GWAS Analysis of Wheat Pre-breeding Germplasm for Terminal Drought Stress Using Next Generation Sequencing Technology

Abstract

Bread wheat (Triticum aestivum L.) is one of the most important cereal crops for food security. Of all the stresses that curtail wheat productivity, drought has the most detrimental effects. Especially terminal drought stress i.e. at the time of flowering imposes a big challenge to sustain grain production. In the current study, 339 pre-breeding lines derived from three-way crosses of exotics x elite lines were evaluated in the irrigated and drought stress environments at Obregon, Mexico for the year 2016 and 2018. Drought significantly reduced yield (Y), spike length (SL), number of grains per spikes (NGS) and thousand kernel weight (TKW) by 46.4, 19.2, 23.5 and 25.9%, respectively in comparison to irrigated conditions. Kernel abortion (KA), highly correlated with Y, increased significantly (11.6%) under drought stress environment. Population structure analysis in this panel revealed three sub-populations and a genome wide linkage disequilibrium (LD) decay was at 2.5 cM. Single marker and haplotypes-based genome wide association study (GWAS) revealed significant associations on three chromosomes; 4A (HB10.7), 2D (HB6.10) and 3B (HB8.12) with Y, SL and TKW, respectively. Likewise, associations on chromosomes 6B (HB17.1) and 3A (HB7.11) were identified for NGS and on 3A (HB7.12) for KA. Five traits i.e. normalized difference vegetation index (NDVI), canopy temperature depression (CTD) days to heading (DTH), NGS, KA were associated at chromosome 3A both under irrigated and drought conditions however, different haplotypes were estimated. Twenty-six SNPs were part of 10 haplotype blocks associated with Y, SL, TKW, NGS and KA. In silico analysis of the associated SNPs/haplotypes showed hits with candidate genes known to confer abiotic stress resistance in model species and crops. Potential candidate genes include those coding for sulfite exporter TauE/SafE family in Arabidopsis thaliana, TBC domain containing protein in Oryza sativa subsp. Japonica and heat shock proteins in Aegilops tauschii subsp. tauschii were revealed. The SNPs linked to the promising genes identified in the study can be used for marker-assisted selection.

Haplotype-Based, Genome-Wide Association Study Reveals Stable Genomic Regions for Grain Yield in CIMMYT Spring Bread Wheat

“…We constructed haplotypes using an LD-based approach and conducted a haplotype-based GWAS and epistatic scan to dissect the genetic architecture of GY under contrasting sets of environments and across seven EYTs. The total number of genome-wide haplotype blocks obtained was in a similar range as reported in the recent studies using same marker platform (Singh et al, 2018;Ledesma-Ramírez et al, 2019;Shokat et al, 2020). Li et al (2019) used a much higher density of markers from two platforms (wheat 90K and 660K Illumina SNP arrays) and thus were able to obtain much higher numbers of haplotype blocks per chromosome and across the genome.…”

“…Although high-density markers, such as genotyping-bysequencing (GBS) or SNP arrays, have been used extensively in wheat to explore the genetic architecture of GY and yield components using GWAS (Neumann et al, 2011;Zhang et al, 2013;Edae et al, 2014;Ain et al, 2015;Azadi et al, 2015;Lopes et al, 2015;Sukumaran et al, 2015;Sehgal et al, 2016;Qaseem et al, 2018;Garcia et al, 2019;Li et al, 2019Li et al, , 2020Ward et al, 2019;Shokat et al, 2020), panel sizes have been relatively small to dissect such a complex trait, and results therefore were quite variable, identifying hundreds of small-effect QTL. GWAS reports in larger germplasm panels are still rare (Sehgal et al, 2017(Sehgal et al, , 2020Juliana et al, 2019).…”

“…As specific sets of alleles observed on a single chromosome, haplotypes are inherited together with little chance of contemporary recombination. Recent studies on wheat and other crops have shown that GWAS analysis with haplotypes can be superior to single marker-based analysis in terms of statistical significance (better p-values) and in estimating allelic effects (Hao et al, 2012;Lu et al, 2012;N'Diaye et al, 2017;Ledesma-Ramírez et al, 2019;Li et al, 2019;Sehgal et al, 2020;Shokat et al, 2020).…”

Identification of novel leaf rust seedling resistance loci in Iranian bread wheat germplasm using genome-wide association mapping

“…The GWAS method has been successfully applied in different plants for various traits. Different wheat traits have been studied using GWAS including agronomic traits (Safdar et al 2020; Pang et al 2020), quality (Yang et al 2020; Muqaddasi et al 2020), drought stress (Abou-Elwafa et al 2021; Shokat et al 2020; Rahimi et al 2019), leaf rust (Spakota et al 2019; Muqaddasi et al 2021), and stem rust resistance (Saremi et al 2021; Gao et al 2017). For leaf rust resistance, Spakota et al (2019) employed GWAS to identify related genomic areas in wheat genotypes, and eleven QTLs (Quantitative Trait Loci) were identified on nine chromosomes.…”

“…Using the structure software, the population of 286 accessions was structured into three 245 subpopulations, Sub1, Sub2, and Sub_3 (Figure 2). Sub_1 included 84 To better evaluate population structure and investigate genetic relationships among wheat accessions, PCA of original and imputed SNPs was performed in 286 wheat accessions. For the original datasets, the two major components described a total of 18.59% of the genetic variance (Figure 3a), whereas it was 23.1% for the imputed datasets (Figure 3b).…”