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The ever-rising population of the twenty-first century together with the prevailing challenges, such as deteriorating quality of arable land and water, has placed a big challenge for plant breeders to satisfy human needs for food under erratic weather patterns. Rice, wheat, and maize are the major staple crops consumed globally. Drought, waterlogging, heat, salinity, and mineral toxicity are the key abiotic stresses drastically affecting crop yield. Conventional plant breeding approaches towards abiotic stress tolerance have gained success to limited extent, due to the complex (multigenic) nature of these stresses. Progress in breeding climate-resilient crop plants has gained momentum in the last decade, due to improved understanding of the physiochemical and molecular basis of various stresses. A good number of genes have been characterized for adaptation to various stresses. In the era of novel molecular markers, mapping of QTLs has emerged as viable solution for breeding crops tolerant to abiotic stresses. Therefore, molecular breeding-based development and deployment of high-yielding climate-resilient crop cultivars together with climate-smart agricultural practices can pave the path to enhanced crop yields for smallholder farmers in areas vulnerable to the climate change. Advances in fine mapping and expression studies integrated with cheaper prices offer new avenues for the plant breeders engaged in climate-resilient plant breeding, and thereby, hope persists to ensure food security in the era of climate change.

The world faces a grave situation of nutrient deficiency as a consequence of increased uptake of calorie-rich food that threaten nutritional security. More than half the world’s population is affected by different forms of malnutrition. Unhealthy diets associated with poor nutrition carry a significant risk of developing non-communicable diseases, leading to a high mortality rate. Although considerable efforts have been made in agriculture to increase nutrient content in cereals, the successes are insufficient. The number of people affected by different forms of malnutrition has not decreased much in the recent past. While legumes are an integral part of the food system and widely grown in sub- Saharan Africa and South Asia, only limited efforts have been made to increase their nutrient content in these regions. Genetic variation for a majority of nutritional traits that ensure nutritional security in adverse conditions exists in the germplasm pool of legume crops. This diversity can be utilized by selective breeding for increased nutrients in seeds. The targeted identification of precise factors related to nutritional traits and their utilization in a breeding program can help mitigate malnutrition. The principal objective of this review is to present the molecular mechanisms of nutrient acquisition, transport and metabolism to support a biofortification strategy in legume crops to contribute to addressing malnutrition.

We report characteristics of soybean genetic diversity and structure from the resequencing of 481 diverse soybean accessions, comprising 52 wild (Glycine soja) selections and 429 cultivated (Glycine max) varieties (landraces and elites). This data was used to identify 7.8 million SNPs, to predict SNP effects relative to genic regions, and to identify the genetic structure, relationships, and linkage disequilibrium. We found evidence of distinct, mostly independent selection of lineages by particular geographic location. Among cultivated varieties, we identified numerous highly conserved regions, suggesting selection during domestication. Comparisons of these accessions against the whole U.S. germplasm genotyped with the SoySNP50K iSelect BeadChip revealed that over 95% of the re-sequenced accessions have a high similarity to their SoySNP50K counterparts. Probable errors in seed source or genotype tracking were also identified in approximately 5% of the accessions., Measurement(s) genetic variation • SNP • Linkage Disequilibrium Technology Type(s) DNA sequencing • bioinformatics analysis • computational phylogenetic analysis Sample Characteristic - Organism Glycine soja • Glycine max Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13568552

Genetic enhancement for resistance against the pod borer, Helicoverpa armigera is crucial for enhancing production and productivity of chickpea. Here we provide some novel insights into the genetic architecture of natural variation in H. armigera resistance in chickpea, an important legume, which plays a major role in food and nutritional security. An interspecific recombinant inbred line (RIL) population developed from a cross between H. armigera susceptible accession ICC 4958 (Cicer arietinum) and resistant accession PI 489777 (Cicer reticulatum) was evaluated for H. armigera resistance component traits using detached leaf assay and under field conditions. A high-throughput AxiomCicerSNP array was utilized to construct a dense linkage map comprising of 3,873 loci and spanning a distance of 949.27 cM. Comprehensive analyses of extensive genotyping and phenotyping data identified nine main-effect QTLs and 955 epistatic QTLs explaining up to 42.49% and 38.05% phenotypic variance, respectively, for H. armigera resistance component traits. The main-effect QTLs identified in this RIL population were linked with previously described genes, known to modulate resistance against lepidopteran insects in crop plants. One QTL cluster harbouring main-effect QTLs for three H. armigera resistance component traits and explaining up to 42.49% of the phenotypic variance, was identified on CaLG03. This genomic region, after validation, may be useful to improve H. armigera resistance component traits in elite chickpea cultivars.

Globally, chickpea production is severely affected by salinity stress. Understanding the genetic basis for salinity tolerance is important to develop salinity tolerant chickpeas. A recombinant inbred line (RIL) population developed using parental lines ICCV 10 (salt-tolerant) and DCP 92-3 (salt-sensitive) was screened under field conditions to collect information on agronomy, yield components, and stress tolerance indices. Genotyping data generated using Axiom&reg, CicerSNP array was used to construct a linkage map comprising 1856 SNP markers spanning a distance of 1106.3 cM across eight chickpea chromosomes. Extensive analysis of the phenotyping and genotyping data identified 28 quantitative trait loci (QTLs) explaining up to 28.40% of the phenotypic variance in the population. We identified QTL clusters on CaLG03 and CaLG06, each harboring major QTLs for yield and yield component traits under salinity stress. The main-effect QTLs identified in these two clusters were associated with key genes such as calcium-dependent protein kinases, histidine kinases, cation proton antiporter, and WRKY and MYB transcription factors, which are known to impart salinity stress tolerance in crop plants. Molecular markers/genes associated with these major QTLs, after validation, will be useful to undertake marker-assisted breeding for developing better varieties with salinity tolerance.

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm ‘package’ that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

Ploidy difference between wild Arachis species and cultivated genotypes hinder transfer of useful alleles for agronomically important traits. To overcome this genetic barrier, two synthetic tetraploids, viz., ISATGR 1212 (A. duranensis ICG 8123 × A. ipaensis ICG 8206) and ISATGR 265-5A (A. kempff-mercadoi ICG 8164 × A. hoehnei ICG 8190), were used to generate two advanced backcross (AB) populations. The AB-populations, namely, AB-pop1 (ICGV 91114 × ISATGR 1212) and AB-pop2, (ICGV 87846 × ISATGR 265-5A) were genotyped with DArT and SSR markers. Genetic maps were constructed for AB-pop1 and AB-pop2 populations with 258 loci (1415.7 cM map length and map density of 5.5 cM/loci) and 1043 loci (1500.8 cM map length with map density of 1.4 cM/loci), respectively. Genetic analysis identified large number of wild segments in the population and provided a good source of diversity in these populations. Phenotyping of these two populations identified several introgression lines with good agronomic, oil quality, and disease resistance traits. Quantitative trait locus (QTL) analysis showed that the wild genomic segments contributed favourable alleles for foliar disease resistance while cultivated genomic segments mostly contributed favourable alleles for oil quality and yield component traits. These populations, after achieving higher stability, will be useful resource for genetic mapping and QTL discovery for wild species segments in addition to using population progenies in breeding program for diversifying the gene pool of cultivated groundnut.

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype × environment (G × E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.

Core Ideas Quantitative trait locus (QTL) analyses for carotenoids in chickpea were completed for three F2 populations. A moderate number of QTLs and candidate genes associated with carotenoid concentration in chickpea seeds were identified. Green cotyledon color is positively associated with provitamin A carotenoids. Three F2 populations derived from crosses between cultivars with green and yellow cotyledon colors were used to identify quantitative trait loci (QTLs) associated with carotenoid components in chickpea (Cicer arietinum L.) seeds developed by the Crop Development Centre (CDC). Carotenoids including violaxanthin, lutein, zeaxanthin, β‐cryptoxanthin, and β‐carotene were assessed in the F2:3 seeds via high‐performance liquid chromatography (HPLC). In the ‘CDC Jade’ × ‘CDC Frontier’ population, 1068 bin markers derived from the 50K Axiom CicerSNP array were mapped onto eight linkage groups (LGs). Eight QTLs, including two each for β‐carotene and zeaxanthin and one each for total carotenoids, β‐cryptoxanthin, β‐carotene, and violaxanthin were identified in this population. In the ‘CDC Cory’ × ‘CDC Jade’ population, 694 bin markers were mapped onto eight LGs and one partial LG. Quantitative trait loci for β‐cryptoxanthin, β‐carotene, violaxanthin, lutein, and total carotenoids were identified on LG8. A map with eight LGs was developed from 581 bin markers in the third population derived from the ‘ICC4475’ × ‘CDC Jade’ cross. One QTL for β‐carotene and four QTLs, one each for β‐cryptoxanthin, β‐carotene, lutein, and total carotenoids, were identified in this population. The highest phenotypic variation explained by the QTLs was for β‐carotene, which ranged from 58 to 70% in all three populations. A major gene for cotyledon color was mapped on LG8 in each population. A significant positive correlation between cotyledon color and carotenoid concentration was observed. Potential candidate genes associated with carotenoid components were obtained and their locations on the chickpea genome are presented.

Much of the world’s population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.

Salinity is a major abiotic stress that is a global threat to crop production, including chickpea. This study focused on understanding the complex molecular mechanisms underlying salinity tolerance using comparative transcriptome analysis of tolerant (ICCV 10, JG 11) and sensitive (DCP 92-3, Pusa 256) chickpea genotypes in control and salt-stressed environments. A total of 530 million reads were generated from root samples of four genotypes using Illumina HiSeq-2500. A total of 21,698 differentially expressed genes (DEGs) were identified, of which 11,456 and 10,242 were up- and down-regulated, respectively, in comparative analysis. These DEGs were associated with crucial metabolic pathways, including hormone signaling, photosynthesis, lipid and carbohydrate metabolism, and cell wall biogenesis. Gene ontology (GO) examination revealed an enrichment of transcripts involved in salinity response. A total of 4257 differentially expressed GO terms were categorized into 64 functional groups; of which, GO terms like, integral component of membrane, organelle, and cellular anatomical entity were highly represented in tolerant genotypes under salt stress. Significant up-regulation of transcripts encoding potassium transporter family HAK/KUP proteins, MIP/aquaporin protein family, NADH dehydrogenase, pectinesterase, and PP2C family proteins occurred under salt stress. The tolerant lines (ICCV 10 and JG 11) engaged highly efficient machinery in response to elevated salt stress, especially for signal transduction, transport and influx of K+ ions, and osmotic homeostasis. The overall study highlights the role of potential candidate genes and their regulatory networks which can be utilized in breeding salt tolerant chickpea cultivars.

Summary Terminal drought is a major constraint to chickpea productivity. Two component traits responsible for reduction in yield under drought stress include reduction in seeds size and root length/root density. QTL‐seq approach, therefore, was used to identify candidate genomic regions for 100‐seed weight (100SDW) and total dry root weight to total plant dry weight ratio (RTR) under rainfed conditions. Genomewide SNP profiling of extreme phenotypic bulks from the ICC 4958 × ICC 1882 population identified two significant genomic regions, one on CaLG01 (1.08 Mb) and another on CaLG04 (2.7 Mb) linkage groups for 100SDW. Similarly, one significant genomic region on CaLG04 (1.10 Mb) was identified for RTR. Comprehensive analysis revealed four and five putative candidate genes associated with 100SDW and RTR, respectively. Subsequently, two genes (Ca_04364 and Ca_04607) for 100SDW and one gene (Ca_04586) for RTR were validated using CAPS/dCAPS markers. Identified candidate genomic regions and genes may be useful for molecular breeding for chickpea improvement.

Chickpea has a profound nutritional and economic value in vegetarian society. Continuous decline in chickpea productivity is attributed to insufficient genetic variability and different environmental stresses. Chickpea like several other legumes is highly susceptible to terminal drought stress. Multiple genes control drought tolerance and ASR gene plays a key role in regulating different plant stresses. The present study describes the molecular characterization and functional role of Abscissic acid and stress ripening (ASR) gene from chickpea (Cicer arietinum) and the gene sequence identified was submitted to NCBI Genbank (MK937569). Molecular analysis using MUSCLE software proved that the ASR nucleotide sequences in different legumes show variations at various positions though ASR genes are conserved in chickpea with only few variations. Sequence similarity of ASR gene to chickpea putative ABA/WDS induced protein mRNA clearly indicated its potential involvement in drought tolerance. Physiological screening and qRT-PCR results demonstrated increased ASR gene expression under drought stress possibly enabled genotypes to perform better under stress. Conserved domain search, protein structure analysis, prediction and validation, network analysis using Phyre2, Swiss-PDB viewer, ProSA and STRING analysis established the role of hypothetical ASR protein NP_001351739.1 in mediating drought responses. NP_001351739.1 might have enhanced the ASR gene activity as a transcription factor regulating drought stress tolerance in chickpea. This study could be useful in identification of new ASR genes that play a major role in drought tolerance and also develop functional markers for chickpea improvement.

Climate change has increased the occurrence of extreme weather patterns globally, causing significant reductions in crop production, and hence threatening food security. In order to meet the food demand of the growing world population, a faster rate of genetic gains leading to productivity enhancement for major crops is required. Grain legumes are an essential commodity in optimal human diets and animal feed because of their unique nutritional composition. Currently, limited water is a major constraint in grain legume production. Root system architecture (RSA) is an important developmental and agronomic trait, which plays vital roles in plant adaptation and productivity under water-limited environments. A deep and proliferative root system helps extract sufficient water and nutrients under these stress conditions. The integrated genetics and genomics approach to dissect molecular processes from genome to phenome is key to achieve increased water capture and use efficiency through developing better root systems. Success in crop improvement under drought depends on discovery and utilization of genetic variations existing in the germplasm. In this review, we summarize current progress in the genetic diversity in major legume crops, quantitative trait loci (QTLs) associated with RSA, and the importance and applications of recent discoveries associated with the beneficial root traits towards better RSA for enhanced drought tolerance and yield.

Chickpea is one of the most important food legumes that holds the key to meet rising global food and nutritional demand. In order to deploy molecular breeding approaches in crop improvement programs, user friendly and cost effective marker resources remain prerequisite. The advent of next generation sequencing (NGS) technology has resulted in the generation of several thousands of markers as part of several large scale genome sequencing and re-sequencing initiatives. Very recently, PCR based Insertion-deletions (InDels) are becoming a popular gel based genotyping solution because of their co-dominant, inexpensive, and highly polymorphic nature. With an objective to expand marker resources for genomics assisted breeding (GAB) in chickpea, whole genome re-sequencing data generated on five parental lines of one interspecific (ICC 4958 × PI 489777) and two intra-specific (ICC 283 × ICC 8261 and ICC 4958 × ICC 1882) mapping populations, were used for identification of InDels. A total of 231,658 InDels were identified using Dindel software with default parameters. Further, a total of 8,307 InDels with ≥20 bp size were selected for development of gel based markers, of which primers could be designed for 7,523 (90.56%) markers. On average, markers appeared at a frequency of 1,038 InDels/LG with a maximum number of markers on CaLG04 (1,952 InDels) and minimum on CaLG08 (360 InDels). In order to validate these InDels, a total of 423 primer pairs were randomly selected and tested on the selected parental lines. A high amplification rate of 80% was observed ranging from 46.06 to 58.01% polymorphism rate across parents on 3% agarose gel. This study clearly reflects the usefulness of available sequence data for the development of genome-wide InDels in chickpea that can further contribute and accelerate a wide range of genetic and molecular breeding activities in chickpea.

Chickpea plays a major role in food and nutritional security worldwide. Its productivity is severely affected by various biotic and abiotic stresses; hence development of stress resilience varieties that can yield higher under stress environment remains the call of the hour. Conventional breeding approaches clubbed with the genome information, commonly known as genomic-assisted breeding (GAB) have the potential to accelerate the crop improvement efforts. In order to deploy the GAB for crop improvement in chickpea, there was need to convert an orphan crop chickpea into the genomic resource-rich crop. Advent of sequencing technology has resulted in reduction of cost and led to development of huge genomic resources in chickpea. A variety of markers have been developed, used for various mapping studies including linkage mapping and association mapping and finally deployed for developing the superior varieties using GAB approached such as marker assisted backcrossing and genomic selection. The chapter reviews the journey of chickpea status from orphan crop with almost no marker resources to a genome resource-rich crop, which are being used for achieving the genetic gains at a momentum .

Legumes play a major role in food and nutritional security across the world. The current rate of genetic gains in legume breeding programs is not enough to meet the food and nutritional requirement of an ever increasing global population. To feed this growing population, it is essential to enhance the rate of genetic gains for increased productivity of these legumes. Genomics tools have great potential in developing improved cultivars faster and more precisely by deploying modern breeding approaches. Marker-assisted backcrossing (MABC) and marker-assisted recurrent selection (MARS) approaches have been successfully deployed in several legume crops for improving traits with simple genetic behaviour. However, it is difficult to address the complex traits using MABC and MARS as several large and small effect quantitative trait loci (QTLs) are involved in their expression. Genomic selection (GS) has potential to capture small and large effect genetic factors and deal with the complex traits. Over the last decade, large scale genomic resources have been developed in majority of the legume crops, which provide a perfect platform to deploy genome-wide information in selecting breeding material for enhancing the rate of genetic gain. Many legume breeders have already took initiatives towards deploying GS breeding by developing training populations, standardizing the GS models, studying effect of marker density, size of training population, and genotype and environment interaction. This chapter presents an overview on the current status of GS and presents the future prospects of its deployment in some legume breeding programs.

Genomic selection (GS) unlike marker-assisted backcrossing (MABC) predicts breeding values of lines using genome-wide marker profiling and allows selection of lines prior to field-phenotyping, thereby shortening the breeding cycle. A collection of 320 elite breeding lines was selected and phenotyped extensively for yield and yield related traits at two different locations (Delhi and Patancheru, India) during the crop seasons 2011–12 and 2012–13 under rainfed and irrigated conditions. In parallel, these lines were also genotyped using DArTseq platform to generate genotyping data for 3000 polymorphic markers. Phenotyping and genotyping data were used with six statistical GS models to estimate the prediction accuracies. GS models were tested for four yield related traits viz. seed yield, 100 seed weight, days to 50% flowering and days to maturity. Prediction accuracy for the models tested varied from 0.138 (seed yield) to 0.912 (100 seed weight), whereas performance of models did not show any significant difference for estimating prediction accuracy within traits. Kinship matrix calculated using genotyping data reaffirmed existence of two different groups within selected lines. There was not much effect of population structure on prediction accuracy. In brief, present study establishes the necessary resources for deployment of GS in chickpea breeding.

Legumes play a vital role in ensuring global nutritional food security and improving soil quality through nitrogen fixation. Accelerated higher genetic gains is required to meet the demand of ever increasing global population. In recent years, speedy developments have been witnessed in legume genomics due to advancements in next-generation sequencing (NGS) and high-throughput genotyping technologies. Reference genome sequences for many legume crops have been reported in the last 5 years. The availability of the draft genome sequences and re-sequencing of elite genotypes for several important legume crops have made it possible to identify structural variations at large scale. Availability of large-scale genomic resources and low-cost and high-throughput genotyping technologies are enhancing the efficiency and resolution of genetic mapping and marker-trait association studies. Most importantly, deployment of molecular breeding approaches has resulted in development of improved lines in some legume crops such as chickpea and groundnut. In order to support genomics-driven crop improvement at a fast pace, the deployment of breeder-friendly genomics and decision support tools seems appear to be critical in breeding programs in developing countries. This review provides an overview of emerging genomics and informatics tools/approaches that will be the key driving force for accelerating genomics-assisted breeding and ultimately ensuring nutritional and food security in developing countries.

Diversity as a function of drought tolerance may be identified by morphological characters, and molecular tools used to find the most divergent genotypes for breeding programs for drought tolerance in future. The narrow genetic base of chickpea can be circumvented by using diverse lines in breeding programs. Forty chickpea genotypes were studied for their morphological and molecular diversity with an objective of identifying the most diverse drought-tolerant lines. In total, 90 alleles were detected with 3.6 alleles per locus. Polymorphism information content (PIC) values ranged from 0.155 to 0.782 with an average value of 0.4374 per locus. The size of amplified products ranged from 160 bp to 390 bp. Primer TA136 with eight alleles showed the highest PIC value of 0.7825, indicating its ability to differentiate the genotypes at molecular level. DARwin neighbour-joining tree analysis based on dissimilarity estimates was done for the molecular data and sequential agglomerative hierarchical non-overlapping (SAHN) grouping for the morphological data. It could clearly discriminate the tolerance and the sensitivity of genotypes. Two-dimensional principal coordinates analysis (PCoA) plot indicated good diversity for drought tolerance. The genetic similarity coefficients ranged from 0.115 (genotypes BGD72 to ICCV 5308) to 0.828 (genotypes ICCV 10316 to ICCV 92337).

Key messageIntegration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts.AbstractThe implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.