Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates

Whole genome resequencing and phenotyping of MAGIC population for high resolution mapping of drought tolerance in chickpea

Terminal drought is one of the major constraints to crop production in chickpea (Cicer arietinum L.). In order to map drought tolerance related traits at high resolution, we sequenced multi-parent advanced generation intercross (MAGIC) population using whole genome resequencing approach and phenotyped it under drought stress environments for two consecutive years (2013-14 and 2014-15). A total of 52.02 billion clean reads containing 4.67 TB clean data were generated on the 1136 MAGIC lines and eight parental lines. Alignment of clean data on to the reference genome enabled identification of a total, 932,172 of SNPs, 35,973 insertions, and 35,726 deletions among the parental lines. A high-density genetic map was constructed using 57,180 SNPs spanning a map distance of 1606.69 cM. Using compressed mixed linear model, genome-wide association study (GWAS) enabled us to identify 737 markers significantly associated with days to 50% flowering, days to maturity, plant height, 100 seed weight, biomass, and harvest index. In addition to the GWAS approach, an identity-by-descent (IBD)-based mixed model approach was used to map quantitative trait loci (QTLs). The IBD-based mixed model approach detected major QTLs that were comparable to those from the GWAS analysis as well as some exclusive QTLs with smaller effects. The candidate genes like FRIGIDA and CaTIFY4b can be used for enhancing drought tolerance in chickpea. The genomic resources, genetic map, marker-trait associations, and QTLs identified in the study are valuable resources for the chickpea community for developing climate resilient chickpeas

Allelic relationships of flowering time genes in chickpea

Flowering time and crop duration are the most important traits for adaptation of chickpea (Cicer arietinum L.) to different agro-climatic conditions. Early flowering and early maturity enhance adaptation of chickpea to short season environments. This study was conducted to establish allelic relationships of the early flowering genes of ICC 16641, ICC 16644 and ICCV 96029 with three known early flowering genes, efl-1 (ICCV 2), ppd or efl-2 (ICC 5810), and efl-3 (BGD 132). In all cases, late flowering was dominant to early-flowering. The results indicated that the efl-1 gene identified from ICCV 2 was also present in ICCV 96029, which has ICCV 2 as one of the parents in its pedigree. ICC 16641 and ICC 16644 had a common early flowering gene which was not allelic to other reported early flowering genes. The new early flowering gene was designated efl-4. In most of the crosses, days to flowering was positively correlated with days to maturity, number of pods per plant, number of seeds per plant and seed yield per plant and negatively correlated or had no correlation with 100-seed weight. The double-pod trait improved grain yield per plant in the crosses where it delayed maturity. The information on allelic relationships of early flowering genes and their effects on yield and yield components will be useful in chickpea breeding for desired phenology

Inheritance and relationships of flowering time and seed size in kabuli chickpea

Flowering time and seed size are the important traits for adaptation in chickpea. Early phenology (time of flowering, podding and maturity) enhance chickpea adaptation to short season environments. Along with a trait of consumer preference, seed size has also been considered as an important factor for subsequent plant growth parameters including germination, seedling vigour and seedling mass. Small seeded kabuli genotype ICC 16644 was crossed with four genotypes (JGK 2, KAK 2, KRIPA and ICC 17109) to study inheritance of flowering time and seed size. The relationships of phenology with seed size, grain yield and its component traits were studied. The study included parents, F1, F2 and F3 of four crosses. The segregation data of F2 indicated flowering time in chickpea was governed by two genes with duplicate recessive epistasis and lateness was dominant to earliness. Two genes were controlling 100-seed weight where small seed size was dominant over large seed size. Early phenology had significant negative or no association (ICC 16644 × ICC 17109) with 100-seed weight. Yield per plant had significant positive association with number of seeds per plant, number of pods per plant, biological yield per plant, 100-seed weight, harvest index and plant height and hence could be considered as factors for seed yield improvement. Phenology had no correlation with yield per se (seed yield per plant) in any of the crosses studied. Thus, present study shows that in certain genetic background it might be possible to breed early flowering genotypes with large seed size in chickpea and selection of early flowering genotypes may not essentially have a yield penalty

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Not AvailableChickpea (Cicer arietinum L.) is known to be sensitive to many herbicides and, therefore, choices for using post-emergence herbicides for weed control are limited. The present study was aimed at identifying sources of tolerance to two herbicides with different modes of action (imazethapyr—amino acid synthesis inhibitor; and metribuzin—photosynthesis inhibitor) for use in breeding herbicide tolerant cultivars. Screening of 300 diverse chickpea genotypes (278 accessions from the reference set and 22 breeding lines) revealed large genetic variations for tolerance to herbicides imazethapyr and metribuzin. In general, the sensitivity of the genotypes to metribuzin was higher compared to that for imazethapyr. Several genotypes tolerant to metribuzin (ICC 1205, ICC 1164, ICC 1161, ICC 8195, ICC 11498, ICC 9586, ICC 14402 ICC 283) and imazethapyr (ICC 3239, ICC 7867, ICC 1710, ICC 13441, ICC 13461, ICC 13357, ICC 7668, ICC 13187) were identified, based on average herbicide tolerance scores from two experimental locations each. The herbicide tolerant lines identified in this study will be useful resources for development of herbicide tolerant cultivars and for undertaking genetic and physiological studies on herbicide tolerance in chickpea.Not Availabl

Comprehensive Transcriptome Profiling Uncovers Molecular Mechanisms and Potential Candidate Genes Associated with Heat Stress Response in Chickpea

Chickpea (Cicer arietinum L.) production is highly susceptible to heat stress (day/night temperatures above 32/20 °C). Identifying the molecular mechanisms and potential candidate genes underlying heat stress response is important for increasing chickpea productivity. Here, we used an RNA-seq approach to investigate the transcriptome dynamics of 48 samples which include the leaf and root tissues of six contrasting heat stress responsive chickpea genotypes at the vegetative and reproductive stages of plant development. A total of 14,544 unique, differentially expressed genes (DEGs) were identified across different combinations studied. These DEGs were mainly involved in metabolic processes, cell wall remodeling, calcium signaling, and photosynthesis. Pathway analysis revealed the enrichment of metabolic pathways, biosynthesis of secondary metabolites, and plant hormone signal transduction, under heat stress conditions. Furthermore, heat-responsive genes encoding bHLH, ERF, WRKY, and MYB transcription factors were differentially regulated in response to heat stress, and candidate genes underlying the quantitative trait loci (QTLs) for heat tolerance component traits, which showed differential gene expression across tolerant and sensitive genotypes, were identified. Our study provides an important resource for dissecting the role of candidate genes associated with heat stress response and also paves the way for developing climate-resilient chickpea varieties for the future

Transcriptome profiling reveals the expression and regulation of genes associated with Fusarium wilt resistance in chickpea (Cicer arietinum L.)

Abstract Fusarium wilt (FW) is one of the most significant biotic stresses limiting chickpea production worldwide. To dissect the molecular mechanism of FW resistance in chickpea, comparative transcriptome analyses of contrasting resistance sources of chickpea genotypes under control and Fusarium oxysporum f. sp. ciceris (Foc) inoculated conditions were performed. The high‐throughput transcriptome sequencing generated about 1137 million sequencing reads from 24 samples representing two resistant genotypes, two susceptible genotypes, and two near‐isogenic lines under control and stress conditions at two‐time points (7th‐ and 12th‐day post‐inoculation). The analysis identified 5182 differentially expressed genes (DEGs) between different combinations of chickpea genotypes. Functional annotation of these genes indicated their involvement in various biological processes such as defense response, cell wall biogenesis, secondary metabolism, and disease resistance. A significant number (382) of transcription factor encoding genes exhibited differential expression patterns under stress. Further, a considerable number of the identified DEGs (287) co‐localized with previously reported quantitative trait locus for FW resistance. Several resistance/susceptibility‐related genes, such as SERINE/THREONINE PROTEIN KINASE, DIRIGENT, and MLO exhibiting contrasting expression patterns in resistant and susceptible genotypes upon Foc inoculation, were identified. The results presented in the study provide valuable insights into the transcriptional dynamics associated with FW stress response in chickpea and provide candidate genes for the development of disease‐resistant chickpea cultivars

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea

Genomic selection (GS) by selecting lines prior to field phenotyping using genotyping data has the potential to enhance the rate of genetic gains. Genotype × environment (G × E) interaction inclusion in GS models can improve prediction accuracy hence aid in selection of lines across target environments. Phenotypic data on 320 chickpea breeding lines for eight traits for three seasons at two locations were recorded. These lines were genotyped using DArTseq (1.6 K SNPs) and Genotyping-by-Sequencing (GBS; 89 K SNPs). Thirteen models were fitted including main effects of environment and lines, markers, and/or naïve and informed interactions to estimate prediction accuracies. Three cross-validation schemes mimicking real scenarios that breeders might encounter in the fields were considered to assess prediction accuracy of the models (CV2: incomplete field trials or sparse testing; CV1: newly developed lines; and CV0: untested environments). Maximum prediction accuracies for different traits and different models were observed with CV2. DArTseq performed better than GBS and the combined genotyping set (DArTseq and GBS) regardless of the cross validation scheme with most of the main effect marker and interaction models. Improvement of GS models and application of various genotyping platforms are key factors for obtaining accurate and precise prediction accuracies, leading to more precise selection of candidates

A Scintillating Journey of Genomics in Simplifying Complex Traits and Development of Abiotic Stress Resilient Chickpeas

Chickpea (Cicer arietinum L.) is an important cool season food legume cultivated in more than 55 countries across the globe. In the context of climate change, productivity of chickpea is hampered by higher incidence of abiotic and biotic stresses. Among abiotic stresses, drought, heat, cold and salinity are the most important yield limiting factors. Advanced genomics technologies have great potential to accelerate mapping, gene discovery, marker development and genomics-assisted breeding. Integration of precise phenotypic data along with sequence information will help in developing cultivars tolerant to various abiotic stresses. In this chapter, we discuss the impact of various abiotic stresses on chickpea production and provide an update on potential strategies to develop stress-tolerant chickpea cultivars. In addition, we also summarize the systematic efforts of simplifying the complex traits in chickpea as well as development of improved varieties with tolerance to abiotic stresses during last decade. In addition, we also highlight the emerging stresses and future strategies to combat the abiotic stresses