Identification of genetic loci associated with major agronomic traits of wheat (Triticum aestivum L.) based on genome-wide association analysis

Abstract

Background Bread wheat (Triticum aestivum L.) is one of the most widely consumed cereal crops, but its complex genome makes it difficult to investigate the genetic effect on important agronomic traits. Genome-wide association (GWA) analysis is a useful method to identify genetic loci controlling complex phenotypic traits. With the RNA-sequencing based gene expression analysis, putative candidate genes governing important agronomic trait can be suggested and also molecular markers can be developed. Results We observed major quantitative agronomic traits of wheat; the winter survival rate (WSR), days to heading (DTH), days to maturity (DTM), stem length (SL), spike length (SPL), awn length (AL), liter weight (LW), thousand kernel weight (TKW), and the number of seeds per spike (SPS), of 287 wheat accessions from diverse country origins. A significant correlation was observed between the observed traits, and the wheat genotypes were divided into three subpopulations according to the population structure analysis. The best linear unbiased prediction (BLUP) values of the genotypic effect for each trait under different environments were predicted, and these were used for GWA analysis based on a mixed linear model (MLM). A total of 254 highly significant marker-trait associations (MTAs) were identified, and 28 candidate genes closely located to the significant markers were predicted by searching the wheat reference genome and RNAseq data. Further, it was shown that the phenotypic traits were significantly affected by the accumulation of favorable or unfavorable alleles. Conclusions From this study, newly identified MTA and putative agronomically useful genes will help to study molecular mechanism of each phenotypic trait. Further, the agronomically favorable alleles found in this study can be used to develop wheats with superior agronomic traits.

Genome-wide association study of coleoptile length with Shanxi wheat

“…DNA was sent to the MOL-BREEDING Company (Shijiazhuang, China) for high-throughput genotyping using wheat GenoBaits technology (14868 mSNP, 37669 SNP). After eliminating markers with an allele frequency (MAF) < 0.05, > 10% missing data, or > 20% heterozygosity ( Arora et al, 2017 ; Jung et al, 2021 ), a total of 9,793 high-quality SNPs were identified for GWAS analyses ( Zheng et al, 2021 ).…”

Molecular characterization of wheat floret development‐related F‐box protein (TaF‐box2): Possible involvement in regulation of Arabidopsis flowering

“…In our previous study (Jung et al, 2021), we used GWAS and RNAseq analysis and proposed that TaF‐box2 may be related to the flowering time of wheat. Increased expression of TaF‐box2 was observed until the late stage of floret development in wheat (Figure 1).…”

“…In our previous study, we observed that several F-box protein-coding genes were associated with spike maturity (Jung et al, 2021). Among them, TraesCS6A01G407500 (TaF-box2) showed particularly high gene expression in flowering-related tissues such as peduncle and spike.…”

Effects of genetic components of plant development on yield-related traits in wheat (Triticum aestivum L.) under stress-free conditions

“…The appropriateness of the model used for association analysis in the present study was tested by drawing a QQ (quantile-quantile) plot between expected and observed -log10(P) values. The significance threshold was set by an application of an overall cut-off significance level of −log10 (P-value) ≥ 3.0, where one false positive was expected within 1,000 events ( Zanke et al., 2015 ; Jung et al., 2021 ). The QQ- and circular-Manhattan plots were drawn by the CMplot package in R ( Yin et al., 2021 ).…”

“…None of these megaenvironments, however, is homogenous, but they represent rather rich combinations of various environmental factors ( Benaouda et al., 2022 ). The meteorological and climatic parameters of the location–especially the maximum temperatures in spring–play the most decisive roles in influencing plant development ( Jung et al., 2021 ; Benaouda et al., 2022 ). Thus, in addition to the two basic features, the determination of plant developmental patterns includes the sets of interconnected earliness per se genes that together ensure the fine-tuning of ecological adaptation ( Cockram et al., 2007 ; Griffiths et al., 2009 ; Dreisigacker et al., 2021 ).…”