Investigating Drought Tolerance in Chickpea Using Genome-Wide Association Mapping and Genomic Selection Based on Whole-Genome Resequencing Data

Drought and heat stress: insights into tolerance mechanisms and breeding strategies for pigeonpea improvement

MAIN CONCLUSION

Pigeonpea has potential to foster sustainable agriculture and resilience in evolving climate change; understanding bio-physiological and molecular mechanisms of heat and drought stress tolerance is imperative to developing resilience cultivars. Pigeonpea is an important legume crop that has potential resilience in the face of evolving climate scenarios. However, compared to other legumes, there has been limited research on abiotic stress tolerance in pigeonpea, particularly towards drought stress (DS) and heat stress (HS). To address this gap, this review delves into the genetic, physiological, and molecular mechanisms that govern pigeonpea's response to DS and HS. It emphasizes the need to understand how this crop combats these stresses and exhibits different types of tolerance and adaptation mechanisms through component traits. The current article provides a comprehensive overview of the complex interplay of factors contributing to the resilience of pigeonpea under adverse environmental conditions. Furthermore, the review synthesizes information on major breeding techniques, encompassing both conventional methods and modern molecular omics-assisted tools and techniques. It highlights the potential of genomics and phenomics tools and their pivotal role in enhancing adaptability and resilience in pigeonpea. Despite the progress made in genomics, phenomics and big data analytics, the complexity of drought and heat tolerance in pigeonpea necessitate continuous exploration at multi-omic levels. High-throughput phenotyping (HTP) is crucial for gaining insights into perplexed interactions among genotype, environment, and management practices (GxExM). Thus, integration of advanced technologies in breeding programs is critical for developing pigeonpea varieties that can withstand the challenges posed by climate change. This review is expected to serve as a valuable resource for researchers, providing a deeper understanding of the mechanisms underlying abiotic stress tolerance in pigeonpea and offering insights into modern breeding strategies that can contribute to the development of resilient varieties suited for changing environmental conditions.

Mousa K, Istanbuli T, Abd El-Maksoud MM, Tawkaz S, Hamwieh A. Unveiling the genetic basis of Fusarium wilt resistance in chickpea using GWAS analysis and characterization of candidate genes

Introduction: Chickpea is a legume crop that thrives in regions with semi-arid or temperate climates. Its seeds are an excellent source of proteins, carbohydrates, and minerals, especially high-quality proteins. Chickpea cultivation faces several challenges including Fusarium wilt (FW), a major fungal disease that significantly reduces productivity. Methods: In this study, a Genome-wide Association Analysis (GWAS) was conducted to identify multiple genomic loci associated with FW resistance in chickpea. We conducted a comprehensive evaluation of 180 chickpea genotypes for FW resistance across three distinct locations (Ethiopia, Tunisia, and Lebanon) during the 2-year span from 2015 to 2016. Disease infection measurements were recorded, and the wilt incidence of each genotype was calculated. We employed a set of 11,979 single nucleotide polymorphisms (SNPs) markers distributed across the entire chickpea genome for SNP genotyping. Population structure analysis was conducted to determine the genetic structure of the genotypes. Results and Discussion: The population structure unveiled that the analyzed chickpea germplasm could be categorized into four sub-populations. Notably, these sub-populations displayed diverse geographic origins. The GWAS identified 11 SNPs associated with FW resistance, dispersed across the genome. Certain SNPs were consistent across trials, while others were specific to particular environments. Chromosome CA2 harbored five SNP markers, CA5 featured two, and CA4, CA6, CA7, and CA8 each had one representative marker. Four SNPs demonstrated an association with FW resistance, consistently observed across a minimum of three distinct environments. These SNPs included SNP5826041, SNP5825086, SNP11063413, SNP5825195, which located in CaFeSOD, CaS13like, CaNTAQ1, and CaAARS genes, respectively. Further investigations were conducted to gain insights into the functions of these genes and their role in FW resistance. This progress holds promise for reducing the negative impact of the disease on chickpea production.

Omics-driven exploration and mining of key functional genes for the improvement of food and fiber crops

The deployment of omics technologies has obtained an incredible boost over the past few decades with the advances in next-generation sequencing (NGS) technologies, innovative bioinformatics tools, and the deluge of available biological information. The major omics technologies in the limelight are genomics, transcriptomics, proteomics, metabolomics, and phenomics. These biotechnological advances have modernized crop breeding and opened new horizons for developing crop varieties with improved traits. The genomes of several crop species are sequenced, and a huge number of genes associated with crucial economic traits have been identified. These identified genes not only provide insights into the understanding of regulatory mechanisms of crop traits but also decipher practical grounds to assist in the molecular breeding of crops. This review discusses the potential of omics technologies for the acquisition of biological information and mining of the genes associated with important agronomic traits in important food and fiber crops, such as wheat, rice, maize, potato, tomato, cassava, and cotton. Different functional genomics approaches for the validation of these important genes are also highlighted. Furthermore, a list of genes discovered by employing omics approaches is being represented as potential targets for genetic modifications by the latest genome engineering methods for the development of climate-resilient crops that would in turn provide great impetus to secure global food security.

Genomic selection for target traits in the Australian lentil breeding program

Genomic selection (GS) uses associations between markers and phenotypes to predict the breeding values of individuals. It can be applied early in the breeding cycle to reduce the cross-to-cross generation interval and thereby increase genetic gain per unit of time. The development of cost-effective, high-throughput genotyping platforms has revolutionized plant breeding programs by enabling the implementation of GS at the scale required to achieve impact. As a result, GS is becoming routine in plant breeding, even in minor crops such as pulses. Here we examined 2,081 breeding lines from Agriculture Victoria's national lentil breeding program for a range of target traits including grain yield, ascochyta blight resistance, botrytis grey mould resistance, salinity and boron stress tolerance, 100-grain weight, seed size index and protein content. A broad range of narrow-sense heritabilities was observed across these traits (0.24-0.66). Genomic prediction models were developed based on 64,781 genome-wide SNPs using Bayesian methodology and genomic estimated breeding values (GEBVs) were calculated. Forward cross-validation was applied to examine the prediction accuracy of GS for these targeted traits. The accuracy of GEBVs was consistently higher (0.34-0.83) than BLUP estimated breeding values (EBVs) (0.22-0.54), indicating a higher expected rate of genetic gain with GS. GS-led parental selection using early generation breeding materials also resulted in higher genetic gain compared to BLUP-based selection performed using later generation breeding lines. Our results show that implementing GS in lentil breeding will fast track the development of high-yielding cultivars with increased resistance to biotic and abiotic stresses, as well as improved seed quality traits.

Enhancing climate change resilience in agricultural crops

Climate change threatens global food and nutritional security through negative effects on crop growth and agricultural productivity. Many countries have adopted ambitious climate change mitigation and adaptation targets that will exacerbate the problem, as they require significant changes in current agri-food systems. In this review, we provide a roadmap for improved crop production that encompasses the effective transfer of current knowledge into plant breeding and crop management strategies that will underpin sustainable agriculture intensification and climate resilience. We identify the main problem areas and highlight outstanding questions and potential solutions that can be applied to mitigate the impacts of climate change on crop growth and productivity. Although translation of scientific advances into crop production lags far behind current scientific knowledge and technology, we consider that a holistic approach, combining disciplines in collaborative efforts, can drive better connections between research, policy, and the needs of society.

Leveraging prior biological knowledge improves prediction of tocochromanols in maize grain

With an essential role in human health, tocochromanols are mostly obtained by consuming seed oils; however, the vitamin E content of the most abundant tocochromanols in maize (Zea mays L.) grain is low. Several large-effect genes with cis-acting variants affecting messenger RNA (mRNA) expression are mostly responsible for tocochromanol variation in maize grain, with other relevant associated quantitative trait loci (QTL) yet to be fully resolved. Leveraging existing genomic and transcriptomic information for maize inbreds could improve prediction when selecting for higher vitamin E content. Here, we first evaluated a multikernel genomic best linear unbiased prediction (MK-GBLUP) approach for modeling known QTL in the prediction of nine tocochromanol grain phenotypes (12-21 QTL per trait) within and between two panels of 1,462 and 242 maize inbred lines. On average, MK-GBLUP models improved predictive abilities by 7.0-13.6% when compared with GBLUP. In a second approach with a subset of 545 lines from the larger panel, the highest average improvement in predictive ability relative to GBLUP was achieved with a multi-trait GBLUP model (15.4%) that had a tocochromanol phenotype and transcript abundances in developing grain for a few large-effect candidate causal genes (1-3 genes per trait) as multiple response variables. Taken together, our study illustrates the enhancement of prediction models when informed by existing biological knowledge pertaining to QTL and candidate causal genes.

Integrating genome-wide association study into genomic selection for the prediction of agronomic traits in rice (Oryza sativa L.)

Accurately identifying varieties with targeted agronomic traits was thought to contribute to genetic selection and accelerate rice breeding progress. Genomic selection (GS) is a promising technique that uses markers covering the whole genome to predict the genomic-estimated breeding values (GEBV), with the ability to select before phenotypes are measured. To choose the appropriate GS models for breeding work, we analyzed the predictability of nine agronomic traits measured from a population of 459 diverse rice varieties. By the comparison of eight representative GS models, we found that the prediction accuracies ranged from 0.407 to 0.896, with reproducing kernel Hilbert space (RKHS) having the highest predictive ability in most traits. Further results demonstrated the predictivity of GS is altered by several factors. Moreover, we assessed the method of integrating genome-wide association study (GWAS) into various GS models. The predictabilities of GS combined peak-associated markers generated from six different GWAS models were significantly different; a recommendation of Mixed Linear Model (MLM)-RKHS was given for the GWAS-GS-integrated prediction. Finally, based on the above result, we experimented with applying the P-values obtained from optimal GWAS models into ridge regression best linear unbiased prediction (rrBLUP), which benefited the low predictive traits in rice.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11032-023-01423-y.

Impact of Terminal Heat and Combined Heat-Drought Stress on Plant Growth, Yield, Grain Size, and Nutritional Quality in Chickpea (Cicer arietinum L.)

Chickpea is the third most consumed pulse and provides a kit of essential nutrients for an exponential population. High temperatures and drought stress are two major abiotic stresses that cause serious effects on chickpea growth and development. The comprehension of abiotic stresses' impact on chickpea productivity and nutritional quality will permit the selection of promising genotypes. The current study aimed to assess the impact of heat and drought stresses on plant growth, grain yield and its components, grain size, and nutritional quality in chickpea. For this purpose, 43 international chickpea genotypes were evaluated under normal, heat, and combined heat-drought stress conditions. The findings revealed a significant decrease of over 50% in plant height, biological yield, and seed yield under both stress conditions. Grain size and hundred-seed weight were the most heritable traits under normal, heat, and combined heat-drought stress. Proteins were accumulated under both stresses, evolving from 20.26% for normal conditions to 22.19% for heat stress and to 21.94% for combined heat-drought stress. For minerals, significant variation between treatments was observed for Mn, Mg, and Na. Our results also showed a significant impact of genotype and genotype-environment interaction factors only on K content. Using selection indices, 22 genotypes were identified as highly tolerant to the combined heat-drought stress, while eleven genotypes were heat-tolerant. Mineral profile analysis according to the contrasting tolerance clusters revealed decreased potassium content in susceptible genotypes, indicating genetic potential in the studied chickpea collection, ensuring tolerance to both stresses while maintaining good grain quality.

Genetic mapping of quantitative trait loci associated with drought tolerance in chickpea (Cicer arietinum L.)

Elucidation of the genetic basis of drought tolerance is vital for genomics-assisted breeding of drought tolerant crop varieties. Here, we used genotyping-by-sequencing (GBS) to identify single nucleotide polymorphisms (SNPs) in recombinant inbred lines (RILs) derived from a cross between a drought tolerant chickpea variety, Pusa 362 and a drought sensitive variety, SBD 377. The GBS identified a total of 35,502 SNPs and subsequent filtering of these resulted in 3237 high-quality SNPs included in the eight linkage groups. Fifty-one percent of these SNPs were located in the genic regions distributed throughout the genome. The high density linkage map has total map length of 1069 cm with an average marker interval of 0.33 cm. The linkage map was used to identify 9 robust and consistent QTLs for four drought related traits viz. membrane stability index, relative water content, seed weight and yield under drought, with percent variance explained within the range of 6.29%-90.68% and LOD scores of 2.64 to 6.38, which were located on five of the eight linkage groups. A genomic region on LG 7 harbors quantitative trait loci (QTLs) explaining > 90% phenotypic variance for membrane stability index, and > 10% PVE for yield. This study also provides the first report of major QTLs for physiological traits such as membrane stability index and relative water content for drought stress in chickpea. A total of 369 putative candidate genes were identified in the 6.6 Mb genomic region spanning these QTLs. In-silico expression profiling based on the available transcriptome data revealed that 326 of these genes were differentially expressed under drought stress. KEGG analysis resulted in reduction of candidate genes from 369 to 99, revealing enrichment in various signaling pathways. Haplotype analysis confirmed 5 QTLs among the initially identified 9 QTLs. Two QTLs, qRWC1.1 and qYLD7.1, were chosen based on high SNP density. Candidate gene-based analysis revealed distinct haplotypes in qYLD7.1 associated with significant phenotypic differences, potentially linked to pathways for secondary metabolite biosynthesis. These identified candidate genes bolster defenses through flavonoids and phenylalanine-derived compounds, aiding UV protection, pathogen resistance, and plant structure.The study provides novel genomic regions and candidate genes which can be utilized in genomics-assisted breeding of superior drought tolerant chickpea cultivars.

Plant breeding for harmony between sustainable agriculture, the environment, and global food security: an era of genomics-assisted breeding

MAIN CONCLUSION

Genomics-assisted breeding represents a crucial frontier in enhancing the balance between sustainable agriculture, environmental preservation, and global food security. Its precision and efficiency hold the promise of developing resilient crops, reducing resource utilization, and safeguarding biodiversity, ultimately fostering a more sustainable and secure food production system. Agriculture has been seriously threatened over the last 40 years by climate changes that menace global nutrition and food security. Changes in environmental factors like drought, salt concentration, heavy rainfalls, and extremely low or high temperatures can have a detrimental effects on plant development, growth, and yield. Extreme poverty and increasing food demand necessitate the need to break the existing production barriers in several crops. The first decade of twenty-first century marks the rapid development in the discovery of new plant breeding technologies. In contrast, in the second decade, the focus turned to extracting information from massive genomic frameworks, speculating gene-to-phenotype associations, and producing resilient crops. In this review, we will encompass the causes, effects of abiotic stresses and how they can be addressed using plant breeding technologies. Both conventional and modern breeding technologies will be highlighted. Moreover, the challenges like the commercialization of biotechnological products faced by proponents and developers will also be accentuated. The crux of this review is to mention the available breeding technologies that can deliver crops with high nutrition and climate resilience for sustainable agriculture.

Genome-wide association study as a powerful tool for dissecting competitive traits in legumes

Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.

Genome-wide discovery of di-nucleotide SSR markers based on whole genome re-sequencing data of Cicer arietinum L. and Cicer reticulatum Ladiz

Simple sequence repeats (SSRs) are valuable genetic markers due to their co-dominant inheritance, multi-allelic and reproducible nature. They have been largely used for exploiting genetic architecture of plant germplasms, phylogenetic analysis, and mapping studies. Among the SSRs, di-nucleotide repeats are the most frequent of the simple repeats distributed throughout the plant genomes. In present study, we aimed to discover and develop di-nucleotide SSR markers by using the whole genome re-sequencing (WGRS) data from Cicer arietinum L. and C. reticulatum Ladiz. A total of 35,329 InDels were obtained in C. arietinum, whereas 44,331 InDels in C. reticulatum. 3387 InDels with 2 bp length were detected in C. arietinum, there were 4704 in C. reticulatum. Among 8091 InDels, 58 di-nucleotide regions that were polymorphic between two species were selected and used for validation. We tested primers for evaluation of genetic diversity in 30 chickpea genotypes including C. arietinum, C. reticulatum, C. echinospermum P.H. Davis, C. anatolicum Alef., C. canariense A. Santos & G.P. Lewis, C. microphyllum Benth., C. multijugum Maesen, C. oxyodon Boiss. & Hohen. and C. songaricum Steph ex DC. A total of 244 alleles were obtained for 58 SSR markers giving an average of 2.36 alleles per locus. The observed heterozygosity was 0.08 while the expected heterozygosity was 0.345. Polymorphism information content was found to be 0.73 across all loci. Phylogenetic tree and principal coordinate analysis clearly divided the accessions into four groups. The SSR markers were also evaluated in 30 genotypes of a RIL population obtained from an interspecific cross between C. arietinum and C. reticulatum. Chi-square (χ²) test revealed an expected 1:1 segregation ratio in the population. These results demonstrated the success of SSR identification and marker development for chickpea with the use of WGRS data. The newly developed 58 SSR markers are expected to be useful for chickpea breeders.

A genome-wide association study and genomic prediction for Phakopsora pachyrhizi resistance in soybean

Soybean brown rust (SBR), caused by Phakopsora pachyrhizi, is a devastating fungal disease that threatens global soybean production. This study conducted a genome-wide association study (GWAS) with seven models on a panel of 3,082 soybean accessions to identify the markers associated with SBR resistance by 30,314 high quality single nucleotide polymorphism (SNPs). Then five genomic selection (GS) models, including Ridge regression best linear unbiased predictor (rrBLUP), Genomic best linear unbiased predictor (gBLUP), Bayesian least absolute shrinkage and selection operator (Bayesian LASSO), Random Forest (RF), and Support vector machines (SVM), were used to predict breeding values of SBR resistance using whole genome SNP sets and GWAS-based marker sets. Four SNPs, namely Gm18_57,223,391 (LOD = 2.69), Gm16_29,491,946 (LOD = 3.86), Gm06_45,035,185 (LOD = 4.74), and Gm18_51,994,200 (LOD = 3.60), were located near the reported P. pachyrhizi R genes, Rpp1, Rpp2, Rpp3, and Rpp4, respectively. Other significant SNPs, including Gm02_7,235,181 (LOD = 7.91), Gm02_7234594 (LOD = 7.61), Gm03_38,913,029 (LOD = 6.85), Gm04_46,003,059 (LOD = 6.03), Gm09_1,951,644 (LOD = 10.07), Gm10_39,142,024 (LOD = 7.12), Gm12_28,136,735 (LOD = 7.03), Gm13_16,350,701(LOD = 5.63), Gm14_6,185,611 (LOD = 5.51), and Gm19_44,734,953 (LOD = 6.02), were associated with abundant disease resistance genes, such as Glyma.02G084100, Glyma.03G175300, Glyma.04g189500, Glyma.09G023800, Glyma.12G160400, Glyma.13G064500, Glyma.14g073300, and Glyma.19G190200. The annotations of these genes included but not limited to: LRR class gene, cytochrome 450, cell wall structure, RCC1, NAC, ABC transporter, F-box domain, etc. The GWAS based markers showed more accuracies in genomic prediction than the whole genome SNPs, and Bayesian LASSO model was the ideal model in SBR resistance prediction with 44.5% ~ 60.4% accuracies. This study aids breeders in predicting selection accuracy of complex traits such as disease resistance and can shorten the soybean breeding cycle by the identified markers.

Genetic diversity, population structure, and genome-wide association study for the flowering trait in a diverse panel of 428 moth bean (Vigna aconitifolia) accessions using genotyping by sequencing

BACKGROUND

Moth bean (Vigna aconitifolia) is an underutilized, protein-rich legume that is grown in arid and semi-arid areas of south Asia and is highly resistant to abiotic stresses such as heat and drought. Despite its economic importance, the crop remains unexplored at the genomic level for genetic diversity and trait mapping studies. To date, there is no report of SNP marker discovery and association mapping of any trait in this crop. Therefore, this study aimed to dissect the genetic diversity, population structure and marker-trait association for the flowering trait in a diversity panel of 428 moth bean accessions using genotyping by sequencing (GBS) approach.

RESULTS

A total of 9078 high-quality single nucleotide polymorphisms (SNPs) were discovered by genotyping of 428 moth bean accessions. Model-based structure analysis and PCA grouped the moth bean accessions into two subpopulations. Cluster analysis revealed accessions belonging to the Northwestern region of India had higher variability than accessions from the other regions suggesting that this region represents its center of diversity. AMOVA revealed more variations within individuals (74%) and among the individuals (24%) than among the populations (2%). Marker-trait association analysis using seven multi-locus models including mrMLM, FASTmrEMMA FASTmrEMMA, ISIS EM-BLASSO, MLMM, BLINK and FarmCPU revealed 29 potential genomic regions for the trait days to 50% flowering, which were consistently detected in three or more models. Analysis of the allelic effect of the major genomic regions explaining phenotypic variance of more than 10% and those detected in at least 2 environments showed 4 genomic regions with significant phenotypic effect on this trait. Further, we also analyzed genetic relationships among the Vigna species using SNP markers. The genomic localization of moth bean SNPs on genomes of closely related Vigna species demonstrated that maximum numbers of SNPs were getting localized on Vigna mungo. This suggested that the moth bean is most closely related to V. mungo.

CONCLUSION

Our study shows that the north-western regions of India represent the center of diversity of the moth bean. Further, the study revealed flowering-related genomic regions/candidate genes which can be potentially exploited in breeding programs to develop early-maturity moth bean varieties.

White Lupin Drought Tolerance: Genetic Variation, Trait Genetic Architecture, and Genome-Enabled Prediction

White lupin is a high-protein crop requiring drought tolerance improvement. This study focused on a genetically-broad population of 138 lines to investigate the phenotypic variation and genotype × environment interaction (GEI) for grain yield and other traits across drought-prone and moisture-favourable managed environments, the trait genetic architecture and relevant genomic regions by a GWAS using 9828 mapped SNP markers, and the predictive ability of genomic selection (GS) models. Water treatments across two late cropping months implied max. available soil water content of 60-80% for favourable conditions and from wilting point to 15% for severe drought. Line yield responses across environments featured a genetic correlation of 0.84. Relatively better line yield under drought was associated with an increased harvest index. Two significant QTLs emerged for yield in each condition that differed across conditions. Line yield under stress displayed an inverse linear relationship with the onset of flowering, confirmed genomically by a common major QTL. An adjusted grain yield computed as deviation from phenology-predicted yield acted as an indicator of intrinsic drought tolerance. On the whole, the yield in both conditions and the adjusted yield were polygenic, heritable, and exploitable by GS with a high predictive ability (0.62-0.78). Our results can support selection for climatically different drought-prone regions.

Influence of Bacteria of the Genus Pseudomonas on Leguminous Plants and Their Joint Application for Bioremediation of Oil Contaminated Soils

The modern approach to the creation of biological products to stimulate plant growth is based on the study of specific inter-bacterial interactions. This study describes the impact that the introduction of strains of the genus Pseudomonas has on annual and perennial leguminous plants and the ecosystem of the leguminous plant-the indigenous microbial community. The objects of research under the conditions of vegetation experiments were plants of field peas (Pisum sativum L.), white lupine (Lupinus albus L.), chickpea (Cicer arietinum L.), alfalfa (Medicago sativa subsp. varia (Martyn) Arcang.), and white sweet clover (Melilotus albus Medik.). For the treatment of plant seeds, a liquid culture of strains of growth-stimulating bacteria Pseudomonas koreensis IB-4, and P. laurentiana ANT 17 was used. The positive effect of the studied strains on the germination, growth and development of plants was established. There was no inhibitory effect of inoculants on rhizobia; on the contrary, an increase in nodule formation was observed. The possibility of recultivation of oil-contaminated soil using chickpea and alfalfa as phytomeliorants and growth-stimulating strains P. koreensis IB-4, P. laurentiana ANT 17 as inoculants was evaluated. It is proved that seed treatment improved the morphological parameters of plants, as well as the efficiency of oil destruction.

Molecular Breeding and Drought Tolerance in Chickpea

Cicer arietinum L. is the third greatest widely planted imperative pulse crop worldwide, and it belongs to the Leguminosae family. Drought is the utmost common abiotic factor on plants, distressing their water status and limiting their growth and development. Chickpea genotypes have the natural ability to fight drought stress using certain strategies viz., escape, avoidance and tolerance. Assorted breeding methods, including hybridization, mutation, and marker-aided breeding, genome sequencing along with omics approaches, could be used to improve the chickpea germplasm lines(s) against drought stress. Root features, for instance depth and root biomass, have been recognized as the greatest beneficial morphological factors for managing terminal drought tolerance in the chickpea. Marker-aided selection, for example, is a genomics-assisted breeding (GAB) strategy that can considerably increase crop breeding accuracy and competence. These breeding technologies, notably marker-assisted breeding, omics, and plant physiology knowledge, underlined the importance of chickpea breeding and can be used in future crop improvement programmes to generate drought-tolerant cultivars(s).

Perspective for genomic-enabled prediction against black sigatoka disease and drought stress in polyploid species

Genomic selection (GS) in plant breeding is explored as a promising tool to solve the problems related to the biotic and abiotic threats. Polyploid plants like bananas (Musa spp.) face the problem of drought and black sigatoka disease (BSD) that restrict their production. The conventional plant breeding is experiencing difficulties, particularly phenotyping costs and long generation interval. To overcome these difficulties, GS in plant breeding is explored as an alternative with a great potential for reducing costs and time in selection process. So far, GS does not have the same success in polyploid plants as with diploid plants because of the complexity of their genome. In this review, we present the main constraints to the application of GS in polyploid plants and the prospects for overcoming these constraints. Particular emphasis is placed on breeding for BSD and drought-two major threats to banana production-used in this review as a model of polyploid plant. It emerges that the difficulty in obtaining markers of good quality in polyploids is the first challenge of GS on polyploid plants, because the main tools used were developed for diploid species. In addition to that, there is a big challenge of mastering genetic interactions such as dominance and epistasis effects as well as the genotype by environment interaction, which are very common in polyploid plants. To get around these challenges, we have presented bioinformatics tools, as well as artificial intelligence approaches, including machine learning. Furthermore, a scheme for applying GS to banana for BSD and drought has been proposed. This review is of paramount impact for breeding programs that seek to reduce the selection cycle of polyploids despite the complexity of their genome.

Recent advancement in OMICS approaches to enhance abiotic stress tolerance in legumes

The world is facing rapid climate change and a fast-growing global population. It is believed that the world population will be 9.7 billion in 2050. However, recent agriculture production is not enough to feed the current population of 7.9 billion people, which is causing a huge hunger problem. Therefore, feeding the 9.7 billion population in 2050 will be a huge target. Climate change is becoming a huge threat to global agricultural production, and it is expected to become the worst threat to it in the upcoming years. Keeping this in view, it is very important to breed climate-resilient plants. Legumes are considered an important pillar of the agriculture production system and a great source of high-quality protein, minerals, and vitamins. During the last two decades, advancements in OMICs technology revolutionized plant breeding and emerged as a crop-saving tool in wake of the climate change. Various OMICs approaches like Next-Generation sequencing (NGS), Transcriptomics, Proteomics, and Metabolomics have been used in legumes under abiotic stresses. The scientific community successfully utilized these platforms and investigated the Quantitative Trait Loci (QTL), linked markers through genome-wide association studies, and developed KASP markers that can be helpful for the marker-assisted breeding of legumes. Gene-editing techniques have been successfully proven for soybean, cowpea, chickpea, and model legumes such as Medicago truncatula and Lotus japonicus. A number of efforts have been made to perform gene editing in legumes. Moreover, the scientific community did a great job of identifying various genes involved in the metabolic pathways and utilizing the resulted information in the development of climate-resilient legume cultivars at a rapid pace. Keeping in view, this review highlights the contribution of OMICs approaches to abiotic stresses in legumes. We envisage that the presented information will be helpful for the scientific community to develop climate-resilient legume cultivars.

Genome-wide association mapping of nutritional traits for designing superior chickpea varieties

Micronutrient malnutrition is a serious concern in many parts of the world; therefore, enhancing crop nutrient content is an important challenge. Chickpea (Cicer arietinum L.), a major food legume crop worldwide, is a vital source of protein and minerals in the vegetarian diet. This study evaluated a diverse set of 258 chickpea germplasm accessions for 12 key nutritional traits. A significant variation was observed for several nutritional traits, including crude protein (16.56-24.64/100 g), β-Carotene (0.003-0.104 mg/100 g), calcium (60.69-176.55 mg/100 g), and folate (0.413-6.537 mg/kg). These data, combined with the available whole-genome sequencing data for 318,644 SNPs, were used in genome-wide association studies comprising single-locus and multi-locus models. We also explored the effect of varying the minor allele frequency (MAF) levels and heterozygosity. We identified 62 significant marker-trait associations (MTAs) explaining up to 28.63% of the phenotypic variance (PV), of which nine were localized within genes regulating G protein-coupled receptor signaling pathway, proteasome assembly, intracellular signal transduction, and oxidation-reduction process, among others. The significant effect MTAs were located primarily on Ca1, Ca3, Ca4, and Ca6. Importantly, varying the level of heterozygosity was found to significantly affect the detection of associations contributing to traits of interest. We further identified seven promising accessions (ICC10399, ICC1392, ICC1710, ICC2263, ICC1431, ICC4182, and ICC16915) with superior agronomic performance and high nutritional content as potential donors for developing nutrient-rich, high-yielding chickpea varieties. Validation of the significant MTAs with higher PV could identify factors controlling the nutrient acquisition and facilitate the design of biofortified chickpeas for the future.

Genetic mapping of QTLs for drought tolerance in chickpea (Cicer arietinum L.)

Chickpea yield is severely affected by drought stress, which is a complex quantitative trait regulated by multiple small-effect genes. Identifying genomic regions associated with drought tolerance component traits may increase our understanding of drought tolerance mechanisms and assist in the development of drought-tolerant varieties. Here, a total of 187 F8 recombinant inbred lines (RILs) developed from an interspecific cross between drought-tolerant genotype GPF 2 (Cicer arietinum) and drought-sensitive accession ILWC 292 (C. reticulatum) were evaluated to identify quantitative trait loci (QTLs) associated with drought tolerance component traits. A total of 21 traits, including 12 morpho-physiological traits and nine root-related traits, were studied under rainfed and irrigated conditions. Composite interval mapping identified 31 QTLs at Ludhiana and 23 QTLs at Faridkot locations for morphological and physiological traits, and seven QTLs were identified for root-related traits. QTL analysis identified eight consensus QTLs for six traits and five QTL clusters containing QTLs for multiple traits on linkage groups CaLG04 and CaLG06. The identified major QTLs and genomic regions associated with drought tolerance component traits can be introgressed into elite cultivars using genomics-assisted breeding to enhance drought tolerance in chickpea.

Ensuring Global Food Security by Improving Protein Content in Major Grain Legumes Using Breeding and 'Omics' Tools

Grain legumes are a rich source of dietary protein for millions of people globally and thus a key driver for securing global food security. Legume plant-based 'dietary protein' biofortification is an economic strategy for alleviating the menace of rising malnutrition-related problems and hidden hunger. Malnutrition from protein deficiency is predominant in human populations with an insufficient daily intake of animal protein/dietary protein due to economic limitations, especially in developing countries. Therefore, enhancing grain legume protein content will help eradicate protein-related malnutrition problems in low-income and underprivileged countries. Here, we review the exploitable genetic variability for grain protein content in various major grain legumes for improving the protein content of high-yielding, low-protein genotypes. We highlight classical genetics-based inheritance of protein content in various legumes and discuss advances in molecular marker technology that have enabled us to underpin various quantitative trait loci controlling seed protein content (SPC) in biparental-based mapping populations and genome-wide association studies. We also review the progress of functional genomics in deciphering the underlying candidate gene(s) controlling SPC in various grain legumes and the role of proteomics and metabolomics in shedding light on the accumulation of various novel proteins and metabolites in high-protein legume genotypes. Lastly, we detail the scope of genomic selection, high-throughput phenotyping, emerging genome editing tools, and speed breeding protocols for enhancing SPC in grain legumes to achieve legume-based dietary protein security and thus reduce the global hunger risk.

A Comprehensive Review on Chickpea (Cicer arietinum L.) Breeding for Abiotic Stress Tolerance and Climate Change Resilience

Chickpea is one of the most important pulse crops worldwide, being an excellent source of protein. It is grown under rain-fed conditions averaging yields of 1 t/ha, far from its potential of 6 t/ha under optimum conditions. The combined effects of heat, cold, drought, and salinity affect species productivity. In this regard, several physiological, biochemical, and molecular mechanisms are reviewed to confer tolerance to abiotic stress. A large collection of nearly 100,000 chickpea accessions is the basis of breeding programs, and important advances have been achieved through conventional breeding, such as germplasm introduction, gene/allele introgression, and mutagenesis. In parallel, advances in molecular biology and high-throughput sequencing have allowed the development of specific molecular markers for the genus Cicer, facilitating marker-assisted selection for yield components and abiotic tolerance. Further, transcriptomics, proteomics, and metabolomics have permitted the identification of specific genes, proteins, and metabolites associated with tolerance to abiotic stress of chickpea. Furthermore, some promising results have been obtained in studies with transgenic plants and with the use of gene editing to obtain drought-tolerant chickpea. Finally, we propose some future lines of research that may be useful to obtain chickpea genotypes tolerant to abiotic stress in a scenario of climate change.

Orphan Crops: A Best Fit for Dietary Enrichment and Diversification in Highly Deteriorated Marginal Environments

Orphan crops are indigenous and invariably grown by small and marginal farmers under subsistence farming systems. These crops, which are common and widely accepted by local farmers, are highly rich in nutritional profile, good for medicinal purposes, and well adapted to suboptimal growing conditions. However, these crops have suffered neglect and abandonment from the scientific community because of very low or no investments in research and genetic improvement. A plausible reason for this is that these crops are not traded internationally at a rate comparable to that of the major food crops such as wheat, rice, and maize. Furthermore, marginal environments have poor soils and are characterized by extreme weather conditions such as heat, erratic rainfall, water deficit, and soil and water salinity, among others. With more frequent extreme climatic events and continued land degradation, orphan crops are beginning to receive renewed attention as alternative crops for dietary diversification in marginal environments and, by extension, across the globe. Increased awareness of good health is also a major contributor to the revived attention accorded to orphan crops. Thus, the introduction, evaluation, and adaptation of outstanding varieties of orphan crops for dietary diversification will contribute not only to sustained food production but also to improved nutrition in marginal environments. In this review article, the concept of orphan crops vis-à-vis marginality and food and nutritional security is defined for a few orphan crops. We also examined recent advances in research involving orphan crops and the potential of these crops for dietary diversification within the context of harsh marginal environments. Recent advances in genomics coupled with molecular breeding will play a pivotal role in improving the genetic potential of orphan crops and help in developing sustainable food systems. We concluded by presenting a potential roadmap to future research engagement and a policy framework with recommendations aimed at facilitating and enhancing the adoption and sustainable production of orphan crops under agriculturally marginal conditions.

Towards the Development, Maintenance and Standardized Phenotypic Characterization of Single-Seed-Descent Genetic Resources for Chickpea

Here we present the approach used to develop the INCREASE "Intelligent Chickpea" Collections, from analysis of the information on the life history and population structure of chickpea germplasm, the availability of genomic and genetic resources, the identification of key phenotypic traits and methodologies to characterize chickpea. We present two phenotypic protocols within H2O20 Project INCREASE to characterize, develop, and maintain chickpea single-seed-descent (SSD) line collections. Such protocols and related genetic resource data from the project will be available for the legume community to apply the standardized approaches to develop Chickpea Intelligent Collections further or for multiplication/seed-increase purposes. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Characterization of chickpea seeds for seed-trait descriptors Basic Protocol 2: Characterization of chickpea lines for plant-trait descriptors specific for primary seed increase.

Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs

The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.

Genome-wide discovery of DNA polymorphisms via resequencing of chickpea cultivars with contrasting response to drought stress

Drought stress limits plant growth, resulting in a significant yield loss in chickpea. The diversification in genome sequence and selective sweep of allele(s) in different genotypes of a crop plant may play an important role in the determination of agronomic traits, including drought stress response. We investigated, via whole genome resequencing, the DNA polymorphisms between two sets of chickpea genotypes with contrasting drought stress responses (3 drought-sensitive vs. 6 drought-tolerant). In total, 36,406 single nucleotide polymorphisms (SNPs) and 3407 insertions or deletions (InDels) differentiating drought-sensitive and drought-tolerant chickpea genotypes were identified. Interestingly, most (91%) of these DNA polymorphisms were located in chromosomes 1 and 4. The genes harboring DNA polymorphisms in their promoter and/or coding regions and exhibiting differential expression under control and/or drought stress conditions between/within the drought-sensitive and tolerant genotypes were found implicated in the stress response. Furthermore, we identified DNA polymorphisms within the cis-regulatory motifs in the promoter region of abiotic stress-related and QTL-associated genes, which might contribute to the differential expression of the candidate drought-responsive genes. In addition, we revealed the effect of nonsynonymous SNPs on mutational sensitivity and stability of the encoded proteins. Taken together, we identified DNA polymorphisms having relevance in drought stress response and revealed candidate genes to engineer drought tolerance in chickpea.

The genetics of vigour-related traits in chickpea (Cicer arietinum L.): insights from genomic data

QTL controlling vigour and related traits were identified in a chickpea RIL population and validated in diverse sets of germplasm. Robust KASP markers were developed for marker-assisted selection. To understand the genetic constitution of vigour in chickpea (Cicer arietinum L.), genomic data from a bi-parental population and multiple diversity panels were used to identify QTL, sequence-level haplotypes and genetic markers associated with vigour-related traits in Australian environments. Using 182 Recombinant Inbred Lines (RILs) derived from a cross between two desi varieties, Rupali and Genesis836, vigour QTL independent of flowering time were identified on chromosomes (Ca) 1, 3 and 4 with genotypic variance explained (GVE) ranging from 7.1 to 28.8%. Haplotype analysis, association analysis and graphical genotyping of whole-genome re-sequencing data of two diversity panels consisting of Australian and Indian genotypes and an ICRISAT Chickpea Reference Set revealed a deletion in the FTa1-FTa2-FTc gene cluster of Ca3 significantly associated with vigour and flowering time. Across the RIL population and diversity panels, the impact of the deletion was consistent for vigour but not flowering time. Vigour-related QTL on Ca4 co-located with a QTL for seed size in Rupali/Genesis836 (GVE = 61.3%). Using SNPs from this region, we developed and validated gene-based KASP markers across different panels. Two markers were developed for a gene on Ca1, myo -inositol monophosphatase (CaIMP), previously proposed to control seed size, seed germination and seedling growth in chickpea. While associated with vigour in the diversity panels, neither the markers nor broader haplotype linked to CaIMP was polymorphic in Rupali/Genesis836. Importantly, vigour appears to be controlled by different sets of QTL across time and with components which are independent from phenology.

Major Genomic Regions for Wheat Grain Weight as Revealed by QTL Linkage Mapping and Meta-Analysis

Grain weight is a key determinant for grain yield potential in wheat, which is highly governed by a type of quantitative genetic basis. The identification of major quantitative trait locus (QTL) and functional genes are urgently required for molecular improvements in wheat grain yield. In this study, major genomic regions and putative candidate genes for thousand grain weight (TGW) were revealed by integrative approaches with QTL linkage mapping, meta-analysis and transcriptome evaluation. Forty-five TGW QTLs were detected using a set of recombinant inbred lines, explaining 1.76-12.87% of the phenotypic variation. Of these, ten stable QTLs were identified across more than four environments. Meta-QTL (MQTL) analysis were performed on 394 initial TGW QTLs available from previous studies and the present study, where 274 loci were finally refined into 67 MQTLs. The average confidence interval of these MQTLs was 3.73-fold less than that of initial QTLs. A total of 134 putative candidate genes were mined within MQTL regions by combined analysis of transcriptomic and omics data. Some key putative candidate genes similar to those reported early for grain development and grain weight formation were further discussed. This finding will provide a better understanding of the genetic determinants of TGW and will be useful for marker-assisted selection of high yield in wheat breeding.

Discovery of DNA polymorphisms via whole genome resequencing and their functional relevance in salinity stress response in chickpea

Salinity stress is one of the major constraints for plant growth and yield. The salinity stress response of different genotypes of crop plants may largely be governed by DNA polymorphisms. To determine the molecular genetic factors involved in salinity stress tolerance in chickpea, we performed a whole genome resequencing data analysis of three each of salinity-sensitive and salinity-tolerant genotypes. A total of 6173 single nucleotide polymorphisms and 920 insertions and deletions differentiating the chickpea genotypes with contrasting salinity stress responses were identified. Gene ontology analysis revealed the enrichment of functional terms related to stress response and development among the genes harboring DNA polymorphisms in their promoter and/or coding regions. DNA polymorphisms located within the cis-regulatory motifs of the quantitative trait loci (QTL)-associated and abiotic stress related genes were identified, which may influence salinity stress response via modulating binding affinity of the transcription factors. Several genes including QTL-associated and abiotic stress response related genes harboring DNA polymorphisms exhibited differential expression in response to salinity stress especially at the reproductive stage of development in the salinity-tolerant genotype. Furthermore, effects of non-synonymous DNA polymorphisms on mutational sensitivity and structural integrity of the encoded proteins by the candidate QTL-associated and abiotic stress response related genes were revealed. The results suggest that DNA polymorphisms may determine salinity stress response via influencing differential gene expression in genotype and/or stage-dependent manner. Altogether, we provide a high-quality set of DNA polymorphisms and candidate genes that may govern salinity stress tolerance in chickpea.

Genomic and common garden approaches yield complementary results for quantifying environmental drivers of local adaptation in rubber rabbitbrush, a foundational Great Basin shrub

The spatial structure of genomic and phenotypic variation across populations reflects historical and demographic processes as well as evolution via natural selection. Characterizing such variation can provide an important perspective for understanding the evolutionary consequences of changing climate and for guiding ecological restoration. While evidence for local adaptation has been traditionally evaluated using phenotypic data, modern methods for generating and analyzing landscape genomic data can directly quantify local adaptation by associating allelic variation with environmental variation. Here, we analyze both genomic and phenotypic variation of rubber rabbitbrush (Ericameria nauseosa), a foundational shrub species of western North America. To quantify landscape genomic structure and provide perspective on patterns of local adaptation, we generated reduced representation sequencing data for 17 wild populations (222 individuals; 38,615 loci) spanning a range of environmental conditions. Population genetic analyses illustrated pronounced landscape genomic structure jointly shaped by geography and environment. Genetic-environment association (GEA) analyses using both redundancy analysis (RDA) and a machine-learning approach (Gradient Forest) indicated environmental variables (precipitation seasonality, slope, aspect, elevation, and annual precipitation) influenced spatial genomic structure and were correlated with allele frequency shifts indicative of local adaptation at a consistent set of genomic regions. We compared our GEA-based inference of local adaptation with phenotypic data collected by growing seeds from each population in a greenhouse common garden. Population differentiation in seed weight, emergence, and seedling traits was associated with environmental variables (e.g., precipitation seasonality) that were also implicated in GEA analyses, suggesting complementary conclusions about the drivers of local adaptation across different methods and data sources. Our results provide a baseline understanding of spatial genomic structure for E. nauseosa across the western Great Basin and illustrate the utility of GEA analyses for detecting the environmental causes and genetic signatures of local adaptation in a widely distributed plant species of restoration significance.

Legume Breeding for the Agroecological Transition of Global Agri-Food Systems: A European Perspective

Wider and more profitable legume crop cultivation is an indispensable step for the agroecological transition of global agri-food systems but represents a challenge especially in Europe. Plant breeding is pivotal in this context. Research areas of key interest are represented by innovative phenotypic and genome-based selection procedures for crop yield, tolerance to abiotic and biotic stresses enhanced by the changing climate, intercropping, and emerging crop quality traits. We see outmost priority in the exploration of genomic selection (GS) opportunities and limitations, to ease genetic gains and to limit the costs of multi-trait selection. Reducing the profitability gap of legumes relative to major cereals will not be possible in Europe without public funding devoted to crop improvement research, pre-breeding, and, in various circumstances, public breeding. While most of these activities may profit of significant public-private partnerships, all of them can provide substantial benefits to seed companies. A favorable institutional context may comprise some changes to variety registration tests and procedures.

Features and applications of haplotypes in crop breeding

Climate change with altered pest-disease dynamics and rising abiotic stresses threatens resource-constrained agricultural production systems worldwide. Genomics-assisted breeding (GAB) approaches have greatly contributed to enhancing crop breeding efficiency and delivering better varieties. Fast-growing capacity and affordability of DNA sequencing has motivated large-scale germplasm sequencing projects, thus opening exciting avenues for mining haplotypes for breeding applications. This review article highlights ways to mine haplotypes and apply them for complex trait dissection and in GAB approaches including haplotype-GWAS, haplotype-based breeding, haplotype-assisted genomic selection. Improvement strategies that efficiently deploy superior haplotypes to hasten breeding progress will be key to safeguarding global food security.

Characterization of heterosis and genomic prediction-based establishment of heterotic patterns for developing better hybrids in pigeonpea

Whole-genome resequencing (WGRS) of 396 lines, consisting of 104 hybrid parental lines and 292 germplasm lines, were used to study the molecular basis of mid-parent heterosis (MPH) and to identify complementary heterotic patterns in pigeonpea [Cajanus cajan (L.) Millsp.] hybrids. The lines and hybrids were assessed for yield and yield-related traits in multiple environments. Our analysis showed positive MPH values in 78.6% of hybrids, confirming the potential of hybrid breeding in pigeonpea. By using genome-wide prediction and association mapping approaches, we identified 129 single nucleotide polymorphisms and 52 copy number variations with significant heterotic effects and also established a high-yielding heterotic pattern in pigeonpea. In summary, our study highlights the role of WGRS data in the study and use of heterosis in crops where hybrid breeding is expected to boost selection gain in order to ensure global food security.

The application of pangenomics and machine learning in genomic selection in plants

Genomic selection approaches have increased the speed of plant breeding, leading to growing crop yields over the last decade. However, climate change is impacting current and future yields, resulting in the need to further accelerate breeding efforts to cope with these changing conditions. Here we present approaches to accelerate plant breeding and incorporate nonadditive effects in genomic selection by applying state-of-the-art machine learning approaches. These approaches are made more powerful by the inclusion of pangenomes, which represent the entire genome content of a species. Understanding the strengths and limitations of machine learning methods, compared with more traditional genomic selection efforts, is paramount to the successful application of these methods in crop breeding. We describe examples of genomic selection and pangenome-based approaches in crop breeding, discuss machine learning-specific challenges, and highlight the potential for the application of machine learning in genomic selection. We believe that careful implementation of machine learning approaches will support crop improvement to help counter the adverse outcomes of climate change on crop production.

Genome, Transcriptome, and Germplasm Sequencing Uncovers Functional Variation in the Warm-Season Grain Legume Horsegram Macrotyloma uniflorum (Lam.) Verdc

Horsegram is a grain legume with excellent nutritional and remedial properties and good climate resilience, able to adapt to harsh environmental conditions. Here, we used a combination of short- and long-read sequencing technologies to generate a genome sequence of 279.12Mb, covering 83.53% of the estimated total size of the horsegram genome, and we annotated 24,521 genes. De novo prediction of DNA repeats showed that approximately 25.04% of the horsegram genome was made up of repetitive sequences, the lowest among the legume genomes sequenced so far. The major transcription factors identified in the horsegram genome were bHLH, ERF, C2H2, WRKY, NAC, MYB, and bZIP, suggesting that horsegram is resistant to drought. Interestingly, the genome is abundant in Bowman-Birk protease inhibitors (BBIs), which can be used as a functional food ingredient. The results of maximum likelihood phylogenetic and estimated synonymous substitution analyses suggested that horsegram is closely related to the common bean and diverged approximately 10.17 million years ago. The double-digested restriction associated DNA (ddRAD) sequencing of 40 germplasms allowed us to identify 3,942 high-quality SNPs in the horsegram genome. A genome-wide association study with powdery mildew identified 10 significant associations similar to the MLO and RPW8.2 genes. The reference genome and other genomic information presented in this study will be of great value to horsegram breeding programs. In addition, keeping the increasing demand for food with nutraceutical values in view, these genomic data provide opportunities to explore the possibility of horsegram for use as a source of food and nutraceuticals.

Fab Advances in Fabaceae for Abiotic Stress Resilience: From 'Omics' to Artificial Intelligence

Legumes are a better source of proteins and are richer in diverse micronutrients over the nutritional profile of widely consumed cereals. However, when exposed to a diverse range of abiotic stresses, their overall productivity and quality are hugely impacted. Our limited understanding of genetic determinants and novel variants associated with the abiotic stress response in food legume crops restricts its amelioration. Therefore, it is imperative to understand different molecular approaches in food legume crops that can be utilized in crop improvement programs to minimize the economic loss. 'Omics'-based molecular breeding provides better opportunities over conventional breeding for diversifying the natural germplasm together with improving yield and quality parameters. Due to molecular advancements, the technique is now equipped with novel 'omics' approaches such as ionomics, epigenomics, fluxomics, RNomics, glycomics, glycoproteomics, phosphoproteomics, lipidomics, regulomics, and secretomics. Pan-omics-which utilizes the molecular bases of the stress response to identify genes (genomics), mRNAs (transcriptomics), proteins (proteomics), and biomolecules (metabolomics) associated with stress regulation-has been widely used for abiotic stress amelioration in food legume crops. Integration of pan-omics with novel omics approaches will fast-track legume breeding programs. Moreover, artificial intelligence (AI)-based algorithms can be utilized for simulating crop yield under changing environments, which can help in predicting the genetic gain beforehand. Application of machine learning (ML) in quantitative trait loci (QTL) mining will further help in determining the genetic determinants of abiotic stress tolerance in pulses.

Agrobiodiversity Index scores show agrobiodiversity is underutilized in national food systems

The diversity of plants, animals and microorganisms that directly or indirectly support food and agriculture is critical to achieving healthy diets and agroecosystems. Here we present the Agrobiodiversity Index (based on 22 indicators), which provides a monitoring framework and informs food systems policy. Agrobiodiversity Index calculations for 80 countries reveal a moderate mean agrobiodiversity status score (56.0 out of 100), a moderate mean agrobiodiversity action score (47.8 out of 100) and a low mean agrobiodiversity commitment score (21.4 out of 100), indicating that much stronger commitments and concrete actions are needed to enhance agrobiodiversity across the food system. Mean agrobiodiversity status scores in consumption and conservation are 14-82% higher in developed countries than in developing countries, while scores in production are consistently low across least developed, developing and developed countries. We also found an absence of globally consistent data for several important components of agrobiodiversity, including varietal, functional and underutilized species diversity.

Systems biology for crop improvement

In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.

Genomics and breeding innovations for enhancing genetic gain for climate resilience and nutrition traits

KEY MESSAGE

Integrating genomics technologies and breeding methods to tweak core parameters of the breeder's equation could accelerate delivery of climate-resilient and nutrient rich crops for future food security. Accelerating genetic gain in crop improvement programs with respect to climate resilience and nutrition traits, and the realization of the improved gain in farmers' fields require integration of several approaches. This article focuses on innovative approaches to address core components of the breeder's equation. A prerequisite to enhancing genetic variance (σ2g) is the identification or creation of favorable alleles/haplotypes and their deployment for improving key traits. Novel alleles for new and existing target traits need to be accessed and added to the breeding population while maintaining genetic diversity. Selection intensity (i) in the breeding program can be improved by testing a larger population size, enabled by the statistical designs with minimal replications and high-throughput phenotyping. Selection priorities and criteria to select appropriate portion of the population too assume an important role. The most important component of breeder's equation is heritability (h2). Heritability estimates depend on several factors including the size and the type of population and the statistical methods. The present article starts with a brief discussion on the potential ways to enhance σ2g in the population. We highlight statistical methods and experimental designs that could improve trait heritability estimation. We also offer a perspective on reducing the breeding cycle time (t), which could be achieved through the selection of appropriate parents, optimizing the breeding scheme, rapid fixation of target alleles, and combining speed breeding with breeding programs to optimize trials for release. Finally, we summarize knowledge from multiple disciplines for enhancing genetic gains for climate resilience and nutritional traits.

Introgression of "QTL-hotspot" region enhances drought tolerance and grain yield in three elite chickpea cultivars

With an aim of enhancing drought tolerance using a marker-assisted backcrossing (MABC) approach, we introgressed the "QTL-hotspot" region from ICC 4958 accession that harbors quantitative trait loci (QTLs) for several drought-tolerance related traits into three elite Indian chickpea (Cicer arietinum L.) cultivars: Pusa 372, Pusa 362, and DCP 92-3. Of eight simple sequence repeat (SSR) markers in the QTL-hotspot region, two to three polymorphic markers were used for foreground selection with respective cross-combinations. A total of 47, 53, and 46 SSRs were used for background selection in case of introgression lines (ILs) developed in genetic backgrounds of Pusa 372, Pusa 362, and DCP 92-3, respectively. In total, 61 ILs (20 BC₃ F₃ in Pusa 372; 20 BC₂ F₃ in Pusa 362, and 21 BC₃ F₃ in DCP 92-3), with >90% recurrent parent genome recovery were developed. Six improved lines in different genetic backgrounds (e.g. BGM 10216 in Pusa 372; BG 3097 and BG 4005 in Pusa 362; IPC(L4-14), IPC(L4-16), and IPC(L19-1) in DCP 92-3) showed better performance than their respective recurrent parents. BGM 10216, with 16% yield gain over Pusa 372, has been released as Pusa Chickpea 10216 by the Central Sub-Committees on Crop Standards, Notification and Release of Varieties of Agricultural Crops, Ministry of Agriculture and Farmers Welfare, Government of India, for commercial cultivation in India. In summary, this study reports introgression of the QTL-hotspot for enhancing yield under rainfed conditions, development of several introgression lines, and release of Pusa Chickpea 10216 developed through molecular breeding in India.

Integrated genomic selection for rapid improvement of crops

An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.

Small RNAs and their targets are associated with the transgenerational effects of water-deficit stress in durum wheat

Water-deficit stress negatively affects wheat yield and quality. Abiotic stress on parental plants during reproduction may have transgenerational effects on progeny. Here we investigated the transgenerational influence of pre-anthesis water-deficit stress by detailed analysis of the yield components, grain quality traits, and physiological traits in durum wheat. Next-generation sequencing analysis profiled the small RNA-omics, mRNA transcriptomics, and mRNA degradomics in first generation progeny. Parental water-deficit stress had positive impacts on the progeny for traits including harvest index and protein content in the less stress-tolerant variety. Small RNA-seq identified 1739 conserved and 774 novel microRNAs (miRNAs). Transcriptome-seq characterised the expression of 66,559 genes while degradome-seq profiled the miRNA-guided mRNA cleavage dynamics. Differentially expressed miRNAs and genes were identified, with significant regulatory patterns subject to trans- and inter-generational stress. Integrated analysis using three omics platforms revealed significant biological interactions between stress-responsive miRNA and targets, with transgenerational stress tolerance potentially contributed via pathways such as hormone signalling and nutrient metabolism. Our study provides the first confirmation of the transgenerational effects of water-deficit stress in durum wheat. New insights gained at the molecular level indicate that key miRNA-mRNA modules are candidates for transgenerational stress improvement.

Genomic resources in plant breeding for sustainable agriculture

Climate change during the last 40 years has had a serious impact on agriculture and threatens global food and nutritional security. From over half a million plant species, cereals and legumes are the most important for food and nutritional security. Although systematic plant breeding has a relatively short history, conventional breeding coupled with advances in technology and crop management strategies has increased crop yields by 56 % globally between 1965-85, referred to as the Green Revolution. Nevertheless, increased demand for food, feed, fiber, and fuel necessitates the need to break existing yield barriers in many crop plants. In the first decade of the 21st century we witnessed rapid discovery, transformative technological development and declining costs of genomics technologies. In the second decade, the field turned towards making sense of the vast amount of genomic information and subsequently moved towards accurately predicting gene-to-phenotype associations and tailoring plants for climate resilience and global food security. In this review we focus on genomic resources, genome and germplasm sequencing, sequencing-based trait mapping, and genomics-assisted breeding approaches aimed at developing biotic stress resistant, abiotic stress tolerant and high nutrition varieties in six major cereals (rice, maize, wheat, barley, sorghum and pearl millet), and six major legumes (soybean, groundnut, cowpea, common bean, chickpea and pigeonpea). We further provide a perspective and way forward to use genomic breeding approaches including marker-assisted selection, marker-assisted backcrossing, haplotype based breeding and genomic prediction approaches coupled with machine learning and artificial intelligence, to speed breeding approaches. The overall goal is to accelerate genetic gains and deliver climate resilient and high nutrition crop varieties for sustainable agriculture.

Orphan Crops and their Wild Relatives in the Genomic Era

More than half of the calories consumed by humans are provided by three major cereal crops (rice, maize, and wheat). Orphan crops are usually well adapted to low-input agricultural conditions, and they not only play vital roles in local areas but can also contribute to food and nutritional needs worldwide. Interestingly, many wild relatives of orphan crops are important weeds of major crops. Although orphan crops and their wild relatives have received little attentions from researchers for many years, genomic studies have recently been performed on these plants. Here, we provide an overview of genomic studies on orphan crops, with a focus on orphan cereals and their wild relatives. The genomes of at least 12 orphan cereals and/or their wild relatives have been sequenced. In addition to genomic benefits for orphan crop breeding, we discuss the potential ways for mutual utilization of genomic data from major crops, orphan crops, and their wild relatives (including weeds) and provide perspectives on genetic improvement of both orphan and major crops (including de novo domestication of orphan crops) in the coming genomic era.

Genomic interventions for sustainable agriculture

Agricultural production faces a Herculean challenge to feed the increasing global population. Food production systems need to deliver more with finite land and water resources while exerting the least negative influence on the ecosystem. The unpredictability of climate change and consequent changes in pests/pathogens dynamics aggravate the enormity of the challenge. Crop improvement has made significant contributions towards food security, and breeding climate-smart cultivars are considered the most sustainable way to accelerate food production. However, a fundamental change is needed in the conventional breeding framework in order to respond adequately to the growing food demands. Progress in genomics has provided new concepts and tools that hold promise to make plant breeding procedures more precise and efficient. For instance, reference genome assemblies in combination with germplasm sequencing delineate breeding targets that could contribute to securing future food supply. In this review, we highlight key breakthroughs in plant genome sequencing and explain how the presence of these genome resources in combination with gene editing techniques has revolutionized the procedures of trait discovery and manipulation. Adoption of new approaches such as speed breeding, genomic selection and haplotype-based breeding could overcome several limitations of conventional breeding. We advocate that strengthening varietal release and seed distribution systems will play a more determining role in delivering genetic gains at farmer's field. A holistic approach outlined here would be crucial to deliver steady stream of climate-smart crop cultivars for sustainable agriculture.

Genome-based trait prediction in multi- environment breeding trials in groundnut

KEY MESSAGE

Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K 'Axiom_Arachis' SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400-0.600) were obtained for pods/plant, shelling  %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

A Dual Strategy of Breeding for Drought Tolerance and Introducing Drought-Tolerant, Underutilized Crops into Production Systems to Enhance Their Resilience to Water Deficiency

Water scarcity is the primary constraint on crop productivity in arid and semiarid tropical areas suffering from climate alterations; in accordance, agricultural systems have to be optimized. Several concepts and strategies should be considered to improve crop yield and quality, particularly in vulnerable regions where such environmental changes cause a risk of food insecurity. In this work, we review two strategies aiming to increase drought stress tolerance: (i) the use of natural genes that have evolved over time and are preserved in crop wild relatives and landraces for drought tolerance breeding using conventional and molecular methods and (ii) exploiting the reservoir of neglected and underutilized species to identify those that are known to be more drought-tolerant than conventional staple crops while possessing other desired agronomic and nutritive characteristics, as well as introducing them into existing cropping systems to make them more resilient to water deficiency conditions. In the past, the existence of drought tolerance genes in crop wild relatives and landraces was either unknown or difficult to exploit using traditional breeding techniques to secure potential long-term solutions. Today, with the advances in genomics and phenomics, there are a number of new tools available that facilitate the discovery of drought resistance genes in crop wild relatives and landraces and their relatively easy transfer into advanced breeding lines, thus accelerating breeding progress and creating resilient varieties that can withstand prolonged drought periods. Among those tools are marker-assisted selection (MAS), genomic selection (GS), and targeted gene editing (clustered regularly interspaced short palindromic repeat (CRISPR) technology). The integration of these two major strategies, the advances in conventional and molecular breeding for the drought tolerance of conventional staple crops, and the introduction of drought-tolerant neglected and underutilized species into existing production systems has the potential to enhance the resilience of agricultural production under conditions of water scarcity.

Breeding and biotechnological interventions for trait improvement: status and prospects

Present review describes the molecular tools and strategies deployed in the trait discovery and improvement of major crops. The prospects and challenges associated with these approaches are discussed. Crop improvement relies on modulating the genes and genomic regions underlying key traits, either directly or indirectly. Direct approaches include overexpression, RNA interference, genome editing, etc., while breeding majorly constitutes the indirect approach. With the advent of latest tools and technologies, these strategies could hasten the improvement of crop species. Next-generation sequencing, high-throughput genotyping, precision editing, use of space technology for accelerated growth, etc. had provided a new dimension to crop improvement programmes that work towards delivering better varieties to cope up with the challenges. Also, studies have widened from understanding the response of plants to single stress to combined stress, which provides insights into the molecular mechanisms regulating tolerance to more than one stress at a given point of time. Altogether, next-generation genetics and genomics had made tremendous progress in delivering improved varieties; however, the scope still exists to expand its horizon to other species that remain underutilized. In this context, the present review systematically analyses the different genomics approaches that are deployed for trait discovery and improvement in major species that could serve as a roadmap for executing similar strategies in other crop species. The application, pros, and cons, and scope for improvement of each approach have been discussed with examples, and altogether, the review provides comprehensive coverage on the advances in genomics to meet the ever-growing demands for agricultural produce.