College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, China.
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
Theor Appl Genet. 2024 Jan 11;137(1):18. doi: 10.1007/s00122-023-04522-8.
Eleven QTLs for agronomic traits were identified by RTM- and MLM-GWAS, putative candidate genes were predicted and two markers for grain weight were developed and validated. Foxtail millet (Setaria italica), the second most cultivated millet crop after pearl millet, is an important grain crop in arid regions. Seven agronomic traits of 408 diverse foxtail millet accessions from 15 provinces in China were evaluated in three environments. They were clustered into two divergent groups based on genotypic data using ADMIXTURE, which was highly consistent with their geographical distribution. Two models for genome-wide association studies (GWAS), namely restricted two-stage multi-locus multi-allele (RTM)-GWAS and mixed linear model (MLM)-GWAS, were used to dissect the genetic architecture of the agronomic traits based on 13,723 SNPs. Eleven quantitative trait loci (QTLs) for seven traits were identified using two models (RTM- and MLM-GWAS). Among them, five were considered stable QTLs that were identified in at least two environments using MLM-GWAS. One putative candidate gene (SETIT_006045mg, Chr4: 744,701-746,852) that can enhance grain weight per panicle was identified based on homologous gene comparison and gene expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10×) resequencing data (unpublished). In addition, homologous gene comparison and haplotype analysis identified one putative foxtail millet ortholog (SETIT_032906mg, Chr2: 5,020,600-5,029,771) with rice affecting the target traits. Two markers (cGWP6045 and kTGW2906) were developed and validated and can be used for marker-assisted selection of foxtail millet with high grain weight. The results provide a fundamental resource for foxtail millet genetic research and breeding and demonstrate the power of integrating RTM- and MLM-GWAS approaches as a complementary strategy for investigating complex traits in foxtail millet.
利用 RTM- 和 MLM-GWAS 鉴定了 11 个农艺性状的数量性状位点(QTL),预测了潜在的候选基因,并开发和验证了两个用于粒重的标记。黍稷(Setaria italica)是继珍珠粟之后第二大栽培小米作物,是干旱地区的重要粮食作物。在中国 15 个省的 408 个不同黍稷品种中,对 7 个农艺性状进行了三个环境的评估。根据基因型数据,利用 ADMIXTURE 将它们分为两个不同的组,这与它们的地理分布高度一致。使用两种全基因组关联研究(GWAS)模型,即限制两阶段多基因多等位基因(RTM)-GWAS 和混合线性模型(MLM)-GWAS,基于 13723 个 SNPs 解析农艺性状的遗传结构。使用两种模型(RTM-和 MLM-GWAS)鉴定了 7 个性状的 11 个数量性状位点(QTL)。其中,5 个被认为是稳定 QTL,这些 QTL 使用 MLM-GWAS 在至少两个环境中被鉴定出来。根据同源基因比较和基因表达分析,确定了一个可以提高每穗粒重的推定候选基因(SETIT_006045mg,Chr4:744701-746852),并通过对具有高深度(10×)重测序数据的 330 个品种进行单倍型分析进行了验证(未发表)。此外,通过同源基因比较和单倍型分析,鉴定了一个与水稻影响目标性状的黍稷同源基因(SETIT_032906mg,Chr2:5020600-5029771)。开发和验证了两个标记(cGWP6045 和 kTGW2906),可用于辅助选择具有高粒重的黍稷。这些结果为黍稷遗传研究和育种提供了基础资源,并证明了整合 RTM-和 MLM-GWAS 方法作为一种互补策略来研究黍稷复杂性状的有效性。