Efficient identification of QTL for agronomic traits in foxtail millet (Setaria italica) using RTM- and MLM-GWAS

文献类型: 外文期刊

第一作者: Dai, Keli

作者: Dai, Keli;Wang, Xin;Liu, Hanxiao;Qiao, Pengfei;Wang, Jiaxue;Shi, Weiping;Guo, Jie;Diao, Xianmin

作者机构:

期刊名称:THEORETICAL AND APPLIED GENETICS ( 影响因子:5.4; 五年影响因子:5.7 )

ISSN: 0040-5752

年卷期: 2024 年 137 卷 1 期

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收录情况: SCI

摘要: 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expression analysis and was validated by haplotype analysis of 330 accessions with high-depth (10x) resequencing data (unpublished).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.

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