Genome-wide generation and genotyping of informative SNPs to scan molecular signatures for seed yield in chickpea

Abstract

We discovered 2150 desi and 2199 kabuli accessions-derived SNPs by cultivar-wise individual assembling of sequence-reads generated through genotyping-by-sequencing of 92 chickpea accessions. Subsequent large-scale validation and genotyping of these SNPs discovered 619 desi accessions-derived (DAD) SNPs, 531 kabuli accessions-derived (KAD) SNPs, 884 multiple accessions-derived (MAD) SNPs and 1083 two accessions (desi ICC 4958 and kabuli CDC Frontier)-derived (TAD) SNPs that were mapped on eight chromosomes. These informative SNPs were annotated in coding/non-coding regulatory sequence components of genes. The MAD-SNPs were efficient to detect high intra-specific polymorphic potential and wide natural allelic diversity level including high-resolution admixed-population genetic structure and precise phylogenetic relationship among 291 desi and kabuli accessions. This signifies their effectiveness in introgression breeding and varietal improvement studies targeting useful agronomic traits of chickpea. Six trait-associated genes with SNPs including quantitative trait nucleotides (QTNs) in combination explained 27.5% phenotypic variation for seed yield per plant (SYP). A pentatricopeptide repeat (PPR) gene with a synonymous-coding SNP/QTN significantly associated with SYP trait was found most-promising in chickpea. The essential information delineated can be of immense utility in genomics-assisted breeding applications to develop high-yielding chickpea cultivars.

Whole Genome Diversity, Population Structure, and Linkage Disequilibrium Analysis of Chickpea (Cicer arietinum L.) Genotypes Using Genome-Wide DArTseq-Based SNP Markers

“…Marker-assisted breeding in chickpea is usually hindered due to low genetic diversity and low intra-specific polymorphism among Desi and Kabuli chickpea geneotypes. Therefore, development and implementation of large-scale informative markers like SNPs assist breeders in differentiating the chickpea germplasm at a genome-wide scale [21]. DArTseq-SNP markers conducted by GBS technology is a rapid, low cost, and efficient method for genotyping, providing a broad genome coverage, and therefore has been increasingly were used in different plants species [23,24,25], as well as in chickpea [26].…”

“…SNP markers are mainly developed based on next generation sequencing technology. Fast development of SNP markers through genotyping-by-sequencing (GBS) has paved the road to facilitating genomics-assisted breeding through quantitative trait loci (QTL) and genome-wide association analysis in diverse crops [20,21]. Recently, diversity array technology (DArT) developed a GBS method called “DArTseq” for genotyping with high-density SNP in different crop species such as wheat [22], common bean [23], sesame [8], tomato [24], snake melon [25], and chickpea [26].…”

Is Chickpea a Potential Substitute for Soybean? Phenolic Bioactives and Potential Health Benefits

“…Changes of the isoflavones content upon germination has very likely a genetic regulation explanation, although no QTLs/genes associated with this phenomenon have been reported so far. Most of the genetic studies of chickpea have focused on the identification of genetic regions using different methodologies associated to improving agronomic traits, such as increasing seed yield and the yield of components [95,96,97,98,99,100,101,102,103]. In addition, a genetic approach has been used to develop drought/heat tolerance [70,104,105,106,107,108], as well as disease resistance, for example, Ascochyta blight [109,110].…”

The Future of Digital Sequence Information for Plant Genetic Resources for Food and Agriculture

“…Pan-genomes, genomes, transcriptomes, metabolomics, and phenotypic data can be used in genome-wide association studies (GWAS) to identify relevant traits, being a single allelic variant that is linked to an agronomic trait or to gene networks (Lipka et al, 2015; Halewood et al, 2018b). Genotyping-by-sequencing and GWAS frameworks were applied based on genome variability in a diverse array of crops like maize (Yano et al, 2016), sorghum (Morris et al, 2013), pearl millet (Varshney et al, 2017), chickpea (Basu et al, 2018), peanut (Zhang et al, 2017), banana (Sardos et al, 2016), cassava (Kayondo et al, 2018; Zhang et al, 2018), and cowpea (Burridge et al, 2017). Metabolite-based GWAS was also reported as an important tool to improve genomics-assisted selection for crop improvement (Fernie and Schauer, 2009; Luo, 2015).…”