Zero hunger and good health could be realized by 2030 through effective conservation, characterization and utilization of germplasm resources1. So far, few chickpea (Cicerarietinum) germplasm accessions have been characterized at the genome sequence level2. Here we present a detailed map of variation in 3,171 cultivated and 195 wild accessions to provide publicly available resources for chickpea genomics research and breeding. We constructed a chickpea pan-genome to describe genomic diversity across cultivated chickpea and its wild progenitor accessions. A divergence tree using genes present in around 80% of individuals in one species allowed us to estimate the divergence of Cicer over the last 21 million years. Our analysis found chromosomal segments and genes that show signatures of selection during domestication, migration and improvement. The chromosomal locations of deleterious mutations responsible for limited genetic diversity and decreased fitness were identified in elite germplasm. We identified superior haplotypes for improvement-related traits in landraces that can be introgressed into elite breeding lines through haplotype-based breeding, and found targets for purging deleterious alleles through genomics-assisted breeding and/or gene editing. Finally, we propose three crop breeding strategies based on genomic prediction to enhance crop productivity for 16 traits while avoiding the erosion of genetic diversity through optimal contribution selection (OCS)-based pre-breeding. The predicted performance for 100-seed weight, an important yield-related trait, increased by up to 23% and 12% with OCS- and haplotype-based genomic approaches, respectively.

Abstract.Terminal drought is one of the major constraints in chickpea (Cicer arietinum L.), causing more than 50% production losses. With the objective of accelerating genetic understanding and crop improvement through genomicsassisted breeding, a draft genome sequence has been assembled for the CDC Frontier variety. In this context, 544.73 Mb of sequence data were assembled, capturing of 73.8% of the genome in scaffolds. In addition, large-scale genomic resources including several thousand simple sequence repeats and several million single nucleotide polymorphisms, high-density diversity array technology (15 360 clones) and Illumina GoldenGate assay genotyping platforms, high-density genetic maps and transcriptome assemblies have been developed. In parallel, by using linkage mapping approach, one genomic region harbouring quantitative trait loci for several drought tolerance traits has been identified and successfully introgressed in three leading chickpea varieties (e.g. JG 11, Chefe, KAK 2) by using a marker-assisted backcrossing approach. A multilocation evaluation of these marker-assisted backcrossing lines provided several lines with 10-24% higher yield than the respective recurrent parents.Modern breeding approaches like marker-assisted recurrent selection and genomic selection are being deployed for enhancing drought tolerance in chickpea. Some novel mapping populations such as multiparent advanced generation intercross and nested association mapping populations are also being developed for trait mapping at higher resolution, as well as for enhancing the genetic base of chickpea. Such advances in genomics and genomics-assisted breeding will accelerate precision and efficiency in breeding for stress tolerance in chickpea.

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 QTLhotspot 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 BC 3 F 3 in Pusa 372; 20 BC 2 F 3 in Pusa 362, and 21 BC 3 F 3 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 SubCommittees 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

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®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.

Unravelling the genetic architecture underlying yield components and agronomic traits is important for enhancing crop productivity. Here, a recombinant inbred line (RIL) population, developed from ICC 4958 and DCP 92–3 cross, was used for constructing linkage map and QTL mapping analysis. The RIL population was genotyped using a high-throughput Axiom®CicerSNP array, which enabled the development of a high-density genetic map consisting of 3,818 SNP markers and spanning a distance of 1064.14 cM. Analysis of phenotyping data for yield, yield components and agronomic traits measured across three years together with genetic mapping data led to the identification of 10 major-effect QTLs and six minor-effect QTLs explaining up to 59.70% phenotypic variance. The major-effect QTLs identified for 100-seed weight, and plant height possessed key genes, such as C3HC4 RING finger protein, pentatricopeptide repeat (PPR) protein, sugar transporter, leucine zipper protein and NADH dehydrogenase, amongst others. The gene ontology studies highlighted the role of these genes in regulating seed weight and plant height in crop plants. The identified genomic regions for yield, yield components, and agronomic traits, and the closely linked markers will help advance genetics research and breeding programs in chickpea.

Rice exhibits relatively high glycemic index (GI) value compared to other carbohydrate rich foods. Eating rice as a staple food and leading sedentary life style may lead to obesity and type‐II diabetes. National Rice Research Institute (ICAR‐NRRI), Cuttack, India released varieties were analyzed for low GI and high resistant starch (RS) content. Large variations were observed in the GI (57.5–76.4) and RS content (0.28–2.94%) among the varieties. The rice Shaktiman had the lowest GI (57.50) with relatively high RS content (2.11%) while Gayatri had the highest RS (2.94%) with relatively low GI (60.31) value. Addition of pigeon pea to rice resulted in lowering of GI and increased RS content compared to other pulses. Addition of ghee (clarified butter) to rice during cooking resulted in lowering of GI and increase in RS content. The effect was not so pronounced with vegetable oils. Addition of vegetables like fenugreek and cauliflower to rice caused much more lowering of GI as compared to other vegetables. The findings emphasize the importance of identifying and developing rice with low GI and high RS content. This also highlights the importance of formulating food combinations of rice with other edible commodities that suit diabetics.

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