Genetic dissection of marker trait associations for grain micro-nutrients and thousand grain weight under heat and drought stress conditions in wheat

Abstract

IntroductionWheat is grown and consumed worldwide, making it an important staple food crop for both its calorific and nutritional content. In places where wheat is used as a staple food, suboptimal micronutrient content levels, especially of grain iron (Fe) and zinc (Zn), can lead to malnutrition. Grain nutrient content is influenced by abiotic stresses, such as drought and heat stress. The best method for addressing micronutrient deficiencies is the biofortification of food crops. The prerequisites for marker-assisted varietal development are the identification of the genomic region responsible for high grain iron and zinc contents and an understanding of their genetics.MethodsA total of 193 diverse wheat genotypes were evaluated under drought and heat stress conditions across the years at the Indian Agricultural Research Institute (IARI), New Delhi, under timely sown irrigated (IR), restricted irrigated (RI) and late sown (LS) conditions. Grain iron content (GFeC) and grain zinc content (GZnC) were estimated from both the control and treatment groups. Genotyping of all the lines under study was carried out with the single nucleotide polymorphisms (SNPs) from Breeder’s 35K Axiom Array.Result and DiscussionThree subgroups were observed in the association panel based on both principal component analysis (PCA) and dendrogram analysis. A large whole-genome linkage disequilibrium (LD) block size of 3.49 Mb was observed. A genome-wide association study identified 16 unique stringent marker trait associations for GFeC, GZnC, and 1000-grain weight (TGW). In silico analysis demonstrated the presence of 28 potential candidate genes in the flanking region of 16 linked SNPs, such as synaptotagmin-like mitochondrial-lipid-binding domain, HAUS augmin-like complex, di-copper center-containing domain, protein kinase, chaperonin Cpn60, zinc finger, NUDIX hydrolase, etc. Expression levels of these genes in vegetative tissues and grain were also found. Utilization of identified markers in marker-assisted breeding may lead to the rapid development of biofortified wheat genotypes to combat malnutrition.

Unravelling the Genetic Basis of Moisture Deficit Stress Tolerance in Wheat for Seedling Vigour-Related Traits and Root Traits Using Genome-Wide Association Study

“…The significant LD block size for the complete genome in the current investigation is 3.49 Mb. The LDs for the subgenomes for the A, B, and D genomes were determined to be 2.48 Mb, 4.29 Mb, and 3.82 Mb, respectively [28]. In agreement with this, Pang et al [52] reported a huge LD block size of 4.4 Mb.…”

“…Seeds harvested from field experiments conducted during the 2020-2021 and 2021-2022 Rabi seasons at the Indian Council of Agricultural Research (ICAR) Institute in New Delhi (28.6550 • N latitude, 77.1888 • E longitude, elevation 228.61 m) were utilised in our laboratory studies, representing the two distinct seasons. More information about the origins (Supplementary Table S1) and population structure has been published by Devate et al [28].…”

“…All the core set lines are clustered together as one group and the varieties of Indian origin were grouped in another cluster in the GWAS panel [28]. Linkage disequilibrium (LD) decay across genetic distance in a population determines the density of marker coverage required to conduct GWAS.…”

“…The SNPs were filtered, and monomorphic markers and markers with a minor allele frequency (MAF) of 5%, missing data of more than 10%, and heterozygote frequency greater than 50% were eliminated from the study. The remaining 13,947 SNPs were analysed further, as used by Devate et al [28].…”

“…Several studies have explored quantitative traits linked to moisture deficit stress tolerance in wheat during its productive phase. For instance, QTL investigations in wheat have delved into the genetic factors governing yield and related traits like kernel weight, grain count per spike, grain yield, grain size, spike length, and spike shape [26][27][28]. The genetic basis underlying complex traits was deciphered using an effective method, GWAS.…”