GWAS in a Collection of Bulgarian Old and Modern Bread Wheat Accessions Uncovers Novel Genomic Loci for Grain Protein Content and Thousand Kernel Weight

Abstract

Genetic enhancement of grain production and quality is a priority in wheat breeding projects. In this study, we assessed two key agronomic traits-grain protein content (GPC) and thousand kernel weight (TKW)-across 179 Bulgarian contemporary and historic varieties and landraces across three growing seasons. Significant phenotypic variation existed for both traits among genotypes and seasons, and no discernible difference was evident between the old and modern accessions. To understand the genetic basis of the traits, we conducted a genome-wide association study with MLM using phenotypic data from the crop seasons, best linear unbiased estimators, and genotypic data from the 25K Infinium iSelect array. As a result, we detected 16 quantitative trait nucleotides (QTNs) associated with GPC and 15 associated with TKW, all of which passed the false discovery rate threshold. Seven loci favorably influenced GPC, resulting in an increase of 1.4% to 8.1%, while four loci had a positive impact on TKW with increases ranging from 1.9% to 8.4%. While some loci confirmed previously published associations, four QTNs linked to GPC on chromosomes 2A, 7A, and 7B, as well as two QTNs related to TKW on chromosomes 1B and 6A, may represent novel associations. Annotations for proteins involved in the senescence-associated nutrient remobilization and in the following buildup of resources required for seed germination have been found for selected putative candidate genes. These include genes coding for storage proteins, cysteine proteases, cellulose-synthase, alpha-amylase, transcriptional regulators, and F-box and RWP-RK family proteins. Our findings highlight promising genomic regions for targeted breeding programs aimed at improving grain yield and protein content.

Keywords: TKW; Triticum aestivum L.; association mapping; candidate genes; grain protein content; grain quality; grain yield.

Figure 1

Probability density and box plots for (a,c) grain protein content (GPC) and (b,d) thousand kernel weight (TKW) across three growing seasons and based on best linear unbiased estimators (BLUEs) in a set of 179 Bulgarian bread wheat accessions. *** is the significant differences between means at p < 0.001.

Figure 2

Box plots for (a) grain protein content (GPC, in %) and (b) thousand kernel weight (TKW, in g) across three growing seasons and based on the best linear unbiased estimators (BLUEs) in the three subpopulations (SPs) distinguished by the Q-matrix [32] within a population of 179 Bulgarian bread wheat accessions. *, *** are the significant differences between means at p < 0.05 and 0.001, respectively.

Figure 3

Proportion of accessions (a–c) from the three subpopulations distinguished by the Q-matrix [32] and (d) within the entire population of 179 Bulgarian bread wheat accessions with respect to grain protein content (GPC); classification is according to [2].

Figure 4

Manhattan plots of the genome-wide association scan for (a) grain protein content (GPC) and (b) thousand kernel weight (TKW) based on the MLM (Q + K) model and the phenotypic data from three growing seasons, along with the calculated BLUE values in a set of 179 Bulgarian bread wheat accessions. The blue color line depicts the threshold of –log₁₀ (p) = FDR, determined for each trait and environment. The significant quantitative trait nucleotides (QTNs) are above the blue line. QTNs with positive additive effects on the traits are marked with red color triangles.