Skim-based genotyping by sequencing

Association study of morpho-phenological traits in quinoa (Chenopodium quinoa Willd.) using SSR markers

In this study, the genetic and molecular diversity of 60 quinoa accessions was assessed using agronomically important traits related to grain yield as well as microsatellite (SSR) markers, and informative markers linked to the studied traits were identified using association study. The results showed that most of the studied traits had a relatively high diversity, but grain saponin and protein content showed the highest diversity. High diversity was also observed in all SSR markers, but KAAT023, KAAT027, KAAT036, and KCAA014 showed the highest values for most of the diversity indices and can be introduced as the informative markers to assess genetic diversity in quinoa. Population structure analysis showed that the studied population probably includes two subclusters, so that out of 60 quinoa accessions, 29 (48%) and 23 (38%) accessions were assigned to the first and second subclusters, respectively, and eight (13%) accessions were considered as the mixed genotypes. The study of the population structure using Structure software showed two possible subgroups (K = 2) in the studied population and the results of the bar plot confirmed it. Association study using the general linear model (GLM) and mixed linear model (MLM) identified the number of 35 and 32 significant marker-trait associations (MTAs) for the first year (2019) and 37 and 35 significant MTAs for the second year (2020), respectively. Among the significant MTAs identified for different traits, the highest number of significant MTAs were obtained for grain yield and 1000-grain weight with six and five MTAs, respectively.

Imputation strategies for genomic prediction using nanopore sequencing

BACKGROUND

Genomic prediction describes the use of SNP genotypes to predict complex traits and has been widely applied in humans and agricultural species. Genotyping-by-sequencing, a method which uses low-coverage sequence data paired with genotype imputation, is becoming an increasingly popular SNP genotyping method for genomic prediction. The development of Oxford Nanopore Technologies' (ONT) MinION sequencer has now made genotyping-by-sequencing portable and rapid. Here we evaluate the speed and accuracy of genomic predictions using low-coverage ONT sequence data in a population of cattle using four imputation approaches. We also investigate the effect of SNP reference panel size on imputation performance.

RESULTS

SNP array genotypes and ONT sequence data for 62 beef heifers were used to calculate genomic estimated breeding values (GEBVs) from 641 k SNP for four traits. GEBV accuracy was much higher when genome-wide flanking SNP from sequence data were used to help impute the 641 k panel used for genomic predictions. Using the imputation package QUILT, correlations between ONT and low-density SNP array genomic breeding values were greater than 0.91 and up to 0.97 for sequencing coverages as low as 0.1 × using a reference panel of 48 million SNP. Imputation time was significantly reduced by decreasing the number of flanking sequence SNP used in imputation for all methods. When compared to high-density SNP arrays, genotyping accuracy and genomic breeding value correlations at 0.5 × coverage were also found to be higher than those imputed from low-density arrays.

CONCLUSIONS

Here we demonstrated accurate genomic prediction is possible with ONT sequence data from sequencing coverages as low as 0.1 × , and imputation time can be as short as 10 min per sample. We also demonstrate that in this population, genotyping-by-sequencing at 0.1 × coverage can be more accurate than imputation from low-density SNP arrays.

Construction of High-Density Genetic Map and QTL Mapping for Grain Shape in the Rice RIL Population

Grain shape is an important agronomic trait directly associated with yield in rice. In order to explore new genes related to rice grain shape, a high-density genetic map containing 2193 Bin markers (526957 SNP) was constructed by whole-genome resequencing of 208 recombinant inbred (RILs) derived from a cross between ZP37 and R8605, with a total genetic distance of 1542.27 cM. The average genetic distance between markers was 0.76 cM, and the physical distance was 201.29 kb. Quantitative trait locus (QTL) mapping was performed for six agronomic traits related to rice grain length, grain width, length-to-width ratio, thousand-grain weight, grain cross-sectional area, and grain perimeter under three different environments. A total of 39 QTLs were identified, with mapping intervals ranging from 8.1 kb to 1781.6 kb and an average physical distance of 517.5 kb. Among them, 15 QTLs were repeatedly detected in multiple environments. Analysis of the genetic effects of the identified QTLs revealed 14 stable genetic loci, including three loci that overlapped with previously reported gene positions, and the remaining 11 loci were newly identified loci associated with two or more environments or traits. Locus 1, Locus 3, Locus 10, and Locus 14 were novel loci exhibiting pleiotropic effects on at least three traits and were detected in multiple environments. Locus 14, with a contribution rate greater than 10%, influenced grain width, length-to-width ratio, and grain cross-sectional area. Furthermore, pyramiding effects analysis of three stable genetic loci showed that increasing the number of QTL could effectively improve the phenotypic value of grain shape. Collectively, our findings provided a theoretical basis and genetic resources for the cloning, functional analysis, and molecular breeding of genes related to rice grain shape.

Genomic architecture of migration timing in a long-distance migratory songbird

The impact of climate change on spring phenology poses risks to migratory birds, as migration timing is controlled predominantly by endogenous mechanisms. Despite recent advances in our understanding of the underlying genetic basis of migration timing, the ways that migration timing phenotypes in wild individuals may map to specific genomic regions requires further investigation. We examined the genetic architecture of migration timing in a long-distance migratory songbird (purple martin, Progne subis subis) by integrating genomic data with an extensive dataset of direct migratory tracks. A moderate to large amount of variance in spring migration arrival timing was explained by genomics (proportion of phenotypic variation explained by genomics = 0.74; polygenic score R² = 0.24). On chromosome 1, a region that was differentiated between migration timing phenotypes contained genes that could facilitate nocturnal flights and act as epigenetic modifiers. Overall, these results advance our understanding of the genomic underpinnings of migration timing.

Mapping QTLs for Reproductive Stage Salinity Tolerance in Rice Using a Cross between Hasawi and BRRI dhan28

Salinity stress is a major constraint to rice production in many coastal regions due to saline groundwater and river sources, especially during the dry season in coastal areas when seawater intrudes further inland due to reduced river flows. Since salinity tolerance is a complex trait, breeding efforts can be assisted by mapping quantitative trait loci (QTLs) for complementary salt tolerance mechanisms, which can then be combined to provide higher levels of tolerance. While an abundance of seedling stage salinity tolerance QTLs have been mapped, few studies have investigated reproductive stage tolerance in rice due to the difficulty of achieving reliable stage-specific phenotyping techniques. In the current study, a BC₁F₂ mapping population consisting of 435 individuals derived from a cross between a salt-tolerant Saudi Arabian variety, Hasawi, and a salt-sensitive Bangladeshi variety, BRRI dhan28, was evaluated for yield components after exposure to EC 10 dS/m salinity stress during the reproductive stage. After selecting tolerant and sensitive progeny, 190 individuals were genotyped by skim sequencing, resulting in 6209 high quality single nucleotide polymorphic (SNP) markers. Subsequently, a total of 40 QTLs were identified, of which 24 were for key traits, including productive tillers, number and percent filled spikelets, and grain yield under stress. Importantly, three yield-related QTLs, one each for productive tillers (qPT3.1), number of filled spikelets (qNFS3.1) and grain yield (qGY3.1) under salinity stress, were mapped at the same position (6.7 Mb or 26.1 cM) on chromosome 3, which had not previously been associated with grain yield under salinity stress. These QTLs can be investigated further to dissect the molecular mechanisms underlying reproductive stage salinity tolerance in rice.

High-Density Mapping of Quantitative Trait Loci Controlling Agronomically Important Traits in Quinoa (Chenopodium quinoa Willd.)

Quinoa is a pseudocereal originating from the Andean regions. Despite quinoa's long cultivation history, genetic analysis of this crop is still in its infancy. We aimed to localize quantitative trait loci (QTL) contributing to the phenotypic variation of agronomically important traits. We crossed the Chilean accession PI-614889 and the Peruvian accession CHEN-109, which depicted significant differences in days to flowering, days to maturity, plant height, panicle length, and thousand kernel weight (TKW), saponin content, and mildew susceptibility. We observed sizeable phenotypic variation across F2 plants and F3 families grown in the greenhouse and the field, respectively. We used Skim-seq to genotype the F2 population and constructed a high-density genetic map with 133,923 single nucleotide polymorphism (SNPs). Fifteen QTL were found for ten traits. Two significant QTL, common in F2 and F3 generations, depicted pleiotropy for days to flowering, plant height, and TKW. The pleiotropic QTL harbored several putative candidate genes involved in photoperiod response and flowering time regulation. This study presents the first high-density genetic map of quinoa that incorporates QTL for several important agronomical traits. The pleiotropic loci can facilitate marker-assisted selection in quinoa breeding programs.

Measurement of Transgene Copy Number in Plants Using Droplet Digital PCR

Transgenic plants are produced both to investigate gene function and to confer desirable traits into crops. Transgene copy number is known to influence expression levels, and consequently, phenotypes. Similarly, knowledge of transgene zygosity is desirable for making quantitative assessments of phenotype and tracking the inheritance of transgenes in progeny generations. Since the first transgenic plants were produced, several methods for determining copy number have been applied, including Southern blotting, quantitative real-time PCR, and more recently, sequencing methods; however, each method has specific disadvantages, compromising throughput, accuracy, or expense. Digital PCR (dPCR) divides reactions into partitions, converting the exponential, analogue nature of PCR into a linear, digital signal that allows the frequency of occurrence of specific sequences to be accurately estimated. Confidence increases with the number of partitions; therefore, the availability of emulsion technologies that enable reactions to be divided into tens of thousands of nanodroplets allows accurate determination of copy number in what has become known as digital droplet PCR (ddPCR). ddPCR offers similar benefits of low costs and scalability as other PCR techniques but with superior accuracy and reliability. Graphic abstract: Digital PCR (dPCR) divides reactions into partitions, converting the exponential, analogue nature of PCR into a linear, digital signal that allows the frequency of transgene copy number to be accurately assessed.

Accurate Imputation of Untyped Variants from Deep Sequencing Data

The quality, statistical power, and resolution of genome-wide association studies (GWAS) are largely dependent on the comprehensiveness of genotypic data. Over the last few years, despite the constant decrease in the price of sequencing, whole-genome sequencing (WGS) of association panels comprising a large number of samples remains cost-prohibitive. Therefore, most GWAS populations are still genotyped using low-coverage genotyping methods resulting in incomplete datasets. Imputation of untyped variants is a powerful method to maximize the number of SNPs identified in study samples, it increases the power and resolution of GWAS and allows to integrate genotyping datasets obtained from various sources. Here, we describe the key concepts underlying imputation of untyped variants, including the architecture of reference panels, and review some of the associated challenges and how these can be addressed. We also discuss the need and available methods to rigorously assess the accuracy of imputed data prior to their use in any genetic study.

Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation

Genotype imputation is a process that estimates missing genotypes in terms of the haplotypes and genotypes in a reference panel. It can effectively increase the density of single nucleotide polymorphisms (SNPs), boost the power to identify genetic association and promote the combination of genetic studies. However, there has been a lack of high-quality reference panels for most plants, which greatly hinders the application of genotype imputation. Here, we developed Plant-ImputeDB (http://gong_lab.hzau.edu.cn/Plant_imputeDB/), a comprehensive database with reference panels of 12 plant species for online genotype imputation, SNP and block search and free download. By integrating genotype data and whole-genome resequencing data of plants from various studies and databases, the current Plant-ImputeDB provides high-quality reference panels of 12 plant species, including ∼69.9 million SNPs from 34 244 samples. It also provides an easy-to-use online tool with the option of two popular tools specifically designed for genotype imputation. In addition, Plant-ImputeDB accepts submissions of different types of genomic variations, and provides free and open access to all publicly available data in support of related research worldwide. In general, Plant-ImputeDB may serve as an important resource for plant genotype imputation and greatly facilitate the research on plant genetic research.

Skim-Sequencing Based Genotyping Reveals Genetic Divergence of the Wild and Domesticated Population of Black Tiger Shrimp (Penaeus monodon) in the Indo-Pacific Region

The domestication of a wild-caught aquatic animal is an evolutionary process, which results in genetic discrimination at the genomic level in response to strong artificial selection. Although black tiger shrimp (Penaeus monodon) is one of the most commercially important aquaculture species, a systematic assessment of genetic divergence and structure of wild-caught and domesticated broodstock populations of the species is yet to be documented. Therefore, we used skim sequencing (SkimSeq) based genotyping approach to investigate the genetic structure of 50 broodstock individuals of P. monodon species, collected from five sampling sites (n = 10 in each site) across their distribution in Indo-Pacific regions. The wild-caught P. monodon broodstock population were collected from Malaysia (MS) and Japan (MJ), while domesticated broodstock populations were collected from Madagascar (MMD), Hawaii, HI, USA (MMO), and Thailand (MT). After various filtering process, a total of 194,259 single nucleotide polymorphism (SNP) loci were identified, in which 4983 SNP loci were identified as putatively adaptive by the pcadapt approach. In both datasets, pairwise F_ST estimates high genetic divergence between wild and domesticated broodstock populations. Consistently, different spatial clustering analyses in both datasets categorized divergent genetic structure into two clusters: (1) wild-caught populations (MS and MJ), and (2) domesticated populations (MMD, MMO and MT). Among 4983 putatively adaptive SNP loci, only 50 loci were observed to be in the coding region. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that non-synonymous mutated genes might be associated with the energy production, metabolic functions, respiration regulation and developmental rates, which likely act to promote adaptation to the strong artificial selection during the domestication process. This study has demonstrated the applicability of SkimSeq in a highly duplicated genome of P. monodon specifically, across a range of genetic backgrounds and geographical distributions, and would be useful for future genetic improvement program of this species in aquaculture.

Integrating genomics for chickpea improvement: achievements and opportunities

Integration 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. The 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.

Development of Genome-Wide SNP Markers for Barley via Reference- Based RNA-Seq Analysis

Marker-assisted selection of crop plants requires DNA markers that can distinguish between the closely related strains often used in breeding. The availability of reference genome sequence facilitates the generation of markers, by elucidating the genomic positions of new markers as well as of their neighboring sequences. In 2017, a high quality genome sequence was released for the six-row barley (Hordeum vulgare) cultivar Morex. Here, we developed a de novo RNA-Seq-based genotyping procedure for barley strains used in Japanese breeding programs. Using RNA samples from the seedling shoot, seedling root, and immature flower spike, we mapped next-generation sequencing reads onto the transcribed regions, which correspond to ∼590 Mb of the whole ∼4.8-Gbp reference genome sequence. Using 150 samples from 108 strains, we detected 181,567 SNPs and 45,135 indels located in the 28,939 transcribed regions distributed throughout the Morex genome. We evaluated the quality of this polymorphism detection approach by analyzing 387 RNA-Seq-derived SNPs using amplicon sequencing. More than 85% of the RNA-Seq SNPs were validated using the highly redundant reads from the amplicon sequencing, although half of the indels and multiple-allele loci showed different polymorphisms between the platforms. These results demonstrated that our RNA-Seq-based de novo polymorphism detection system generates genome-wide markers, even in the closely related barley genotypes used in breeding programs.

Toward the sequence-based breeding in legumes in the post-genome sequencing era

Efficiency of breeding programs of legume crops such as chickpea, pigeonpea and groundnut has been considerably improved over the past decade through deployment of modern genomic tools and technologies. For instance, next-generation sequencing technologies have facilitated availability of genome sequence assemblies, re-sequencing of several hundred lines, development of HapMaps, high-density genetic maps, a range of marker genotyping platforms and identification of markers associated with a number of agronomic traits in these legume crops. Although marker-assisted backcrossing and marker-assisted selection approaches have been used to develop superior lines in several cases, it is the need of the hour for continuous population improvement after every breeding cycle to accelerate genetic gain in the breeding programs. In this context, we propose a sequence-based breeding approach which includes use of independent or combination of parental selection, enhancing genetic diversity of breeding programs, forward breeding for early generation selection, and genomic selection using sequencing/genotyping technologies. Also, adoption of speed breeding technology by generating 4-6 generations per year will be contributing to accelerate genetic gain. While we see a huge potential of the sequence-based breeding to revolutionize crop improvement programs in these legumes, we anticipate several challenges especially associated with high-quality and precise phenotyping at affordable costs, data analysis and management related to improving breeding operation efficiency. Finally, integration of improved seed systems and better agronomic packages with the development of improved varieties by using sequence-based breeding will ensure higher genetic gains in farmers' fields.

Dissecting genomic hotspots underlying seed protein, oil, and sucrose content in an interspecific mapping population of soybean using high-density linkage mapping

The cultivated [Glycine max (L) Merr.] and wild [Glycine soja Siebold & Zucc.] soybean species comprise wide variation in seed composition traits. Compared to wild soybean, cultivated soybean contains low protein, high oil, and high sucrose. In this study, an interspecific population was derived from a cross between G. max (Williams 82) and G. soja (PI 483460B). This recombinant inbred line (RIL) population of 188 lines was sequenced at 0.3× depth. Based on 91 342 single nucleotide polymorphisms (SNPs), recombination events in RILs were defined, and a high-resolution bin map was developed (4070 bins). In addition to bin mapping, quantitative trait loci (QTL) analysis for protein, oil, and sucrose was performed using 3343 polymorphic SNPs (3K-SNP), derived from Illumina Infinium BeadChip sequencing platform. The QTL regions from both platforms were compared, and a significant concordance was observed between bin and 3K-SNP markers. Importantly, the bin map derived from next-generation sequencing technology enhanced mapping resolution (from 1325 to 50 Kb). A total of five, nine, and four QTLs were identified for protein, oil, and sucrose content, respectively, and some of the QTLs coincided with soybean domestication-related genomic loci. The major QTL for protein and oil were mapped on Chr. 20 (qPro_20) and suggested negative correlation between oil and protein. In terms of sucrose content, a novel and major QTL were identified on Chr. 8 (qSuc_08) and harbours putative genes involved in sugar transport. In addition, genome-wide association using 91 342 SNPs confirmed the genomic loci derived from QTL mapping. A QTL-based haplotype using whole-genome resequencing of 106 diverse soybean lines identified unique allelic variation in wild soybean that could be utilized to widen the genetic base in cultivated soybean.

An imputation platform to enhance integration of rice genetic resources

As sequencing and genotyping technologies evolve, crop genetics researchers accumulate increasing numbers of genomic data sets from various genotyping platforms on different germplasm panels. Imputation is an effective approach to increase marker density of existing data sets toward the goal of integrating resources for downstream applications. While a number of imputation software packages are available, the limitations to utilization for the rice community include high computational demand and lack of a reference panel. To address these challenges, we develop the Rice Imputation Server, a publicly available web application leveraging genetic information from a globally diverse rice reference panel assembled here. This resource allows researchers to benefit from increased marker density without needing to perform imputation on their own machines. We demonstrate improvements that imputed data provide to rice genome-wide association (GWA) results of grain amylose content and show that the major functional nucleotide polymorphism is tagged only in the imputed data set.

Revolution in Genotyping Platforms for Crop Improvement

In the past decade, the application of high-throughput sequencing to crop genotyping has given rise to novel platforms capable of genotyping tens of thousands of genome-wide DNA markers. Coupled with the decreasing costs of sequencing, this rapid increase in markers allows accelerated and highly accurate genotyping of entire crop populations and diversity sets using single nucleotide polymorphisms (SNPs). These revolutionary advances accelerate crop improvement by facilitating a more precise connection of phenotype to genotype through association studies, linkage mapping and diversity analysis. The platforms driving the advances in genotyping are array technologies and genotyping by sequencing (GBS) methods, which include both low-coverage whole genome resequencing (skim sequencing) and reduced representation sequencing (RRS) approaches. Here, we outline and compare these genotyping platforms and provide a perspective on the promising future of crop genotyping. While SNP arrays provide high quality, simple handling, and unchallenging analysis, the lower cost of RRS and the greater data volume produced by skim sequencing suggest that use of GBS will become more prevalent in crop genomics as sequencing costs decrease and data analysis becomes more streamlined. Graphical Abstract.

Genotyping-by-sequencing approaches to characterize crop genomes: choosing the right tool for the right application

In the last decade, the revolution in sequencing technologies has deeply impacted crop genotyping practice. New methods allowing rapid, high-throughput genotyping of entire crop populations have proliferated and opened the door to wider use of molecular tools in plant breeding. These new genotyping-by-sequencing (GBS) methods include over a dozen reduced-representation sequencing (RRS) approaches and at least four whole-genome resequencing (WGR) approaches. The diversity of methods available, each often producing different types of data at different cost, can make selection of the best-suited method seem a daunting task. We review the most common genotyping methods used today and compare their suitability for linkage mapping, genomewide association studies (GWAS), marker-assisted and genomic selection and genome assembly and improvement in crops with various genome sizes and complexity. Furthermore, we give an outline of bioinformatics tools for analysis of genotyping data. WGR is well suited to genotyping biparental cross populations with complex, small- to moderate-sized genomes and provides the lowest cost per marker data point. RRS approaches differ in their suitability for various tasks, but demonstrate similar costs per marker data point. These approaches are generally better suited for de novo applications and more cost-effective when genotyping populations with large genomes or high heterozygosity. We expect that although RRS approaches will remain the most cost-effective for some time, WGR will become more widespread for crop genotyping as sequencing costs continue to decrease.

Emerging Genomic Tools for Legume Breeding: Current Status and Future Prospects

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.

Prioritization of candidate genes in "QTL-hotspot" region for drought tolerance in chickpea (Cicer arietinum L.)

A combination of two approaches, namely QTL analysis and gene enrichment analysis were used to identify candidate genes in the “QTL-hotspot” region for drought tolerance present on the Ca4 pseudomolecule in chickpea. In the first approach, a high-density bin map was developed using 53,223 single nucleotide polymorphisms (SNPs) identified in the recombinant inbred line (RIL) population of ICC 4958 (drought tolerant) and ICC 1882 (drought sensitive) cross. QTL analysis using recombination bins as markers along with the phenotyping data for 17 drought tolerance related traits obtained over 1–5 seasons and 1–5 locations split the “QTL-hotspot” region into two subregions namely “QTL-hotspot_a” (15 genes) and “QTL-hotspot_b” (11 genes). In the second approach, gene enrichment analysis using significant marker trait associations based on SNPs from the Ca4 pseudomolecule with the above mentioned phenotyping data, and the candidate genes from the refined “QTL-hotspot” region showed enrichment for 23 genes. Twelve genes were found common in both approaches. Functional validation using quantitative real-time PCR (qRT-PCR) indicated four promising candidate genes having functional implications on the effect of “QTL-hotspot” for drought tolerance in chickpea.