinations of crosses between the Nsel best individuals will be made. Better-half: the crossing between the last selected genotypes will be avoided (Bernardo and Charcosset 2006). Manual selection: selection of different pairs of parents for crossing will be possible.

3.7

Application of MARS in Different Crops

MARS is used to improve the breeding lines in different crops. The list of traits improved is given in Table 3.1. Table 3.1 Application of MARS in trait improvement of different crops Sl. no. 1

Crop Maize

Traits improved Grain yield, grain moisture during harvest, and abiotic stress adaption (early vigour under cold conditions). Genetic gain achieved through MARS was almost twice than that through phenotypic selection The frequency of the favourable marker allele sweet corn F2 population was increased by MARS Improved stover quality and grain yield

2

Maize

3

Maize

4

Maize

5

Maize

6

Maize

7

Maize

8 9

Maize Maize

10 11

Upland cotton Rice

12 13 14

Wheat Wheat Sorghum

Improved grain yield in sub-Saharan Africa under drought stress and non-stress situations Improved grain yield in sub-Saharan Africa under drought stress and non-stress situations Genetic gain of the population for grain yield improved under stress and non-stress conditions Improved grain yield using biparental population Improved provitamin-A content in two tropical maize synthetics Genetic gain in yield and resistance to Helicoverpa armigera Improved the maintainer line population of genetic male sterility for drought and salinity tolerance Bread-making related traits Crown rot resistance of bread wheat Drought tolerance

15 16

Sorghum Chickpea

Grain quality Enhancement of drought tolerance

References

Eathington et al. (2007) Mayor and Bernardo (2009) Massman et al. (2013) Beyene et al. (2016a) Beyene et al. (2016b)

Bankole et al. (2017) Kebede et al. (2021) Yi et al. (2004) Suryendra et al. (2020) Charmet et al. (2001) Rahman et al. (2020) Abdallah et al. (2009) Guindo et al. (2014) Thudi et al. (2014)

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Conclusion

The benefit of the MARS scheme is that it may be used to improve complicated variables like grain yield, biotic stress, and abiotic stress, which are regulated by polygenes or QTLs with tiny but additive gene effects. Using a selection of markers that are significantly related to the target traits, it seeks to accumulate a large number of QTLs in a particular population. As a result, compared to the MABC approach, the genetic gain obtained through MARS is greater. When one gets information about a limited number of markers as opposed to molecular markers that cover the entire genome, MARS has an advantage over GS. Marker-aided recurrent selection is particularly useful for speeding up the breeding process, choosing individuals, and selecting qualities that are difficult or expensive to quantify, such pest, and drought tolerance. The choice of a MARS technique in this situation is strongly influenced by the breeding goal, the species genome, the breeder’s resources, and the nature of the trait (or characteristics) that need to be improved.

References Abdallah AA, Ali AM, Geiger HH, Parzies HK (2009) Marker-assisted recurrent selection for increased out crossing in caudatum–race sorghum. In: Proceedings of the international conference on applied biotechnology, 28–30 September 2009, Khartoum, Sudan Bankole F, Menkir A, Olaoye G, Crossa J, Hearne S, Unachukwu N (2017) Genetic gains in yield and yield related traits under drought stress and favorable environments in a maize population improved using marker assisted recurrent selection. Front Plant Sci 8:808 Bernardo R, Charcosset A (2006) Usefulness of gene information in marker-assisted recurrent selection: a simulation appraisal. Crop Sci 46:614–621 Beyene Y, Semagn K, Crossa J, Mugo S, Atlin GN, Tarekegne A, Meisel B, Sehabiague P, Vivek BS, Oikeh S, Alvarado G, Machida L, Olsen M, Prasanna BM, Bänziger M (2016a) Improving maize grain yield under drought stress and non-stress environments in sub-Saharan Africa using marker-assisted recurrent selection. Crop Sci 56:344–353 Beyene Y, Semagn K, Mugo S, Prasanna BM, Tarekegne A, Gakunga J, Sehabiague P, Meisel B, Oikeh SO, Olsen M, Crossa J (2016b) Performance and grain yield stability of maize populations developed using marker-assisted recurrent selection and pedigree selection procedures. Euphytica 208:285–297 Charmet G, Robert N, Perretant MR, Sourdille P, Groos C, Bernard S, Bernard M (2001) Marker assisted recurrent selection for cumulating QTLs for bread-making related traits. Euphytica 119: 89–93 Eathington SR, Dudley JW, Rufener GK II. (1997a) Marker effects estimated from testcrosses of early and late generations of inbreeding in maize. Crop Sci 37:1679–1685 Eathington SR, Dudley JW, Rufener GK II. (1997b) Usefulness of marker-QTL associations in early generation selection. Crop Sci 37:1686–1693 Eathington SR, Crosbie TM, Edwards MD, Reiter RS, Bull JK (2007) Molecular markers in a commercial breeding program. Crop Sci 47:S154–S163 Edwards MD, Johnson L (1994) RFLP for rapid recurrent selection. In analysis of molecular marker data. Proc. Joint Symp. Am. Soc. Hort. Sci. and Crop Sci. Soc. Am., Corvallis, OR, 5–6 Aug. 1994, Univ. of Oregon, Corvallis, pp 33–40 Edwards MD, Page NJ (1994) Evaluation of marker-assisted selection through computer simulation. Theor Appl Genet 88:376–382

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Edwards MD, Stuber CW, Wendel JF (1987) Molecular marker-facilitated investigations of quantitative trait loci in maize. I. Numbers, distribution, and types of gene action. Genetics 116:113–125 Gimelfarb A, Lande R (1994) Simulation of marker assisted selection hybrid populations. Genet Res 63:39–47 Guindo D, Gnimdu AK, Guitton B, Fliedel G, Davrieux F, Vaksmann M, Teme N, Rami JF (2014) Evaluation of sorghum grain quality for marker assisted recurrent selection (MARS), conference: plant and animal genome conference (PAG) XXII Hallauer AR (1992) Recurrent selection in maize. Plant BreedRev 9:115–179 Hallauer AR, Miranda JB (1988) Quantitative genetics in maize breeding. Iowa State Univ. Press, Ames Hoeschele I, Uimari P, Grignola FE, Zhang Q, Gage KM (1997) Advances in statistical methods to map quantitative trait loci in outbred populations. Genetics 147:1445–1457 Hospital F, Charcosset A (1997) Marker-assisted introgression of quantitative trait loci. Genetics 147:1469–1485 Kebede D, Menkir W, Abe A, Oliveira ALG, Gedil M (2021) Marker based enrichment of provitamin a content in two tropical maize synthetics. Sci Rep 11:14998 Knapp JS (1998) Marker-assisted selection as a strategy for increasing the probability of selecting superior genotypes. Crop Sci 38:1164–1174 Knott SA, Elsen JM, Haley CS (1996) Methods for multiple marker mapping of quantitative trait loci in half-sib populations. Theor ApplGenet 93:71–80 Lande R, Thompson R (1990) Efficiency of marker-assisted selection in the improvement of quantitative traits. Genetics 124:743–756 Massman JM, Jung HJG, Bernardo R (2013) Genome-wide selection versus marker-assisted recurrent selection to improve grain yield and stover-quality traits for cellulosic ethanol in maize. Crop Sci 53:58 Mayor PJ, Bernardo R (2009) Genome wide selection and marker-assisted recurrent selection in doubled haploid versus F2 populations. Crop Sci 49:1719–1725 Moreau L, Charcosset A, Gallais A (2004) Experimental evaluation of several cycles of markerassisted selection in maize. Euphytica 137:111–118 Openshaw S, Frascaroli E (1997) QTL detection and marker assisted selection for complex traits in maize. In: Proceedings of the 52nd annual corn and sorghum research conference. Washington, DC: American Seed Trade Association, 44–53 Peleman JD, Van Der Voort JR (2003) Breeding by design. Trends Plant Sci 7:330–334 Ragot JM, Sisco PH, Hoisington DA, Stuber CW (1995) Molecular-marker mediated characterization of favorable exotic alleles at quantitative trait loci in maize. Crop Sci 35:1306–1315 Rahman M, Davies P, Bansal U, Pasam R, Hayden M, Trethowan R (2020) Marker-assisted recurrent selection improves the crown rot resistance of bread wheat. Mol Breed 40(3):1–14 Ribaut JM, Hoisington DA, Deutsch JA, Jiang C, Gonzalez-de-Leon D (1997) Identification of quantitative trait loci under drought conditions in tropical maize. I. Flowering parameters and the anthesis-silking interval. Theor Appl Genet 92:905–914 Smith C (1967) Improvement of metric traits through specific genetic loci. Anim Prod 9:349–358 Sprague GF, Eberhart SA (1977) Corn breeding. In: Sprague GF (ed) Corn and corn improvement, Agron. Monogr, vol 18. ASA, Madison. Stromberg, pp 305–362 Stam P (1995) Marker-assisted breeding. In: Van Ooijen JW, Jansen J (eds) Biometrics in plant breeding: applications of molecular markers. Proc. 9th Mtg. EUCARPIA section on biometrics in plant breeding, pp 32–44 Stromberg LD, Dudley JW, Rufener GK (1994) Comparing conventional early generation selection with molecular marker assisted selection in maize. Crop Sci 34:1221–1225 Stuber CW, Edwards MD, Wendel JF (1987) Molecular marker-facilitated investigations of quantitative trait loci in maize. II. Factors influencing yield and its component traits. Crop Sci 27:639–648

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Suryendra PJ, Revathi P, Singh AK, Viraktamath BC (2020) Marker assisted recurrent selection for genetic male sterile population improvement in rice. Electron J. Plant Breed 11(1):149–155 Thudi M, Gaur PM, Krishnamurthy L, Mir RR, Kudapa H, Fikre A, Kimurto P, Tripathi S, Soren KR, Mulwa R, Bharadwaj C, Datta S, Chaturvedi SK, Varshney RK (2014) Genomics-assisted breeding for drought tolerance in chickpea. Funct Plant Biol 41:1178–1190 Van Berloo R, Stam P (1998) Marker-assisted selection in autogamous RIL populations: a simulation study. Theor Appl Genet 96:147–154 Van Berloo R, Stam P (2001) Simultaneous marker-assisted selection for multiple traits in autogamous crops. Theor Appl Genet 102:1107–1112 Xie C, Xu S (1998a) Strategies of marker-assisted selection. Crop Sci 38:1526–1535 Xie C, Xu S (1998b) Efficiency of multistage marker-assisted selection in the improvement of multiple quantitative traits. Heredity 80:489–498 Xu S, Atchley WR (1995) A random model approach to interval mapping of quantitative trait loci. Genetics 141:1189–1197 Yi C, Guo W, Zhu X, Min L, Zhang T (2004) Pyramiding breeding by marker-assisted recurrent selection in upland cotton: selected effects on resistance to Helicoverpa armigera. Agric Sci China 3:330–339 Zehr BE, Dudley JW, Chojecki J (1992) Some practical considerations for using RFLP markers to aid in selection during inbreeding of maize. Theor Appl Genet 84:704–708

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Concepts and Employment of Molecular Markers in Crop Breeding Varsha Kumari, S. B. Yeri, Priyanka Kumawat, Sharda Choudhary, Shyam Singh Rajput, Ashok Kumar Meena, Ram Kishor Meena, Raj Kumar Meena, and Poonam Kumari

4.1

Introduction

Crop breeding is the art and science of improving genetic makeup of plant to make them suitable economically. It is fusion of principles and methods to recognize and improve characters of economic importance utilizing scientific tools (Farooq and Azam 2002). Mendelian genetics and cytogenetics are the basis of modern crop breeding, although the existence of crop breeding is found approximately 10,000 years ago and more. Mendelian genetics is the pillars of crop breeding V. Kumari (✉) · S. S. Rajput · A. K. Meena Department of Plant Breeding and Genetics, Sri Karan Narendra Agriculture University, JobnerJaipur, India e-mail: [email protected] S. B. Yeri Department of Plant Biotechnology, Zonal Agricultural Research Station, Kalaburagi, University of Agricultural Sciences, Raichur, India P. Kumawat Department of Agronomy, SKN College of Agriculture, Sri Karan Narendra Agriculture University, Jobner-Jaipur, India S. Choudhary National Research Centre on Seed Spices, Ajmer, India R. K. Meena Department of Entomology, SKN College of Agriculture, Sri Karan Narendra Agriculture University, Jobner-Jaipur, India R. K. Meena Department of Dairy Science, College of Bharatpur, Sri Karan Narendra Agriculture University, Jobner-Jaipur, India P. Kumari Department of Plant Pathology, Sri Karan Narendra Agriculture University, Jobner-Jaipur, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_4

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after its rediscovery by three scientists in 1900 which was originally published in 1866 in Proceeding of Natural History Society of Baunn (Mendel 1866). Mendel has introduced the concept of factor called as genes, responsible for the transfer of character from one generation to others. Crop breeders are trying to identify genes to incorporate into one variety for more economical and useful for human beings. Traditional crop breeding involves hybridization between the parents for contrasting traits like yield and disease. Individual plant selections are practices followed by preliminary, advanced, and multiplication trials which take around 12–15 years of time in selfing cycle and around BC10 for only recovery of 90% genome in backcrossing for releasing a variety of desirable traits (Murray et al. 1988). The development and employment of molecular marker in crop breeding have solved the problem of limitations which arises by conventional breeding. This landmark has begun in the 1980s with the discovery of PCR-based DNA markers, viz. RAPD, RFLF, AFLP, SSR, and SNPs, with wide applications in crop breeding and genomics (Nadeem et al. 2018). They are majorly applied in genomic analysis, diversity analysis, and marker-assisted selection for various characters. Further, the nextgeneration sequencing and association mapping have opened the path for discovery of novel molecular markers, viz. DArT, EST, STS, P450-based analogue markers, and tubulin-based polymorphism, which have facilitated genotyping of multiple traits and complex populations. In this chapter, we have discussed various molecular markers and their present implications to assure major crop breeding objectives in order to achieve yield target for food security.

4.2

Molecular Markers

They are sequences of DNA with a known location on chromosome and occur in close proximity to gene of interest, and its inheritance can be detected. It mentions to any distinctive DNA sequence which can be used in PCR, DNA hybridization, or restriction mapping experiments to detect that particular sequence. A good marker should have the following characters as follows: • • • • • • • • • •

Should be highly polymorphic. Should be codominant. Commonly distributed throughout the genome. Simple inheritance. Easy. Fast and cheap to detect and reproducible. Easy exchange of data between laboratories. Abundantly occurs throughout the genome. Exhibits minimum pleiotropic effect. Its detection is not dependent on the developmental stage of the plant, etc.

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Classification of Molecular Markers

Molecular markers have been classified based on gene action, mode of detection (hybridization or PCR), and banding pattern (codominant or dominant markers) (Fig. 4.1). Various types of molecular markers were discovered and used in genetics and crop breeding successfully for different types of crop plants (Semagn et al. 2006). A comparison of salient features of commonly used markers has been described in Table 4.1.

4.2.1.1 Restriction Fragment Length Polymorphism (RFLP) This was the first DNA marker that was discovered before discovery of PCR and evolved from the difference in nucleotide sequences of diverse plants. The difference in the length of restriction fragment is the basis of polymorphism. It is only marker

Fig. 4.1 Classification of DNA molecular markers based on PCR and hybridization

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Table 4.1 Evaluation of the most extensively used different DNA markers in plant systems Feature and description PCR-based Dominance Level of polymorphism Radioactive detection Amount of DNA required Quality of DNA required Reproducibility/ reliability Genotyping throughput Time demanding Cost per analysis Number of polymorphic loci per analysis Primary application Cloning/ sequencing

RFLP No Codominant Moderate

RAPD Yes Dominant High

AFLP Yes Codominant High

SSR Yes Codominant High

SNP Yes Codominant High

Generally yes Large (5–50 μg)

No

Yes or no

No

Moderate (0.5–1.0 μg)

High

Small (0.01– 0.1 μg) Moderate

High

Low

High

Generally no Small (0.05– 0.12 μg) Moderate to high High

Low

Low

High

High

High

High High 1.0–3.0

Low Low 1.5–5.0

Moderate Moderate 20–100

Low Low 1.0–3.0

Low Low 1.0

Genetics

Diversity

Yes

No

Diversity and genetics No

All purposes Yes

All purposes Yes

High

Small (≥0.05 μg) High High

based on restriction and hybridization, while all other are PCR-based. Protocol involves the isolation of genomic DNA followed by restriction digestion to cut DNA at the restriction site. Restriction digestion results in large number of DNA fragments of different sizes. Polyacrylamide gel electrophoresis is done to separate those DNA fragments. DNA strands are double-stranded, so HCl or NaOH is applied to gel to make them single stranded which were transferred to nitrocellulose paper also called as blotting followed by baking at high temperature (Semagn et al. 2006). DNAs are tagged with radioactive isotopes followed by hybridization with probe and detection of positive bands (Botstein et al. 1980). Insertion/deletions (InDels), inversion, translocation, and point mutations are regions of variation in RFLP (Madhumati 2014).

4.2.1.2 Randomly Amplified Polymorphic DNA (RAPD) RAPD is a PCR-based marker system which is dominant in nature and similar to PCR reactions requires short oligonucleotide, e.g. 10–15-bp-long random primers for reaction (Fig. 4.2). Polymorphism is identified by the absence or presence of bands (Babu et al. 2021). Random primers are used without any sequence information. Multiple bands are amplified, and polymorphism is found by amplification or

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Fig. 4.2 Steps of RAPD

Fig. 4.3 Polymorphism pattern of RAPD marker

not. Polymorphism pattern of RAPD markers has been given in Fig. 4.3. Major application of this marker is emphasized in diversity analysis to find contrasting parents for traits of interest to be used in hybridization.

4.2.1.3 Simple Sequence Repeats (SSRs) SSRs are tandem repeats of mononucleotide, dinucleotide, trinucleotide, tetranucleotide, and pentanucleotide motifs (e.g. A, AT, GAA, AATC, and AAAAG) which vary in the number of repeats at a given locus. They are DNA fragments with a tandem repeat motif of 1–6 nucleotides. Specific primers are required with prior sequence information for the detection of polymorphism. They are codominant and highly polymorphic in nature, which are highly dispersed throughout genome and arbitrarily abundant throughout the mitochondrial and chloroplast genomes (Kalia et al. 2011; Koelling et al. 2012). Variation in the numbers of repeat motifs is the basis of polymorphism in SSR markers between

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individuals and populations. Two primer sets, viz. forward and reverse primers, are used for SSR marker loci, whereas single primers are utilized in RAPDs, SCARs, and ISSRs. One primer anneals to the template in forward orientation, and another primer hybridizes to DNA template in a reverse direction. The creation of DNA library of SSR motifs is the first step in SSR marker development followed by the identification of unique SSR motifs. Primer designing is carried out to perform PCR, and SSR amplicons are separated by gel electrophoresis. SSR band profiling is done for the assessment of polymorphism, and SSR analysis is done using software (Singh et al. 2020; Kumar et al. 2021). SSR markers have a role in mapping, QTLs analysis, sequencing, marker-assisted selection (MAS), etc.

4.2.1.4 Amplified Fragment Length Polymorphism (AFLP) Amplified fragment length polymorphism (AFLP) is a PCR-based technique that combines both RAPD and RFLP techniques where digestion of DNA is performed followed by PCR. It uses selective amplification of a subset of fragmented DNA fragments to generate and relate distinctive fingerprints for gene of interest. The AFLP procedure was initially defined by Vos and Zabeau in 1993. AFLP overcomes the lacunae of RAPD and RFLP technique. The AFLP polymorphisms are the outcome of differences in the restriction sites in DNA fragments. It is codominant marker, and no prior sequence information is required and also is very cost-effective. The procedure involves the restriction of DNA with restriction enzyme and ligation of adaptor to restriction fragments. Primers are designated in such a way that they should contain 1–5 nucleotides from adaptor. Selective amplification of some of these fragments with two PCR primers has corresponding adaptor and restriction site-specific sequences followed by gel electrophoresis stained with AgNO3 or by autoradiography (Vos et al. 1995). 4.2.1.5 Single-Nucleotide Polymorphism (SNP) Single-nucleotide polymorphism (SNP) in genome of organism arises due to difference in a single nucleotide. Insertion/deletions (InDels) are the major cause of SNPs which may be either transitions (C/T or G/A) or transversions (C/G, A/T, C/A, or T/G) (Sobrino et al. 2005). SNPs are highly abundant in genome and can give maximum numbers of markers due to single-nucleotide base which is the smallest unit of inheritance. SNPs are situated in coding, non-coding, and intergenic regions of genomes with a rate of one SNP for 100–250 bps of DNA sequence. SNPs are the most catchy marker in recent days due to its frequency and could be extensively used in high-throughput genotyping, QTL mapping, saturation of map in linkage mapping, next-generation sequencing technology, etc. (Fusari et al. 2008). SNPs difference occurs when restriction site is present in one allele and absent in another of a restriction enzyme which gives length polymorphism of restriction fragments separated by gel electrophoresis (Long et al. 2017).

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4.3

Employment of Molecular Markers in Crop Breeding

4.3.1

Determination of Genetic Diversity

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Genetic diversity analysis is done to find diverse genotypes for further selection in crop breeding in large germplasm. It is also helpful in the study of similarity, evolution, synteny, population structure, and comparative genomics. RAPD, SNPs, and DArT markers are mostly utilized for the assessment of genetic diversity in numerous crop plants (Baloch et al. 2017; Nawaz et al. 2017).

4.3.2

Linkage Map Construction

A linkage map works as route map that determines relative distance between markers (Paterson 1996). Logarithm of odd called as LOD is ratio of linkage, and no linkage is utilized to calculate linkage of markers. LOD score should be more than 3 for linkage map construction. SSRs and SNPs are majorly used for the construction of a linkage map which is the basis of QTL mapping.

4.3.3

QTL Mapping

Quantitative trait loci mapping is applied for the mapping of markers controlling quantitative traits controlled by polygenes. Quantitative characters are controlled by many genes with small and additive effects and showed continuous variation. Mapping populations such as recombinant inbred lines (RILs), near-isogenic lines (NILs), backcrossing populations (BCs), double haploids (DHs), and F2 populations are created by hybridizing diverse parents for different traits like yield, disease, stress, or any other traits (Collard et al. 2005). Steps of QTL mapping include phenotyping of mapping population, genotyping to incur marker data, construction of linkage map, finding of molecular makers linked with trait of interest, and identification of QTLs through statistical analysis.

4.3.4

Marker-Assisted Selection (MAS)

Marker-assisted selection is indirect selection in which phenotypic selection is done based on a marker linked to the trait of interest. MAS overcomes the problems associated with conventional breeding like precise selection, less time taking, season independent, and screening of complex traits. Marker-assisted backcross breeding, gene pyramiding, and marker-assisted recurrent selection are major steps of MAS which are done with SSRs, SNPs, AFLP, DArT, etc. (Francia et al. 2005).

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Association Mapping

Association mapping is the association of molecular markers with phenotypic characters. It facilitates a greater number of recombination and is more time effective than linkage mapping. A larger number of alleles can be identified because of more genetic variation (Zhang et al. 2016a, b).

4.3.6

Evolution and Phylogenic Study

Phylogenic and evolution are studied using molecular markers nowadays which were totally dependent on geographical change in the past days. Plant phylogeny studies are dependent on chloroplast genome sequence because of their stable genome sequence (Tong et al. 2011).

4.3.7

Detection of Heterosis

Heterosis is a superiority of F1 over both the parents. SSR markers have been used in recent days to study genetic diversity and heterosis in many crops such as rice, wheat, maize, and rapeseed. (Betran et al. 2003; Wu et al. 2013).

4.3.8

Identification of Haploids

Haploids are plants with a single set of chromosomes. Haploids and double haploids (DHs) are used as a mapping population for QTL mapping. SSR and SNP markers have been used to identify haploids, double haploids, and isogenic lines (Shahid et al. 2013).

4.3.9

Genome-Wide Association Study (GWAS)

Genome-wide association study is deployed to find the connection between natural variation and gene of interest. Phenotyping is done multiple times after genotyping of inbred lines. They are cost-effective methods with high resolution and throughput. Billions of SNPs are generated through GWAS, and it has been applied in numerous crops like rice, maize, and sorghum. (Atwell et al. 2010).

4.3.10 Marker-Assisted Backcrossing (MABC) Marker-assisted backcrossing is a method of backcrossing where markers are used. It involves foreground and background selection. Linked markers are used to identify donor gene linked with marker in foreground selection. Markers distributed over

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genomes are used to select an individual with a large amount of recurrent parent genomes with traits under transfer in background selection (Collard and Mackill 2008).

4.3.11 Marker-Assisted Gene Pyramiding Marker-assisted gene pyramiding is used to stack many desired genes simultaneously in a single variety using molecular markers. Majorly used for pyramiding disease or insect resistance gene in various crops (Luo et al. 2012).

4.3.12 Targeting Induced Local Lesions in Genome (TILLING) Targeting Induced Local Lesions In Genome (TILLING) was first discovered by McCallum in the 1990s. Steps of TILLING involve the generation of mutated population and identification of mutations in the targeted sequence, and bioinformatical tools are used for the analysis of mutants. It is cost-effective and less time-consuming technique for the identification of novel alleles (McCallum et al. 2000; Kurowska et al. 2011).

4.3.13 Genome Editing (CRISPR-Cas) CRISPR-Cas is a gene editing technique used in numerous crops successfully. CRISPR and Cas protein are the two components in the CRISPR-Cas technique where cleavage of a particular site is done with endonuclease activity of Cas driven by CRISPR guide RNA. Genome editing has been performed successfully in crops like wheat maize and arabidopsis. (Feng et al. 2013; Hsu et al. 2013).

4.4

Conclusions

Molecular markers are versatile tools for resolving problems associated with traditional crop breeding. It is highly productive and cost-effective technology to estimate the biology of gene of interest. Molecular markers offer numerous applications in diversity analysis, evolution and phylogeny, MAS, MABB, gene pyramiding, genome editing, TILLING, GWAS, linkage mapping, identification of haploids, QTL mapping, association mapping, etc., in various cereals, legumes, oil seeds, fibres, and clonally propagated crops for the advent of new crop varieties. Declaration The author declares no conflicts of interest.

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sorghum research conference (Wilkinson et al., eds.). American Seed Trade Association, Washington, pp 72–78 Nadeem MA, Nawaz MA, Shahid MQ et al (2018) DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol Biotechnol Equip 32(2):261–285 Nawaz MA, Rehman HM, Baloch FS et al (2017) Genome and transcriptome-wide analysis of cellulose synthase gene superfamily in soybean. J Plant Physiol 215:163–175 Paterson AH (1996) Making genetic maps. In: Paterson AH (ed) Genome mapping in plants. R.G. Landes Company, Austin, pp 23–39 Semagn K, Bjørnstad A, Ndjiondjop MN (2006) An overview of molecular marker methods for plants. Afr J Biotechnol 2540:25–68 Shahid MQ, Chen FY, Li HY et al (2013) Double-neutral genes, and, for pollen fertility in rice to overcome hybrid sterility. Crop Sci 53(1):164–176 Singh RB, Mahenderakar MD, Jugran AK, Singh RK, Srivastava RK (2020) Assessing genetic diversity and population structure of sugarcane cultivars, progenitor species and genera using microsatellite (SSR) markers. Gene 753:144800 Sobrino B, Brion M, Carracedo A (2005) SNPs in forensic genetics: a review on SNP typing methodologies. Forensic Sci Int 154(2):181–194 Tong J, Li Y, Yang Y et al (2011) Molecular evolution of rice S5n allele and functional comparison among different sequences. Chin Sci Bull 56(19):2016–2024 Vos P, Hogers R, Bleeker M et al (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23(21):4407–4414 Wu JW, Hu CY, Shahid MQ et al (2013) Analysis on genetic diversification and heterosis in autotetraploid rice. Springer Plus 2(1):439 Zhang P, Zhong K, Shahid MQ et al (2016a) Association analysis in rice: from application to utilization. Front Plant Sci 7:1202 Zhang P, Zhong K, Tong H et al (2016b) Association mapping for aluminum tolerance in a core collection of rice landraces. Front Plant Sci 7:1415

5

Microsatellites as Potential Molecular Markers for Genetic Diversity Analysis in Plants Tania Sagar, Nisha Kapoor, and Ritu Mahajan

5.1

Introduction

Plants are universally identified as crucial part of the biological diversity and a key supply for the planet. As per the World Health Organization, more than 80% of the world’s population depends directly or indirectly on the plants as they are bestowed with rich bioactive compounds that have therapeutic properties (Palhares et al. 2015). These natural products have distinctive chemical diversity and innovative mode of action, thence they play an essential role in the formulation of several important medications (Kumar and Sharma 2016). Various efforts have been undertaken to categorize diversity among the plants on the basis of their anatomy, biochemistry, and molecular traits, thereby unravelling the evolutionary relationships between species particularly for wild plants (Mikulic-Petkovsek et al. 2012). Genetic diversity is variation occurring within or between various genotypes of a same species (Bhandari et al. 2017). The main reason for genetic diversity is sexual recombination which facilitates the emergence of new combinations. However, factors like inbreeding, qualitative mutation, and directional and stabilizing selection reduce genetic diversity, while quantitative mutation, out-breeding, and disruptive selection increase genetic diversity (Yilmaz and Boydak 2006). Due to the presence of genetic variation within and between the plant species, breeders and researchers are able to choose superior genotypes either as a direct parent or as a new variety in hybridization programs. Thus, it becomes easier to develop the varieties with specific characteristics like improvement in quality and resilience to both biotic and abiotic stresses (Marcedo et al. 2021). Diversity in nature is premise for the plant survival and for the crop enhancement. The presence of genetic diversity can be delineated in wild species including closely T. Sagar · N. Kapoor · R. Mahajan (✉) School of Biotechnology, University of Jammu, Jammu, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_5

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related species and mutant lines that may act as a source of beneficial alleles and help plant breeders in creating varieties that are more tolerant to climatic changes (Brake et al. 2022). Breeding of climate-adaptable varieties demands the development of unique traits such as resistance to a variety of soil and air contaminants, extreme heat and cold, and tolerance to pests as well as diseases. Additionally, to achieve heterosis and to produce transgressive segregants, two parents must be genetically diverse (Bhandari et al. 2017). Genetic diversity permits change in the genetic composition in order to manage the changes in the environment for the reliability of species. Genetic diversity assessment could be beneficial for finding new germplasm resources that when combined with actual plant varieties would produce both qualitatively and quantitatively improved yields (Swarup et al. 2020). In recent years, next-generation sequencing (NGS) techniques have facilitated the generation of enormous sequences at an affordable cost in several model and non-model plants, which has further facilitated the molecular markers development, including SSRs for phylogeographical studies (Li et al. 2016; Taheri et al. 2018; Naranpanawa et al. 2020).

5.2

Biodiversity

Biodiversity refers to biological variability and diversity of life on earth. All presently existing species have developed different characteristics that distinguish them from other species. The term biodiversity includes diverse ecosystem and diversity at species and genetic levels within and between the species (Sandifer et al. 2015). Biodiversity is censorious to support various ecosystem services. Different studies concur that plant biodiversity supports and regulates ecosystem services such as cycling of soil nutrients, control of soil erosion, and productivity (Chen et al. 2018; Peri et al. 2019). Plant communities also provide requisite habitats for several varieties of species (Wen et al. 2020). Various factors influence the distribution of species, where environmental diverseness (like climatic conditions, soil, and topographical features) is the primary cause for diversity of the species (Li et al. 2020a). The hotspots of biodiversity are mainly determined by species richness, whereas hotspots of agro-biodiversity refer to the hubs of origin and heterogeneity of local varieties or landraces as well as wild progenitors of the crops that harbour plant genetic resources which are important for plant breeding (Zimmerer et al. 2019). Pironon et al. (2020) observed the temporal compatibility between hotspots of biodiversity and agro-biodiversity. They considered that not only species abundance but also the numerous benefits are offered by the plants to mankind in terms of functioning of ecosystem and agro-ecosystem. They also suggested the provision of genetic resources for plant breeding programs and variability in chemical constituents of plants for human medicinal and nutritional use. Genetic diversity refers to variation of sets of genes underpinned by diverse organisms. It exists not only at a small scale between individuals of the similar population but also on large scale between individuals in diverse populations from

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similar species and also between distantly and closely related species (Muriira et al. 2018). Genetic diversity is a basic pillar of biodiversity and creates primal matter for evolution. In agro-biodiversity, variation in plant genetic resources imparts a chance for plant breeders to produce improved and new cultivars with breeder-preferred and farmer-preferred traits. Conserved plant genetic resources are important for the improvement of crops in order to encounter future global challenges in regard to nutrition and food security (Begna 2021).

5.3

Analysis of Genetic Diversity

Genetic markers have constituted a significant advancement in plant breeding. These are specific DNA sequences or genes with a known position on a chromosome that controls a gene or a trait. They act as indicators due to their linkage to the target gene (Kebriyaee et al. 2012). These are categorized into two groups: classical and molecular markers. Classical markers are of three types: morphological, biochemical, and cytological markers, whereas molecular markers include PCR-based and hybridization-based markers (Jiang 2013). Morphological markers are visual signals of phenotypically varying characters like shape of seed, colour of flower, development habits, and other crucial agronomic characteristics. These markers do not require any special instrument for their use. The main drawback of these markers is that they are restricted in quantity, controlled by growth stages of plants and influenced by several environmental factors (Karaköy et al. 2014), while cytological markers are the markers that are associated with changes in chromosome numbers, patterns of banding, shape, size, order, and position. These changes reflect discrepancies in euchromatin and heterochromatin distributions. These chromosomal landmarks are used in differentiating normal and mutant chromosomes (Jiang 2013). Biochemical markers are multi-molecular allelic variants of enzymes that are encoded by various genes but carry out the similar activity (Bayley 1983). They are used to assess the gene and genotype frequencies. These markers are co-dominant, inexpensive, and simple to use. Consequently, they are limited in number, ascertain lesser polymorphism level, and are influenced by different separation techniques, plant tissues, and growth phases (Mondini et al. 2009).

5.4

Molecular Markers

Plant breeding fate has changed during the 1980s due to the development and applications of molecular markers. Since then, several molecular markers and their applications have been delineated in different areas of genomics and plant breeding which in turn provide deeper knowledge about the available diversity within and between diverse plant populations (Nadeem et al. 2018). The recent advancements in molecular marker technology greatly aided the research in nearly every domain of crop development and its improvement (Hailu and Asfere 2020). A molecular

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marker is a nucleotide sequence and can be examined via the polymorphism that exists between the nucleotide sequences of diverse individuals. Molecular markers facilitate the incorporation of desirable attributes from wild to cultivated species for important phylogenetic facts (Reddy et al. 2021). Molecular markers are recognized as genetic markers. An ideal molecular marker should be co-dominant, highly reproducible, polymorphic, uniformly, and frequently distributed within genome, easy, and cheap to detect with no damaging effect on the phenotype (Kordrostami and Rahimi 2015). These markers are either PCR-based or hybridization-based or non-PCR-based markers. One of the imperative PCR-based markers is SSR markers.

5.5

SSR (Simple Sequence Repeat) Markers

SSR markers are also referred as microsatellites (Tautz 1989) or short tandem repeats (Schlotteröer et al. 1991). They are co-dominant, highly polymorphic, abundant, and distributed randomly within the nuclear, mitochondrial, and chloroplast genomes of various species (Kalia et al. 2011). SSRs are tandemly arranged repeat motifs of mononucleotides to hexanucleotides with variable lengths. However, most commonly motifs are mononucleotide, dinucleotide, trinucleotide, and tetranucleotide. It is postulated that SSRs may be ensued from slippage of single-strand DNA, nucleotide mismatches, relocation of jumping genes, and double-strand DNA recombination (Koelling et al. 2012). SSRs represent lower number of repeats per locus with higher degree of polymorphism. This is because the application of PCR in SSR assay increases the capability to detect higher polymorphism level in microsatellite regions (Padmakar et al. 2015). The existence of SSRs in expressed sequence tags (ESTs) and protein-coding genes is also confirmed (Yan et al. 2017a). These markers are considered to be more variable, robust, and informative in comparison to other markers. These are studied as best markers for intervarietal polymorphism detection, beneficial for parentage analysis and also for estimating the relatedness degree among different individuals or groups (Senan et al. 2014).

5.6

Types of SSR Markers

SSR markers are grouped into genomic SSRs (g-SSRs) and expressed sequence tag SSRs (EST-SSRs).

5.6.1

Genomic SSR Markers

Genomic SSRs are more widespread in plant genome, highly polymorphic, show high reproducibility, and are co-dominant in nature (Koubouris et al. 2019). Beghè et al. (2015) demonstrated that the extensive utilization of dinucleotide loci resulted in relatively close size neighbouring alleles which made it onerous to differentiate between them and thereby cause misidentification and generate uncertainty. Thus,

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SSR marker development with richer elemental repeats all over the genome is informative and effective to a greater extent in the identification and genetic analysis of olive accessions (Li et al. 2020b). In Ethiopian commercial varieties of Coffea arabica, genetic diversity was assessed using genomic SSR markers, where genetic similarity coefficient values ranged from 0.14 to 0.50, thus revealing the existence of distant inherent association across varieties (Benti et al. 2021).

5.6.2

EST-SSR Markers

EST-SSRs are positioned in transcribed regions of DNA and are recognized by NGS technique. They are more economical, are more evolutionarily sustained, and possess higher transferable capability to related species as compared to traditional genomic SSRs (Gadissa et al. 2018). They are anticipated to have lesser polymorphism level because of considerable DNA sequence maintenance in transcribed regions and are also less inclined to null alleles (Postolache et al. 2014). The position of EST-SSRs ascertains their functional roles in 5′ and 3′ untranslated regions that impact transcription or translation of gene and transcription slippage or gene silencing respectively (Gao et al. 2013). Also, EST-SSRs are efficient molecular markers for analysing the genetic variation in plants with higher transferability rate (Zeng et al. 2018; Li et al. 2021b). Parthiban et al. (2018) evaluated relative efficiency of EST-SSRs and genomic SSRs in Saccharum officinarum and observed that EST-SSRs provide a mean genetic relatedness of 0.70 across 59 sugarcane accessions, whereas genomic SSRs gave a genetic relatedness value of 0.63. However, EST-SSRs had a lower polymorphism level as their PIC value was low as compared to genomic SSRs, and the genetic diversity shown was deemed to be promising. Their results indicated that the higher polymorphism and low genetic similarity of genomic SSRs could be useful in recognition of variety-specific markers, true hybrid selection, and genetic diversity analysis. Wang et al. (2021) discovered EST-SSRs and genomic SSRs by examining the transcriptome as well as genome of Hippophae rhamnoides where EST-SSRs showed higher transferability rate to Hippophae in comparison with genomic SSRs. However, parentage analysis revealed that as genomic SSRs have higher polymorphism level, they were more efficient in determining parentage. In their study, EST-SSR and genomic SSR markers comparison shed light on trade-off between distinction and polymorphism in the selection of markers for breeding programs.

5.7

ISSR (Inter-Simple Sequence Repeat) Markers

ISSRs can be defined as the genomic fragments flanked by SSR. They are randomly distributed across the genome and are based on the production of multi-locus flags via PCR amplification of the genomic segments which are flanked by similar

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microsatellite repeats that are inversely aligned at each end (Sarwat et al. 2016). This marker system involves single primer PCR reaction; however, for analysis, anchored primers are usually preferred (Sharafi et al. 2017). Yadav et al. (2019a) revealed that the high reliability of these markers is due to longer PCR products and high annealing temperature of primers. These markers show dominant inheritance and are highly polymorphic. ISSRs technique is simple, easy, and cost-efficient in comparison to other dominant markers (Yadav et al. 2019b).

5.8

miRNA-Derived SSRs

MicroRNAs (miRNA) delineate a category of non-coding RNA that ranges in length from 19 to 22 nucleotides (Bartel 2004). These endogenous miRNAs have been revealed to play a significant role in controlling the expression of genes in plants, animals, and fungi with the majority of their sequences being connected to transcription factors (Bartel and Bartel 2003). The genes that are involved in various processes, such as pathogen invasion (Jones-Rhoades et al. 2006), response to diverse biotic and abiotic stimuli (Khraiwesh et al. 2012; Kompelli et al. 2015), various development, and protein breakdown processes (Eldem et al. 2013), are mediated by miRNA. In pre-miRNA sequences, SSRs have been identified in different plant species despite the fact that majority of SSRs are located in the protein-coding (Li et al. 2004), non-coding (Riley and Krieger 2009), or untranslated (Mondal and Ganie 2014) regions of the plant genome. Ordinarily, SSRs are frequently located in the non-coding regions but comparatively infrequent in the protein-coding regions (Madsen et al. 2008). (Ganie and Mondal 2015) reported the salt-responsive miRNA-SSRs in the rice genome and correlated them to gene expression analysis and plant phenotype. Additionally, research on the significance of transcriptional epitomizing of SSRs specific non-coding RNAs in banana and sugarcane supports the idea that SSRs play a significant role in the non-coding regions of the genome (Cardoso-Silva et al. 2014; Yang et al. 2015). Kumar et al. (2017) mined 147 miRNA-SSRs in pre-miRNA transcripts of Arabidopsis thaliana and analysed them to be in the non-coding region and concluded these miRNAbased SSRs might undergo additional testing for allelic variation, act as a source of incredibly useful molecular markers, and also serve as a guide for marker-assisted plant breeding. Patil et al. (2021) observed unique miRNA-based SSRs from 761 pomegranate miRNA precursor sequences for seed-type attribute that would act as a useful genetic source for quick and targeted enhancement of the pomegranate seed-type trait in near future.

5.9

Analysis Using SSR Markers

SSR markers have repeat motifs of mononucleotides to hexanucleotides. SSR analysis involves two primers in a single PCR reaction. Both the primers show complementary annealing to specific DNA template which flanks the SSR sequence.

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Table 5.1 Applications of SSR markers in plant improvement S. no. 1. 2. 3. 4. 5.

Plant species Citrus aurantifolia and Citrus limon Elymus sibiricus Oryza sativa Etlingera elatior Zea mays

Trait improvement Hybrid identification

6.

Glycine max

7.

Punica granatum

8.

Psidium guajava

Genetic diversity and population structure analysis Genetic diversity

9. 10. 11. 12. 13.

Gossypium hirsutum Triticum aestivum Cicer arietinum Ricinus communis Manihot esculenta

QTL mapping and characterization QTL mapping Association mapping Mapping Marker-assisted selection

14.

Carthamus tinctorius

Genetic and physical mapping

Hybrid identification Assessment of heterosis in breeding Genetic diversity evaluation Identification of haploid or diploid plants Bulk segregant analysis

References Guzmán et al. (2017) Zhao et al. (2017) Pavani et al. (2018) Ismail et al. (2019) Rádi et al. (2020) Sreenivasa et al. (2020) Patil et al. (2020, 2021) Kumar et al. (2020) Li et al. (2021a) Xu et al. (2021) Jha et al. (2021) Kim et al. (2021) Olasanmi et al. (2021) Jegadeeswaran et al. (2021)

These markers have been utilized in genetic variability analysis, population structure studies, fingerprinting, linkage, and association mapping (Table 5.1). These markers are also applicable in specific breeding via marker-assisted selection (Zhao et al. 2017; Kumar et al. 2021). Patil et al. (2021) reported three major types of SSRs on the basis of motif length and repeats which include: class I (a set of hypervariable, > 30 nucleotides), class II (a set of potentially variable; 20–30 nucleotides), and class III (a set of variable SSRs; < 20 nucleotides) types. Due to their co-dominance nature and rich allelic variability, SSR markers display high capability in characterizing levels of genetic diversity and genetic structure (Ismail et al. 2019). Being locus-specific, SSRs are very informative and their discovery serves as a valuable genomic resource. In various crop species, genetic and physical maps based on SSRs have played an essential role to analyse collinearity and synteny (Kirungu et al. 2018; Kumari et al. 2020). The development of highdensity physical map on the basis of hypervariable SSRs could function as a reference map for exploring genotyping data for diverse populations (Patil et al. 2021). Several studies have indicated the requisition of physical map based on SSR markers in the fine mapping of proclaimed QTLs (Zhao et al. 2017). Reports published revealed the use of SSR markers for the analysis of genetic studies and population structure, fingerprinting, and linkage studies (Mahajan et al. 2012; Li et al. 2021a; Jian et al. 2021). In legumes, the development of legume SSR database—a genomic Web server can serve as a useful resource for the improvement

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of germplasm of various legumes and marker-assisted breeding (Duhan and Kaundal 2021).

5.10

Use of Next-Generation Sequencing in Developing SSR Markers

The advancement in sequencing techniques like NGS produces innumerable amount of data that can be interpreted in a cost-efficient manner (Singh et al. 2015). NGS techniques when coupled with potent computational methods have transfigured omics-based research (Yadav et al. 2016). These methods being cost-effective are used to develop substantial genomic and transcriptomic resources in diverse plant species (Thumilan et al. 2016) (Table 5.2). The generation of transcriptome data via sequencing of RNA has been prosperously delineated for the development of SSR marker in non-model plants as de novo sequencing (Singh et al. 2017). Before NGS Table 5.2 Development of SSR markers using NGS techniques in plants Species Hevea brasiliensis Juglans cathayensis Punica granatum

SSR type SSR ESTSSR SSR

Dipteronia oliver

SSR

Elymus sibiricus

ESTSSR ESTSSR SSR

Psophocarpus tetragonolobus Two Hemarthria species Zantedeschia rehmannii Jatropha curcas

ESTSSR SSR

Trifolium pratense Cyamopsis tetragonoloba Punica granatum

SSR SSR

Stephanandra incisa Artocarpus heterophyllous

ESTSSR ESTSSR

SSR

NGS technology used Roche 454 sequencing platform Illumina HiSeq 2000 sequencing platform Roche 454 pyrosequencing Illumina HiSeq 2000 sequencing platform Illumina HiSeq 2000 sequencing platform Roche 454 genome sequencer FLX HiSeq TM 2500 sequencing platform Illumina HiSeq 2000 sequencing platform Roche 454 genome sequencer Illumina HiSeq 2000 sequencing platform Illumina HiSeq 2000 sequencing platform Illumina HiSeq 2000 sequencing platform Illumina HiSeq 2000 sequencing platform Illumina NextSeq 1000 platform

Total number of discovered SSRs 1397 22,484 567 12,377 8769 12,956 10,888 9933 9798 15 5773 19,000 5555 16,853

References Salgado et al. (2014) Dang et al. (2015) Ravishankar et al. (2015) Zhou et al. (2016a) Zhou et al. (2016b) Vatanparast et al. (2016) Huang et al. (2016) Wei et al. (2016) Tian et al. (2017) Kovi et al. (2017) Tanwar et al. (2017) Simsek et al. (2018) Zhang et al. (2021) Singh et al. (2021)

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technology, SSR marker development process was laborious, time-consuming, and less economical due to the requisite condition of constructing genomic libraries. Presently, Illumina sequencing (sequencing by synthesis) is most extensively used NGS rostrum for the development of SSRs (Grohme et al. 2013; Vieira et al. 2016). Tanwar et al. (2017) used Illumina HiSeq 2000 instrument to develop 5773 SSRs from non-redundant 62,146 unigenes in Cyamopsis tetragonoloba. They developed 20 primer pairs, out of which 13 pairs were effectively amplified in two varieties. However, the failure of amplification by other 7 SSRs was imputed to the probability of flanking primers extending through a splice site with chimeric cDNA contigs or an intron. Ravishankar et al. (2015) reported 7361 contigs using pyrosequencing technique, out of which 567 contained SSRs. Primer pairs for 171 SSR loci were designed out of which 167 generated polymorphic bands in 12 pomegranate genotypes. Huang et al. (2016) used Illumina high-throughput sequencing technique and identified 10,888 SSR loci in two Hemarthria species. They designed and synthesized high-quality PCR primers to certify the identified SSRs and randomly tested 54 primers. They observed that most of these primers effectively performed the amplification of the desired product. In another study, 12,377 SSR loci were identified in 99,358 unigenes generated by Illumina pair-end sequencing in an endangered endemic Chinese genus Dipteronia oliver and 435 primers were randomly selected to detect polymorphism (Zhou et al. 2016a). Similarly, Wei et al. (2016) reported 9933 EST-SSRs in Zantedeschia rehmannii and designed 200 pairs of primers, out of which 58 pairs show polymorphism among 21 accessions. However, Li et al. (2012) performed de novo transcriptomic investigation in Hevea brasiliensis for producing EST data sets utilized for SSR marker development and identified a total of 39,257 EST-SSR loci. RNA sequencing being a simple and reliable technique has been employed for the development of EST-SSRs in various other species like Vigna radiata (Liu et al. 2016) and Phyllostachys edulis (Gao et al. 2014). Also, the development of transcriptome data set and EST-SSR markers demonstrated a deep perception on genetic background of north-eastern and eastern Indian collection of Artocarpus heterophyllous (Singh et al. 2021). Zhang et al. (2021) identified 5555 EST-SSRs among 35,251 unigenes in Stephanandra incisa using de novo sequencing. Magandhi et al. (2021) demonstrated the development of SSR sequences in Durio testudinarius genome using NGS data and found that trinucleotide motifs were abundant throughout the genome. Li et al. (2018a) reported the development of unique SSRs in Cinnamomum camphora and analysed the genetic variability on the basis of transcriptome sequencing. They observed that mononucleotide repeats were most abundant, followed by dinucleotide and trinucleotide repeats. These novel markers are not only beneficial for analysis of genetic divergence and conservation of wild genomic resources in Cinnamomum camphora, but also contribute to investigate the evolutionary history and genetic differentiation arrangement of Cinnamomum. Similarly, Simsek et al. (2018) determined 19,000 SSRs using next-generation sequencing technique and

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designed and tested 20 pairs of SSR primers in 40 pomegranate genotypes, out of which five pairs were recorded to be polymorphic.

5.11

SSR Markers in Characterization of Genetic Diversity

Genetic diversity characterization in plant breeding program is important before selection for the protection and efficient management of available genetic variation. The exploration of genetic dissimilarities among genotypes is crucial for protection of species and sustainable utilization of genetic resources of plants (Pan et al. 2017). However, the characterization of germplasm is an analytical interpretation of accessions on the basis of penetrability qualitative characters like plant height, flower colour, and growth habit depicting the accessions population, utilizing a set of documented traits. Phenotypic and morphological traits are greatly affected by their surroundings, and so many times, it becomes difficult to differentiate between two species, generally when their fruits and flowers are in the juvenile stage. Hence, it is important to employ a technique that is unaffected by the surroundings to substantiate the phenotypic traits. In recent times, SSR marker approach has been utilized to enhance the number of attainable genomic markers that are appropriate for the characterization and analysis of phylogenetic relationships among different plant germplasm. In Saudi Phoenix dactylifera cultivars, genomic SSRs development and their utilization for characterization of genetic variability revealed that 71% of SSR motifs were observed to be dinucleotides, 25% trinucleotides, 3% tetranucleotides, and 1% pentanucleotides and the polymorphism percentage was 100%. Thus, knowledge on developed SSR markers added values to the characterization tools of date palm that can further be employed by researchers in cultivar identification, population genetics, and genetic resource investigation and management (Al-Faifi et al. 2016). SSR markers have been intensively utilized to analyse genetic variability and comprehending the population structure evaluation. Hasnaoui et al. (2010) used 11 SSRs for genetic variation assessment in 27 Tunisian pomegranate accessions and observed 25 alleles, while Jian et al. (2012) used 18 EST-SSRs for genetic diversity characterization among 42 pomegranate cultivars from east-central China and detected 2–5 alleles with an average value of 2.80. However, Singh et al. (2015) offered the first database of pomegranate for association mapping analysis by investigating 88 accessions of pomegranate and classifying them into four clusters, where they recorded a clade of cultivated pomegranates adjacent to wild-type accessions. Zarei and Sahraroo (2018) analysed various novel alleles in diverse populations of pomegranate from five diverse areas of Iran and indicated the significance of these collected pomegranate accessions for the protection of genetic resources, Mahajan et al. (2018) studied the phylogenetic relationships between cultivated and wild pomegranate accessions collected from Maharashtra using 17 SSR markers, while Gunnaiah et al. (2021) reported that the wild accessions of Himalayan region displayed modest genetic variability than cultivated accessions.

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Li et al. (2018b) reported genome-wide characterization of genetic diversity in spinach Chinese germplasm collection using 41 polymorphic SSRs. They observed that tetranucleotide and trinucleotide repeat motifs were most abundant and accounting for 33.2% and 27.7% of total number of motifs identified in the genome, respectively. The generated dendrogram showed a low genetic divergence; as a result, their findings provide little information for future breeding programs and facilitate further explorations on the organization of SSRs in Chinese spinach genome. Kapoor et al. (2020) characterized distinct collection of Asparagus species from various regions of Northwest India for studying the genetic diversity using highly polymorphic genomic SSR markers, which resulted in conservative genetic backdrop for utmost species of Asparagus. Only, the cultivars of Asparagus adscendens breach into two different clusters, thus prompting a comprehensive genetic base of such species in comparison with other species. Kumar et al. (2020) studied novel genomic SSR markers and their development in Psidium guajava via library enrichment techniques and validated them through genetic variability and population structure analysis. The developed novel SSRs revealed high cross-transferability rate among wild species of guava. Furthermore, genetic diversity was also analysed in some genotypes of chilli using SSR markers where 10 alleles were detected for 5 polymorphic SSR loci with high PIC value. Twenty genotypes of chilli were divided into two main groups in dendrogram. From the findings, they concluded that the genetic base of chilli is wide, which can result in future improvement in breeding programs (Sharmin et al. 2018). Feng et al. (2016) developed 218 SSR loci using Chrysanthemum morifolium EST data sets to appraise the genetic diversity, and they recorded that out of all loci, hexanucleotide repeat motifs were highest in number and the dendrogram segregated 32 cultivars of Chrysanthemum morifolium into two main clusters. Nachimuthu et al. (2015) reported the use of genomic SSR markers in 192 different germplasm lines of Oryza sativa, which resulted in 205 alleles. The germplasm lines were categorized into two different sub-groups using distance-based and model-based approaches. Li et al. (2021c) proposed the development of EST-SSRs and their application in the characterization of genetic variability and analysis of population structure in 127 Allium sativum accessions and observed 79 polymorphic SSR loci with a PIC value of 0.36. Their findings were important for genetic studies and improvement of garlic germplasm. Zea mays accessions developed with tolerance to low soil nitrogen and drought were assessed for genetic diversity using 31 SSR loci. An aggregate of 288 alleles among the germplasm was observed, thus indicating a great polymorphism level among the maize populations (Adu et al. 2019). Similarly, El-Rawy and Hassan (2021) differentiated durum and bread wheat genotypes under drought-stressed field conditions with 30 SSR markers which specified substantial genetic variation between and within durum and bread wheat genotypes. Singh et al. (2022) characterized 87 varieties of Indian mustard using 200 genomic SSRs and observed 552 alleles with 189 SSRs. The generated dendrogram segregated 87 varieties into two main groups and provided a deeper knowledge on

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the genetic composition of Indian varieties of mustard and for developing future breeding plans for genetic improvement of Indian mustard. Donde et al. (2019) characterized 16 genotypes of introgressed rice from wild species for tolerance/susceptibility to drought stress using 63 microsatellite markers. They observed diverse genotypes with segments from wild ancestors against drought tolerance and can be utilized as recurrent parents developing rice varieties against abiotic stresses. Similarly, Hassan and Hama-Ali (2022) investigated 62 rice accessions collected from Kurdistan region of Iraq, using 37 polymorphic SSRs. They segregated diverse accessions of rice into japonica and indica subpopulations which can be useful for rice breeding programs and the domestication of new rice species.

5.12

SSR Markers and Their Cross-Species Transferability

The development of SSRs and the utilization of new statistical aids could be beneficial in characterizing the genetic variability present within and between different populations. These markers are widely used to assess the genetic pattern in diverse species. Moreover, species-specific SSR markers development is less economical and time-consuming and involves the amelioration of enriched libraries, sequencing of aimed genomic sections, and synthesis of flanking primers. This apprehension reveals that the development and requisition of SSRs in species of low economic value are limited (Miah et al. 2013). Alternatively, a widely utilized technique implements species-specific SSRs via cross-species amplification on the basis of species closeness without incurring additional expenditures. In addition to it, the amplifying sections of species from the same genus or closely related taxa yield better results. This indicates that the success of any DNA sequence crossamplification is inversely associated with the evolutionary divergence between two species (Huang et al. 2014). Rai et al. (2017) utilized a set of 18 SSR primers developed for Prosopis alba, Prosopis flexuosa, and Prosopis chilensis in Prosopis cineraria to examine their cross-species transferability. They observed that SSRs tested for Prosopis cineraria had significant levels of heterozygosity and thus had better opportunities for utilizing them in genetic diversity analysis and population genetics. Kaldate et al. (2017) characterized 117 genomic SSRs in 48 different lines of Macrotyloma uniflorum and also investigated the cross-transferability of novel 47 SSRs in 9 legume species, where 43 SSRs showed cross-amplification. Thus, these 43 markers can be used for genetic evaluation in other relevant crops of legumes which are lacking in SSR genomic resources. Similarly, Yan et al. (2017b) reported 114 highly polymorphic EST-SSR primer pairs in 15 Morus albus accessions and used these primer pairs against 18 Melilotus species to analyse their transferability. From the study, they concluded that 70 EST-SSRs were transferable to 18 Melilotus species. Their results offer an important genomic resource for genetic dissimilarity studies and molecular-aided breeding of Melilotus germplasm resources.

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Babu et al. (2018) applied Oryza sativa and Eleusine coracana genomic SSR markers for assessing the cross-transferability to the genotypes of barnyard millet and observed 71% cross-transferability for Oryza sativa SSRs of which 48% were polymorphic, while 100% of Eleusine coracana SSR markers were crosstransferable with 91% of polymorphism. Kim et al. (2019) analysed the cross-transferability of 102 EST-SSRs obtained from transcriptomic sequences of Hibiscus cannabinus and utilized them to evaluate the genetic variability and correlation of 7 cultivars as well as 87 accessions of Hibiscus species. The cross-transferability rate of EST-SSRs ranged from 83% to 99% with a mean value of 89.9%. The developed EST-SSRs obtained from the Hibiscus cannabinus transcriptome could provide valuable information regarding the genomic resources in this genus. Ngangkham et al. (2019) assessed the potency of genomic SSRs for the recognition of cross-species transferability in different genomes in both cultivated and wild relatives of Oryza sativa. The overall transferability rate of 70 SSRs across 18 genotypes varied from 38.9% to 100% with a mean value of 76.58%. In addition to it, the transferability rate across chromosome varied from 54.4% to 86.5% with a mean value of 74.35%. Their findings facilitate a better understanding of complicated mechanisms involved in origin and evolutionary processes of various Oryza species and their wild relatives. Jiang et al. (2020) demonstrated the cross-species transferability of 60 EST-SSRs developed from transcriptomic data of Lycoris aurea and used them in genetic analysis of Lycoris species. Their study provides a high rate of amplification, transferability, and efficiency of these SSRs, which in turn made genetic analysis and breeding in Lycoris genus easier. Aiello et al. (2020) evaluated already reported SSR primers from Daucus carota in commercial varieties and breeding lines of Foeniculum vulgare to check crossgenera transferability and to determine how effective they were at measuring genetic diversity. From their analysis, they observed that the transferability rate of SSR between two species was minimal. Similarly, Liu et al. (2021) developed EST-SSRs from transcriptomic profile of Cephalotaxus oliveri and observed their crosstransferability rate in Cephalotaxus fortune, Pseudotaxus chienii, and Amentotaxus argotaenia using 28 highly polymorphic EST-SSRs, with higher transferability observed in Cephalotaxus fortune.

5.13

Conclusion

Microsatellite markers are one of the significant and choicest markers in plant breeding. In the last few years, the use of microsatellite-based markers has considerably increased because of their small size, co-dominant, and repetitive nature. Being highly polymorphic and genome-specific, they have identified several important genetic loci. The use of NGS has laid foundation for the development of genomic and EST-SSR markers at lost cost and reduced effort. Thus, SSR markers have proved to be powerful tool in studies on the genetic structure and genetic

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relationships within and between different plant species which can help in the management of germplasm collection in near future.

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Application of Molecular Markers in Assessment of Genetic Diversity of Medicinal Plants R. S. Sharma, Nairita Vaidya, S. R. Maloo, Ashish Kumar, Stuti Sharma, R. Shiv Ramkrishnan, and Varsha Kumari

6.1

Introduction

Over eons, medicinal plants have been used for averting as well as healing numerous diseases. They have been used traditionally as home remedies and also in different medicinal formulations as curatives against many disorders. Presently, ~21,000 compounds derived from medicinal plants form the main composition of popular medicines, drugs and even cosmetics. As of today, the global market value of products derived from medicinal plants stands at US$120 billion. It has been estimated that in India there will be an increase in market value for medical plants from US$56.6 million to US$188.6 million by 2026 (https://www.ibef.org). A medicinal plant can be used, either whole or in parts, directly as medicine or used in synthesis of medicinal compounds (Sofowora et al. 2013). They are also edible and can be used as spices or perfumery plants. Around 7500 species of medicinal plants are cultivated in India, thus making our country second in terms of export (Kala et al. 2006). They are the cornerstone of Indian medicinal system AYUSH that consists of Ayurveda, Yoga, Unani, Siddha and Homeopathy (Adhikari and Paul 2018). The principle of using traditional and determinate methods to prevent onset of diseases and practicing these methods to achieve healthy body and mind forms the basis of AYUSH. All the medicines and products used in this system are derived from nature, i.e. from plants, animals or R. S. Sharma (✉) · N. Vaidya · A. Kumar · S. Sharma · R. S. Ramkrishnan Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur, India e-mail: [email protected] S. R. Maloo Pacific College of Agriculture, Pacific University, Udaipur, India V. Kumari Department of Plant Breeding and Genetics, SKN College of Agriculture, Sri Karan Narendra Agriculture University, Jobner-Jaipur, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_6

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mineral origin. The galenical preparation from medicinal plants acts as immunity modulators and, hence, is effective against respiratory ailments. Therefore, they were recommended as precautionary measures during Covid-19 pandemic (Ahmed et al. 2021). Secondary metabolites derived from medicinal plants, such as sesquiterpene and lactones, show static effects against bacteria, fungi and protozoa. A flavonoid Quercetin-3-O-glucoside extracted from leaves and seeds of Moringa oleifera was found to be cytotoxic to cancer cells (Caco-2-cell line) and apoptotic against non-cancer cell lines in vitro (Maiyo et al. 2016). Compounds like Taxol from Taxus and vincristine and vinblastine derived from Catharanthus species are anticancer agents. Anti-malarial agents like quinine, artemisinin and quinoline are isolated from Cinchona ledgeriana and Artemsia annua, while serpentine from roots of Rauwolfia serpentine alleviates hypertension (Lobay 2015). Other chemicals like Atropine and Hyoscyamine isolated from Atropa belladonna and Hyoscymus niger, respectively, are curative against nervous system related disorders. Some metabolites having anti-inflammatory and antioxidants derived from medicinal plants are Curcumin, Ferulic acid and Myricetin. Leaves of Abelmoschus manihot have analgesic and antioxidant chemicals that are reported to be effective against cardiac ischemia and diabetes mellitus. Metabolites derived from Abuliton indicum are anti-microbial, anti-asthmatic and anti-inflammatory, and isolates are used to treat wounds, ulcers, infections and haemorrhoids. Extracts from flowers of Datura metel are reported to be counteractive against skin ulcers, asthma, diarrhoea and bronchitis, while it also provides relief against convulsions, hysteria, skin ailments and cataract. Seed, leaf and roots of Mucuna pruriens have analgesic, anti-inflammatory, aphrodisiac and anti-diabetic properties (Ulu et al. 2018). Averrhoa bilimbi has remedial properties in its leaves and hence is used to provide relief from swelling and high-blood pressure and cures boils and skin eruptions. Essential oils derived from leaves and stem of Acillea millefolium restrict microbial activities of Streptococcus pneumonia, Candida albicans and Clostridium perfringes (Candan et al. 2003). The herbal extracts of Valeriana have valepotriates that can act as sedative tranquilizers and anti-tumour agents (Pande and Shukla 1993). Acorus calamus is an aromatic herb that has pharmaceutical compounds, which lowers cholesterol by reducing its biosynthesis. Thus, in nutshell, medicinal plants have anti-microbial, analgesic, anti-inflammatory, purgative, laxative, carminative and aphrodisiac attributes.

6.2

Genetic Diversity

Biological diversity is prevalence of intra- and inter-specific variations in the living world. It is quintessential for crop improvement programmes. Absence of any variation limits any opportunity for selection of favourable crop lines or varieties as well restricts any improvement in plant performances which is necessary for changing environments. Biological diversity can be stratified into four tiers; ecosystem diversity, species diversity, genetic diversity and genomic diversity (Bhandari et al. 2017). First, ecosystem diversity encompasses variability present in different

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populations of species. Second species diversity refers to different species present in an ecosystem and the relative abundance of each species in relation to other. Third, genetic diversity is the prevalence of variations among various genotypes within a particular species. Genetic diversity forms the cornerstone of crop breeding programmes. And finally, genomic diversity is defined at individual level and represents variation at gene level. Every crop breeding program aims at producing new cultivars keeping in interests of farmers’ preferences like yield, quality parameters, etc. as well as breeders’ preferences like tolerance to abiotic and biotic stresses, etc. Genetic diversity has a huge significance in survival of plants as well as in selection of plants having traits of interest. Better genotypes can be selected directly as varieties or used as parents in hybridization programs. Genetically diverse plants are chosen as parents so as to increase heterosis. Transgressive segregants can also be derived from such populations. There are various landraces, wild species, breeding stocks, etc., which possess favourable alleles that are exploited in crop improvement programs. There is an increased adaptability of plants to changing environmental conditions which has an important role in breeding of climate resilient varieties as well providing tolerance to various biotic stresses like insect-pests and diseases (Bhandari et al. 2017). Factors like mutation, migration, selection and genetic drift alter the Hardy–Weinberg equilibrium and hence shape the genetic diversity. Genetic erosion is a chronic problem leading to loss of genetic diversity caused by monocultures, replacing landraces by popular varieties, deforestation, overharvesting of crops, etc. (Begna 2021). Protection and preservation of biological diversity, in general, and genetic diversity, in particular, have to be done to de-escalate genetic diversity and prevent narrowing of genetic base of plants. Since medicinal plants are important sources for manufacturing popular medicines and herbal products, they have higher demand and hence are overharvested leading to their extinction. Medicinal plants account for 50,000–80,000 flowering plant species from which about 15,000 species are endangered with extinction because of habitat destruction, burgeoning demand leading to overharvesting and plant consumption (Bentley 2010) with elevated risk in African and Asian countries which harbour huge population of medicinal plants. There are two broad strategies of conservation: in situ and ex situ conservation. In situ conservation strategies protect groves of medicinal plants at their indigenous habitats preserving their ecosystem as a whole. Such conservation methods are applied to plants that cannot be cultured in vitro and include wild nurseries and natural reserves. Natural reserves are mainly protected areas and address the extinction issue caused due to destruction of habitats. There are about 12,700 natural reserves on Earth covering an area of 13.2 million km2. Wild nurseries are prepared for cultivation of medicinal plants shorter distances from their natural habitats (Chen et al. 2016). Ex situ conservation programs are implied to endangered species with low-growth rate, less abundance and high susceptibility to diseases (Hamilton 2004; Havens et al. 2006; Yu et al. 2010). It includes botanical gardens and seed banks. Botanical gardens harbour living collections of endangered plant species and contain taxonomically categorized medicinal plants. They are also important in spreading

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knowledge and awareness about medicinal plants. Seed banks are superior in ex situ conservation to botanical gardens and they protect the genetic diversity of medicinal flora while allowing access to plant samples whenever required. Good agricultural practices (GAP) and organic farming are more sustainable approaches of cultivation of medicinal plants as these methods provide quality assurance and assists in standardization of herbal products while preserving the medicinal plants and the ecosystem (Chan et al. 2012). Tissue culture can also be used as an alternative to produce desirable secondary metabolites from medicinal plants with advantages of easy transportation, storage and high-regeneration rates and in vitro conservation.

6.3

Methods for Assessing Genetic Diversity in Medicinal Plants

Genetic diversity can be assessed by the use of morphological, cytological, biochemical and molecular markers and all having the following common steps (Fig. 6.1). • Elucidating the diversity present within population or in between populations and encompass to units such as areas and regions. • Calculating the relationships in terms of geometric or genetic distances among all contents in the study. • Expressing these relationships with classification or ordination method at hand.

6.3.1

Morphological Markers

Morphological markers are conventionally used, and analysis of genetic diversity is done by planting the germplasms in large tracts of land in a particular experimental Fig. 6.1 Tools for assessment of genetic diversity

MOLECULAR MARKERS

MORPHOLOGICAL MARKERS

ASSESSMENT OF GENETIC DIVERSITY

CYTOLOGICAL MARKERS

BIOCHEMICAL MARKERS

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design. Visually observable characters such as seed shape, flower colour, growth habits, etc., are used to characterize different entries grown in the field. The evaluations are inexpensive, relatively easy and do not necessitate expensive and sophisticated technology. They are also affected by environmental constraints. The herbage yield and essential oil yield are the two characters used as morphological markers in medicinal plants.

6.3.2

Cytological Markers

Cytological analysis detects the unique features of chromosomes like knob and satellite, the number of nucleoli in the nucleus, defective plastids, chromosome size, arm ratio, position of centromere, chromosome volume, constitutive heterochromatin patterns, G banding, etc. However, availability of limited markers and expensive and laborious protocols restricts their applications.

6.3.3

Biochemical Markers

Different proteins and allelic variants of enzymes (isozymes) are employed as biochemical markers as separation by electrophoresis and specific staining techniques form specific banding patterns. Some merits of biochemical markers include its co-dominant nature, simple inheritance and spotting the differences at gene level. However, less abundance of isozyme markers and limited resolution reduces the use of such markers.

6.4

Molecular Markers

Molecular markers are extensively employed for genetic diversity analysis in medicinal crops. DNA markers have an edge over other markers as they are not affected by environmental changes and are reproducible, heritable, polymorphic and detect variations in coding and non-coding regions. DNA markers provide easily scorable information which is unique for each species. There are dominant and co-dominant markers available. Broad classification of molecular markers includes polymerase chain reaction (PCR)-based markers, hybridization-based markers and DNA sequence-based markers.

6.4.1

Hybridization-Based Markers

Restriction fragment length polymorphisms (RFLP) are the first hybridization-based markers developed in 1980. Firstly, a digestion of genomic DNAs using restriction enzymes is done followed by hybridization to specific oligonucleotide primers of two or more individuals of same species to identify their diverseness. RFLP markers

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are mainly used in creation of genetic maps. ITS-RFLP has been used for molecular diversity analysis in wild accessions of medicinal plant Peganum harmala L. (El-Bakatoushi and Ahmed 2018). Internal transcribed spacer (ITS) is present between 18S rDNA, 5.8S rDNA and 26S rDNA which present in a large segment of nuclear ribosomal DNA region. ITS-RFLP was done using restriction enzymes EcoRV, HaeIII and AluI. The level of polymorphism was found to be 66.67%. However, certain disadvantages like use of radioactivity, low rate of detection of polymorphism, the necessity of large quantities of DNA and highly skilled manpower limits the uses of RFLP markers.

6.4.2

PCR-Based Markers

6.4.2.1 Randomly Amplified Polymorphic DNA Randomly amplified polymorphic DNA (RAPD) is one of the first PCR-based markers developed based on the principle of using short-arbitrary oligonucleotide primers to amplify a set of DNA segments distributed throughout the genome. The primers are usually 10 bp long, present in opposite orientation and generate PCR products. The appearance of band confirms the presence of dominant allele while the absence confirms the presence of recessive allele. RAPDs are beneficial in terms of small quantity of DNA used, absence of any radioactive assays or use of any speciesspecific probe or need of any prior sequence information about the genome in question (Kumar and Gurusubramanium 2011; Choudhary et al. 2019). Arbitrarily primed polymerase chain reaction (AP-PCR) markers vary from RAPD as they have 15-bp long nucleotide sequences and have diverse amplification and electrophoretic conditions. DAF or DNA amplification fingerprinting is also a variant of RAPD. However, being a dominant marker, it fails to distinguish a heterozygous and homozygous locus. Other problems like sensitivity to changes in quality of DNA, PCR conditions and mismatches between primer and the template restrict the use of RAPD markers. Silybum marianum or milk thistle is a common weed grown in Mediterranean climatic regions (Andrzejewska et al. 2015). The fruits are source of Silymarin that is used for treating liver-related ailments (AbouZid et al. 2016). RAPD markers are used for population studies and provide information about genetic diversity in milk thistle. It was found that RAPD markers showed broad level of polymorphism (73.2%) using 12 RAPD primers out of which only 8 gave reproducible polymorphic DNA pattern (Hamouda 2019). 6.4.2.2 Amplified Fragment Length Polymorphism Amplified fragment length polymorphism (AFLP) is also a PCR-based technique that was demonstrated by Vos et al. (1995). It combines mechanisms of both RFLP and RAPD as it relies upon amplification of DNA fragments derived from restriction digestion of genomic DNA by particular restriction endonucleases. AFLP is an efficient technique for detection of polymorphisms and points out linkages present in a segregating population (Paun and Schönswetter 2012). Other advantages include multiplexing and applied in amplification of numerous genomic fragments

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from many individuals at same time. The AFLP assay can give accurate results only when following factors are optimized such as reagents, PCR reaction conditions and a sound electrophoresis system followed by accurate sizing software. Another feature that makes AFLP preferable over other markers is the capability to change the number of AFLP bands per assay by reducing or increasing the number of nucleotides throughout the selective amplification (Vos et al. 1995). This advantage is employed in genetic diversity assessment of species with big genome size (Han et al. 1999; Yue 2001). Dendrobium thyrsiflorum is an endangered medicinal Orchid which usually grows in India, South China, Thailand and in different South Asian countries. It is beneficial as it releases certain secondary metabolites that are helpful in prevention of various degenerative diseases. AFLP markers are run in D. thyrsiflorum populations and about 97% of genome was found to be polymorphic (Bhattacharya et al. 2017). AFLP markers were also found to show significant ( p < 0.05) association with anti-oxidant activity measured by DPPH (1,1-diphenyl-2-picrylhydrazyl) activity. All these findings suggest that AFLPs have significant role in early progeny selection and genetic mapping which leads to formulate conservation practices to preserve the medicinal orchid.

6.4.2.3 Microsatellite Markers Simple sequence repeats (SSR), short-tandem repeats (STRs), simple sequence length polymorphisms (SSLP) and sequence tagged microsatellite sites (STMS) are the short tandem repeats of length 1–10 bp and constitute microsatellite markers. These repeated sequences can be of varying length of 2–8 bp, 1–6 bp or 1–5 bp and total number of repeats range between 10 and 100. SSR is a PCR-based marker, and flanking primers are used and annealed to 3′ and 5′ ends of the DNA template. The flanking regions are likely to be conserved within the species or higher taxonomic levels. The merits of SSR markers are co-dominant, highly polymorphic, uniformly distributed in the genome, which are abundant, easily automated and have high reproducibility. Hence, SSR markers are widely used and popular markers. EST-SSRs are an alternative to SSR markers and are developed on basis of EST databases that are available. Single read sequences generated from partial sequencing of group of mRNAs that have been reverse transcribed into cDNA constitute EST (Expressed sequence tags) (Mondini et al. 2009). EST-SSRs are easily constructed and are accurate and inexpensive as compared to genomic SSRs. Construction of genomic libraries and sequencing of large number of clones to discover SSR containing DNA regions is detoured in this approach and thus construction of EST-SSRs consumes less time. The ESTs lack introns which may be a drawback because of loss of priming sites which leads to failed amplification. EST-SSRs are also less polymorphic than normal SSRs. SSRs are used for genetic diversity assessment in Chrysanthemum morifolium where the average polymorphism information content was found to be 0.972 (Feng et al. 2016). SSR markers revealed the phylogenetic relationship between Chrysanthemum moriflorum cultivars, which were very reliable with the classification of the medicinal crop according to their origin and natural distribution.

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6.4.2.4 Inter Simple Sequence Repeats Inter simple sequence repeats (ISSR) markers are used to amplify sequences, and flanking SSR markers present in opposite orientation. They were first discovered by Zietkiewicz et al. (1994). There are many applications of ISSR markers owing to its many advantages of being rapid, highly reproducible, easier, automated and having no need of any prior information about genome of species. The applications consist of identification of cultivars and varieties, characterization of accessions and assessing genetic diversity between closely related cultivars (Kumar et al. 2008; Sharma et al. 2018). The resulting band confirms a DNA sequence bordered by two inverted microsatellites. ISSR markers are used to assess genetic diversity of Zizyphus spinachristi. The leaf extracts of Zizyphus are neuroprotective and therapeutic, and ease ailments like wounds and ulcers. It was found that maximum value of genetic diversity was found to be 90% in Zizyphus populations (Alansi et al. 2016). 6.4.2.5 Sequence-Related Amplified Polymorphism Sequence-related amplified polymorphism (SRAP) was first discovered by Li and Quiros in 2001 and developed uniquely for gene tagging in Brassica oleracea. Here, ambiguous primers are used to amplify open reading frames (ORFs) of genes (Li and Quiros 2001). The primers, 17–18 bases long, having core sequences of 13–14 nucleotides length which in turn contains filler sequences at 5′ end, followed by CCGG in the forward primer and AATT in the reverse primer and ending with three selective nucleotides at 3′ end are used. The reliability, reproducibility, simplicity, genome wide coverage and effectiveness of SRAP markers have led to its application in characterization of plant genetic resources and ecological populations and other uses (Aneja et al. 2012; Robarts and Wolfe 2014). SRAP markers were used for genetic diversity assessment of Lavender and analysis of molecular variance with the markers showed high-level of genetic variation (96.60% of the total variations) among the assessed groups of Bulgarian and foreign varieties (Zagorcheva et al. 2020). 6.4.2.6 Cleaved Amplified Polymorphic Sequences Cleaved amplified polymorphic sequences (CAPS) or PCR-RFLP is the assay that combines principles of RFLP with amplification of short fragments of DNA rather than the entire genome (Heubl 2010, 2013; Hu et al. 2014). It includes PCR amplification of target gene using specific set of primers and restriction digestion of PCR amplicons with restriction endonucleases and separation of digestion products by agarose gel electrophoresis. A CAP is co-dominant, inexpensive and easily interpreted, but suffers from limitations of being less adaptable for high throughput automatic or robotic systems. Some modern techniques have been used for the development of CAPS markers such as genotyping-by-sequencing or whole genome sequencing (WGS) (Jun et al. 2012; Cui et al. 2012) and SNP-detecting method based on Illumina Golden Gate technology (Cui et al. 2012).

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6.4.2.7 Single Nucleotide Polymorphism Single nucleotide polymorphism (SNPs) denote to the single nucleotide changes present in genomes of different individuals. It is the most abundant marker that is extensively distributed throughout the genome. SNPs are widespread in the non-coding domain of the genome. SNPs in coding regions create non-synonymous and synonymous mutations resulting in production of different proteins and different phenotypes. SNP genotyping is done on the basis of allele specific hybridization, followed by oligonucleotide ligation and primer extension or invasive cleavage (Mondini et al. 2009). 6.4.2.8 Diversity Arrays Technology Diversity arrays technology (DArT) assay is developed as an alternative to other molecular markers such as RFLP, AFLP and SSR as it subdues the disadvantages of the mentioned markers. DArT covers several thousand loci in one assay and hence usually runs in large genomes of polyploid species such as wheat. It is a highthroughput technology and cost-effective whole genome fingerprinting tool (Govindaraj et al. 2014). Several steps are carried out such as complexity reduction of metagenome which employs restriction digestion, adapter ligation and amplification followed by cloning of genomic representation and formation of microarrays resulting in a “discovery array” and hybridization with fluorophore resulting in hybridization signal which intensifies for different individuals. This was ended with data extraction and analysis. 6.4.2.9 Sequence-Characterized Amplified Region The genomic DNA fragment at one locus amplified in a PCR reaction involving a pair of specific oligonucleotide primers comprises sequence-characterized amplified region (SCAR) marker (Paran and Michelmore 1993). SCAR markers are useful as they are less prone to PCR reaction conditions and are highly reproducible and scored effortlessly as compared to RAPD markers. RAPD markers can be improved for reliability by converting them into SCAR markers. 6.4.2.10 Start Codon Targeted Markers Start codon targeted markers (SCoT) markers involve small conserved region in genes covering the ATG or initiation codon and uses a single 18 nucleotide long primer in Polymerase Chain Reaction and a high annealing temperature of 50 °C is maintained (Collard and Mackill 2009). Lower recombination occurs between SCoT markers and gene of interest than other markers making it useful in marker assisted selection. QTL mapping, DNA fingerprinting and analysis of genetic diversity and structure are some areas that uses SCoT markers. Here no information about genome sequence is needed. 6.4.2.11 Random Amplified Microsatellite Polymorphisms A new generation of markers known as random amplified microsatellite polymorphisms (RAMPs) is developed so as to overcome demerits of two popular markers SSR and RAPD. RAMPs were developed by Wu et al. and involve

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amplification of genomic DNA by a SSR primer in the presence or absence of RAPD primers. The PCR products were run in submarine agarose gel electrophoresis. RAMPs are widely dispensed in whole genome, which show high polymorphism and are rapid inexpensive assays.

6.4.2.12 Selective Amplification of Microsatellite Polymorphic Loci Selective amplification of microsatellite polymorphic loci (SAMPL) was developed by Morgante and Vogel in 1994, which have steps similar to AFLAP but contains a slight modification and is selective amplification that employs a microsatellite primer along with an AFLP primer and this step decides the number of PCR products produced. 6.4.2.13 DNA Amplification Fingerprinting DNA amplification fingerprinting (DAF) is an alternative to normal PCR-based markers as it involves two-step program rather than usual three steps PCR done under various stringent conditions. The resultant PCR products are separated in polyacrylamide agarose gel electrophoresis led by silver staining. Here, 5–8 bp long primers were used. Fluorescent tagging of primers and predigestion of DNA template are also done. 6.4.2.14 Directed Amplification of Minisatellite Region DNA Directed amplification of minisatellite region DNA (DAMD) uses minisatellites to check polymorphism. It was developed by Heath et al., in 1993. The assay is carried out under higher stringent conditions in PCR. They have high reproducibility than RAPD (Saleh 2019).

6.5

Diversity Assessment Works in Medicinal Plants

Naik et al. (2017) employed a total of 25 RAPD primers and 20 ISSR primers to study the genetic diversity analysis of Costus pictus. Analysis of RAPD markers showed 343 loci among 124 were found polymorphic and the average polymorphism was 4.96 loci per primer. The ISSR markers analysis showed 177 loci among 77 were found polymorphic and the average polymorphism was 3.85 loci per primer. The UPGMA dendrogram generated after the analysis revealed low level of genetic divergence in the accessions collected from South and West regions. AFLP markers are used to analyse genetic diversity in Egyptian Origanum. Four selective primer combinations were employed to assess polymorphisms among the studied plant species, and Polymorphism Information Content (PIC) was found to be 0.98 and 0.99 (El-Demerdash et al. 2019). SSR markers were used to evaluate genetic diversity in 30 accessions of Centella asiatica (Sakthipriya et al. 2018). The molecular screening showed low polymorphism of 0.019 between samples analysed in the study. Ma et al. (2021) studied genetic diversity of eight cultivated populations of Amomum tsaoko, which were analysed using SRAP and ISSR markers. Based on

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their study, results revealed that 139 (99.29%) of 140 and 185 (99.46%) of 186 bands were polymorphic by SRAP and ISSR primers amplification, respectively. SNP markers are used in genetic diversity studies in Vitex negundo, an aromatic medicinal plant having anti-tumour properties. Fourteen SNPs were discovered in five different populations when they were sequenced by direct partial sequencing of genes. Assessment of SNP variation in Vitex negundo showed high levels of genetic variation (Sujin 2015). Valeriana officinalis is a medicinal plant that produces essential oils, valepotriates and sesquiterpenic acids which cures anxiety. DArT sequence is employed in 188 individuals of Valeriana representing 19 accessions and resulted in 65,000 SNPs with high-reproducibility coefficients which implies DArT sequence is effective and useful in genetic analysis of V. officinalis (Boczkowska et al. 2020). Coneflower plants (Echinacea) have many therapeutic properties and are also used for ornamental purposes. SCoT-PCR analysis led to identification of different accessions of Echinacea. Polymorphism between species as confirmed by nine SCoT primers proved the merits of SCoT markers in the assessment of genetic diversity (Jedrzejczyk 2019). RAMPs were used for analysis of genetic diversity in Lycium species (Liu et al. 2020). They were used to analyse the genetic association and distance in Lycium varieties. SAMPL was used for characterization of genetic diversity in Azadirachta indica (Singh et al. 2002). A useful application of DAF is genetic diversity assessment in Ipomoea batatas (Guohao et al. 1995). DAMD markers were in assessment of genetic diversity as well chemical diversity among six natural populations of Dendrobium nobile (Bhattacharyya et al. 2015).

6.6

Conclusion

Our world is a treasure trove of many medicinal plants which have huge significance in treatment of serious diseases and also in common ailments. However, these medicinal plants are threatened by many anthropogenic factors and hence are needed to be conserved. Assessment of genetic diversity of different medicinal plants by using molecular markers is an important activity in germplasm conservation. Important markers like RAPD, AFLP, RFLP, SSR, SNP and many more are employed in this respect. Many Transposable elements based molecular markers such as sequence-specific amplified polymorphism (S-SAP), inter-retrotransposon amplified polymorphism (IRAP), retrotransposon-based amplified polymorphism (RBIP), etc., and RNA-based molecular markers can also be used for genetic diversity assessment.

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Non-coding RNA Based Marker: A New Weapon in Armory of Molecular Markers Ravi S. Singh, Prakash Singh, Sweta Sinha, Ujjwal Kumar, Ruchi Kumari, and Sanjeev Kumar

7.1

Introduction

The so-called junk regions of DNA are now being realized as “Gold mines” for useful genomic resources. Non-coding RNAs (ncRNAs) are the ones among these genomic resources that comprise very diverse RNA molecules such as microRNA (miRNA), small interfering RNAs (siRNAs), piwi-interacting RNAs (piRNAs), snRNA, small nucleolar RNAs (snoRNA), promoter-associated transcripts (PATs), enhancer RNAs (eRNAs), circular RNAs (circRNAs), long non-coding RNAs (lncRNAs), etc. (Taft et al. 2010; Ma et al. 2013; Bhatia et al. 2020). These are important players in the gene regulation at epigenetic and genetic level. Out of these ncRNAs, miRNAs (~22 nt long) are abundant ncRNAs that act negatively at transcriptional and translational level, while lncRNAs (>200 nt) play both up- as R. S. Singh (✉) Department of Genetics and Plant Breeding, Bihar Agricultural University, Sabour, India P. Singh Department of Genetics and Plant Breeding, Veer Kunwer Singh College of Agriculture, Bihar Agricultural University, Sabour, India S. Sinha Department of Molecular Biology and Genetic Engineering, Bihar Agricultural University, Sabour, India U. Kumar Department of Community & Family Medicine, All India Institute of Medical Sciences, Deoghar, India R. Kumari Department of Home Science-Food Science and Nutrition, Tilka Manjhi Bhagalpur University, Bhagalpur, India S. Kumar Department of Plant Breeding and Genetics, K.K. University, Nalanda, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_7

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well as down-regulatory roles at the transcriptional level or post-transcriptional level or even epigenetic level. LncRNAs have been classified according to their four major features, viz., genomic location and context, effect exerted on DNA sequences, mechanism of functioning and their targeting mechanism (Ma et al. 2013). Though, lncRNAs lack the coding potential but possess important roles in regulating gene expression, in response to biotic and abiotic stresses (Summanwar et al. 2021; Bhatia et al. 2020, 2021). The field of lncRNAs is an emerging field that has gained considerable interests of researchers in recent years. Though the cost of high-throughput sequencing work has come down heavily and is cost effective now, molecular markers like simple sequence repeats (SSRs) are microsatellite markers found throughout the genome. These markers, due to hyper variability and co-dominant nature, can be utilized in molecular characterization of germplasm and diversity analysis. Due to advances in in silico analyses techniques, finding lncRNA-derived SSRs seems easier now, but their validation is still cumbersome for researchers. Despite availability of so many types of molecular markers, the lncRNA-derived SSR markers could be the marker of choice as these show high level of polymorphism and cross-transferability. These markers could be important assets for genetic studies and breeding programs. In this book chapter, we discuss mainly on lncRNAs type of ncRNA with updated information on lncRNA-related databases, tools, research and on the aspect of lncRNA-derived SSRs as a new generation of molecular markers for higher efficiency, specificity and high potentiality of their use in markers-assisted selection in molecular breeding of crops.

7.1.1

Detection of LncSSRs

SSRs can be found throughout the genome and serve as genomic landmarks helping identity of individual bearing it. Such molecular markers are crucial for genetic studies including gene tagging, QTL mapping, germplasm characterization, and marker-assisted breeding of crops. Advances in high-throughput next generation sequencing technologies have made it economical and feasible to analysis genome as well as transcriptome of any organism including ncRNA transcriptome such as lncRNA transcriptome sequencing. The data generated can be analysed for discovery of ubiquitous SSRs in genome and transcriptome. The lncRNAs transcripts usually analysed for the detection of SSRs by Krait v1.1.0 (Du et al. 2018) or MISA tools which are commonly used tools for the genome-wide identification of SSRs. Types of LncRNA motifs based on number of nucleotides per repeats and arrangement of nucleotides per repeats are shown in Table 7.1. Several marker systems have been in use in markers-assisted selection (MAS) and genetic diversity analyses in crops such as random amplified polymorphic DNA (RAPD), restriction fragment length polymorphism (RFLP), start codon targeted (SCoT), amplified fragment length polymorphism (AFLP), inter-simple sequence repeat (ISSR), simple sequence repeats (SSR), single nucleotide polymorphism (SNP), etc. Although there was surge in SNPs markers discovery due to approaches

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Table 7.1 Types of LncRNA motifs SSR motif type Some SSR motif sequences I. Based on number of nucleotides per repeats Mono A/T/G/C Di AC/GT, AG/CT, AT/AT, CA/TG, GA/TC, TA/TA Tri AAC/AAG/AAT/ACA/ACC/AGA/AGG/ATA/CAA/ CAC/CCA/GAA/TAA/TAC/TGA Tetra AAAG/AAAT/AATA/ACAA/ATAA/TAAA/TTTC/ AAAAT Penta GGGTG/CACCC Hexa AAAAGA/GAAAAA II. Based on arrangement of nucleotides per repeats Perfect GTGTGT Compound CTCTGAGA Imperfect ATATATGCTCT Mixed CTCTGAGACACA

Number of repeats (na) (A)10 (AC)6 (AAC)5 (AAAG)4 (GGGTG)3 (AAAAGA)3 (GTGTGT)3 (CTCTGAGA)2 (ATATATGCTCT)2 (CTCTGAGACACA) 2

na = variable number

like Genotyping-By-Sequencing (GBS) recent years, SSRs are still widely used due to their co-dominant nature and comparatively higher reproducibility among various markers. SSRs are ubiquitous in genome, so attempts have been made to find them in different regions of genome including non-coding transcribed regions. Among ncRNAs, miRNAs are largely transcribed from intergenic regions, but some exonic miRNAs have also been reported. In contrast to miRNAs, the lncRNAs are transcribed from various genomic regions (exon, intron, UTRs, promoter, intergenic regions) (Bhandawat et al. 2020). In recent years, lncRNA-based SSR markers are being developed for use in marker-assisted selection in plant breeding. The comparison among these molecular markers has been enlisted in Table 7.2.

7.1.2

Distribution in Genome

Genes for LncRNAs are widely distributed in the genome, they may be found residing in intergenic intergenic, intronic, on either strand of DNA (Sense or antisense) and named accordingly as intergenic lncRNA, intronic lncRNA, sense lncRNA and antisense lncRNA. The lncRNAs can also be catergorized into different groups based on their length: small-lncRNA (200–950 nt), medium-lncRNA (950–4800 nt) and large-lncRNA (4800 nt~) (Ma et al. 2013).

7.1.3

Development of LncRNA-SSRs Markers

The flowchart with descriptions of SSR discovery to marker development has been shown in Fig. 7.1. In brief, the raw data of lncRNA-Seq is generated first or retrieved

Moderate

High

Cross species— transferability Rating among markers

Cost effectiveness

High

Hybridization based Low

Method followed

Degree of polymorphism

RFLP Co-dominant

Molecular markers Dominance

Low to moderate Low to moderate Low to moderate Low to moderate

PCR-based

AFLP Dominant

Low to moderate Low to moderate Low to moderate Low to moderate

PCR-based

RAPD/ISSR Dominant

Moderate

Moderate to high

High

PCR-based/sequence based High

SNP Co-dominant

High

High

Moderate

Low

High

High

SSR Co-dominant GenicEST-SSR SSR PCRPCRbased based High Low

Table 7.2 Comparison of molecular markers (Beckmann and Soller 1986; Williams et al. 1990; Powell et al. 1996; Vos et al. 1995)

Low

High

High

High

LncRNA SSR PCR-based

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Raw data of Lnc RNA-Seq [generated or retrieved from NCBI SRA database]

Adapter Removal [Used to filter out the adapters sequences and to perform quality trimming at Q30]

Assembly [For example, software like Trinity used for assembling the preprocessed reads: a tool de novo transcriptome assembly of RNA-seq data]

Clustering [cd-hit-est a package of CD-HIT to cluster the assemblies created by trinity]

SSR discovery [using Krait/MISA]

BLAST

CO Annotation

[for similarity search/assigning function]

Primer Designing [using Primer 3]

Validation of primers (Polymorphism test/cross-transferability of primers)

LncRNA-SSR marker

Fig. 7.1 LncRNA SSR discovery and marker development

from NCBI SRA database (https://www.ncbi.nlm.nih.gov/sra), filter out the adapters sequences and to perform quality trimming at Q30 and cluster the assemblies created by Trinity (cd-hit-est a package of CD-HIT). Then use tool like Krait/MISA to find the frequency and distribution of SSRs on the identified putative lncRNA. Further, BLAST for similarity search/assigning function and GO Annotation may be performed to see if discovered SSR is near to any know gene. Then primers flanking these SSRs can be designed using Primer3 (http://primer3.ut.ee). These primers can be validated by polymorphism test/cross-transferability. After this validation, we chose most polymorphic and highly transferable as the lncRNA-SSR for use in genotyping.

7.1.4

Databases/Tools for LncRNA

Recent surge in studies on lncRNAs and report on diverse role across disease, genomic regulation and development has led to the development of several databases and prediction methods for lncRNAs. These databases and prediction methods are helping in silico analysis of lncRNAs for localization, formation of a triplex with a DNA sequence, determining which particular regions of DNA the

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Table 7.3 LncRNA Databases/tools useful for study on LncRNA-derived SSRs Name of databases MISA-web SA-SSR

SSR locator

LncTar

PLncDB V2.0

NONCODEV6 EVLncRNAs2.0

CANTATAdb

Locate-R

Capsule-LPI SEEKR

LNCipedia version 5.2

Description A web server for SSRs discovery A software tool developed to find SSRs in a sequence (presumably of DNA or RNA) A tool for searching of SSRs combined with primer design and PCR simulation A tool for predicting RNA targets of lncRNAs and can be used for predicting putative interactions among various types of RNA molecules (mRNA, ncRNAs including lncRNAs, pre-miRNAs, and other types of noncoding RNAs) Harbours a large number of plant lncRNAs from more than 80 species Contains 94,697 lncRNAs from 23 plant species The database of experimentally validated functional lncRNAs • This is a lncRNA database for plants only. • It contains lncRNAs from different model plant species Subcellular localization of lnc RNAs based on nucleotide compositions A tool for prediction of interaction between LncRNA and protein A tool to compare lncRNAs to infer their function (k-mer-based classification) A database for lncRNA sequence and annotation

Web address http://misaweb.ipkgatersleben.de/ http://github.com/ ridgelab/SA-SSR

References Beier et al. (2017)

https:// microsatellite.org/ ssr.php http://www.cuilab. cn/lnctar

da Maia et al. (2008) Li et al. (2015)

https://www. tobaccodb.org/ plncdb/ http://www. noncode.org/ https://www.sdklabbiophysics-dzu.net/ EVLncRNAs2/ http://cantata.amu. edu.pl/

Jin et al. (2020)

http://locate-r. azurewebsites.net/ http://csbg-jlu.site/ lpc/predict https://app.med.unc. edu/seekr/home https://lncipedia. org/

Zhao et al. (2020) Zhou et al. (2020) Szcześniak et al. (2019) Ahmad et al. (2020) Li et al. (2021) Kirk et al. (2018) https:// lncipedia. org/

lncRNA interact with, etc. Some of the LncRNA Databases/Tools useful for study on LncRNA-derived SSRs have been compiled in Table 7.3. The LncRNA-derived SSRs are being reported from various crops in their studies (Table 7.4).

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Table 7.4 LncRNA-derived SSRs reported in crops Crops Chickpea

Study on Identification and functional prediction of salt-stress related lncRNAs LncRNA-derived-SSR markers for Dongxiang wild3 rice (Oryza rufipogon Griff.)

Rice

Wheat

Identification and characterization of novel ncRNA-derived SSRs

Brassica napus

Development of lncRNA-based SSR markers from lncRNAs induced in response to infection by Plasmodiophora brassicae

Capsicum

NcRNA based SSR markers in Capsicum species

Flax

Regulatory gene-derived SSRs use in genetic diversity and population structure analysis

7.2

LncRNA SSR identified Around 614 lncRNA-SSRs were identified with higher efficiency and specificity A total of 1878 SSR loci were detected from the 30 lncRNA sequences of Dongxiang wild rice, and 1258 lncRNAderived-SSR markers were developed Identified a total of 661 SSRs from pre-miRNA (15), small nuclear RNA (25) and lncRNA (621) The SSR markers were identified within 196 differentially expressed lncRNAs. One of these marker was capable of detecting the resistance in 98% of the DH lines 120 ncRNA-SSRs (including 60 each miRNASSRs and lncRNASSRs) were used for genotyping of Capsicum species 74 lncRNAs regulatory genederived SSRs (ReG-SSR) markers generated 76 alleles, with an average of 2.5 alleles per primer

References Kumar et al. (2021) Yang et al. (2021)

Bhandawat et al. (2020)

Summanwar et al. (2021)

Jaiswal et al. (2020)

Saha et al. (2019)

Challenges and Future Prospects

As day-by-day genomic and RNA-seq data on LncRNAs are being generated and so more number of SSRs are being discovered. But challenges persist in fishing out of these SSRs residing on LncRNAs, viz., (1) issue of finding all functional lncRNAs via RNA/DNA sequencing or array-based methods, as many lncRNAs are often lowly expressed and/or even expressed in specific conditions, (2) as many RNA-seq protocols follow oligo(dT) selection and thereby enable to sequence poly-adenylated RNAs only and missing many of the lncRNA transcripts lacking poly(A) tails. Hence, Ribo-Zero-Seq (rRNA depletion protocol based) and DSN-Seq (DuplexSpecific Nuclease (DSN) degradation based provide precise quantification of transcripts in contrast to microarrays or mRNA-Seq (poly A selection) and substantially more information on non-poly (A) RNA (Zhao et al. 2014).

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Though the cost of high-throughput sequencing work has come down heavily and is cost effective now, and advances in in silico analyses techniques, finding LncRNA-SSRs seems easier now, but their validation is still cumbersome for researchers. The prospect of LncRNA-derived SSRs is very high as these markers are generally tissue specific and expressed in specific conditions. The lncRNAs may up-regulate or down-regulate the gene expression (Jaiswal et al. 2020). Therefore, it would be more helpful in distinguishing one genotype from another, and thus help in our pre-breeding and breeding programme.

7.3

Conclusion

Despite availability of so many types of molecular markers, the lncRNA-derived SSR markers could be the marker of choice as these are highly polymorphic, transferable and is recent one. These molecular markers could be of great use in marker-assisted selection and genetic diversity analyses, thus boosting the crop improvement programme. Acknowledgements Authors duly acknowledge the Bihar Agricultural University, Sabour for the grant of research project entitled “Genome-wide screening and identification of long non-coding RNAs (lncRNAs)-related to alternaria blight in Indian mustard (Brassica juncea (L.) Czern. & Coss” [State funded (Project code: SNP/CI/Rabi/2019-13)] to initiate the research work on LncRNA.

References Ahmad A, Lin H, Shatabda S (2020) Locate-R: subcellular localization of long non-coding RNAs using nucleotide compositions. Genomics 112(3):2583–2589 Beckmann JS, Soller M (1986) Restriction fragment length polymorphisms and genetic improvement of agricultural species. Euphytica 35:111–124. https://doi.org/10.1007/BF00028548 Beier S, Thiel T, Münch T, Scholz U, Mascher M (2017) MISA-web: a web server for microsatellite prediction. Bioinformatics 33(16):2583–2585 Bhandawat A, Sharma H, Pundir N, Madhawan A, Roy J (2020) Genome-wide141 identification and characterization of novel non-coding RNA-derived SSRs in142 wheat. Mol Biol Rep 47: 6111–6125 Bhatia G, Singh A, Verma D, Sharma S, Singh K (2020) Genome-wide investigation of regulatory roles of lncRNAs in response to heat and drought stress in Brassica juncea (Indian mustard). Environ Exp Bot 171:103922 Bhatia G, Upadhyay SK, Upadhyay A et al (2021) Investigation of long non-coding RNAs as regulatory players of grapevine response to powdery and downy mildew infection. BMC Plant Biol 21:265 da Maia LC, Palmieri DA, de Souza VQ, Kopp MM, de Carvalho FI, Costa de Oliveira A (2008) SSR locator: tool for simple sequence repeat discovery integrated with primer design and PCR simulation. Int J Plant Genomics 2008:412696. https://doi.org/10.1155/2008/412696 Du L, Zhang C, Liu Q, Zhang X, Yue B (2018) Krait: an ultrafast tool for genome-wide survey of microsatellites and primer design. Bioinformatics 34(4):681–683. https://doi.org/10.1093/ bioinformatics/btx665

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Jaiswal V, Rawoof A, Dubey M, Chhapekar SS, Sharma V, Ramchiary N (2020) Development and characterization of non-coding RNA based simple sequence repeat markers in Capsicum species. Genomics 112(2):1554–1564 Jin J, Lu P, Xu Y, Li Z, Yu S, Liu J, Wang H, Chua NH, Cao P (2020) PLncDB V2.0: a comprehensive encyclopedia of plant long noncoding RNAs. Nucleic Acids Res 49:1489–1495 Kirk JM, Kim SO, Inoue K, Smola MJ, Lee DM, Schertzer MD, Wooten JS, Baker AR, Sprague D, Collins DW et al (2018) Functional classification of long non-coding RNAs by k-mer content. Nat Genet 50:1474–1482 Kumar N, Bharadwaj C, Sahu S, Shiv A, Shrivastava AK, Reddy SPP, Soren KR, Patil BS, Pal M, Soni A, Roorkiwal M, Varshney RK (2021) Genome-wide identification and functional prediction of saltstress related long non-coding RNAs (lncRNAs) in chickpea (Cicer arietinum L.). Physiol Mol Biol Plants 27(11):2605–2619 Li J, Ma W, Zeng P, Wang J, Geng B, Yang J, Cui Q (2015) LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Brief Bioinform 16(5):806–812 Li Y, Sun H, Feng S et al (2021) Capsule-LPI: a LncRNA–protein interaction predicting tool based on a capsule network. BMC Bioinform 22:246 Ma L, Bajic VB, Zhang Z (2013) On the classification of long non-coding RNAs. RNA Biol 10(6): 925–933. https://doi.org/10.4161/rna.24604 Powell W, Morgante M, Andre C et al (1996) The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol Breed 2:225–238. https://doi.org/10.1007/ BF00564200 Saha D, Rana RS, Das S, Datta S, Mitra J, Cloutier SJ, You FM (2019) Genome-wide regulatory gene-derived SSRs reveal genetic differentiation and population structure in fiber flax genotypes. J Appl Genet 60(1):13–25. https://doi.org/10.1007/s13353-018-0476-z Summanwar A, Basu U, Kav NNV, Rahman H (2021) Identification of lncRNAs in response to infection by Plasmodiophora brassicae in Brassica napus and development of lncRNA-based SSR markers. Genome 64(5):547–566. https://doi.org/10.1139/gen-2020-0062 Szcześniak MW, Bryzghalov O, Ciomborowska-Basheer J, Makałowska I (2019) CANTATAdb 2.0: expanding the collection of plant long noncoding RNAs. Methods Mol Biol 1933:415–429 Taft RJ, Pang KC, Mercer TR, Dinger M, Mattick JS (2010) Non-coding RNAs: regulators of disease. J Pathol 220(2):126–139. https://doi.org/10.1002/path.2638 Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M et al (1995) AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res 23 (21):4407–4414. https://doi.org/10.1093/nar/23.21.4407. PMID: 7501463; PMCID: PMC307397 Williams JGK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV (1990) DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res 18:6531–6535 Yang W, Fan Y, Chen Y, Ding G, Liu H, Xie J, Zhang F (2021) Genome-wide development of lncRNA-derived-SSR markers for Dongxiang wild3 rice (Oryza rufipogon Griff) Zhao W, He X, Hoadley KA et al (2014) Comparison of RNA-Seq by poly (a) capture, ribosomal RNA depletion, and DNA microarray for expression profiling. BMC Genomics 15:419. https:// doi.org/10.1186/1471-2164-15-419 Zhao L, Wang J, Li Y, Song T, Wu Y, Fang S, Bu D, Li H, Sun L, Pei D et al (2020) NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants. Nucleic Acids Res 49:165–171 Zhou B, Ji B, Liu K, Hu G, Wang F, Chen Q, Yu R, Huang P, Ren J, Guo C et al (2020) EVLncRNAs 2.0: an updated database of manually curated functional long non-coding RNAs validated by low-throughput experiments. Nucleic Acids Res 49:86–91

8

Molecular Marker Techniques in Niger Crop Improvement Suvarna

8.1

Introduction

Niger (Guizotia abyssinica (L. f.) Cass.) is a minor oilseed crop grown in East Africa and the Indian subcontinent (Getinet and Sharma 1996). It is a small-scale, minor oilseed crop grown on marginal soils in India for both its seed and oil. In many regions of the nation, the niger is known by the common names ramtil, jagnior, ramtal (Gujrati), jatangi (Hindi), khurasani (Marathi), karaleor, uchchellu, gurellu (Kannada), verrinuvvulu (Telugu), payellu (Tamil), sarguza (Bengali), alashi (Oriya), ramtil. Its names in Ethiopia are noug and noog (Getinet and Sharma 1996). Niger seed is used for human consumption, for bird feed and for extraction of oil. The oil is having pale yellow colour with a pleasant odour and a nutty taste. The raw oil encompasses low acidity, which can directly be used for cooking. Normally, it has got a poor shelf life and will turn rancid when stored for a long period. The oil is used for anointing, lighting, painting and cleaning of machinery. It is used as an alternate for sesame oil in pharmaceutical industries and it is also used in soapmaking. The seed contains about 30–50% of quality oil with four fatty acids, comprising two major unsaturated fatty acids, viz., linoleic acid (18:2) (75–80%) and oleic acid (18:1) (5–8%) and two main saturated fatty acids, viz., palmitic acid (16:0) (7–8%) and stearic acid (18:0) (7–8%) (Getinet and Teklewold 1995). The Indian types contain 55% linoleic acids and 25% oleic acids (Nasirullah et al. 1982). The oil has good keeping quality with 70% toxin-free unsaturated fatty acids. The oil is appraised for its high content of linoleic acid (70–75 g/100 g) (Dasthagiriah and Nagaraj 1993; Datta et al. 1994). The physical characteristics of oil and its fatty acid composition are provided in Tables 8.1, 8.2, and 8.3. Niger seed oil can be utilized as a biodiesel plant (Yadav et al. 2012a and Ramesh babu and Godiganur 2015).

Suvarna (✉) College of Agriculture, Raichur, University of Agricultural Sciences, Raichur, Karnataka, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_8

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Table 8.1 Basic nutritional components of niger seeds

Component Oil Proteins Soluble sugars Crude fibre Moisture

Percentage (%) 30–40 10–25 12–18 10–20 10–11

Table 8.2 Physical characteristics of oil (Nasirullah et al. 1982)

Characteristic Refractive index 40 °C Saponification value Iodine value Unsaponifiable matter Moisture Bellier turbidity temperature F.F.A

Range 1.4655–1.4673 187–195 112–129.0 0.5–1.0% 0.5–0.75% 24.5–27.8 °C 0.2–2.0%

Table 8.3 Fatty acid composition of niger seed oil (Nasirullah et al. 1982)

Fatty acid Myristic acid (C14:0) Palmitic acid (C16:0) Stearic acid (C18:0) Oleic acid (C18:1) Linoleic acid (C18:2)

Percentage (%) 1.7–3.4 5.8–13.0 5.0–7.5 13.4–39.3 45.5–65.8

An oil cake that is produced after extraction of oil is used as manure or animal feed. Approximately 42% of the de-oiled flour is protein (Nagaraj 1990). Methionine, cystine, lysine, leucine and isoleucine were discovered to be the limiting amino acids and account for 30% of the protein in the meal (Getinet and Sharma 1996). Poorly managed marginal, unproductive fields and hilly areas are used to grow niger crop. The crop can withstand attacks from wild animals and insect pests as well as diseases. Due to its high potential for biofertilizer use, soil conservation and land rehabilitation, the crop that comes after niger is always of high quality. Plants can be used a ‘bee plant’. Generally, it is cultivated during kharif seasons, but in some regions of India, it is also grown in rabi season. It is majorly grown in Madhya Pradesh, Orissa, Bihar, Maharashtra, Karnataka and Andhra Pradesh. During 2020–2021, it occupied an area of 134,000 ha in India, resulting in production of 42,000 tonnes with the productivity of 317 kg/ha. In Karnataka, it was cultivated over 1000 ha with a total production of 0.25000 tonnes bearing a productivity of 247 kg/ha (Anonymous 2022).

8.2

Botanical Description

The Guizotia genus belongs to the tribe Heliantheae and subtribe Coreopsidinae in the family Compositae/Asteraceae. The genus has its distribution, centre of origin and genetic diversity in Ethiopia, where G. abyssinica has been domesticated. The

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Fig. 8.1 Niger crop and seed

genus Guizotia consists of six species, among which five species, including niger, are native to the Ethiopian highlands (Baagøe 1974). The six species are G. abyssinica (L. f.) Cass.; G. arborescens I. Friis; G. reptans Hutch; G. scabra (Vis.) Chiov.; G. villosa Sch. Bip. and G. zavattarii Lanza. Guizotia scabra includes two subspecies, schimperi and scabra. Guizotia scabra subsp. schimperi is a commonly found annual weed in Ethiopia. Five species of Guizotia are found in Ethiopia. Of the six species, G. reptans is not present in Ethiopia. The cultivated niger Guizotia abyssinica (2n = 2x = 30) is an annual dicotyledonous herb (Fig. 8.1). Niger plant contains inflorescence called as capitulum (Plural Capitula) or head. Involucral bracts are present at the base of the inflorescence. The two types of florets are present in the inflorescence: one is ray floret and another is disc floret. The head diameters are in the range of 15–50 mm with 5–20 mm long ray florets. The ray florets are yellow and present at the periphery of the head and are pistillate. The disc florets are present inside the head and are bisexual. It contains two scaly sepals, gamopetalous with five petals, five synganaceous anthers, epipetalous, inferior ovary with bifid stigma and the fruit is achene. It is a highly cross-pollinated crop, and the cross pollination is carried out by insects.

8.3

Genetic Problems in Niger Crop Improvement

The crop is highly cross pollinated and self-incompatible. Studies on floral biology and self-incompatibility and to identify self-compatible lines were conducted (Kumar et al. 2006; Patil and Duhoon 2006; Geleta and Bryngelsson 2010; Suvarna and Lokesha 2014). Maintenance of genetic purity and inbred development is difficult, because of self-incompatibility. The flowers are very small and difficult to emasculate. Male sterile sources are not available in niger. As only few improved varieties are available in niger, farmers’ still relying on their local varieties which are low in their yield levels. The conventional methods of plant breeding are still being used in niger like selection, poly cross and recurrent selection, and therefore not

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much improvement has been reported. So far on the other hand, the usage of molecular markers in niger crop improvement may hasten the breeding cycle irrespective of environmental influence.

8.4

Molecular Markers

Molecular markers are certain DNA fragments that may be detected throughout the entire genome and are present in particular places. These molecular markers are phenotypically neutral and are mainly used to ‘flag’ the presence of a specific gene or inheritance of a specific trait.

8.4.1 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.

Characteristics of a Good Molecular Marker

High level of polymorphism. Selectively neutral. The assay for detecting markers have to be simple to perform and quick. Markers must commonly occur within the genome. The marker (gene) should exhibit co-dominant inheritance pattern. They should be highly reproducible. When utilizing multiple markers at once, they should not interact with other markers. Clearly distinguishable allelic characteristics that make it simple to distinguish between the various alleles. Single copy and no pleiotropy. Cost-effective marker development and genotyping that is easy to use. Highly available (unrestricted use) and suitability for replication/multiplication (to accumulate data and to share between laboratories). Be particular to a genome (especially with polyploids). No negative phenotypic impact.

8.4.2

Types of Molecular Markers

There may be various classes of markers, depending on the method employed for marker detection and amplification. 1. Based on target DNA restriction site alterations and subsequent hybridization with probe DNA (a) Restriction fragment length polymorphism (RFLP) 2. Based on the target DNA’s primer annealing site having a mutation (a) Random amplified polymorphic DNA (RAPD) (b) Sequence characterized amplified region (SCAR) (c) Sequence tagged sites (STS)

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3. Based on both mutations at the primer annealing site in the target DNA and alterations to the restriction sites. (a) Cleaved amplified polymorphic sequence (CAPS) and (b) Amplified fragment length polymorphism (AFLP) Other markers like single nucleotide polymorphism (SNP), simple sequence repeat (SSR) and inter simple sequence repeat (ISSR) markers are also available. To detect SNPs, nevertheless, extremely specific methods are needed.

8.4.3 1. 2. 3. 4. 5.

Applications of Molecular Markers

Used to study genetic diversity, phylogenetic analysis. Mapping genes on the chromosomes and gene tagging. Marker trait association. Population structure studies. Distinct strain identification and parental recognition etc.

8.5

Application of Molecular Markers in Niger Crop Improvement

Applications of molecular markers for the improvement of niger crop are very limited and efforts are made to study the diversity and identification of markers for salt and heat stress tolerance. The molecular markers used to study these are reviewed and explained below.

8.5.1

Genetic Diversity

The variation and diversity exist within the germplasm for different characters. Analysis and exploitation of genetic variation and diversity helps in developing improved varieties. The first step in plant breeding is to evaluate germplasm and study variability and diversity. There are few studies involved in the assessment of diversity in niger using morphological characteristics in Indian germplasm (Yadav et al. 2012b; Bisen et al. 2016; Ahirwar et al. 2017; Patil et al. 2019; Suryanarayana et al. 2019, Bhoite et al. 2021 and Thorat et al. 2021) and few in Ethiopian germplasm (Mengistu et al. 2019, Aboye 2021 and Gebeyehu et al. 2022). But molecular markers are the best method to study genetic diversity which has advantages over morphological and biochemical markers. It detects the polymorphism at the molecular level. Various molecular markers are available RAPD, AFLP, SSR, ISSR, SNP, etc. However, a very few studies have been reported in niger using the molecular markers for genetic diversity studies at national and international level (Tiwari et al. 2011; Terefe and Girma 2022) viz., RAPD (Nagella

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et al. 2008), AFLP (Geleta et al. 2008), RAPD and AFLP (Geleta et al. 2007), ISSR (Petros et al. 2007, Petros 2008, Hussain et al. 2015 and Moraa 2021) and SSR markers (Misganaw and Abera 2017 and Mengistu et al. 2019). Genotyping by sequencing is a recent method and helps in understanding the diversity at the nucleotide level by comparing the SNPs. This helps in identification of traits linked to markers, i.e. marker trait association which is very useful in improving the crop varieties. On the other hand, a very less efforts have been made in this area in niger (Tsehay et al. 2020). Other markers reported in niger are ESTs (Dempewolf et al. 2010), MicroRNAs (Prathiba et al. 2017, Naik and Devaraj 2020 and Prathiba et al. 2020) and ITS (Bekele et al. 2007).

8.5.1.1 Random Amplified Polymorphic DNA Marker (RAPD) The scientists Welsh, McClelland and Williams developed the RAPD marker in 1990. The approach randomly amplifies the DNA region using a short, arbitrary primer of length 8–12 bp. Same primer can act as a forward and a backward primer. The process begins when a single primer anneals to the genomic DNA at two different locations on the complementary strand of the DNA template. DNA segment amplification is dependent on places that are complementary to the sequence of the primers. The RAPD fragments range in size from 0.2 kb to 5.0 kb, and they can be examined using agarose gel or polyacrylamide gel electrophoresis stained with ethidium bromide. It is a dominant marker. Genetic diversity in 70 populations representing the growing regions of niger in Ethiopia was assessed using RAPD marker. About 97% of the loci studied were shown to be polymorphic. They estimated Nei gene diversity and Shanon Weaver Diversity Index which revealed to be 0.158 and 0.395, respectively. Higher proportion of the variation resided within the population (64.58%) relative to the variation among the population (35.72%) (Geleta et al. 2007). Similarly, genetic diversity among 18 cultivars of niger from India was studied using RAPD markers with 17 primers (Nagella et al. 2008). About 41.20% of the total bands (124) were polymorphic. Minimum and maximum distance between the cultivars was reported and identified two major clusters. One contained early maturing cultivars and another one contained late maturing cultivars. 8.5.1.2 ISSR Markers This technique works based on polymerase chain reaction (PCR) and reported by the scientist Ztetikiewicz et al. in the year 1994. ISSR markers can be developed by using a solitary primer containing microsatellite core regions between two similar microsatellites repeat regions are amplified DNA segments by PCR method. The primers used can usually be 16–25 bp long and attached or unattached at 3′ or 5′ end. Using ISSR markers, genetic diversity of the 37 populations of niger from Ethiopia was examined. A whole of 118 genomic DNA fragments were amplified using five primers, 106 of which were polymorphic (89.33%). Entire genetic diversity (Ht) and genetic differentiation coefficient (Gst) were reported to be 0.3738 and 0.03776, respectively, indicating that there may be more genetic variation within populations than between them (Petros et al. 2007).

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Twelve wild populations of niger from the Moiben sub-country were evaluated for genetic diversity using ISSR markers (Moraa 2021). For all of the investigated primers, polymorphic bands were obtained. The fact that each primer had an allele frequency 15 times. Transcription factors (SPL, MYB, GRF, NAC and GRAS) and oxidative stress were the main targets found.

8.6

Conclusion

Niger (Guizotia abyssinica) is a minor neglected oilseed crop in India and cultivated in limited area that too restricted to marginal and degraded land. Recently, more importance has been given for the genetic improvement of niger. However, owing to its inherent genetic problems such as self-incompatibility and also non-availability of male sterile lines in niger has hindered the application of conventional approaches in the improvement of niger crop. On the other hand, application of molecular

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markers in niger crop improvement seems to be a better option. As of now, molecular markers are being used in niger to study the genetic diversity and abiotic stress responses; however, this work is limited. The markers linked self-incompatibility/ compatibility loci needs to be identified, so that it can be used to identify the selfcompatible lines and further improvement in yield levels of niger, a long way to go.

References Aboye BM (2021) Cluster, divergence and principal component analysis of niger seed (Guizotia abyssinica (L. f.) Cass.) genotypes. Int J Res Stud Agric Sci 7(2):17–22 Ahirwar AD, Tiwari VN, Rai GK, Ahirwar SK (2017) Analysis of genetic divergence in niger [Guizotia abyssinica (l.f.) cass.] germplasm. Plant Arch 17(1):115–117 Anonymous (2022) Area, production and productivity of India and Karnataka. www.Indiastat.com Baagøe J (1974) The genus Guizotia (Compositae). A taxonomic revision. Bot Tidsskrift 69:1–39 Bekele E, Geleta M, Dagne K, Jones AL, Barnes I, Bradman N, Thomas MG (2007) Molecular phylogeny of genus Guizotia (Asteraceae) using DNA sequences derived from ITS. Genet Resour Crop Evol 54:1419–1427 Bhoite KD, Pardeshi SR, Patil SD, Patil HM, Sonawane KM, Kusalkar DV (2021) Analysis of genetic divergence in niger (Guizotia abyssinica (L.f) Cass.). Pharma Innov J 10(5):1634–1636 Bisen R, Panday AK, Jain S, Sahu R, Malviya M (2016) Estimation of genetic divergence among the niger germplasm. J Anim Plant Sci 26(5):1320–1325 Bouck A, Vision T (2007) The molecular ecologist’s guide to expressed sequence tags. Mol Ecol 16:907–924 Dasthagiriah P, Nagaraj G (1993) Seed and oil quality characteristics of some niger genotypes. J Oil Technol Assoc India 25:42–44 Datta PC, Helmersson S, Kebedu E, Alemaw G (1994) Variation in lipid composition of niger seed (Guizotia abyssinica cass.) samples collected from different regions in Ethiopia. J Am Oil Chem Soc 71:839–843 Dempewolf H, Kane NC, Ostevik KL, Geleta M, Barker MS, Lai Z, Stewart ML, Bekele E, Engels JMM, Cronk QCB, Rieseberg LH (2010) Establishing genomic tools and resources for Guizotia abyssinica (L.f.) Cass.—the development of a library of expressed sequence tags, microsatellite loci, and the sequencing of its chloroplast genome. Mol Ecol Resour 10:1048–1058 Gebeyehu A, Hammenhag C, Tesfaye K, Vetukuri RR, Ortiz R, Geleta M (2022) RNA-Seq provides novel genomic resources for noug (Guizotia abyssinica) and reveals microsatellite frequency and distribution in its transcriptome. Front Sci 13:882136 Geleta M (2007) Genetic diversity, phylogenetics and molecular systematics of Guizotia Cass. Asteraceae), Doctoral thesis Swedish University of Agricultural Sciences, Alnarp Geleta M, Bryngelsson T (2010) Erratum to: population genetics of self-incompatibility and developing self-compatible genotypes in niger (Guizotia abyssinica). Euphytica 176:431–432 Geleta M, Bryngelsson T, Bekele E, Dagne K (2007) Genetic diversity of Guizotia abyssinica (L. f.) Cass. (Asteraceae) from Ethiopia as revealed by random amplified polymorphic DNA (RAPD). Genet Resour Crop Evol 54:601–614 Geleta M, Bryngelsson T, Bekele E, Dagne K (2008) Assessment of genetic diversity of Guizotia abyssinica (L.f.) Cass. (Asteraceae) from Ethiopia using amplified fragment length polymorphism. Plant Genet Resour: Characterization and Utilization 6(1):41–51 Getinet A, Sharma SM (1996) Niger [Guizotia abyssinica (L.F.)] Cass. Promoting the conservation and use of underutilized and neglected crops. In: Institute of plant genetics and crop plant research. International Plant Genetic Resources Institute, Rome, p 58 Getinet A, Teklewold A (1995) An agronomic and seed-quality evaluation of niger (Guizotia abyssinica Cass.) germplasm grown in Ethiopia. Plant Breed 114(4):375e6

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Hussain Z, Yadav S, Kumar S, Suneja P, Nizar MA, Yadav SK, Dutta M (2015) Molecular characterization of niger [Guizotia abyssinica (L.f.) Cass.] germplasms diverse for oil parameters. Indian J Biotechnol 14:344–350 Kumar GNV, Gangappa E, Mahadevu P (2006) Studies on floral biology and autogamy in niger [Guizotia abyssinica (l.t.) Cass]. Indian J Genet Plant Breed 66(2):131–133 Mengistu B, Gebrselassie W, Disasa T (2019) Estimating the genetic diversity of Ethiopian Noug (Guizotia abyssinica (L.f.) Cass.) genotypes using SSR markers. Adv Crop Sci Technol 7:2 Misganaw A, Abera S (2017) Genetic diversity assessment of Guzoita abyssinica using EST derived simple sequence repeats (SSRs) markers. African J Plant Sci 11(4):79–85 Moraa OL (2021) Assessment of genetic diversity of niger plant (Guizotia abyssinica L.) in Moiben Sub County, Kenya, using inter simple sequence repeat markers. Int J Res Appl Sci Biotechnol 8(1):126–131 Nadeem MA, Nawaz MA, Shahid MQ, Doğan Y, Comertpay G, Yıldız M, Hatipoğlu R, Ahmad F, Alsaleh A, Labhane N, Özkan H, Chung G, Baloch FS (2017) DNA molecular markers in plant breeding: current status and recent advancements in genomic selection and genome editing. Biotechnol Biotechnol Equip 32(2):1–25 Nagaraj G (1990) Fatty acid composition of niger varieties. J Oil Technol Assoc India 22:88–89 Nagella P, Hosakatte NM, Ravishankar KV, Hahn E-J, Paek K-Y (2008) Analysis of genetic diversity among Indian niger [Guizotia abyssinica(L.f.) Cass.] cultivars based on randomly amplified polymorphic DNA markers. Electron J Biotechnol 11(1):1–5 Naik HK, Devaraj VR (2020) Genomic identification of salt induced microRNAs in niger (Guizotia abyssinica Cass.). Plant Gene 23:100242 Nasirullah MT, Rajalakshmi S, Pashupathi KS, Ankaiah KN, Vibhakar S, Krishna Murthy MN, Nagaraja KV, Kapur OP (1982) Studies on niger (Guizotia abyssinica) seed oil. J Food Sci Technol 19:147–149 Patil HS, Duhoon SS (2006) Self incompatibility, male sterility and pollination mechanism in niger (Guizotia abyssinica (L.F.) Cass.) – a review. Agric Rev 27(2):113–121 Patil SG, Bhavsar VV, Deokar SD, Girase VS (2019) Genetic divergence in niger (Guizotia abyssinica (L.f) Cass). Inter J Curr Microbiol Appl Sci 8(9):1891–1902 Petros Y (2008) Genetic diversity and oil quality of Guizotia abyssinica (L.f) Cass. (Asteraceae). Doctoral Thesis submitted to Swedish University of Agricultural Sciences, Alnarp Petros Y, Merker A, Zeleke H (2007) Analysis of genetic diversity of Guizotia abyssinica from Ethiopia using inter simple sequence repeat markers. Hereditas 144:18–24 Prathiba KY, Usha S, Suchithra B, Jyothi MN, Devaraj VR, Nageshbabu R (2017) Computational identification of miRNAs and their targets from niger (Guizotia abyssinica). J Appl Biol Biotechnol 5(02):053–058 Prathiba KY, Usha S, Suchithra B, Jyothi MN, Ulfath TKS, Sharadamma N, Hoor FS, Nageshbabu R (2020) Analysing the response of non-coding RNA of niger, Guizotia abyssinica towards high temperature and associated functional predictions. Biosc Biotech Res Comm 13(3) Ramesh babu G, Godiganur S (2015) Niger seed (Guizotia abyssinica) as a source of biodiesel in India. Int J Eng Res Technol 3(7):1–4 Suryanarayana L, Sekhar D, Tejeswara Rao K (2019) Assessment of genetic divergence in niger (Guizotia abyssinica L.). Int J Chem Stud 7(3):4061–4063 Suvarna, Lokesha R (2014) Self compatible niger (Guizotia abyssinica L): boost production and aid poorest farmers to harness income. Abstract published in regional science conference on science and technology for harnessing natural resources towards sustainable development” 4–5 January, 2014. pp 143 Terefe M, Girma D (2022) Development of molecular resources for the genetic improvement of noug (Guizotia abyssinica (L.f) Cass): a mini review. CABI Agric Biosci 3:52 Thorat BS, Bhave SG, Waghmode BD, Mane AV, Kunkerkar RL, Pethe UB, Desai SS (2021) Genetic diversity study in niger (Guizotia abyssinicaL.). Pharma Innov J 10(11):1835–1841

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Tiwari S, Kumar S, Gontia I (2011) Minireview: biotechnological approaches for sesame (Sesamum indicum L.) and niger (Guizotia abyssinica L.f. Cass.). AsPac J Mol Biol Biotechnol 19(1):2–9 Tsehay S, Ortiz R, Johansson E, Bekele E, Tesfaye K, Hammenhag C, Geleta M (2020) New transcriptome-based SNP markers for Noug (Guizotia abyssinica) and their conversion to KASP markers for population genetics analyses. Genes 11:1373 Yadav S, Kumar S, Hussain Z, Suneja P, Yadav SK, Nizar MA, Dutta M (2012a) Guizotia abyssinica (L.f.) cass.: an untapped oilseed resource for the future. Biomass Bioenergy 43: 72–78 Yadav S, Hussain Z, Suneja P, Nizara MA, Yadav SK, Dutta M (2012b) Genetic divergence studies in niger (Guizotia abyssinica) germplasm. Biomass Bioenergy 44:64–69

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Applicability of Molecular Markers in Ascertaining Genetic Diversity and Relationship Between Five Edible Bamboos of North-East India Leimapokpam Tikendra, Hamidur Rahaman, Abhijit Dey, Manas Ranjan Sahoo, and Potshangbam Nongdam

9.1

Introduction

Bamboo ( family Poaceae) is an economically important fastest-growing, perennial, woody grass the with varied uses (Tikendra et al. 2021a). There are approximately 1575 bamboo species belonging to 111 different genera distributed across the globe (Sawarkar et al. 2020). However, only 50 species of these bamboos are routinely cultivated for commercial utilization (Hunter 2003). According to Forest Resources Assessment (FRA 2020) report, the bamboos cover 17.416 million haectares of India’s forest land, which accounts for approximately half the entire bamboo coverage zone of Asia (Tewari et al. 2019). With 136 bamboo species, India has a very rich bamboo diversity (Kumar 2011). Northeastern India is considered the hot spot for bamboo diversity among other bamboo cultivation zones, being home to 58 bamboo species representing 10 genera. Of the 24 genera found in India, 20 are indigenous, and 4 are reported as exotic (Sharma 1987). Bamboos, a materially versatile plants have different uses in industries (paper, handicraft, and furniture), house constructions, making water tubing, storage basins, and other domiciliary items (Scurlock et al. 2000; Nilkanta et al. 2017). The vernal bamboo shoots are often consumed as either fresh or in fermented form and is mainly confined to the Northeastern states of India and Asian countries. It is an integral component of many traditional cuisines of Northeast India, consumed as fresh or fermented shoots (Nongdam and Tikendra 2014). Since fermented L. Tikendra · H. Rahaman · P. Nongdam (✉) Department of Biotechnology, Manipur University, Manipur, India A. Dey Department of Life Sciences, Presidency University, Kolkata, India M. R. Sahoo Central Horticultural Experiment Station, ICAR–Indian Institute of Horticultural Research, Bhubaneswar, India # The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 N. Kumar (ed.), Molecular Marker Techniques, https://doi.org/10.1007/978-981-99-1612-2_9

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bamboo products contributed tremendously to the local food needs and livelihood of this low-income region, there is demand for the safeguard of such modest and primeval knowledge of fermented shoots preparation from the advancing food technologies (Nongdam 2015). Bamboo shoots contain high proteins, carbohydrates, vitamins, and other minerals like calcium, phosphorus, potassium, iron, manganese, magnesium, and sodium (Bisht et al. 2012). The shoots are also considered natural healthy foods for their rich dietary fiber, phytosterols, and less cholesterol (Visuphaka 1985; Xia 1989; Nirmala et al. 2007). The low-fat content makes processed bamboo food products ideal for healthy nutrition, especially for diabetic and cardiothoracic diseases (Kumbhare and Bhargava 2007). Bamboos, by virtue of its extensive utility in domestic and commercial purposes, proper identification, and categorization have becoming highly imperative. The exact identification of bamboo taxa is also obligatory to warrant the safeguard of intellectual property rights for breeders, domestic consumers, and commercial propagators. However, identification and investigating the genetic relationship of natural bamboos are daunting tasks owing to lack of morphological differences and unpredictable flowering patterns (Das et al. 2008). Conventional taxonomy and classification of bamboo based on inflorescence, rhizome, branching, culm sheath, or other vegetative characters failed at the generic level as they are easily tempted by various environmental factors (Soderstrom and Ellis 1988; Stapleton et al. 2009; Yeasmin et al. 2015; Khairi et al. 2020). Several bamboo plant characterization methods were adopted, showing different degrees of precision. For instance, biochemical-based characterization bifurcated the genus Dendrocalamus (Chou and Hwang 1985), while the phenotypic parameters divided the genus into four groups (Wong 1995). Molecular marker-based characterization of bamboos was performed to unriddle the disarray and substantiate the missing link in bamboo genetic studies (Loh et al. 2000; Khairi et al. 2020). Molecular markers have wide applicability in biodiversity, plant, and animal breeding studies, map-based gene cloning, phylogenetic analysis, and forensic investigations (Kalendar et al. 2010; Grover and Sharma 2016). From various studies, it is known that the molecular marker technique provided the scientific community with the tools for genetic analysis of genomes without sequencing, and this has led to a significant advancement in the knowledge of structural and functional genomics of different plant genomes and other organisms (Guo et al. 2014; Zhao et al. 2015; Adhikari et al. 2017; Amom et al. 2018; Aydin et al. 2020). Restriction fragment length polymorphism (RFLP) and amplified fragment length polymorphism (AFLP) markers have been utilized in the genetic variation study of Phyllostachys bamboos and other four genera within the subtribe Bambusinae (Friar and Kochert 1994; Loh et al. 2000). However, intersimple sequence repeats (ISSR) markers are more commonly used than RFLP and ALP markers as they are fast, reliable, require no sequence information, and very less quantity of DNA (Wang et al. 2009; Amom and Nongdam 2017; Dey et al. 2020). ISSR markers which amplify the DNA fragments by anchoring a few nucleotides at 3′- or 5′- end between two simple sequence repeats (SSRs) having opposite orientations, are among the highly employed marker systems for genetic diversity and phylogenetic relationship studies (Zietkiewicz et al. 1994; Wang et al.

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2009; Tikendra et al. 2022b). They can perform effective genetic differentiation at inter and intraspecific levels because of their more specific and reproducible amplifications of genomic regions (Powell et al. 1996; Dey et al. 2019). The relative abundance of ISSR in the genome permits the amplification of genomic DNA to a greater number of DNA fragments per primer than RAPD markers (Saha et al. 2016). Although there are reports on population genetic studies of some bamboo by ISSR markers (Yang et al. 2012), its application in genetic differentiation and molecular phylogenetic studies is paltry. CAAT box-derived polymorphism (CBDP) and start codon targeted (SCoT), on the other hand, are advanced gene-targeted markers found in or nearby the candidate genes which unveiled more detailed information about the genome and genotypic variation (Gianbelli et al. 2001; Heidari et al. 2017; Amom et al. 2020). While CBDP targets the gene promoter CAAT box region of plants, SCoT markers are based on the short conserved regions flanking the ATG start codon (Collard and Mackill 2009; Singh et al. 2014; Tikendra et al. 2021b). SCoT involved using an 18-mer primer with an annealing temperature of 50 °C and above (Collard and Mackill 2009). However, the recently developed iPBS markers are based on the conserved sequence adjacent to the 5′ LTR (long terminal repeat sequence) retrotransposons (Kalendar et al. 2018; Apana et al. 2021; Tikendra et al. 2022a). Evaluating the phylogenetic and genetic relationship of bamboos is vital for proper plant authentication, potential germplasm group identification, hybridization optimization and selection procedures, and effective germplasm conservation. Investigating the efficiency of different marker systems is essential for determining their applicability in bamboo genetic relationship studies. The present chapter aims to reveal the effectiveness and applicability of four different marker systems viz., ISSR, SCoT, CBDP, and iPBS, in ascertaining the genetic relationship and diversity of five different edible bamboos (Bambusa nutan, Bambusa tulda, Dendrocalamus hamiltonii, Dendrocalamus latiflorous, and Dendrocalamus manipureanus) of North-East India.

9.2

Materials and Methods

9.2.1

Sample Collection and DNA Extraction

Young and healthy leaves from ten individual plants each of B. nutan, B. tulda, D. hamiltonii, D. latiflorous, and D. manipureanus were collected after intense sampling from different locations of seven districts in Manipur (Fig. 9.1; Table 9.1). In order to prevent collecting samples from the same clone, every sample collected was given a distance gap of 1 km at the minimum. The CTAB method was employed with slight modifications to extract genomic DNA from the leaf samples (Doyle and Doyle 1987). The extracted genomic DNA was subjected for quality and quantity evaluation using a spectrophotometer at 260 and 280 nm, respectively. The integrity and purity of the extracted genomic DNA were further checked on horizontal electrophoresis with lesser concentration (0.8%) of agarose gel stained with ethidium bromide (EtBr). The resultant band intensities were compared with 1 kb

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Fig. 9.1 District-wise geographical locations of the five different bamboo species. 1—Bishnupur; 2—Tamenglong; 3—Senapati; 4—Imphal East; 5—Imphal West; 6—Thoubal; 7—Chandel (Map source: https://earthexplorer.usgs.gov/) Table 9.1 Sampling details of five different edible bamboo species of Northeast India Species D. latiflorus

D. manipureanus

D. hamiltonii

B. nutans

B. tulda

Sampling area (District) Bishnupur Imphal east Senapati Tamenglong Bishnupur Imphal east Thoubal Bishnupur Chandel Imphal east Bishnupur Imphat east Imphal west Thoubal Bishnupur Imphal east Imphal west Thoubal

Latitude (°N) 24°56′55″ 24°52′53″ 25°16′37″ 24°31′19″ 24°56′55″ 24°52′53″ 24°33′52″ 24°56′55″ 24°16′19″ 24°52′53″ 24°56′55″ 24°52′53″ 24°46′54″ 24°33′52″ 24°56′55″ 24°52′53″ 24°46′54″ 24°33′52″

Longitude (°E) 93°28′31″ 94°04′53″ 94°04′56″ 93°47′18″ 93°28′31″ 94°04′53″ 94°02′24″ 93°28′31″ 94°01′15″ 94°04′53″ 93°28′31″ 94°04′53″ 93°51′22″ 94°02′24″ 93°28′31″ 94°04′53″ 93°51′22″ 94°02′24″

Altitude (m) 822.18 790 1061–1788 1260 822.18 790 790 822.18 880 790 822.18 790 790 790 822.18 790 790 790

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DNA ladder (Thermo Fisher Scientific). The screened DNA samples were then stored at -20 °C for use in succeeding molecular experimentation.

9.2.2

PCR Amplification Reaction with ISSR, SCoT, CBDP, and iPBS Primers

Forty different primers belonging to ISSR, SCoT, CBDP, and iPBS markers (Integrated DNA Technologies (IDT), Germany) were used after an initial screening of 64 primers for the present study. The selected primers of four different marker systems were amplified in 25 μL reaction volume that constituted 1XPCR buffer, 2.5 mM dNTPs, 1.5 mM MgCl2, 1 μM primer, 0.6 units of Taq DNA polymerase, and ultrapure water. The amplification steps were set with an initial denaturation at 94 °C for 4 min, followed by 40 cycles of denaturation for 1 min at 94 °C, 1 min of Tm-5 °C of the respective primers for annealing, and 2 min at 72 °C for primer elongation with a final extension at 72 °C for 4 min. The amplified PCR products were electrophoresed in 1.5% agarose gel containing 0.5 μg/ml ethidium bromide in 0.5× TBE buffer at 100 V for around 1.5 h against 1 kb DNA ladder (Thermo Fisher Scientific) as a band size marker. The separated fragments were visualized under Bio-Print Imaging System, Vilber, Germany.

9.2.3

Data Analysis

Clear and consistently reproducible bands produced by ISSR, SCoT, CBDP, and iPBS primers were scored for their presence (1) or absence (0). From these binary values, input file formats were prepared for genetic software GenAlEx version 6.503 (Peakall and Smouse 2012). The potential of these molecular markers in detecting the genetic relationship was determined by caculating the polymorphic information content (PIC), effective multiplex ratio (EMR), and marker index (MI). PIC was obtained with the formula, PIC = 2 fi (1-fi), where “fi” represented the frequency of amplified DNA fragments and “1-fi” was the frequency of nonamplified fragments (Roldan-Ruiz et al. 2000). EMR was estimated by the formula, EMR = total number of polymorphic loci X proportion of polymorphic loci per their total number (Powell et al. 1996; Nagaraju et al. 2001). The ability to detect polymorphic loci among the genotypes was assessed by deriving MI values which were the product of PIC and EMR (Powell et al. 1996; Nagaraju et al. 2001; Varshney et al. 2007). The genetic divergence of the five bamboo species was examined based on Nie’s unbiased genetic distances and genetic identities (Nei 1978). The genetic relationship at the genus level was also evaluated by constructing UPGMA (Unweighted Pair Group Arithmetic Mean Method)-based dendrograms using Nei’s unbiased genetic distance matrix employing MEGA (Molecular Evolutionary Genetics Analysis) software, version 7.0.26 (Kumar et al. 2016). The correlation betweeen the marker systems was checked by Mantel test from Nei’s genetic distance matrices.

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9.3

Results and Discussion

9.3.1

Polymorphism and Markers Efficacy

Sixty four different primers belonging to ISSR, SCoT, CBDP, and iPBS markers were screened to amplify the extracted genomic DNA. Forty primers—12 ISSR, 10 SCoT, and 9 each for CBDP and iPBS that produced distinct and scorable bands were employed for DNA amplification. The concurrent use of different markers to validate the findings of each marker system was earlier demonstrated by Amom et al. (2020) in bamboos and Khodaee et al. (2021) in other plants of the Poaceae. 100% polymorphism was detected by primers belonging to ISSR, SCoT, CBDP, and iPBS markers (Tables 9.2, 9.3, 9.4, and 9.5). Similar markers also exhibited a high degree of polymorphism in the genetic diversity analysis of other plants (Goyal and Sen 2015; Apana et al. 2021; Tikendra et al. 2021b). The result indicated the effectiveness of these markers systems in differentiating the genotypes of five different bamboo species. Das et al. (2007), using RAPD markers, also observed a high

Table 9.2 Details of primers, banding profile, and markers’ parameters for different ISSR primers ISSR primer UBC 810 UBC 814 UBC 820 UBC 823 UBC 824 UBC 827 UBC 828 UBC 830 UBC 863 UBC 868 UBC 871 UBC 878

Sequence (GA)8T

Primer Tm (° C) 45.4

Total bands 13

PB 13

PPB (%) 100

PIC 0.33

EMR 43.42

MI 14.68

Band size range (bp) 250–1500

(CT)8A

44.7

10

10

100

0.34

38.09

13.09

500–2000

(GT)8C

51.0

8

8

100

0.32

18.49

6.05

500–1500

(TC)8C

48.1

7

7

100

0.33

19.68

6.52

500–1500

(TC)8G

48.5

5

5

100

0.09

0.68

0.07

250–1000

(AC)9G

53.0

16

16

100

0.29

37.85

11.03

250–2000

(TG)8A

52.0

4

4

100

0.26

19.22

5.15

250–1000

(TG)8G

52.7

8

8

100

0.28

19.36

5.38

250–1500

(AGT)6

41.3

11

11

100

0.37

50.10

18.88

250–1500

(GAA)6

43.2

8

8

100

0.21

50.10

10.57

500–2000

(TAT)6

27.1

6

6

100

0.38

40.46

15.66

500–1500

(GGAT)4

47.5

11

11

100

0.34

43.68

15.25

250–1500

0.30

31.76

10.19

Average

8.92

Sequence (5′ ! 3′) KCCA KCCG KCGG KGCC LACG MGCC MGGG NCCA NCAC MGCA Average

Primer Tm (°C) 52.6 53.9 53.9 53.9 58.4 60.7 60.2 60.7 59.1 59.7

Total bands 10 8 12 6 9 10 11 16 7 10 9.9

PB 10 8 12 6 9 10 11 16 7 10

PPB (%) 100 100 100 100 100 100 100 100 100 100

PIC 0.35 0.41 0.44 0.41 0.33 0.37 0.39 0.41 0.46 0.39 0.39

EMR 31.25 44.22 77.04 40.33 25.44 45.00 81.71 141.12 75.91 47.43

MI 11.24 18.04 33.76 16.33 8.46 16.68 32.11 58.52 35.28 18.45 24.89

K = CAACAATGGCTACCA; L = ACGACATGGCGACCA; M = ACCATGGCTACCACC; N = CCATGGCTACCACCG

SCoT S1 S3 S7 S10 S12 S18 S25 S28 S32 S34

Table 9.3 Details of primers, banding profile, and markers’ parameters for different SCoT primers Band size range (bp) 250–1500 500–2000 250–2000 500–1000 250–2000 250–2000 250–2000 250–1000 500–2000 250–1500

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Table 9.4 Details of primers, banding profile, and markers’ parameters for different CBDP primers

CBDP CAAT 1 CAAT 3 CAAT 4 CAAT 5 CAAT 6 CAAT 7 CAAT 8 CAAT 9 CAAT 10 Average

Sequence QAGC

Primer Tm (° C) 52.1

Total bands 8

PB 8

PPB (%) 100

PIC 0.43

EMR 49.00

MI 21.01

Band size range (bp) 250–1500

QACC

51.8

10

10

100

0.36

33.80

12.15

500–2000

QAAG

49.2

6

6

100

0.32

18.25

5.78

500–1500

QCTA

50.2

11

11

100

0.35

48.31

17.00

500–2000

QCAG

52.0

14

14

100

0.22

129.43

28.96

250–2000

QCGA

53.6

9

9

100

0.36

35.28

12.78

250–1500

QCGG

54.9

12

12

100

0.27

44.28

11.95

250–2000

QGAT

51.2

15

15

100

0.39

76.80

29.68

250–1000

QGTT

51.6

6

6

100

0.41

32.67

13.39

250–750

0.34

51.98

16.96

10.11

Q = TGAGCACGATCCAAT

degree of polymorphism among 15 bamboo species. DNA fingerprinting analysis using AFLP markers among 12 bamboo species recorded 87.55% of polymorphism (Gosh et al. 2011). In the analysis by 12 ISSR primers in the present investigation, the five bamboo species produced a total of 107 bands whose band size ranged from 250 bp to 2000 bp. An average number of 8.92 bands were observed per primer. UBC-827 displayed the highest of 16 bands, while the least number of four bands were witnessed in UBC-828. The PIC, which evaluates the marker’s capability to discriminate between genotypes (Hudson 1990), was recorded in the range of 0.21–0.38, except for UBC 824 (PIC = 0.09). The highest PIC (0.38) was produced by UBC 871, but an average PIC of 0.30 per primer was estimated from the alleles detected per locus by 12 ISSR primers. The marker index (MI) was highest (18.88) for UBC 863, while the least (MI = 0.07) was observed for UBC 824 (Table 9.2). Polymorphism examination by ten SCoT primers produced 99 bands ranging between 250 bp and 2000 bp with an average number of 9.9 bands per primer. The PIC of SCoT primers was extended between the highest of 0.46 (SCoT 32) and the lowest of 0.33 (SCoT 12), averaging a PIC value of 0.39 per primer. The MI value of SCoT markers varied from 58.52 for SCoT 28 to 8.46 for SCoT 12 (Table 9.3). A similar investigation by nine CBDP primers generated 91 bands

E = CCA

iPBS 2076 2081 2083 2220 2237 2242 2270 2373 2386

Sequence (5′ ! 3′) GCTCCGTAGE GCAACGGCGE CTTCTAGCGE ACCTGGCTCATGATGE CCCCTACCTGGCGTGE GCCCCA(TGG)2GCGE ACCTGGCGTGE GAACTTACTCCGATGE CTGATCAACE Average

Primer Tm (°C) 46.0 53.6 42.0 56.8 63.1 67.4 54.3 51.7 37.2

Total bands 7 9 5 6 10 11 8 6 8 7.77

PB 7 9 5 6 10 11 8 6 8

PPB (%) 100 100 100 100 100 100 100 100 100 100

Table 9.5 Details of primers, banding profile, and markers’ parameters for different iPBS primers PIC 0.27 0.26 0.37 0.35 0.29 0.16 0.34 0.42 0.12 0.29

EMR 29.72 18.00 30.97 18.75 24.64 5.70 29.70 47.20 2.10 22.97

MI 8.16 4.85 11.48 6.64 7.35 0.95 10.17 20.28 0.264 7.79

Band size range (bp) 250–1500 500–2000 250–1000 500–1500 250–2000 250–2000 250–1500 250–1000 250–1500

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with band sizes ranging from 250 to 2000, averaging 10.11 bands per primer. The PIC for CBDP markers was recorded to be highest (0.43) for CAAT 1 and lowest (0.22) for CAAT 6, averaging 0.34 (PIC) per primer. MI of CBDP markers was maximum (29.68) for CAAT 9, while the least (5.78) was reported for CAAT 4 (Table 9.4) with an average value of 16.96 per primer. From the analysis of nine iPBS primers, a total of 70 scorable bands were produced, with band sizes ranging between 250 bp and 2000 bp and generating an average of 7.77 bands per primer. The highest number of 11 amplified bands was recorded for iPBS 2242 while the least amplification of five bands was detected for iPBS 2083. The estimated PIC values ranged between 0.42 (iPBS 2373) and 0.12 (iPBS 2386) ensuing an average PIC of 0.29 per primer. The overall utility of iPBS markers was also identified by determining MI values which ranged from 20.28 (iPBS 2373) to 0.264 (iPBS 2386), with an average MI of 7.79 per primer (Table 9.5). Among four marker systems employed in the present study, the SCoT marker recorded the highest average MI values per primer (24.89), followed by CBDP markers (16.96). The iPBS marker exhibited the lowest average MI value (7.79). Upon comparing the average bands, PIC and MI values per primer of different marker systems, SCoT generated the highest values indicating their higher informativeness and efficiency among the markers under study. Similar effectiveness of SCoT markers compared to other marker was reported in Triticum turgidum (Heidari et al. 2017) and Clerodendrum serratum (Apana et al. 2021).

9.3.2

Correlation Analysis

The Mantel test allows for determining the intensity of relationship between two matrices of the variables (Mantel 1967). Genetic distances derived from analysis by different markers were considered variables for exploring the correlation between the markers using the Mantel test. The test ensues from the dissimilarity-similarity matrix and can be employed to different variables such as categorical, rank, or interval-scale, and is one of the important test in analysis of genetic diversity (Mohammadi and Prasanna 2003). Only a few studies have compared the results derived from an individual against pooled data sets concerning genetic diversity (Franco et al. 1997; Russell et al. 1997; Ajmone-marsan et al. 1998). Attempts have been made in the present study to determine if a correlation existed between the markers individually and against the pooled data of all the markers (Figs. 9.2a–f, 9.3a–c). A varying degree of positive correlation was observed between the markers. When compared within individual markers, the highest correlation was noticed between iPBS and ISSR (r = 0.348; p = 0.001) (Fig. 9.2a), while the least was recorded between SCoT and CBDP (r = 0.146; p = 0.005) (Fig. 9.2e). The observation of a significant positive correlation between the varied marker systems indicated that the markers could be used in concurrence to determine the genetic relationship and diversity of these five edible bamboos. Further analysis of the correlation strength between pooled data of ISSR-SCoT-CBDP-iPBS and each marker system showed a strong and significant correlation. The highest correlation

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r = 0.217 p = 0.002

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Fig. 9.2 Mantel test showing significant correlation between different markers: (a) ISSR and iPBS, (b) ISSR and CBDP, (c) ISSR and SCoT, (d) SCoT and iPBS, (e) SCoT and CBDP, and (f) iPBS and CBDP

was recorded between CBDP and pooled ISSR-SCoT-CBDP-iPBS data (r = 0.772; 0.001) (Fig. 9.3d) followed by iPBS and pooled ISSR-SCoT-CBDP-iPBS data (r = 0.654; p = 0.001), and SCoT and pooled ISSR-SCoT-CBDP-iPBS data (r = 0.572; p = 0.001) (Fig. 9.3b, c). The lowest among them was observed between ISSR and pooled ISSR-SCoT-CBDP-iPBS data (r = 0.568; p = 0.001) (Fig. 9.3a). Collard and Mackill (2009) reported CBDP a novel, simple, and consistent genetargeted marker systems having more reliability and reproducibility due to longer primers with high annealing temperatures. Similarly, iPBS targets functional genes. Being a retrotransposon-based molecular marker, the multiple insertion sites of LTR retrotransposons which are distributed in the plant genome, make iPBS a useful marker for detecting insertion polymorphism (Nasri et al. 2013; Monden et al. 2014; Sonia et al. 2022). SCoT markers which are based on conserved regions flanking the

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Fig. 9.3 Mantel test showing significant correlation between: (a) ISSR and pooled ISSR-SCoTCBDP-iPBS data, (b) SCoT and pooled ISSR-SCoT-CBDP-iPBS data, (c) iPBS and pooled ISSRSCoT-CBDP-iPBS data, and (d) CBDP and pooled ISSR-SCoT-CBDP-iPBS data

gene initiation codon sequences have proved their ability to detect higher polymorphism and better marker resolvability over other dominant molecular markers like RAPD and ISSR (Gorji et al. 2011; Tikendra et al. 2021c). The correlation analysis conducted in the present investigation also revealed dominant ISSR markers to be least effective compared to the other functional gene-targeted markers.

9.3.3

Genetic Distance and Cluster Analysis by UPGMA

We studied Nei’s genetic distance whose expected value was proportionate to evolutionary time when both effects of mutation and genetic drift were considered. It measured the genetic relatedness or differences between two populations or of the related species computed using the allele frequency data obtained from multiple observed loci (Nei 2013). The Nei’s genetic distance of bamboo genotypes in the present study was recorded from the closest distance (0.067) between DL and DM to the farthest (0.117) between DH and BN (Table 9.6). The three Dendrocalamus species, as expected, showed the least genetic distance of 0.067 (DL and DM), 0.072 (DM and DH), and 0.076 (DL and DH). Similarly, the genetic distance between BN and BT was the closest (0.093). The present study comprehended what the taxonomic classification defines: species of the same genus have more characteristics in common than other genera. The recorded Nei’s genetic identity values between genotypes of five bamboo species also substantiate the genetic distance (Table 9.6).

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Table 9.6 Nei’s genetic matrices for genetic identity (above diagonal) and genetic distance (below diagonal) among the five different bamboo species, based on pooled data of CBDP, ISSR, iPBS, and SCoT markers DL **** 0.067 0.076 0.098 0.099

DM 0.935 **** 0.072 0.117 0.099

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0.927 0.931 **** 0.094 0.098

0.906 0.890 0.910 **** 0.093

0.905 0.905 0.907 0.911 ****

DL DM DH BN BT

Dendrograms were constructed for each marker system using MEGA7.0.26 (Kumar et al. 2016). The radial tree structure constructed from ISSR, SCoT, CBDP, and iPBS markers produced species-specific clustering of different bamboo genotypes (Fig. 9.4a–d). No intermixing of genotypes belonging to different bamboo species was detected, indicating the high discriminability potential of each marker system. The Dendrocalamus bamboos were clubbed together with a cluster of D. manipureanus genotypes located between D. latiflorous and D. hamiltonii. Two Bambusa species were located very close to one another, with B. nutans positioned closer to D. hamiltonii, and B. tulda spotted adjacent to D. latiflorous. The first report on RFLP-based phylogenetic relationships study among Asian bamboos retrieved a clade representing subtribe Bambusinae that includes Bambusa, Dendrocalamus, and Gigantochloa, suggesting the close lineages among these genera (Watanabe et al. 1994). Anoter study on genetic variability study with RAPD markers also grouped species of different genera of Dendrocalamus and Bambusa into the same cluster (Nayak et al. 2003). Thus, based on the previous reports, it was contended that Bambusa and Dendrocalamus are genetically close genera (Loh et al. 2000; Ramanayake et al. 2007). The present study highlighted the applicability and high efficiency of each of the four marker systems under study by corroborating the previous findings of bamboo phylogeny using molecular markers.

9.3.4

Principal Coordinate Analysis (PCoA)

Principal coordinate analysis (PCoA) via covariance matrix with data standardization provides the ability to visualize individual or group differences and the other outlier individuals (Mohammadi and Prasanna 2003). This low-dimensional graphical plot of the data is commended over principal component analysis (PCA) when there are many missing data and fewer individuals than characters (Rohlf 1972). Tikendra et al. (2019a, b) also performed PCoA to check the consistency of genetic diversity between the analyzed genotypes as established by cluster analysis. The two-dimensional PCoA demonstrated the first and second coordinates contributing 37.84% and 32.708% (ISSR); 45.07% and 24.54% (SCoT); 36.50% and 32.11% (CBDP); and 40.62% and 26.79% (iPBS) of the total genetic variation (Table 9.7). The spatial arrangement pattern obtained from the pooled

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B

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Fig. 9.4 Clustering pattern of 50 bamboo individuals belonging to five different species: D. latiflorus (DL), D. manipureanus (DM), D. hamiltonii (DH), B. nutans (BN), and B. tulda (BT) based on (a) ISSR markers, (b) iPBS markers, (c) SCoT markers, and (d) CBDP markers

Table 9.7 Principal coordinate analysis (PCoA) showing the percentage of variation explained by the three axes Axis % of variation Cumulative %

ISSR 1st 37.84 37.84

2nd 32.70 70.54

SCoT 1st 45.07 45.07

2nd 24.54 69.61

CBDP 1st 36.50 36.50

2nd 32.11 68.62

iPBS 1st 40.62 40.62

2nd 26.79 67.41

markers data showed the efficiency of molecular markers in differentiating the genotypes of D. latiflorus, D. manipureanus, D. hamiltonii, B. nutans, and B. tulda. The genetic diversity percentage explained by the two main coordinates of the pooled data was determined at 11.31%, and 8.49%, respectively, which

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Fig. 9.5 Principal coordinate analysis (PCoA) plot for 50 genotypes of bamboo belonging to the five different species of D. latiflorus (DL), D. manipureanus (DM), D.hamiltonii (DH), B. nutans (BN), and B. tulda (BT)

accounted for 27.21% of the total diversity (Fig. 9.5). Species-specific grouping of bamboo genotypes was observed in the PCoA plot, with genotypes of D. latiflorus, D. hamiltonii, B. nutans, and B. tulda occupying different quadrants. The PCoA based on combined marker data revealed similar clustering patterns generated by UPGMA dendrograms. Melchinger (1993), using molecular marker data, showed that PCoA although provides a authentic representation of relationships between major groups of lines, often showed varied distances between close neighbors when a small proportion (