Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Triticum aestivum L.)

Abstract

In wheat, a multi-locus genome-wide association study (ML-GWAS) was conducted for the four grain weight-related traits (days to anthesis, grain filling duration, grain number per ear, and grain weight per ear) using data recorded under irrigated (IR) and rain-fed (RF) conditions. Seven stress-related indices were estimated for these four traits: (i) drought resistance index (DI), (ii) geometric mean productivity (GMP), (iii) mean productivity index (MPI), (iv) relative drought index (RDI), (v) stress tolerance index (STI), (vi) yield index, and (vii) yield stability index (YSI). The association panel consisted of a core collection of 320 spring wheat accessions representing 28 countries. The panel was genotyped using 9,627 single nucleotide polymorphisms (SNPs). The genome-wide association (GWA) analysis provided 30 significant marker-trait associations (MTAs), distributed as follows: (i) IR (15 MTAs), (ii) RF (14 MTAs), and (iii) IR+RF (1 MTA). In addition, 153 MTAs were available for the seven stress-related indices. Five MTAs co-localized with previously reported QTLs/MTAs. Candidate genes (CGs) associated with different MTAs were also worked out. Gene ontology (GO) analysis and expression analysis together allowed the selection of the two CGs, which may be involved in response to drought stress. These two CGs included: TraesCS1A02G331000 encoding RNA helicase and TraesCS4B02G051200 encoding microtubule-associated protein 65. The results supplemented the current knowledge on genetics for drought tolerance in wheat. The results may also be used for future wheat breeding programs to develop drought-tolerant wheat cultivars.

Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study

“…Therefore, TKW is one of the important breeding objectives due to its twin effects on yield and protein. The MTAs have been identified for TKW 48 – 51 , 91 using different compositions of GWAS panels. Therefore, more GWAS studies would be helpful to identify the genomic regions governing nutritional traits in wheat and also to identify the candidate genes to develop biofortified cultivars.…”

Dissecting the Genetic Architecture of Phenology Affecting Adaptation of Spring Bread Wheat Genotypes to the Major Wheat-Producing Zones in India

“…They have been used successfully to dissect several complex traits in wheat ( Breseghello and Sorrells, 2006 ; Crossa et al, 2007 ; Yu et al, 2011 ; Maccaferri et al, 2015 ; Sukumaran et al, 2015 ; Juliana et al, 2019 , 2021 ). While few GWASs in wheat have identified genomic regions associated with phenological traits including heading and maturity ( Zhang et al, 2018 ; Gahlaut et al, 2021 ; Muhammad et al, 2021 ), a comprehensive study to dissect the genetic architecture of these traits in multiple wheat production zones has not been reported. A total of 43 SNPs (single-nucleotide polymorphisms) were consistently detected, including seven across multiple environments by ML-GWAS ( Muhammad et al, 2021 ).…”

“…A total of 43 SNPs (single-nucleotide polymorphisms) were consistently detected, including seven across multiple environments by ML-GWAS ( Muhammad et al, 2021 ). Nine significant marker–trait associates were identified for days to anthesis under drought stress by Gahlaut et al (2021) . Hence, the main objective of this study was to use GWAS to identify consistently significant marker–trait associations for heading and maturity, affecting the adaptation of spring bread wheat to three major zones of India where wheat is cultivated in about 25 mha under diverse environmental and management conditions.…”

Identification of quantitative trait loci (QTL) and meta-QTL analysis for kernel size-related traits in wheat (Triticum aestivum L.)

“…These are complex quantitative traits controlled by polygenes [9][10][11] and are strongly in uenced by both genotypic and environmental factors [12]. In the last two decades, a large number of QTLs underlying wheat kernel size-related traits have been successfully identi ed by traditional bi-parental linkage mapping [7,[9][10][11][13][14][15][16][17] and genome-wide association studies (GWAS) [18][19][20][21][22][23]. However, due to the redundancy of functional genes in three sub-genomes A, B, and D of wheat and the highly repetitive nature of this genome, identifying stable and robust QTLs for kernel size-related and yield traits remains challenging [24,25].…”