Genetic dissection of grain architecture-related traits in a winter wheat population

Abstract

Background The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated. Results Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767–602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture. Conclusions These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.

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

“…A bigger kernel positively affects wheat kernel weight, yield and commercial value [40,53]. Kernel size-related traits influence wheat yield by regulating TKW, and both are associated with high heritability [16,[54][55][56][57][58][59].…”

InDels Identification and Association Analysis with Spike and Awn Length in Chinese Wheat Mini-Core Collection

“…The phenotypic data of spike length conformed to a normal distribution ( Figure 4 A). A total of 87 significant ( p -value < 1.0 × 10 − 4 ) InDels were identified to be associated with spike length ( Figure 4 B) [ 35 ]. Quantile-quantile (QQ) plot of the data showed an acceptable separation of the observed from the expected ( Figure 4 C).…”

Nested association mapping-based GWAS for grain yield and related traits in wheat grown under diverse Australian environments

“…The nature of the lack of GEI for TGW and the stability of TGW across different environments and years can be attributed to the fact that TGW is under strong genetic control (Zanke et al 2015). High heritability estimates and major stable QTL have been reported for TGW (Schierenbeck et al 2021;Yang et al 2020).…”