Department of Plant Biotechnology, Korea University, Seoul, 02841, South Korea.
Department of Biotechnology, Korea University, Seoul, 02841, South Korea.
BMC Plant Biol. 2021 Sep 13;21(1):418. doi: 10.1186/s12870-021-03180-6.
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.
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.
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.
小麦（Triticum aestivum L.）是最广泛食用的谷物作物之一，但由于其基因组复杂，研究其对重要农艺性状的遗传效应较为困难。全基因组关联（GWA）分析是一种用于鉴定控制复杂表型性状的遗传位点的有用方法。通过基于 RNA-seq 的基因表达分析，可以提出控制重要农艺性状的假定候选基因，并开发分子标记。
我们观察了来自不同国家起源的 287 个小麦品种的主要数量农艺性状，包括冬季存活率（WSR）、抽穗期（DTH）、成熟期（DTM）、茎长（SL）、穗长（SPL）、芒长（AL）、千粒重（LW）、千粒重（TKW）和每穗粒数（SPS）。观察到的性状之间存在显著相关性，根据群体结构分析将小麦基因型分为三个亚群。预测了不同环境下每个性状的基因型效应的最佳线性无偏预测值（BLUP），并基于混合线性模型（MLM）进行了 GWA 分析。共鉴定出 254 个高度显著的标记-性状关联（MTA），并通过搜索小麦参考基因组和 RNAseq 数据预测了与显著标记紧密相关的 28 个候选基因。此外，结果表明，表型性状受有利或不利等位基因积累的显著影响。
从这项研究中，新鉴定的 MTA 和假定的农艺有用基因将有助于研究每个表型性状的分子机制。此外，本研究中发现的农艺有利等位基因可用于培育具有优良农艺性状的小麦。
