1 Introduction

Pulse crops, including peas, chickpeas, lentils, and beans, are essential components of global agriculture. They are highly valued for their nutritional benefits, providing a rich source of dietary protein, fiber, vitamins, and minerals (Roy et al., 2010; Asif et al., 2013; Kumar and Pandey, 2020). These crops play a crucial role in food security, particularly in developing countries, where they help alleviate protein and micronutrient malnutrition (Bohra et al., 2014; Bessada et al., 2019). Additionally, pulses contribute to sustainable agriculture by improving soil health through nitrogen fixation, making them an environmentally friendly crop choice (Asif et al., 2013; Bessada et al., 2019).

Despite their importance, pulse crops face significant productivity challenges. These include susceptibility to biotic and abiotic stresses, such as diseases, pests, and adverse environmental conditions (Varshney, 2016; Thudi et al., 2020). Traditional breeding methods have had limited success in overcoming these challenges, resulting in low genetic gains and insufficient yield improvements to meet the growing global demand for food (Bohra et al., 2014; Varshney, 2016). Furthermore, the presence of antinutritional factors in pulses can affect their nutritional quality and digestibility, posing additional hurdles for their widespread adoption (Roy et al., 2010; Bessada et al., 2019).

Recent advancements in genomics offer promising solutions to enhance pulse crop productivity. Genomics-assisted breeding (GAB) has emerged as a powerful tool to accelerate genetic improvements by leveraging genome-wide genetic markers, high-throughput genotyping, and sequencing technologies (Bohra et al., 2014; Varshney, 2016; Thudi et al., 2020). The availability of whole-genome sequences and high-density genetic linkage maps enables precise trait mapping and genomic selection, facilitating the development of superior cultivars with enhanced yield, stress tolerance, and nutritional quality. These innovations have already led to notable successes, such as the development of drought-tolerant chickpea varieties and disease-resistant groundnut lines (Varshney, 2016).

This study aims to explore the role of genomics in advancing pulse crop productivity. Specifically, it will review the current state of pulse crop genomics and the key technological advancements in this field. Additionally, it will discuss the challenges and limitations associated with traditional breeding methods for pulse crops. The potential of genomics-assisted breeding to overcome these challenges and improve pulse crop yields and resilience will be highlighted. The study will present case studies and success stories of genomics applications in pulse crop breeding. Furthermore, it will provide insights into future directions and opportunities for integrating genomics into sustainable pulse crop production. By addressing these objectives, this study seeks to underscore the transformative potential of genomics in enhancing the productivity and sustainability of pulse crops, ultimately contributing to global food security and agricultural resilience.

2 Genomic Tools and Technologies in Pulse Crop Research

2.1 Next-generation sequencing (NGS)

Next-Generation Sequencing (NGS) technologies have revolutionized the field of genomics by enabling rapid and high-throughput sequencing of entire genomes. These technologies have significantly reduced the cost and time required for sequencing, making it feasible to conduct large-scale genomic studies. NGS encompasses various methods, including whole-genome re-sequencing (WGRS), single nucleotide polymorphism (SNP) arrays, and reduced-representation sequencing (RRS) such as genotyping-by-sequencing (GBS) (Abebe, 2019; Torkamaneh et al., 2021). These methods allow for the comprehensive analysis of genetic variation within and between species, facilitating the identification of genetic markers associated with important traits (Pavlopoulos et al., 2013).

NGS technologies have been extensively applied in pulse crop genomics to enhance our understanding of genetic diversity, population structure, and the genetic basis of key agronomic traits. For instance, genotyping-by-sequencing (GBS) has been used to discover and genotype SNPs in large populations, aiding in genome-wide association studies (GWAS) and genomic selection (GS) (He et al., 2014; Abebe, 2019). These applications have led to the identification of genetic loci associated with disease resistance, yield, and other important traits, thereby accelerating the breeding of improved pulse crop varieties (Lee et al., 2015; Liu and Yan, 2018).

2.2 Genome-wide association studies (GWAS)

Genome-Wide Association Studies (GWAS) are a powerful tool for identifying genetic variants associated with phenotypic traits. GWAS involves scanning the genomes of many individuals to find genetic markers that occur more frequently in individuals with a particular trait. This approach leverages the natural genetic variation within a population to link specific genetic loci to phenotypic traits (Xiao et al., 2017; Gupta, 2021). The success of GWAS depends on the availability of high-density genotyping data and appropriate statistical models to detect significant associations (Pavan et al., 2020).

GWAS has been instrumental in identifying genetic loci associated with key traits in pulse crops, such as disease resistance, drought tolerance, and yield. By analyzing large populations with diverse genetic backgrounds, researchers have uncovered numerous genotype-phenotype associations that provide insights into the genetic architecture of complex traits (Liu and Yan, 2018; Torkamaneh et al., 2021). These findings have important implications for breeding programs, as they enable the development of molecular markers for marker-assisted selection (MAS) and the identification of candidate genes for functional studies (Lee et al., 2015; Xiao et al., 2017).

2.3 Genomic selection (GS)

Genomic Selection (GS) is a breeding method that uses genome-wide genetic information to predict the breeding value of individuals. Unlike traditional selection methods that rely on phenotypic data alone, GS incorporates genotypic data to enhance the accuracy of selection. The methodology involves genotyping a training population, estimating the effects of genetic markers on the trait of interest, and using these estimates to predict the performance of selection candidates (He et al., 2014; Abebe, 2019). This approach allows for the selection of superior individuals at an early stage, thereby reducing the breeding cycle time.

GS has been successfully applied in pulse crop breeding to improve traits such as yield, disease resistance, and stress tolerance. By integrating genomic data into breeding programs, researchers can make more informed decisions and accelerate the development of improved varieties. For example, the use of GBS in GS has enabled the efficient genotyping of large populations, facilitating the identification of superior genotypes for breeding. The implementation of GS in pulse crops holds great promise for enhancing productivity and sustainability in these important food crops.

3 Advancements in Pulse Crop Genomics

3.1 Genetic mapping and quantitative trait loci (QTL) analysis

The advent of next-generation sequencing (NGS) technologies has revolutionized QTL mapping by providing high-resolution genetic maps and enabling the rapid identification of QTLs. Techniques such as QTL-seq, which involves whole-genome resequencing of DNA from bulked populations, have significantly accelerated the process of QTL identification. This method has been successfully applied in various crops, including rice, to identify QTLs for traits like disease resistance and seedling vigor (Figure 1) (Takagi et al., 2013). Additionally, the integration of NGS with traditional bulk-segregant analysis (BSA) has enhanced the resolution of QTL mapping, facilitating the detection of causative genes (Nguyen et al., 2019).

Recent studies have identified numerous QTLs associated with key agronomic traits in pulse crops. For instance, in sorghum, an ultra-high-density linkage map constructed using SNPs from high-throughput sequencing revealed 57 major QTLs for traits such as plant height, flowering time, and grain yield under different photoperiods (Zou et al., 2012). Similarly, in cassava, QTL mapping has identified loci associated with productivity and plant architecture traits, highlighting the potential for marker-assisted selection to improve crop yields (Okogbenin and Fregene, 2003). These findings underscore the importance of QTL analysis in understanding the genetic basis of complex traits and enhancing pulse crop productivity.

3.2 Transcriptomics and gene expression profiling

RNA sequencing (RNA-seq) has emerged as a powerful tool for transcriptomic analysis in pulse crops. This technology allows for the comprehensive profiling of gene expression, providing insights into the molecular mechanisms underlying various traits. RNA-seq has been instrumental in identifying differentially expressed genes and understanding their roles in stress responses, growth, and development (Kumar et al., 2017).

Transcriptomic studies have yielded significant findings that contribute to our understanding of pulse crop biology. For example, RNA-seq has been used to identify genes involved in drought tolerance, disease resistance, and nutrient uptake. These studies have revealed the complex regulatory networks that govern these traits and have identified potential targets for genetic improvement. The integration of transcriptomic data with QTL mapping has further enhanced the identification of candidate genes and their functional validation (Shariatipour et al., 2021).

3.3 Functional genomics

The CRISPR-Cas9 system has revolutionized functional genomics by enabling precise gene editing in pulse crops. This technology allows for the targeted modification of specific genes, facilitating the study of gene function and the development of improved crop varieties. CRISPR-Cas9 has been successfully used to knock out genes associated with undesirable traits and to introduce beneficial alleles, thereby enhancing crop performance (Xu et al., 2017).

Functional genomics approaches, such as gene knockout and overexpression studies, have provided valuable insights into the roles of specific genes in pulse crops. These studies have identified key genes involved in traits such as yield, stress tolerance, and nutrient content. For instance, the overexpression of certain genes has been shown to improve drought tolerance and increase biomass production, while gene knockout studies have elucidated the functions of genes involved in disease resistance (Yang et al., 2012). These findings highlight the potential of functional genomics to drive the development of high-yielding, resilient pulse crop varieties.

4 Case Study: Enhancing Drought Tolerance in Soybeans Using Genomic Approaches

4.1 Introduction to drought stress in soybeans

Drought stress is a significant environmental factor that adversely affects soybean productivity, leading to substantial yield losses. Soybeans are particularly sensitive to water-deficit conditions, which limit their growing area and overall production stability (Fuganti-Pagliarini et al., 2017; Shahriari et al., 2022). The increasing frequency and intensity of drought events due to climate change further exacerbate this issue, making it crucial to develop drought-tolerant soybean cultivars (Valliyodan et al., 2016; Dubey et al., 2019).

4.2 Genomic strategies for improving drought tolerance

The identification of drought-responsive genes is a critical step in understanding and improving drought tolerance in soybeans. Comprehensive RNA-seq analyses have revealed numerous differentially expressed genes (DEGs) associated with drought tolerance. For instance, a study identified 4 850 and 6 272 DEGs in drought-tolerant and drought-sensitive soybean genotypes, respectively, highlighting genes involved in water and auxin transport, cell wall/membrane integrity, antioxidant activity, and transcription factor activities (Aleem et al., 2020). Another study identified 2 168 significant DEGs and core modules involved in key biological processes such as photosynthesis and cytokinin dehydrogenase activity, which are crucial for drought tolerance (Shahriari et al., 2022).

Marker-assisted selection (MAS) and breeding strategies have been employed to enhance drought tolerance in soybeans. Genomic resources, including whole-genome sequences and high-throughput marker genotyping platforms, have facilitated the discovery of quantitative trait loci (QTLs) associated with drought tolerance traits such as root system architecture and nitrogen-fixation efficiency (Figure 2) (Valliyodan et al., 2016). Additionally, the identification of specific transcription factors, such as GmWRKY54 and GmNFYB17, which regulate drought-responsive genes, has provided valuable targets for genetic engineering and breeding programs (Wei et al., 2019; Sun et al., 2022).

4.3 Success stories and field trials

Several case studies have demonstrated the successful enhancement of drought tolerance in soybeans through genomic approaches. For example, the overexpression of the GmWRKY54 gene in transgenic soybean plants has been shown to improve drought tolerance by enhancing stomatal closure and activating stress-related genes in the ABA and Ca²⁺ signaling pathways (Wei et al., 2019). Similarly, the overexpression of the GmNFYB17 gene resulted in transgenic plants with better drought resistance, higher root-to-top ratios, and improved yield under limited water conditions (Sun et al., 2022). Field trials of genetically modified soybean lines expressing DREB and AREB transcription factors have also shown promising results, with improved water use efficiency and drought-responsive gene expression (Fuganti-Pagliarini et al., 2017).

The success of these genomic strategies in enhancing drought tolerance in soybeans has significant implications for future breeding programs. The integration of genomic technologies with traditional breeding approaches can accelerate the development of drought-tolerant soybean cultivars. The identification of key drought-responsive genes and QTLs provides valuable markers for MAS, while the use of transcription factors and other regulatory genes offers new avenues for genetic engineering (Valliyodan et al., 2016; Dubey et al., 2019). Continued research and field trials are essential to validate these findings and ensure the stability and effectiveness of drought-tolerant traits under diverse environmental conditions (Fuganti-Pagliarini et al., 2017; Zhang et al., 2019). By leveraging these genomic approaches, researchers and breeders can develop soybean cultivars that are better equipped to withstand drought stress, thereby improving crop productivity and food security in the face of changing climatic conditions.

5 Integrative Genomic Approaches in Pulse Crop Improvement

5.1 Combining genomics with phenotyping

High-throughput phenotyping platforms are essential for the integration of genomics and phenotyping in pulse crop improvement. These platforms enable the rapid and precise measurement of phenotypic traits across large populations, facilitating the identification of genetic markers associated with desirable traits. Advances in automated phenotyping assays have significantly enhanced the ability to link genetic diversity with agronomic phenotypes, thereby accelerating the breeding process (Bevan et al., 2017).

Integrative data analysis combines genomic data with phenotypic information to identify genetic loci associated with important traits. This approach leverages high-density genetic linkage maps, QTL mapping, and genome-wide association studies (GWAS) to pinpoint genes and genomic regions that contribute to yield, stress tolerance, and other agronomic traits. The availability of whole-genome sequences and high-throughput genotyping platforms has made it possible to perform comprehensive integrative analyses, leading to more efficient breeding strategies (Bohra et al., 2014a; Bohra et al., 2014b; Bhat et al., 2018).

5.2 Systems biology and computational approaches

Network analysis and modeling are powerful tools in systems biology that help to understand the complex interactions between genes, proteins, and metabolic pathways. By constructing and analyzing biological networks, researchers can identify key regulatory nodes and pathways that control important traits in pulse crops. These insights can guide the development of targeted breeding strategies and the engineering of metabolic pathways to enhance crop productivity (Morrell et al., 2011; Abberton et al., 2015).

Predictive modeling uses computational approaches to forecast the performance of different genotypes under various environmental conditions. This approach integrates genomic, phenotypic, and environmental data to create models that predict how different genetic combinations will perform in the field. Predictive modeling can help breeders select the best genotypes for specific environments, thereby improving the efficiency and effectiveness of breeding programs (Morrell et al., 2011; Bevan et al., 2017; Nerkar et al., 2022).

5.3 Translational genomics and field applications

Translational genomics involves applying discoveries made in the laboratory to real-world agricultural settings. Several case studies have demonstrated the successful translation of genomic research into field applications. For instance, the Pigeonpea Genomics Initiative (PGI) has developed a range of genomic resources, including mapping populations and molecular markers, which have been used to breed pigeonpea varieties with improved yield and stress tolerance (Varshney et al., 2009). Similarly, genomics-assisted breeding has led to the development of high-yielding and resilient pulse crop varieties in various developing countries (Bohra et al., 2014a; Bohra et al., 2014b).

Despite the significant progress in genomics and molecular breeding, several challenges remain. These include the need for better integration of genomic data with phenotypic and environmental information, the development of more efficient transformation and regeneration systems, and the adaptation of genomic tools to a wider range of pulse crops. However, the opportunities are immense, with genomics offering unprecedented potential to enhance crop yield, quality, and resilience. Continued investment in genomic research and the development of innovative breeding strategies will be crucial for meeting the global food demand in the face of climate change and other challenges (Abberton et al., 2015; Altpeter et al., 2016).

6 Economic and Environmental Impacts of Genomic Advances in Pulse Crops

6.1 Economic benefits for farmers and stakeholders

Genomic interventions in pulse crops have shown significant economic benefits for farmers and stakeholders. The adoption of genomics-assisted breeding (GAB) has led to the development of high-yielding, stress-tolerant pulse varieties, which directly translates to increased productivity and profitability for farmers. For instance, the use of high-throughput genotyping and sequencing platforms has enabled the identification of desirable traits, leading to the rapid development of improved cultivars (Bohra et al., 2014). This has resulted in higher yields and reduced losses due to environmental stresses, thereby enhancing farmer profits. Additionally, the meta-analysis of genetically modified (GM) crops indicates that such technologies can reduce chemical pesticide use by 37%, increase crop yields by 22%, and boost farmer profits by 68% (Klümper and Qaim, 2014). These findings suggest that similar economic benefits can be expected from the application of genomics in pulse crops.

The market impact of genomic advances in pulse crops is substantial, with increased adoption rates observed among farmers. The availability of whole-genome sequences and high-density genetic linkage maps has facilitated the development of superior pulse varieties, which are more resilient to climate change and environmental stresses (Bohra et al., 2014; Abberton et al., 2015). This has led to a higher adoption rate of these improved varieties, as farmers recognize the economic advantages of higher yields and reduced input costs. Moreover, the success of genomics-assisted breeding in other crops, such as the introgression of submergence tolerance in rice, highlights the potential for similar adoption rates in pulse crops (Varshney et al., 2012). The increased market demand for high-quality, stress-tolerant pulse varieties further drives the adoption of genomic technologies, benefiting both farmers and stakeholders.

6.2 Environmental sustainability

Genomic advances in pulse crops contribute to environmental sustainability by reducing the need for chemical inputs such as fertilizers and pesticides. The development of stress-tolerant pulse varieties through genomics-assisted breeding reduces the dependency on chemical inputs, as these crops are better equipped to withstand environmental stresses (Kole et al., 2015). For example, the identification of genetic markers associated with stress tolerance allows for the selection of genotypes that require fewer inputs, thereby minimizing the environmental impact of pulse cultivation (Bevan et al., 2017). Additionally, the reduction in chemical pesticide use, as observed in GM crops, can be extrapolated to pulse crops, leading to a more sustainable agricultural practice (Klümper and Qaim, 2014).

The application of genomics in pulse crops also plays a crucial role in enhancing soil health and biodiversity. Pulses are known for their ability to fix atmospheric nitrogen, which improves soil fertility and reduces the need for synthetic fertilizers (Bohra et al., 2014). Genomic tools can further enhance this trait by identifying and selecting genotypes with superior nitrogen-fixing capabilities. Moreover, the diversification of pulse crops through genomics-assisted breeding promotes biodiversity, as it encourages the cultivation of a wider range of pulse species (Abberton et al., 2015). This not only improves the resilience of agricultural systems but also supports ecosystem health by maintaining a diverse plant population. The integration of genomics in pulse crop breeding thus contributes to both soil health and biodiversity, fostering a more sustainable agricultural landscape.

7 Future Directions and Research Priorities

7.1 Emerging genomic technologies

Single-cell genomics is poised to revolutionize our understanding of plant development and tissue-specific responses to environmental stimuli. This technology allows for the sequencing of DNA and RNA from individual cells, revealing the extent of genomic and transcriptomic heterogeneity within a plant. Recent advances in whole-genome and whole-transcriptome amplification have made it possible to conduct larger and more cost-effective experiments, which can significantly enhance our understanding of cell types and their developmental trajectories (Macaulay and Voet, 2014; Cuperus, 2021). However, there are still technological hurdles to overcome, such as the need for more efficient methods to measure multiple types of molecules simultaneously (Cuperus, 2021).

Epigenomics, which involves the study of chromatin states and modifications, offers a promising avenue for crop improvement. By leveraging chromatin information, researchers can better annotate and decode plant genomes, leading to more precise genome engineering. This approach, known as "epigenome-guided" improvement, can enhance our understanding of complex crop genomes and facilitate the identification of heritable epialleles that control important crop traits. Integrating epigenomic information into crop improvement strategies, particularly through CRISPR/Cas9 gene editing, holds significant potential for advancing pulse crop productivity (Kumar et al., 2020; Zhang et al., 2022).

7.2 Collaborative and multidisciplinary research

The complexity and scale of genomic research necessitate collaborative efforts across multiple disciplines and countries. International consortia and partnerships can pool resources, expertise, and data to accelerate the pace of genomic discoveries and their application in crop improvement. Such collaborations can also facilitate the sharing of genomic data and tools, making them accessible to researchers worldwide (Purugganan and Jackson, 2021). These partnerships are particularly crucial for addressing the challenges faced by developing countries, where pulse crops are a vital source of nutrition and livelihood (Bohra et al., 2014a; Bohra et al., 2014b).

Sustained funding and supportive policies are essential for the continued advancement of genomic research in pulse crops. Governments and funding agencies need to recognize the importance of genomics in addressing global food security and invest accordingly. Policies that promote open access to genomic data and encourage public-private partnerships can further enhance the impact of genomic research (Bevan et al., 2017; Varshney et al., 2021). Additionally, funding should be directed towards training programs to build capacity in genomic technologies and their application in crop breeding (Varshney et al., 2005).

7.3 Addressing societal and ethical considerations

The successful implementation of genomic technologies in agriculture depends on public perception and acceptance. It is crucial to engage with the public and communicate the benefits and potential risks associated with genomic research. Transparent and inclusive dialogues can help build trust and address any concerns related to the use of genomic technologies in food production (Varshney et al., 2005; Purugganan and Jackson, 2021). Educational initiatives that explain the science behind genomics and its potential to improve crop productivity and sustainability can also play a significant role in shaping public opinion (Bevan et al., 2017).

Ethical considerations are paramount in genomic research, particularly when it involves genetic modification and the use of genomic data. Researchers must adhere to ethical guidelines that ensure the responsible use of genomic technologies and protect the rights and privacy of individuals and communities involved in the research. Issues such as biopiracy, the equitable sharing of benefits, and the potential environmental impact of genetically modified crops need to be carefully addressed (Varshney et al., 2005). Establishing robust ethical frameworks and regulatory mechanisms can help navigate these challenges and ensure that genomic research is conducted responsibly and sustainably.

8 Concluding Remarks

The role of genomics in advancing pulse crop productivity has been underscored by several key findings from recent research. Genomics-assisted breeding (GAB) has emerged as a promising approach to address the issues of limited genetic gain and low productivity in various pulse crops. Advances in genome sequencing, high-throughput genotyping, and the development of high-density genetic linkage and QTL maps have significantly enhanced the genetic improvement of pulses. The application of next-generation sequencing (NGS) has allowed for the de novo assembly of whole genomes in lesser-explored pulse crops, providing a robust genetic framework for crop improvement. Additionally, genomics has facilitated the identification of genetic diversity and its association with agronomic phenotypes, thereby laying new foundations for crop-breeding systems. The integration of genomics with molecular breeding approaches has proven effective in enhancing the stress adaptation of pulse crops, making them more resilient to climate change.

The future of pulse crop genomics looks promising, with several avenues for further research and development. Continued advancements in genomic tools and technologies are expected to accelerate the development of superior cultivars with higher yields, enhanced stress tolerance, and wider adaptability. The optimization of genetic transformation techniques and the translation of knowledge from model legumes to pulse crops could lead to the successful commercialization of transgenic pulse crops. Moreover, the integration of omics approaches with genomics is likely to make breeding programs more efficient, enabling the identification and utilization of genes and genomic loci governing important agronomical traits. The development of climate-resilient crops through genomics-assisted breeding will play a crucial role in ensuring global food security in the face of climate change.

In conclusion, genomics has the potential to revolutionize pulse crop productivity by providing new tools and strategies for crop improvement. To fully realize this potential, it is essential to continue investing in genomic research and the development of advanced breeding techniques. Collaborative efforts among scientists, funding agencies, and policymakers are crucial to overcome the challenges associated with the commercialization of transgenic pulse crops and to ensure the successful implementation of genomics-assisted breeding programs. By leveraging the power of genomics, we can enhance the productivity, nutritional quality, and resilience of pulse crops, thereby contributing to global food security and the well-being of millions of people worldwide.

Acknowledgments

The author sincerely thanks the two anonymous peer reviewers for their valuable comments and suggestions on the manuscript.

Conflict of Interest Disclosure

The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

References

Abberton M., Batley J., Bentley A., Bryant J., Cai H., Cockram J., Oliveira A., Cseke L., Dempewolf H., Pace C., Edwards D., Gepts P., Greenland A., Hall A., Henry R., Hori K., Howe G., Hughes S., Humphreys M., Lightfoot D., Marshall A., Mayes S., Nguyen, H., Ogbonnaya F., Ortiz R., Paterson A., Tuberosa R., Valliyodan B., Varshney R., and Yano M., 2015, Global agricultural intensification during climate change: a role for genomics, Plant Biotechnology Journal, 14: 1095-1098.

https://doi.org/10.1111/pbi.12467

Abebe K., 2019, Genotype by sequencing method and its application for crop improvement (a review), Archives of Biochemistry and Biophysics, 7: 1.

https://doi.org/10.11648/j.abb.20190701.11

Aleem M., Raza M., Haider M., Atif R., Ali Z., Bhat J., and Zhao T., 2020, Comprehensive RNA-seq analysis revealed molecular pathways and genes associated with drought tolerance in wild soybean (Glycine soja Sieb. and Zucc.), Physiologia Plantarum, 172(2): 707-732.

https://doi.org/10.1111/ppl.13219

Altpeter F., Springer N., Bartley L., Blechl A., Brutnell T., Citovsky V., Conrad L., Gelvin S., Jackson D., Kausch A., Lemaux P., Medford J., Orozo-Cardenas M., Tricoli D., VanEck J., Voytas D., Walbot V., Wang K., Zhang Z., and Stewart C., 2016, Advancing crop transformation in the era of genome editing, Plant Cell, 28: 1510-1520.

https://doi.org/10.1105/tpc.16.00196

Asif M., Rooney L., Ali R., and Riaz M., 2013, Application and opportunities of pulses in food system: a review, Critical Reviews in Food Science and Nutrition, 53: 1168-1179.

https://doi.org/10.1080/10408398.2011.574804

Bessada S., Barreira J., and Oliveira M., 2019, Pulses and food security: dietary protein, digestibility, bioactive and functional properties, Trends in Food Science and Technology, 93: 53-68.

https://doi.org/10.1016/j.tifs.2019.08.022

Bevan M., Uauy C., Wulff B., Zhou J., Krasileva K., and Clark M., 2017, Genomic innovation for crop improvement, Nature, 543: 346-354.

https://doi.org/10.1038/nature22011

Bhat J., Shivaraj S., Ali S., Mir Z., Islam A., and Deshmukh R., 2018, Genomic resources and omics-assisted breeding approaches for pulse crop improvement, Pulse Improvement: Physiological, Molecular and Genetic Perspectives, 2018: 13-55.

https://doi.org/10.1007/978-3-030-01743-9_2

Bohra A., Pandey M., Jha U., Singh B., Singh I., Datta D., Chaturvedi S., Nadarajan N., and Varshney R., 2014a, Genomics-assisted breeding in four major pulse crops of developing countries: present status and prospects, Theoretical and Applied Genetics, 127: 1263-1291.

https://doi.org/10.1007/s00122-014-2301-3

Bohra A., Jha U., Kishor P., Pandey S., and Singh N., 2014b, Genomics and molecular breeding in lesser explored pulse crops: current trends and future opportunities, Biotechnology Advances, 32(8): 1410-1428.

https://doi.org/10.1016/j.biotechadv.2014.09.001

Cuperus J., 2021, Single-cell genomics in plants: current state, future directions, and hurdles to overcome, Plant Physiology, 188: 749-755.

https://doi.org/10.1093/plphys/kiab478

Dubey A., Kumar A., AbdAllah E., Hashem A., and Khan M., 2019, Growing more with less: breeding and developing drought resilient soybean to improve food security, Ecological Indicators, 105: 425-437.

https://doi.org/10.1016/J.ECOLIND.2018.03.003

Fuganti-Pagliarini R., Ferreira L., Rodrigues F., Molinari H., Marin S., Molinari M., Marcolino-Gomes J., Mertz-Henning L., Farias J., Oliveira M., Neumaier N., Kanamori N., Fujita Y., Mizoi J., Nakashima K., Yamaguchi-Shinozaki K., and Nepomuceno A., 2017, Characterization of soybean genetically modified for drought tolerance in field conditions, Frontiers in Plant Science, 8: 448.

https://doi.org/10.3389/fpls.2017.00448

Gupta P., 2021, GWAS for genetics of complex quantitative traits: genome to pangenome and SNPs to SVs and k‐mers, BioEssays, 43(11): 2100109.

https://doi.org/10.1002/bies.202100109

He J., Zhao X., Laroche A., Lu Z., Liu H., and Li Z., 2014, Genotyping-by-sequencing (GBS), an ultimate marker-assisted selection (MAS) tool to accelerate plant breeding, Frontiers in Plant Science, 5: 484.

https://doi.org/10.3389/fpls.2014.00484

Klümper W., and Qaim M., 2014, A meta-analysis of the impacts of genetically modified crops, PLoS One, 9: e111629.

https://doi.org/10.1371/journal.pone.0111629

Kole C., Muthamilarasan M., Henry R., Edwards D., Sharma R., Abberton M., Batley J., Bentley A., Blakeney M., Bryant J., Cai H., Çakır M., Cseke L., Cockram J., Oliveira A., Pace C., Dempewolf H., Ellison S., Gepts P., Greenland A., Hall A., Hori K., Hughes S., Humphreys M., Iorizzo M., Ismail A., Marshall A., Mayes S., Nguyen H., Ogbonnaya F., Ortiz R., Paterson A., Simon P., Tohme J., Tuberosa R., Valliyodan B., Varshney R., Wullschleger S., Yano M., and Prasad M., 2015, Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects, Frontiers in Plant Science, 6: 563.

https://doi.org/10.3389/fpls.2015.00563

Kumar J., Gupta D., Gupta S., Dubey S., Gupta P., and Kumar S., 2017, Quantitative trait loci from identification to exploitation for crop improvement, Plant Cell Reports, 36: 1187-1213.

https://doi.org/10.1007/s00299-017-2127-y

Kumar R., Jorben J., Yadav R., and Senapati M., 2020, Genomics and its application in crop improvement, Journal of Pharmacognosy and Phytochemistry, 9(1): 547-552.

https://doi.org/10.22271/PHYTO.2020.V9.I1I.10488

Kumar S., and Pandey G., 2020, Biofortification of pulses and legumes to enhance nutrition, Heliyon, 6(3): e03682.

https://doi.org/10.1016/j.heliyon.2020.e03682

Lee T., Kim H., and Lee I., 2015, Network-assisted crop systems genetics: network inference and integrative analysis, Current Opinion in Plant Biology, 24: 61-70.

https://doi.org/10.1016/j.pbi.2015.02.001

Liu H., and Yan J., 2018, Crop genome‐wide association study: a harvest of biological relevance, The Plant Journal, 97: 8-18.

https://doi.org/10.1111/tpj.14139

Macaulay I., and Voet T., 2014, Single cell genomics: advances and future perspectives, PLoS Genetics, 10(1): e1004126.

https://doi.org/10.1371/journal.pgen.1004126

Morrell P., Buckler E., and Ross-Ibarra J., 2011, Crop genomics: advances and applications, Nature Reviews Genetics, 13: 85-96.

https://doi.org/10.1038/nrg3097

Nerkar G., Devarumath S., Purankar M., Kumar A., Valarmathi R., Devarumath R., and Appunu C., 2022, Advances in crop breeding through precision genome editing, Frontiers in Genetics, 13: 880195.

https://doi.org/10.3389/fgene.2022.880195

Nguyen K., Grondin A., Courtois B., and Gantet P., 2019, Next-generation sequencing accelerates crop gene discovery, Trends in Plant Science, 24(3): 263-274.

https://doi.org/10.1016/j.tplants.2018.11.008

Okogbenin E., and Fregene M., 2003, Genetic mapping of QTLs affecting productivity and plant architecture in a full-sib cross from non-inbred parents in cassava (Manihot esculenta Crantz), Theoretical and Applied Genetics, 107: 1452-1462.

https://doi.org/10.1007/s00122-003-1383-0

Pavan S., Delvento C., Ricciardi L., Lotti C., Ciani E., and D’Agostino N., 2020, Recommendations for choosing the genotyping method and best practices for quality control in crop genome-wide association studies, Frontiers in Genetics, 11: 447.

https://doi.org/10.3389/fgene.2020.00447

Pavlopoulos G., Oulas A., Iacucci E., Sifrim A., Moreau Y., Schneider R., Aerts J., and Iliopoulos I., 2013, Unraveling genomic variation from next generation sequencing data, BioData Mining, 6: 1-25.

https://doi.org/10.1186/1756-0381-6-13

Purugganan M., and Jackson S., 2021, Advancing crop genomics from lab to field, Nature Genetics, 53: 595-601.

https://doi.org/10.1038/s41588-021-00866-3

Roy F., Boye J., and Simpson B., 2010, Bioactive proteins and peptides in pulse crops: pea, chickpea and lentil, Food Research International, 43: 432-442.

https://doi.org/10.1016/J.FOODRES.2009.09.002

Shahriari A., Soltani Z., Tahmasebi A., and Poczai P., 2022, Integrative system biology analysis of transcriptomic responses to drought stress in soybean (Glycine max L.), Genes, 13(10): 1732.

https://doi.org/10.3390/genes13101732

Shariatipour N., Heidari B., Tahmasebi A., and Richards C., 2021, Comparative genomic analysis of quantitative trait loci associated with micronutrient contents, grain quality, and agronomic traits in wheat (Triticum aestivum L.), Frontiers in Plant Science, 12: 709817.

https://doi.org/10.3389/fpls.2021.709817

Sun M., Li Y., Zheng J., Wu D., Li C., Li Z., Zang Z., Zhang Y., Fang Q., Li W., Han Y., Zhao X., and Li Y., 2022, A nuclear factor Y-B transcription factor, GmNFYB17, regulates resistance to drought stress in soybean, International Journal of Molecular Sciences, 23(13): 7242.

https://doi.org/10.3390/ijms23137242

Takagi H., Abe A., Yoshida K., Kosugi S., Natsume S., Mitsuoka C., Uemura A., Utsushi H., Tamiru M., Takuno S., Innan H., Cano L., Kamoun S., and Terauchi R., 2013, QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations, The Plant Journal, 74(1): 174-183.

https://doi.org/10.1111/tpj.12105

Thudi M., Palakurthi R., Schnable J., Chitikineni A., Dreisigacker S., Mace E., Srivastava R., Satyavathi C., Odeny D., Tiwari V., Lam H., Hong Y., Singh V., Li G., Xu Y., Chen X., Kaila S., Nguyen H., Sivasankar S., Jackson S., Close T., Shubo W., and Varshney R., 2020, Genomic resources in plant breeding for sustainable agriculture, Journal of Plant Physiology, 257: 153351.

https://doi.org/10.1016/j.jplph.2020.153351

Torkamaneh D., Laroche J., Boyle B., Hyten D., and Belzile F., 2021, A bumper crop of SNPs in soybean through high‐density genotyping‐by‐sequencing (HD‐GBS), Plant Biotechnology Journal, 19: 860-862.

https://doi.org/10.1111/pbi.13551

Valliyodan B., Ye H., Song L., Murphy M., Shannon J., and Nguyen H., 2016, Genetic diversity and genomic strategies for improving drought and waterlogging tolerance in soybeans, Journal of Experimental Botany, 68: 1835-1849.

https://doi.org/10.1093/jxb/erw433

Varshney R., 2016, Exciting journey of 10 years from genomes to fields and markets: some success stories of genomics-assisted breeding in chickpea, pigeonpea and groundnut, Plant Science, 242: 98-107.

https://doi.org/10.1016/j.plantsci.2015.09.009

Varshney R., Bohra A., Yu J., Graner A., Zhang Q., and Sorrells M., 2021, Designing future crops: genomics-assisted breeding comes of age, Trends in Plant Science, 26(6): 631-649.

https://doi.org/10.1016/j.tplants.2021.03.010

Varshney R., Graner A., and Sorrells M., 2005, Genomics-assisted breeding for crop improvement, Trends in Plant Science, 10(12): 621-630.

https://doi.org/10.1016/J.TPLANTS.2005.10.004

Varshney R., Penmetsa R., Dutta S., Kulwal P., Saxena R., Datta S., Sharma T., Rosen B., Carrasquilla-Garcia N., Farmer A., Dubey A., Saxena K., Gao J., Fakrudin B., Singh M., Singh B., Wanjari K., Yuan M., Srivastava R., Kilian A., Upadhyaya H., Mallikarjuna N., Town C., Bruening G., He G., May G., Mccombie R., Jackson S., Singh N., and Cook D., 2009, Pigeonpea genomics initiative (PGI): an international effort to improve crop productivity of pigeonpea (Cajanus cajan L.), Molecular Breeding, 26: 393-408.

https://doi.org/10.1007/s11032-009-9327-2

Varshney R., Ribaut J., Buckler E., Tuberosa R., Rafalski J., and Langridge P., 2012, Can genomics boost productivity of orphan crops, Nature Biotechnology, 30: 1172-1176.

https://doi.org/10.1038/nbt.2440

Wei W., Liang D., Bian X., Shen M., Xiao J., Zhang W., Ma B., Lin Q., Lv J., Chen X., Chen S., and Zhang J., 2019, GmWRKY54 improves drought tolerance through activating genes in ABA and Ca²⁺ signaling pathways in transgenic soybean, The Plant Journal, 10: 14-49.

https://doi.org/10.1111/tpj.14449

Xiao Y., Liu H., Wu L., Warburton M., and Yan J., 2017, Genome-wide association studies in maize: praise and stargaze, Molecular Plant, 10(3): 359-374.

https://doi.org/10.1016/j.molp.2016.12.008

Xu Y., Li P., Yang Z., and Xu C., 2017, Genetic mapping of quantitative trait loci in crops, Crop Journal, 5: 175-184.

https://doi.org/10.1016/J.CJ.2016.06.003

Yang Q., Zhang D., and Xu M., 2012, A sequential quantitative trait locus fine-mapping strategy using recombinant-derived progeny, Journal of Integrative Plant Biology, 54(4): 228-237.

https://doi.org/10.1111/j.1744-7909.2012.01108.x

Zhang L., Li T., Wang Y., Zhang Y., and Dong Y., 2019, FvC5SD overexpression enhances drought tolerance in soybean by reactive oxygen species scavenging and modulating stress-responsive gene expression, Plant Cell Reports, 38: 1039-1051.

https://doi.org/10.1007/s00299-019-02424-y

Zhang Y., Andrews H., Eglitis-Sexton J., Godwin I., Tanurdžić M., and Crisp P., 2022, Epigenome guided crop improvement: current progress and future opportunities, Emerging Topics in Life Sciences, 6: 141-151.

https://doi.org/10.1042/ETLS20210258

Zou G., Zhai G., Feng Q., Yan S., Wang A., Zhao Q., Shao J., Zhang Z., Zou J., Han B., and Tao Y., 2012, Identification of QTLs for eight agronomically important traits using an ultra-high-density map based on SNPs generated from high-throughput sequencing in sorghum under contrasting photoperiods, Journal of Experimental Botany, 63(15): 5451-5462.

https://doi.org/10.1093/jxb/ers205