OMICs-based Techniques for Global Food Security E-Book
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- Sprache: Englisch
OMICs-based Techniques for Global Food Security Forward-thinking resource discussing how to integrate OMICs and novel genome editing technologies for sustainable crop production OMICS-based Techniques for Global Food Security provides an in-depth understanding of the mechanisms of OMICs techniques for crop improvement, details how OMICs techniques can contribute to identifying genes and traits with economic benefits, and explains how to develop crop plants with improved yield, quality, and resistance to stresses through genome editing technologies, providing evidence on the developments of climate resilient crops via applications of genome editing techniques throughout. The text covers the application of OMICs in crop plants, the integration of bioinformatics and multi-OMICs for precision breeding, de-novo domestication, CRISPR/Cas system for crop improvement, hybrid seed production, transgene free breeding, regulation for genome edit crops, bioinformatics and genome editing, and other topics related to OMICs and genome editing. The text also includes a chapter on global regulations for genome edited crops, and explains how these regulations influence novel plant breeding techniques in their adopted countries. Edited by two highly qualified academics, OMICs-based Techniques for Global Food Security covers topics such as: * Crops genome sequencing and their application for crop improvement, and functional characterization of cereal genome * The role of OMICs-based technologies in plant sciences and utilization of different multi-OMICs approaches for crop improvement * Genomic database and genetic resource of cereals, speed breeding for rapid crop improvement, and evolution of genome editing technologies * CRISPR system discovery, history, and future perspective, and CRISPR/Cas system for biotic and abiotic stress resistance in cereals Providing a collection of recent literature focusing on developments and applications of OMICs-based technologies for crop improvement, OMICs-based Techniques for Global Food Security is an important read for plant breeders, molecular biologists, researchers, postdoctoral fellows, and students in disciplines for developing crops with high yield and nutritional potential.
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Veröffentlichungsjahr: 2024
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Table of Contents
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
1 Crop Genome Sequencing and their Application for Crop Improvement
1.1 Crop Genome Sequence
1.2 Genomics and Its Contribution to Agricultural Improvements
1.3 Genome-Assisted Advances in Crops
1.4 Methods of Crop Genome Sequencing
1.5 Application of Crop Genome Sequencing for Crop Improvement
1.6 Identification of Disease Resistance of Genes
1.7 Case Studies of Crop Genome Sequencing
1.8 Discussion of Potential Future Applications and Impacts
1.9 Challenges and Limitations of Crop Genome Sequencing for Crop Improvement
1.10 Possible Solutions
1.11 Lack of Funding for Research in Crop Genome Sequencing
1.12 Possible Solutions
1.13 Lack of Incentives for Private Companies to Invest in Crop Genome Sequencing
1.14 Possible Solutions
1.15 Conclusion
References
2 Functional Characterization of Cereal Genomics
2.1 Introduction
2.2 Cereal Genomes
2.3 Genetic Aspects of Cereal Crops
2.4 Comparison of Cereal Genomes
2.5 Genomic Diversity in Cereals
2.6 Functional Genomics
2.7 Cereal Gene Function Analysis
2.8 Applications of Functional Characterization of Cereal Genomes in the Real World
2.9 Functional Genes’ Role in Stress Response and Cereal Development
2.10 Impact of Genetically Modified Cereals
2.11 Cereal Functional Genomics: Prospects and Challenges for the Future
2.12 Conclusion
References
3 Role of OMICS-Based Technologies in Plant Sciences
3.1 Introduction
3.2 Omics Technologies in Plant Sciences
3.3 Conclusion and Perspective
References
4 OMICS-Based Knowledge for Achieving Food and Nutritional Security
4.1 Introduction
4.2 Applications of OMICS in Agriculture
4.3 Transcriptome Analysis Techniques
4.4 Metabolic Pathways for Nutrient Enrichment
4.5 Bioinformatics and Data Analysis in OMICS
4.6 OMICS Applications in Addressing Nutritional Deficiencies
4.7 Challenges and Ethical Considerations in OMICS-Based Agriculture
4.8 Intellectual Property Rights and Access to Genetic Resources
4.9 Integration of OMICS in Sustainable Agriculture
4.10 Potential Impact on Global Food and Nutritional Security
4.11 Future Prospects and Conclusions
References
5 Utilization of Multi-Omics Approaches for Crop Improvement
5.1 Introduction
5.2 Pre-Omic Evolutionary Era
5.3 Omics Approaches in Crop Breeding
5.4 Integrated-Omics Approaches
References
6 Genomic Databases and Genetic Resources of Cereals
6.1 Introduction
6.2 Wheat (
Triticum aestivum
)
6.3 Rice (
Oryza sativa
)
6.4 Maize (
Zea mays
)
6.5 Barley (
Hordeum vulgare
)
6.6 Sorghum (
Sorghum bicolor
)
6.7 Millets (
Pennisetum glaucum
)
6.8 Triticale
6.9 Oat
6.10 Role of Plant Genetic Resources in Food Security
References
7 Speed Breeding for Rapid Crop Improvement
7.1 Introduction
7.2 Scope of Harvesting Immature Seeds Under Speed Breeding
7.3 Plant Density: A Circumstantial Advantage
7.4 History and Evolution of Seed Breeding (SB)
7.5 Advantages of Speed Breeding
7.6 Success Stories of Speed Breeding
7.7 Challenges (Limitations of Speed Breeding)
7.8 Conclusions
References
8 CRISPR System Discovery, History, and Future Perspective
8.1 Introduction
8.2 Genome Editing: Concepts, Technologies, and Recent Advancements
8.3 History and Origin of CRISPR Technology
8.4 CRISPR-Cas Systems: From Bacterial Immunity to Diverse Applications
8.5 CRISPR Application in Plant Science and Agriculture
8.6 Conclusion
References
9 The Evolution of Genome-Editing Technologies
9.1 Introduction
9.2 The Early Beginnings of Genome Editing: Restriction Enzymes and Transgenic Animals
9.3 The Advent of Programmable Nucleases: Zinc Finger Nucleases and TALENs
9.4 The Rise of CRISPR-Cas9: From Basic Research to Genome Editing Revolution
9.5 Expansion of the CRISPR Toolkit: Alternatives to Cas9 and Advances in Delivery Methods
9.6 The Future of Genome Editing: Integration with Other Technologies and Potential Applications
9.7 Ethical Considerations and Societal Implications of Genome Editing Technology
9.8 The Ongoing Evolution of Genome Editing and Its Impact on Science and Society
9.9 Conclusion
References
10 CRISPR/Cas-Mediated Biotic Stress Resistance in Cereals for Achieving Zero Hunger
10.1 Introduction
10.2 Effect of Biotic Stressors on Cereals
10.3 Plant Genome-Editing Approaches
10.4 CRISPR/Cas Systems
10.5 CRISPR/Cas System in Agriculture for Managing Pathogenic Diseases
10.6 CRISPR-Cas System in Agriculture for Managing Insect Pests
10.7 Conclusion
References
11 CRISPR/Cas System for Achieving Abiotic Stress Tolerance
11.1 Introduction
11.2 CRISPR/Cas System (Genome Editing, Categories, Classes, and Types of CRISPR)
11.3 Identification of Abiotic Corresponding Genes in Crops
11.4 Current Status of Abiotic Stress Tolerance in Cash Crops
11.5 CRISPR/Cas System-Mediated Targeting on Salt Stress
11.6 CRISPR/Cas System-Mediated Targeting on Drought Stress
11.7 CRISPR/Cas System-Mediated Targeting on Mineral Toxicity
11.8 CRISPR/Cas System-Mediated Targeting on Cold Stress
11.9 CRISPR/Cas System in Plants that Combats Cold Stress
11.10 CRISPR/Cas System-Mediated Targeting of Cold Stress Response Genes
11.11 Benefits and Drawbacks of CRISPR/Cas System to Make Genetic Changes in Plants to Withstand Cold Stress
11.12 CRISPR/Cas System-Mediated Targeting on Heat Stress
11.13 Physiological and Molecular Responses of Plants to Heat Stress
11.14 Heat Stress Response for Precise Genetic Alterations in Plants
11.15 CRISPR/Cas System-Mediated Targeting of Heat Stress Response Genes in Plants
11.16 Potential Applications and Future Perspectives
11.17 Conclusion
References
12 Technological Innovations for Abiotic Stress Resistance in Horticultural Crops
12.1 Introduction
12.2 Traditional Approaches to Abiotic Stress Resistance
12.3 Technological Innovations for Abiotic Stress Resistance
12.4 Challenges and Future Directions
12.5 Discussion and Conclusion
References
13 Novel CRISPR-Based Genome Editing Systems for Crop Improvement
13.1 Introduction
13.2 Conventional Methods for Genome Editing
13.3 Overview of the CRISPR/CAS System
13.4 Classification of CRISPR/Cas System
13.5 Modifications of the CRISPR/Cas9
13.6 Gene Targeting by CRISPR/Cas for Genome Modification
13.7 Advantages of CRISPR over Other Gene-Editing Techniques
13.8 Role of CRISPR/Cas in Crop Improvement
13.9 Case Study
13.10 Potential Challenges and Concerns with CRISPR/Cas in Crop Improvement
13.11 Transparency Needs and Public Engagement
13.12 Concluding Remarks and Future Perspective
References
14 Precise Genome Editing of Plants Through Base and Prime Editor
14.1 Introduction
14.2 CRISPR/CAS9
14.3 Base Editing (BE)
14.4 Prime Editing (PE)
14.5 Disease Resistance
14.6 Nutrition and Yield
14.7 Future Prospectus
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 sCrop attributes that have been enhanced through genome editing m...
Chapter 2
Table 2.1 List of genes associated with various traits in cereals.
Table 2.2 Summary regarding cereals quantitative traits loci.
Table 2.3 Effectiveness in editing genes across different cereal plants.
Chapter 4
Table 4.1 Applications of OMICS in achieving food and nutritional security....
Table 4.2 Key OMICS technologies for advancing food and nutritional securit...
Table 4.3 Overall wide range of OMICS categories and their applications.
Chapter 5
Table 5.1 Commonly used databases in plant bioinformatics.
Table 5.2 Multi-omics integration for crop improvement.
Chapter 6
Table 6.1 Genomics databases of major cereals.
Chapter 7
Table 7.1 List of different crops in which speed breeding has increased num...
Chapter 8
Table 8.1 Comparison of ZFN, TALEN, and CRISPR technology.
Chapter 10
Table 10.1 CRISPR/Cas genome editing in cereals for conferring resistance a...
Table 10.2 CRISPR-Cas genome editing in insect pests for crop management.
Table 10.3 CRISPR/Cas genome editing in cereals for conferring resistance a...
Chapter 13
Table 13.1 Table shows some important crops improved by using CRISPR techno...
Chapter 14
Table 14.1 Software used for BE and PE based applications in crop plants.
List of Illustrations
Chapter 1
Figure 1.1 Genomics of field crops and its progress during the last two deca...
Figure 1.2 Challenges and limitations of crop genome sequencing for crop imp...
Chapter 3
Figure 3.1 General idea of multiomics methods in plant sciences. The five co...
Figure 3.2 Systems biology is combined with a multiomics model that uses the...
Figure 3.3 Applications of genomics in plant sciences.
Figure 3.4 Genome editing techniques in plants.
Chapter 4
Figure 4.1 Various OMICs methods used in science.
Chapter 5
Figure 5.1 The utilization of multiomics techniques in crop breeding strateg...
Figure 5.2 Multiomics techniques have found practical applications in the fi...
Chapter 6
Figure 6.1 Gramene genomic database.
Figure 6.2 URGI database.
Figure 6.3 RGAP website hosting integrated genomic data of rice.
Figure 6.4 List of tools available on Rice Genome Hub.
Figure 6.5 Tools and datasets hosted on Maize Genetics and Genomics Database...
Figure 6.6 SorghumBase hosting genomic and genetic diversity data on sorghum...
Chapter 7
Figure 7.1 Schematic diagram of speed breeding chamber.
Figure 7.2 Exploring types of speed breeding.
Chapter 8
Figure 8.1 Historical timeline of CRISPR technology.
Figure 8.2 (a) Schematic diagram of CAS9 protein domain organization; (b) Cr...
Chapter 9
Figure 9.1 Scope of CRISPR/CAS9.
Figure 9.2 Future of genome editing.
Chapter 10
Figure 10.1 CRISPR/Cas system to induce resistance against pathogens (viruse...
Figure 10.2 CRISPR/Cas-based genome editing in insects.
Chapter 11
Figure 11.1 Timeline of genome editing in economically important crops.
Figure 11.2 Schematic diagram of CRISPR/Cas9 inducing drought tolerance in w...
Figure 11.3 CRISPR/Cas9 inducing stress resistance in maize plant.
Chapter 12
Figure 12.1 Mechanism of gene editing via ZFNs, TALENs, and CRISPR/Cas9 (Ahm...
Chapter 13
Figure 13.1 Steps of CRISPR/Cas adaptive immune system: Bacterial or archaea...
Figure 13.2 Classification of CRISPR/Cas system: CRISPR system has been clas...
Figure 13.3 The CRISPR/Cas9-induced double-stranded breaks (DSBs) set the st...
Figure 13.4 Crop improvement by CRISPR/Cas.
Chapter 14
Figure 14.1 Presentation of base editing for C-T (a) and A-G (b) base transi...
Figure 14.2 Prime editing application procedure in crop plant.
Guide
Cover
Table of Contents
Title Page
Copyright
List of Contributors
Preface
Begin Reading
Index
End User License Agreement
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OMICs-based Techniques for Global Food Security
Edited by
Sajid Fiaz
University of Haripur, Haripur, Pakistan
Channapatna S. Prakash
Tuskegee University, Alabama, United States
This edition first published 2024© 2024 John Wiley & Sons Ltd.
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List of Contributors
Asim Abbasi
Kohsar University Murree
Department of Environmental Sciences
Pakistan
Banzeer A. Abbasi
Rawalpindi Women University
Department of Botany
Rawalpindi
Pakistan
Nishat I. Abbasi
Kohsar University Murree
Department of Botany
Murree
Pakistan
Nader R. Abdelsalam
Alexandria University
Agricultural Botany Department
Faculty of Agriculture (Saba Basha)
Alexandria
Egypt
Muhammad Abdullah
Cholistan Institute of Desert Studies (CIDS)
The Islamia University of Bahawalpur
Pakistan
Farooq Ahmad
Department of Plant Breeding and Genetics
PMAS-Arid Agriculture University
Rawalpindi
Pakistan
Hafiz I. Ahmad
Department of Animal Breeding and Genetics
Faculty of Veterinary and Animal Sciences
The Islamia University of Bahawalpur
Bahawalpur
Pakistan
Munir Ahmad
PMAS-Arid Agriculture University
Department of Plant Breeding Genetics
Rawalpindi
Pakistan
Ramala Masood Ahmad
University of Agriculture Faisalabad
Department of Plant Breeding and Genetics
Faisalabad
Pakistan
Usman Ahmad
University of Agriculture Faisalabad
Department of Plant Breeding and Genetics
Faisalabad
Pakistan
Yumna Ahmad
Quaid-i-Azam University
Plant Biochemistry and Molecular Biology Lab
Department of Plant Sciences
Islamabad
Pakistan
Hafiz Ghulam Muhu-Din Ahmed
Department of Plant Breeding and Genetics
Faculty of Agriculture & Environment
The Islamia University of Bahawalpur
Pakistan
Mohammed Al-Dakhil
Advanced Agricultural & Food Technologies Institute
King Abdulaziz City for Science and Technology (KACST)
Riyadh
Saudi Arabia
Saif Alharbi
Advanced Agricultural & Food Technologies Institute
King Abdulaziz City for Science and Technology (KACST)
Riyadh
Saudi Arabia
Faizan Ali
Department of Plant Pathology
University of Agriculture
Faisalabad
Pakistan
Fahad S. Alotaibi
Advanced Agricultural & Food Technologies Institute
King Abdulaziz City for Science and Technology (KACST)
Riyadh
Saudi Arabia
Nimrah Ameen
University of Agriculture
Department of Botany
Faisalabad
Pakistan
Lubna Ansari
Pir Mehr Ali Shah-Arid Agriculture University
Department of Forestry and Range Management
Rawalpindi
Pakistan
Muhammad Arif
DNA Markers and Applied Genomics Lab
National Institute for Biotechnology and Genetic Engineering (NIBGE)
Affiliated College Pakistan institute of Engineering and Applied Sciences Islamabad
Faisalabad
Pakistan
Muhammad Asif
DNA Markers and Applied Genomics Lab
National Institute for Biotechnology and Genetic Engineering (NIBGE)
Affiliated College Pakistan institute of Engineering and Applied Sciences Islamabad
Faisalabad
Pakistan
Asad Aslam
Northeast Forestry University
Key Laboratory for Sustainable Forest Ecosystem Management-Ministry of Education
College of Forestry
Harbin
China
Ayesha Aslam
University of Agriculture Faisalabad
Department of Plant Breeding and Genetics
Faisalabad
Pakistan
Muhammad Aslam
University of Agriculture Faisalabad
Department of Plant Breeding and Genetics
Faisalabad
Pakistan
Muhammad Atiq
Department of Plant Pathology
University of Agriculture
Faisalabad
Pakistan
Iqra Bibi
Kohsar University Murree
Department of Botany
Pakistan
Noreena Bibi
Department of Pathology
University of Veterinary and Animal Sciences
Lahore
Punjab
Pakistan
Amna Chaudhry
University of Agriculture
Centre of Agricultural Biochemistry and Biotechnology (CABB)
Faisalabad
Pakistan
and
University of Agriculture
Center for Advanced Studies in Agriculture and Food Security (CASAFS)
Faisalabad
Pakistan
Anns Faisal
Department of Plant Breeding and Genetics
Faculty of Agriculture & Environment
The Islamia University of Bahawalpur
Pakistan
Noor Fatima
Department of Plant Breeding and Genetics
Faculty of Agriculture & Environment
The Islamia University of Bahawalpur
Pakistan
Zubaria Haakim
Quaid-i-Azam University
Plant Biochemistry and Molecular Biology Lab
Department of Plant Sciences
Islamabad
Pakistan
Muhammad Hammad
PMAS-Arid Agriculture University
Department of Plant Breeding Genetics
Rawalpindi
Pakistan
Akhtar Hameed
Department of Plant Pathology
Institute of Plant Protection
MNS-University of Agriculture Multan
Multan
Pakistan
Mariam Hameed
DNA Markers and Applied Genomics Lab
National Institute for Biotechnology and Genetic Engineering (NIBGE)
Affiliated College Pakistan institute of Engineering and Applied Sciences Islamabad
Faisalabad
Pakistan
and
Centre of Agricultural Biochemistry and Biotechnology
Agriculture University
Faisalabad
Pakistan
Daima Hamid
DNA Markers and Applied Genomics Lab
National Institute for Biotechnology and Genetic Engineering (NIBGE)
Affiliated College Pakistan institute of Engineering and Applied Sciences Islamabad
Faisalabad
Pakistan
Aiman Hina
Nanjing Agricultural University
Ministry of Agriculture (MOA) National Centre for Soybean Improvement
State Key Laboratory for Crop Genetics and Germplasm Enhancement
Nanjing
China
Sajjad Hyder
Department of Botany
Government College Women University Sialkot
Sialkot
Pakistan
Mohammad U. Ijaz
PMAS-Arid Agriculture University
Department of Plant Breeding Genetics
Rawalpindi
Pakistan
Shumaila Ijaz
Bacha Khan University
Department of Botany
Charsadda
Khyber Pakhtunkhwa
Pakistan
Safa Imtiaz
Kohsar University Murree
Department of Botany
Pakistan
Javed Iqbal
Bacha Khan University
Department of Botany
Charsadda
Khyber Pakhtunkhwa
Pakistan
Tasmeya Ishfaq
Kohsar University Murree
Department of Botany
Murree
Pakistan
Abdul Jabbar
Department of Veterinary Medicine
University of Veterinary and Animal Sciences
Lahore
Punjab
Pakistan
Mohsin Kazi
King Saud University
Department of Pharmaceutics
College of Pharmacy
Riyadh
Saudi Arabia
Taimoor Khalid
Department of Plant Breeding and Genetics
PMAS-Arid Agriculture University
Rawalpindi
Pakistan
Eesha A. Khaliq
DNA Markers and Applied Genomics Lab
National Institute for Biotechnology and Genetic Engineering (NIBGE)
Affiliated College Pakistan institute of Engineering and Applied Sciences Islamabad
Faisalabad
Pakistan
Azeem I. Khan
University of Agriculture
Department of Plant Breeding Genetics
Faisalabad
Pakistan
Shazia Kousar
University of Baltistan
Department of Botany
Skardu
Pakistan
Tariq Mahmood
Quaid-i-Azam University
Department of Plant Sciences
Faculty of Biological Sciences
Islamabad
Pakistan
Muhammad Majeed
University of Gujrat
Department of Botany
Punjab
Pakistan
Murad Muhammad
State Key Laboratory of Desert and Oasis Ecology
Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands
Xinjiang Institute of Ecology and Geography
Chinese Academy of Sciences
Urumqi
China
and
University of Chinese Academy of Sciences
Beijing
China
Shanza Nasar
University of Gujrat
Department of Botany
Punjab
Pakistan
Zahra Noreen
Department of Botany
Division of Science and Technology
University of Education
Lahore
Pakistan
Naila Perveen
DNA Markers and Applied Genomics Lab
National Institute for Biotechnology and Genetic Engineering (NIBGE)
Affiliated College Pakistan institute of Engineering and Applied Sciences Islamabad
Faisalabad
Pakistan
Nasir A. Rajput
Department of Plant Pathology
University of Agriculture
Faisalabad
Pakistan
Rashid M. Rana
Department of Plant Breeding and Genetics
PMAS-Arid Agriculture University
Rawalpindi
Pakistan
Madiha Rashid
Department of Botany
Division of Science and Technology
University of Education
Lahore
Pakistan
Abdul Rehman
Department of Plant Pathology
University of Agriculture
Agriculture University Road
Faisalabad
Pakistan
Shazia Rehman
Rawalpindi Women University
Department of Botany
Satellite Town
Rawalpindi
Pakistan
Nadia Riaz
Department of Botany
Lahore College for Women University
Lahore
Pakistan
and
Department of Botany
Division of Science and Technology
University of Education
Lahore
Pakistan
Sohaib Sarfraz
Department of Plant Pathology
University of Agriculture
Agriculture University Road
Faisalabad
Pakistan
Sehar Shahid
Kohsar University Murree
Department of Botany
Pakistan
Ejaz H. Siddiqi
University of Gujrat
Department of Botany
Punjab
Pakistan
Aasma Tufail
Department of Botany
Division of Science and Technology
University of Education
Lahore
Pakistan
Shahid Ullah
University of Peshawar
Department of Botany
Peshawar
Pakistan
Fahad M. Wattoo
Department of Plant Breeding and Genetics
PMAS-Arid Agriculture University
Rawalpindi
Pakistan
Xiaomeng Yang
Biotechnology and Germplasm Resources Institute
Yunnan Academy of Agricultural Sciences
Kunming
China
Tabassum Yaseen
Bacha Khan University
Department of Botany
Charsadda
Khyber Pakhtunkhwa
Pakistan
Afifa Younas
Department of Botany
Lahore College for Women University
Lahore
Pakistan
and
Department of Botany
Division of Science and Technology
University of Education
Lahore
Pakistan
Syeda A. Zahra
Rawalpindi Women University
Department of Botany
Satellite Town
Rawalpindi
Pakistan
Yawen Zeng
Biotechnology and Germplasm Resources Institute
Yunnan Academy of Agricultural Sciences
Kunming
China
Preface
Global population growth, climate change, resource depletion, nutrition, and sustainable food production are all hotspot problems which the world is grappling, inserting pressure on available non-renewable resources. The production of high-quality food with less impact on land and environment is among the greatest challenges of the twenty-first century. In addition, to meet global food demand, growers need to double crop production by 2050, seemingly difficult with current yield potential. Under these circumstances, crop improvement by using OMICs-based technologies may help to ensure food and nutritional security in different parts of the world. The OMICs technologies based on genomics, transcriptomics, proteomics, metabolomics, interactomics, and phenomics have revealed each corresponding molecular biological facet integrated with plant systems. These OMICs-based approaches have proven valuable for exploring the genetic and molecular basis of crop development through modifications in DNA, transcript levels, proteins, metabolites, and mineral nutrients against a backdrop of environmental and physiological stress responses.
In this book, leading researchers with expertise ranging from classical plant breeding and genetics to genome editing provided an updated overview of application of OMICs, genome editing genomic databases, and speed breeding for crop improvement, ensuring food security. In a systematic way, the reader is introduced to well-established and emerging techniques that can potentially streamline the idea of genomic sequencing of agriculturally important crops. Furthermore, the reader is introduced with multidimensional OMICs-based interventions for achieving food and nutritional security. In addition, the importance of genetic diversity among the available germplasm for crop improvement programs. The speed breeding strategy could help plant breeders to achieve the number of generations in minimum time and with greater accuracy. Moreover, the readership community has been made conversant with the breakthrough technology called clustered regularly interspaced short palindromic repeats (CRISPR) system and proof of concepts for achieving biotic and abiotic stress resistance, the advancement in CRISPR technology, that is, base and prime editing and their potential application for crop improvement programs. It has been emphasized that CRISPR technology holds potential to create novel traits in agronomically important crops essential for achieving United National 2nd sustainable development goal.
Overall, this book provides a collection of recent literature focusing on developments and applications of OMICs-based technologies for crop improvement programs. We believe this book will be of interest to plant breeders, molecular biologists, researchers, postdoctoral fellows, and students working in OMICs and related disciplines for developing crops with high yield and nutritional potential. Finally, we would like to gratefully acknowledge the authors for their valuable contribution to this book and the associate editor for making active coordination to complete the project well on time.
8th March 2024
Sajid Fiaz
Haripur, Pakistan
Channapatna S. Prakash
Tuskegee, AL, USA
1Crop Genome Sequencing and their Application for Crop Improvement
Hafiz Ghulam Muhu-Din Ahmed1, Yawen Zeng2, Xiaomeng Yang2, Noor Fatima1, and Anns Faisal1
1Department of Plant Breeding and Genetics, Faculty of Agriculture & Environment, The Islamia University of Bahawalpur, 63100, Pakistan
2Biotechnology and Germplasm Resources Institute, Yunnan Academy of Agricultural Sciences, Kunming 650205, China
1.1 Crop Genome Sequence
Crop genome sequencing refers to the process of determining the complete DNA sequence of a particular crop plant species. This is done by using high-throughput sequencing technologies, which allow for the rapid and accurate sequencing of large amounts of genetic material.
1.2 Genomics and Its Contribution to Agricultural Improvements
In agriculture, the decrease in production, due to climate change and different emerging diseases, is the upcoming and current challenge to ensure an adequate food supply for the world’s population of 9 billion people by the year 2050 (Keating et al. 2014). The trends in the production of yield are inadequate to reach the demands (Ray et al. 2013). For this reason, it is necessary to speed up the process of crop breeding to significantly increase production by using new strategies. The new revolution strategies which are being used up by the researchers are Next-Generation sequencing (NGS), Machine learning Technology (MTL), and advanced phenotyping platforms. Mass sequence of genomes and transcriptomes make it easier to study each genotype’s association with its phenotype which is particular for complex traits (Cannarozzi et al. 2014). Over the last decade, NSG has brought life sciences to an era of big data and omics studies are accelerating more quickly than ever. Through genomics-assisted breeding, which enables the integration of genomes and phenomics by facilitating the genomic selection of phenotype by genotype via genome wide association studies (GWAS) research, the crop has been considerably enhanced. They have greatly improved and facilitated the development of new and improved cultivars with desired attributes (Collard and Mackill 2008). Considering it separately as a whole, the limitations that might arise in determining and identifying gene function are not enough to overcome it. The effectiveness of traditional methods in identifying rare variants and transcripts is limited, leading to constrained outcomes across various developmental stages. Understanding the intricate molecular dynamics governing physiological processes in plants, particularly in terms of exponential advancements, presents a challenge in terms of practical application by breeders. The gap in translating biological insights into actionable solutions contributes to the ambiguity surrounding the utilization of this knowledge.
1.3 Genome-Assisted Advances in Crops
In order to highlight the impact of genetic advances on improving crop yields for hundreds of millions of consumers, we shall focus here on rice and other crops which are most widely grown in 2019, collectively amounting to 360 million hectares.
1.3.1 Genomic Advances in Rice
The most important and consumed cereal crop which is the basic staple food for more than half of the population of the globe, the plant Eudicot arabidopsis has a very small genome size of 430 Mb, varies from other cereal crops. With abundant genetic resources and a highly efficient genetic information system, the rice crop has become a model plant species in the grass family equivalent to E. arabidopsis. Remarkable attempts have been made to explain and gather the genome of rice of specific japonica and indica species. When compared to E. arabidopsis, rice revealed a GC content gradient, a significant proportion of the rice genome has no similarity in E. arabidopsis (Goff et al. 2002). Due to its extremely strong population structure and extensive linkage disequilibrium (LD) caused by self-pollination, rice is a great candidate for population genomic analysis. The two cultivated subspecies of rice were clearly separated by resequencing 527 landraces, which are further divided according to latitudinal photoperiod and temperature clines (Huang et al. 2012a). In an analysis of 446 wild rice species (Oryza rufipogon) conducted in southern China, where domestication into the japonica subspecies is believed to have occurred, 1083 cultivated varieties of O. rufipogon were found. From these varieties, indica rice was created through hybridization with nearby wild rice and subsequently spread throughout Southeast and South Asia (Huang et al. 2012b). Most number of loci which are associated with the agronomical traits in the rice have been exhibited by combining phenotypic and genomic data by using GWAS, while these kinds of analyses can be performed using short sequence reads from different species mapped onto one of the reference genomes, genomic regions not included in the reference will be disregarded. Recent investigations have focused on examining the genomes of additional cultivars that display advantageous agronomic traits (Zhao et al. 2018; Choi et al. 2020). Moreover, efforts have been directed toward the exploration of rice weedy species and other Oryza variants, leading to the assembly and organization of a rice pangenome, which holds promising potential (Wang et al. 2020). A comprehensive study involving 16 de novo assembled genomes from major population genomic groups, referred to as platinum genomes due to their assembly through long reads and optical mapping, was conducted as part of the rice pangenome initiative. This study unveiled a consistent absence of approximately 33.7 Mb of genomic content in all pairwise comparisons (Zhou et al. 2020). These illustrations underscore the necessity of utilizing a single reference to elucidate the variations within a specific segment of the complete crop genome. Notably, the rice pangenome consists predominantly of approximately 90% transposable elements (TEs), indicative of their heightened precision and evolutionary adaptability. Conversely, the remaining 10% comprises potentially protein-coding loci. The availability of these diverse genome resources is anticipated to greatly advance evolutionary investigations and facilitate innovations pertaining to adaptive genetic variation in rice, as depicted in Figure 1.1.
Figure 1.1 Genomics of field crops and its progress during the last two decades. Source: Zhou Fangqi/Wikimedia Commons/CC BY-SA 4.0; zcy/Adobe Stock; sarangib/Pixabay; Bill Ebbesen/Wikimedia Commons/CC BY-SA 3.0; VIVEK/Adobe Stock; Bezvershenko/Adobe Stock; Anna Sedneva/Adobe Stock; dabjola/Adobe Stock.
1.3.2 Genomic Advances in Pepper
In pepper, potyvirus resistance 4 (Pvr4) was tightly linked to molecular markers which were identified by using Illumina technology in combination with bulked segregant analysis (Devran et al. 2015). Using F2 mapping populations, the Pvr4 locus was mapped, and the synthetic areas separating resistant from susceptible offspring were identified. In this approach, more than 5000 SNPs were transformed into CAPS markers. Another similar study used GWAS to identify four novel loci linked to pepper fruit size and shape using 750 thousand GBS-polymorphic sites (Rodriguez et al. 2020).
1.3.3 Genomic Advances in Legumes
Marker-assisted genomics in legumes has made considerable strides as well. Notably, substantial datasets of single nucleotide polymorphisms (SNPs) were generated by mapping diverse collections of genotyping-by-sequencing (GBS) reads in accessions from the primary gene pools of the common bean (Phaseolus vulgaris), encompassing the Andean and Middle American lineages. Utilizing the same methodology, researchers also successfully pinpointed over 200 000 SNPs within each gene pool. These SNPs were subsequently employed in genome-wide association studies (GWAS) to investigate characteristics associated with yield in plants cultivated under conditions of both heat and drought stress. The application of genotyping-by-sequencing (GBS) proved instrumental in constructing a high-density linkage map for chickpea (Cicer arietinum L.), leading to the identification of quantitative trait loci (QTLs) linked to seed traits (Verma et al. 2015). Among these, five robust QTLs contained candidate genes displaying specific expression profiles related to seed development. More recently, by utilizing three distinct mapping populations of chickpeas developed from independent sources of resistance, a significant QTL for phytophthora root rot resistance was identified (Amalraj et al. 2019). This breakthrough enabled the precise mapping of resistance loci, providing the chickpea industry with a valuable tool for facilitated breeding and genomics, as depicted in Figure 1.1.
1.3.4 Genomic Advances in Wheat
Multi-parent advanced generation inter-cross (MAGIC) and core collections have been utilized by researchers to map wheat populations using bi-parental genomic aided breeding (Huang et al. 2012a; Dixon et al. 2018; Saintenac et al. 2018). GWAS have been used in those to dissect the genomic regions underlying primary agronomic traits (Milner et al. 2016) and to determine the resistance against stripe rust (Puccinia striiformis f. sp. Tritici) (Liu et al. 2017). Numerous strides have been taken in developing enhanced rice cultivars referred to as “super varieties.” These advancements stem from the integration of elite alleles recognized for their capacity to elevate both grain yield and quality (Esposito et al. 2019). The research demonstrated the efficacy of employing high-throughput genotyping techniques, which culminate in a meticulously devised marker-assisted selection (MAS) strategy. This streamlined approach significantly simplifies the process of identifying and selecting elite crop varieties, as illustrated in Figure 1.1.
1.4 Methods of Crop Genome Sequencing
There are several methods for crop genome sequencing and the choice of method depends on the size and complexity of the genome, as well as the resources available. Here are some of the most commonly used methods.
1.4.1 Genotype by Sequencing Method (GBS)
The number of molecular markers available for use in crop genetics has significantly increased thanks to genotyping by sequencing GBS, a high-throughput sequencing technique (Poland and Rife 2012; He et al. 2014). Reduction of genome complexity through the use of restriction enzymes and sequencing of pooled samples through the use of barcode adapters are the fundamental components of GBS. The size of the crop’s genome, the degree of LD, the degree of heterozygosity of the panel under study, and cost-efficiency considerations are typically used to select the best GBS method. GBS makes it possible to identify and genotype a huge number of SNPs, in contrast to earlier low-throughput methods based on RFLP or simple sequence repeats (SSRs). These SNPs can be linked to desirable agronomic traits, and their use in marker-assisted breeding or to confirm trait-linked haplotypes in crops can be used to improve crops (Varshney et al. 2014; Thomson 2014). Numerous significant crops have seen success using this method (Spindel et al. 2013). For instance, recombinant inbred line (RIL) populations of rice, maize, and barley were genotyped using GBS techniques (Elshire et al. 2011) and doubled haploid (DH) populations in wheat in view of QTL mapping.
1.4.2 Single Primer Enrichment Technology
The single primer enrichment technology is another targeted genotyping technique used to examine the diversity of tomato and eggplant. Previous knowledge of the target sequences is necessary for the method’s probe design. Compared with previous knowledge and methodologies, this method offers better discrimination between domesticated and wild species, and it has a high degree of transferability between closely related species, making it a viable alternative to random complexity reduction methods and arrays (Barchi et al. 2019). Additionally, users can alter the target marker panel.
1.4.3 Comparison of Whole Genome Sequencing vs Targeted Genome Sequencing
Whole genome sequencing (WGS) and targeted sequencing are two commonly used techniques for sequencing crops. WGS involves sequencing the entire genome of an organism, while targeted sequencing focuses on sequencing specific regions of interest in the genome.
GWAS and the discovery of new genetic variations are made possible by WGS’ complete perspective of the genome. However, it is more expensive and generates a large amount of data that can be challenging to analyze. Targeted sequencing, on the other hand, is more cost-effective and generates less data, making it easier to analyze. It is also useful for studying specific regions of the genome, such as genes or regulatory regions. Rodriguez et al. (2020) compared WGS and targeted sequencing in rice and found that targeted sequencing was more effective for identifying genetic variations in specific genes. However, WGS provided a more comprehensive view of the genome and enabled the identification of new genes and functional elements. Wang et al. (2018) compared WGS and targeted sequencing in maize and found that targeted sequencing was more effective for identifying small-scale genetic variations, while WGS was more effective for identifying large-scale variations.
1.5 Application of Crop Genome Sequencing for Crop Improvement
Crop genome sequencing has changed the way plant breeds and crop improvements have been achieved by providing researchers access to a wide range of genetic data. The genes responsible for desirable traits like pest and disease resistance, stress tolerance, and enhanced yield can be observed using information as mentioned in Table 1.1. It is now feasible to pinpoint the genes responsible for features like disease resistance, drought tolerance, and higher yield; thanks to genome sequencing. The development of new crop varieties with enhanced traits can then be accomplished by incorporating these genes into breeding programs. As an illustration, the sequencing of the wheat genome has enabled the identification of genetic variants’ fungal disease resistance, which can be used to create new varieties with enhanced disease resistance (Yadav et al. 2022). Genome sequencing allows for the identification of specific genetic markers associated with desirable traits, which can be used to develop precise breeding strategies. For example, the sequencing of the soybean genome has allowed for the identification of genes associated with seed composition, which can be used to develop soybean varieties with improved nutritional profiles (Lam et al. 2010).
Table 1.1 sCrop attributes that have been enhanced through genome editing method.
Crops
Targeted genes
Editing genes
Targeted indices
Maize
ZmIPK1, Wx1, ARGOS8
ZFNs, Cas9
Tolerant to herbicides, increase in amylopectin, tolerant to drought
Rice
OsSWEET14, OsBADH2, GS3, ALS
TALENs, CRISPR,
Resistance to bacterial blight, fragrance of rice, increase in size of grain, higher number of grains
Wheat
GW2, EDR1, TaMLO
Cas9, TALENs
Increase weight of grain, resistance to powdery mildew
Soybean
ALS, FAD2-1A, FAD2-1B, FAD3A
TALENs, CRISPR,
Resistance to herbicides, higher oleic and lower linoleic content
Tomato
SIMLO1, SP5G, S1AGL6
Cas9
Resistance to powdery mildew, earlier harvesting, parthenocarpy
Potato
Wx1, ALS
CRISPR
Tolerant to herbicides, increase in amylopectin
The identification of genes responsible for desirable traits is a crucial step in crop improvement through genetic engineering and breeding. Genome sequencing and functional genomics approaches such as transcriptomics, proteomics, and metabolomics have greatly facilitated the identification of such genes. Genes associated with desirable traits in crops include the rice SUB1A gene, which confers tolerance to flooding, the maize ZmMYB167 gene, which is involved in drought tolerance (Singh et al. 2022), and the tomato SlARF7 gene, which is involved in fruit ripening (Kumar et al. 2012).
GS3 acts as a negative regulator for grain size and is a significant QTL for grain length and weight (Fan et al. 2006; Mao et al. 2010). By limiting the number of cells, the novel protein encoded by TGW6 that has indole-3-acetic acid (IAA)-glucose hydrolase activity negatively regulates grain weight (Weng et al. 2008). Grain weight and grain width are negatively regulated by the calmodulin-binding protein GW5/qSW5, which is dependent on the Brussino steroid (BR) signaling pathway (Shomura et al. 2008). A RING-type E3 ubiquitin ligase that is encoded by the gene GW2 also negatively controls grain width, weight, and yield by inhibiting cell division in the shell (Song et al. 2007) as showed in Table 1.1.
DNA sequences known as molecular markers can be used to identify particular genes or genetic characteristics in crop plants. The development of molecular markers has totally transformed plant breeding because they make it possible to select desirable traits like disease resistance, yield potential, and nutrient use efficiency more effectively. DNA-based molecular markers such as restriction fragment length polymorphism, amplified fragment length polymorphism, cleaved amplified polymorphic sequence, and SSRs were harnessed to generate genotypic information and unravel the genetic attributes of the strains. However, the process of data acquisition demands significant time and labor investment (Lau et al. 2015; Shabir et al. 2017). The utilization of these markers to establish reliable genotypic information for identifying candidate gene loci is hindered by challenges such as the limited resolution of QTL mapping, the context-dependent expression of QTLs, and gene epistasis, resulting in an extended timeline (Lau et al. 2015).
For the purpose of wheat breeding, researchers used molecular markers to explore individuals with high grain yield and disease resistance and were able to establish new wheat cultivars with improved yield and resistance as exhibited in Table 1.1. Similarly, in a study of rice breeding, researchers used molecular markers to identify individuals with high nutrient use efficiency and were able to develop new rice cultivars with improved nutrient use efficiency (Jewel et al. 2019). Emerging high-throughput sequencing techniques have the capability to swiftly generate millions of SNP markers. These innovative technologies stand out for their reliability, precision, and enhanced information content (Chen et al. 2014). Due to their broader chromosomal coverage, these SNPs hold significant promise for constructing genetic linkage maps geared toward dissecting QTLs underlying numerous traits (Parida et al. 2012).
Numerous QTLs have been documented thus far for traits associated with tolerance to nitrogen (N), phosphorus (P), and potassium (K) deficiencies in multiple genetic backgrounds of managed populations. These backgrounds encompass diverse types like recombinant inbred lines (RILs), backcross inbred lines (BILs), introgression lines (ILs), doubled haploids (DHs), chromosome segment substitution lines (CSSLs), and BC2F3 families in rice, as elucidated by Vinod and Heuer (2012).
Genetic tolerance to phosphorous deficiency has been observed, with identified Quantitative Trait Loci (QTLs) located on rice chromosomes 1, 2, 6, 11, and 12 (Vinod and Heuer 2012). Tolerance to low potassium According to Wang et al. (2015) three chromosomes – chromosomes 3, 5, and 8 – have QTLs. In contrast to an individual nutrient shortfall, a full N, P, and K deficiency has a major influence on agronomic variables such tillering ability, PH, and LC, which are crucial components in determining total grain production (Wang et al. 2017).
1.5.1 Accelerating Breeding
Accelerated breeding represents a modern plant breeding technique that leverages genomic technologies to increase the breeding process and also enhances the efficiency of plant breeding initiatives. This strategy aims to create crop plants exhibiting improved yield, disease resistance, and other desirable traits. While traditional backcrossing breeding methods were used to incorporate favorable agronomic traits like disease resistance, this approach typically necessitates a minimum of six backcrossing cycle to minimum unwanted linkages within the donor parent (Boopathi and Boopathi 2013). Over the past three decades, various molecular markers associated with specific traits have been identified and employed for marker-assisted backcross breeding, serving as foreground selection (Hospital and Charcosset 1997). The concept of background selection was initially implemented to introduce the Xa21 gene for bacterial blight resistance into the Minghui 63 cultivar (Xu et al. 2012). In recent times, the combination of foreground and background selection methodologies in marker-assisted backcross breeding gained widespread application in the development of novel disease-resistance rice varieties (Hu et al. 2016). Bacterial blight (BB) disease, caused by Xanthomonas oryzae PV. Oryzae (Xoo), has emerged as a highly determined ailment impacting rice production globally. Since the 1960s, breeding for BB resistance has remained a pivotal objective in rice breeding efforts. Several BB resistance (Xa3/Xa/26, Xa4, Xa4b, Xa6, and Xa9) had pinpointed on the terminal region of chromosomes 11. Among these, Xa4 has gained prominence as the most extensively utilized BB resistance gene in the rice breeding endeavors. The anticipated ramification of climate change on the yields of major crop worldwide, as highlighted by Zhao et al. (2017) necessitate profound attention. Despite advancement in crop enhancement, the actualized genetic improvements observed in farmer’s fields, particularly in rain fed conditions, had notably limited. To expedite the rate of genetic advancement and counteract the effect of climate change in order to fulfill the demands of food production, multidisciplinary research approach is imperative, as emphasized by Hickey et al. (2017).
The enhancement in genetic gain observed within research plots is directly linked to several key factors. Primarily, it is positively correlated with the extent of genetic variation (σA) present within the population. Additionally, the degree of selection intensity (i), which pertains to the proportion of individuals that do not contribute to the subsequent generation, also plays a crucial role. Furthermore, the accuracy of selection (r) is another influential factor. Conversely, the number of years required per breeding cycle (y) exhibits an inverse relationship with genetic gain (Varshney et al. 2017). The integration of contemporary genomic techniques, such as next generation sequencing (NGS) and high-throughput genotyping, alongside advanced phenotyping methods (phenomics) and informatics, as well as decision support tools, holds the potential to increase the accrual of genetic advancement over time (Varshney et al. 2014). The extent of selection intensity (i) can be heightened by optimizing the screening process to accommodate more plants within a given timeframe or area. Progress in phenotyping methodologies, such as disease plot studies or controlled screenings in laboratories and green houses, can facilitate the swift evaluation of a substantial number of plants for specific traits of interest (Bevan et al. 2017).
Molecular markers offer a valuable substitute for observable traits, allowing selection to be carried out during early plant stages and initial generations. The accessibility of high-throughput sequencing and genotyping platforms facilitates the swift evaluation of thousands of plants within a relatively condensed timeframe. Augmenting selection accuracy (r) becomes attainable by employing markers associated with specific traits, which in turn enables selection across diverse environments and seasons. For example, during years with ample rainfall, traditional breeding methods encounter challenges in identifying drought-tolerant plants. Nevertheless, robust GAB approaches can be seamlessly implemented regardless of the season or developmental stage, effectively circumventing this limitation (Varshney et al. 2005). Augmenting genetic variance (σA) involves the selection of lines harboring advantageous yet infrequent alleles for specific traits. Reducing the time required per breeding cycle (y) is attainable by cultivating more generations annually, utilizing rapid generation advancement (3–6 crop season annually), embracing speed breeding (Watson et al. 2018), as opposed to the conventional 1 or 2 crop seasons per year. Speed breeding can be synergistically coupled with selection on an individual plant basis using either visual cues or molecular markers.
Globally, approximately 7.4 million accessions are meticulously preserved across 1700 seed banks, constituting a vast repository of natural genetic diversity. These invaluable resources serve as a rich reservoir for breeders seeking to harness genetic variability. By amalgamating gene bank passport information with climatic data, it becomes possible to employ these factors as proxies for abiotic stresses. This strategic approach facilitates the identification of genotype harboring crucial haplotypes, which can subsequently be seamlessly integrated into ongoing breeding endeavors. To amplify the magnitude of genetic advancement, the inclusion of fresh avenues of genetic diversity needs the identification of novel and superior genes originating from crop wild relatives (CWR) and local landraces, both of which are housed within gene banks. Remarkably, modern NGS methodologies have proven highly effective in pinpointing DNA polymorphisms linked to desired traits. A notable illustration is the 3000 Rice Genome and 3000 Chickpea Genome Sequencing Initiative (Varshney et al. 2017) which presents a promising avenue to uncover new varieties across a multitude of genes by scrutinizing genotype-phenotype associations. The process of re-sequencing an extensive array of germplasm accessions yields not only insights into origin, domestication, and population structure, as highlighted by Varshney et al. (2017) but also illuminates lines harboring deleterious mutations within genomes. The identification of these types of mutations offers the opportunity for their elimination, subsequently curbing the genetic load present within crop species.
1.5.2 Speed Breeding (SB)
The protracted generation time required for crops to attain a stable homozygous state after hybridization significantly hampers the advancement of fundamental and applied research. This restriction has led to efforts to shorten generational gaps in a variety of crops, most of which have depended on off-season nursery shuttle breeding, double haploid technology, and in vitro/embryo culture techniques (Bhatta et al. 2021). The application of embryo rescue techniques, coupled with precise management of water levels, light exposure, and temperature manipulation, yields remarkable results. For instance, the integration of these practices enabled the attainment of eight wheat and nine barley generations within a single year (Zheng et al. 2013). Similarly, the establishment of a streamlined biotron breeding system, involving regulation of CO2 levels, photoperiod manipulating, and root volume control, facilitated four crossing cycles in rice (Tanaka et al. 2016). Recently, a novel technique known as simplified breeding (SB) has been introduced to expedite harvest without resorting to intricate tissue culture protocols. Instead, SB depends on high-density planting, early harvesting, the use of growth regulators, and regulating the growing environment, which includes elements like photoperiod and temperature (Ghosh et al. 2018). Especially well-suited for long-day or day-neutral plants, SB protocols have enabled researchers to achieve the cultivation of four to six generations annually in crops such as chickpea, pea, wheat, barley, and canola. The adaptation of SB methods has markedly compressed breeding cycle timelines and is lauded for its efficacy in amplifying selection gains within crop breeding initiatives. Expending the applicability of SB techniques to short-day plants, such as soyabean, has been demonstrated through the utilization of a 10-hour photoperiod and a light spectrum enriched with blue light (Jähne et al. 2020). Nevertheless, SB experiments conducted on other short-day crops like rice and amaranth (Amaranthus spp.) underscore the necessity for crop-specific optimization of light quantity and quantity in influencing plant photomorphogenic responses was previously underscored in study involving grain legumes (Croser et al. 2016). Standardized SB systems found widespread utility across various domains, including aiding hybridization, adult plant trait phenotyping, MAS for target traits, advancing generations via single seed descent (SSD), and facilitating gene editing across multiple crop species.
1.5.3 Rapid Trait Mapping
The progress in genomics has facilitated the development of trait mapping methodologies based on NGS. These approaches have significantly increased the duration of trait mapping initiatives, reducing the process from several years to merely a few months. As an illustration, the application of NGS technologies has made it feasible to enhance and improve time-intensive processes like bulked analysis into rapid high-resolution based on whole genome sequencing (Lobet 2017; Singh et al. 2017). The existence of preliminary genome sequences for various crop species and the decreases in sequencing expenses had made it possible to re-sequence numerous individuals within a genetic population. Consequently, researchers in the genetics field were able to utilize Whole-Genome Resequencing (WGRS) or low-coverage sequencing on the entire population or extreme sample groups. Techniques such as GBS and WGRS of complete mapping populations or exceptional subgroups had generated an abundance of SNPs. This resource has facilitated the execution of high-resolution trait mapping, and many examples have been observed in several crop species (Pandey et al. 2016). Also, in past years for trait mapping, bulk or pool-based sequencing has gained significant popularity (Pandey et al. 2017). Pre-breeding is an important step in the process of identifying and transferring favorable attributes and genes from non-adapted to intermediate materials (Dempewolf et al. 2017). These intermediate materials can be subsequently employed by breeders to develop novel varieties. This initial and critical stage is raising the variety from CWR and other non-adapted material and tying genetic diversity to practical use. To incorporate unique indices from these collections into recently developed varieties, the plant breeder and germplasm curator must work well together.
1.5.4 Crop Evolution
Addressing the significant challenge of securing a sustainable rise in global food production to accommodate the expanding human population within the constraints of limited resources remains a paramount task. Approximately 15 years ago, we introduced the concept of GAB as a mechanism to drive crop improvement through genomic modifications (Varshney et al. 2005). Notably, the publication of a high-quality genome sequence assembly for rice (Oryza sativa) coincided with this proposal, marking a notable milestone as the first genomic sequence for an agricultural plant (Kover et al. 2009). Subsequently, a diverse array of genomic tools and technologies has become readily available for integration into the crop breeding process. The advancements in genomic technologies have led to the introduction of innovative genetic approaches involving multi-parent synthetic populations, expanding the horizons of trait exploration. These designs combine the advantages of association mapping with linkage analysis, which encompass advantages including more genetic diversity, regulated structure, greater power for detecting QTLs, and enhanced mapping accuracy (Yu et al. 2008).
With the creation of more than 130 publicly bred cultivars of various crops over the course of the last 15 years, GAB has accelerated the breeding progress timelines across a wide variety of crop species (Varshney et al. 2005). A significant portion of the notable crop achievement through the utilization of GAB within diverse breeding initiatives involves the development of enhanced cultivars featuring resistance against important diseases such as bacterial blight and blast in rice and rust in wheat. Tolerance to submergence, salt, and drought continued to be the major target qualities for GAB development. Additionally, GAB techniques have had a significant influence on improving quality attributes across a variety of crop species.
1.6 Identification of Disease Resistance of Genes
1.6.1 Genome-Assisted Breeding for Abiotic Stress Tolerance
The predominant objectives of introgression via GAB strategies typically encompass disease resistance and other traits with straightforward inheritance patterns governed by potent QTLs. A notable example in the context of rice is the creation of “Improved Samba Mahsuri” (ISM) through GAB techniques. This variety harbors three BB disease (Xanthomonas oryzae pv. oryzae) resistance genes (Xa21, Xa13, and Xa5) (Varshney et al. 2017). Two important blast disease (Magnaporthe oryzae) resistance genes (Pi-2 and Pi-54) and a BB gene (Xa38) were further stacked into “ISM” (Sundaram et al. 2008). Other improvements include “Pusa Basmati 1121” and “Pusa Basmati 6,” which both have genes for blast resistance (Pi2 and Pi54) and BB resistance (xa13 and Xa21). ’Pusa Basmati 1’pyramided with three (Pi54+Pi1+Pita) and two (Pi2+Pi5) blast genes, respectively (Ellur et al. 2016; Ratna Madhavi et al. 2016). In wheat breeding, a number of DNA markers have been used to enhance stress response as well as other agronomic and quality-related features as shown in Table 1.1. Prominent instances of improved hard red winter wheat (HRWW) cultivars include “Jagger” and “Overley,” which respectively carry genes Yr40/Lr57 and Lr58. Similarly, the spring wheat cultivar “HUW510” harbors Lr34 (Kuraparthy et al. 2009; Vasistha et al. 2017). In 2005, India witnessed the introduction of “HHB 67-improved,” a pearl millet cultivar fortified with downy mildew resistance, for commercial cultivation (Rai et al. 2008). Illustrating the potential of GAB in cereal breeding, the transfer of genes such as Pch1 for eyespot (Rhizoctonia cerealis) resistance, the recessive rym4/rym5 genes against barley yellow mosaic viruses, and Mlo-gene for barley powdery mildew (Blumeria graminis f. sp. hordei) has been observed.
While cereals have experienced significant advancements through GAB, grain legume crops have been somewhat slower in product delivery. Nevertheless, recent strides are being made, with a growing adoption of GAB-based options in breeding projects, as highlighted in Table 1.1. For example, within the USDA-ARS research, the development and official recognition of high-yielding and disease-resistant genotypes, namely “JTN 5503,” “JTN 5303,” “DS 880,” and “JTN 5109,” were achieved through the amalgamation of resistance against distinct races (2, 3, 5, and 14) of soybean cyst nematode (Heterodera glycines) (Rai et al. 2008)(Arelli et al. 2006). A parallel achievement was accomplished by Varshney et al. (2017) in groundnut (Arachis hypogaea). Here, a significant QTLs for rust resistance was successfully introduced into the genetic background of three susceptible cultivars (“ICGV 91114”, “JL 24”, and “TAG 24”), yielding a group of 20 introgression lines. Notably, a well-established chickpea variety known as C 214 has exhibited enhanced resistance against wilt caused by Fusarium oxysporum f. sp. ciceris and blight induced by Ascochyta rabiei
