Genome-wide association mapping of nutritional traits for designing superior chickpea varieties

Abstract

Micronutrient malnutrition is a serious concern in many parts of the world; therefore, enhancing crop nutrient content is an important challenge. Chickpea (Cicer arietinum L.), a major food legume crop worldwide, is a vital source of protein and minerals in the vegetarian diet. This study evaluated a diverse set of 258 chickpea germplasm accessions for 12 key nutritional traits. A significant variation was observed for several nutritional traits, including crude protein (16.56–24.64/100 g), β-Carotene (0.003–0.104 mg/100 g), calcium (60.69–176.55 mg/100 g), and folate (0.413–6.537 mg/kg). These data, combined with the available whole-genome sequencing data for 318,644 SNPs, were used in genome-wide association studies comprising single-locus and multi-locus models. We also explored the effect of varying the minor allele frequency (MAF) levels and heterozygosity. We identified 62 significant marker-trait associations (MTAs) explaining up to 28.63% of the phenotypic variance (PV), of which nine were localized within genes regulating G protein-coupled receptor signaling pathway, proteasome assembly, intracellular signal transduction, and oxidation–reduction process, among others. The significant effect MTAs were located primarily on Ca1, Ca3, Ca4, and Ca6. Importantly, varying the level of heterozygosity was found to significantly affect the detection of associations contributing to traits of interest. We further identified seven promising accessions (ICC10399, ICC1392, ICC1710, ICC2263, ICC1431, ICC4182, and ICC16915) with superior agronomic performance and high nutritional content as potential donors for developing nutrient-rich, high-yielding chickpea varieties. Validation of the significant MTAs with higher PV could identify factors controlling the nutrient acquisition and facilitate the design of biofortified chickpeas for the future.

Disentangling potential genotypes for macro and micro nutrients and polymorphic markers in Chickpea

“…This process is necessary for the development and expansion of chickpea plants as well as for the production of highquality protein for human consumption. Despite having varied degrees of relatedness, the accessions do not cluster according to their biological components 16 . Hence, we investigated the micronutrients nutritional profiles and their association with EST-SSR markes through modulating the plymorphism analysis, minor allele frequency and heterozygosity level.…”

Fatty acid composition and genome-wide associations of a chickpea (Cicer arietinum L.) diversity panel for biofortification efforts

“…Numerous GWAS have been conducted in chickpea to identify SNPs significantly associated with agronomic traits 48 – 51 , stress tolerance 52 , 53 , and nutritional traits 20 , 50 , 54 , 55 . However, no GWAS have been reported for fatty acids in chickpea to date.…”

“…3 a), which follows previous studies in chickpea 67 . However, some studies reported two or three subpopulations in chickpea 20 , 50 , 54 , 55 . These seven subpopulations represent the diverse ancestral background of chickpea.…”

Genetic and genomic interventions in crop biofortification: Examples in millets

“…Radical developments in genomics techniques have led to the development of various marker systems including mapped microsatellite or simple sequence repeat (SSR) markers, SNPs markers, insertion–deletion (InDel) markers, and more recently haplotype-based SNP markers. Linkage mapping and association mapping are the two most used tools for dissecting the genetics of complex nutritional traits in plants ( Roorkiwal et al., 2022 ). Traditional linkage mapping/QTL mapping explores the recombination events and marker–trait associations (MTAs) in bi-parental segregating populations, such as F 2 , doubled haploid (DH), and recombinant inbred lines (RILs).…”

“…This method is very powerful in capturing major genes with larger effects and rare alleles ( Kumar et al., 2016 ). On the other hand, GWAS explores functional variations within genetically diverse panels through linkage disequilibrium (LD) analysis, which is very efficient and effective for identifying new genomic regions ( Yu and Buckler, 2006 ; Roorkiwal et al., 2022 ). Here, we highlight some key genomics efforts undertaken in millets to identify genomic regions associated with grain nutrient content ( Table 2 ).…”