Wang Xiaoqian, Pang Yunlong, Wang Chunchao, Chen Kai, Zhu Yajun, Shen Congcong, Ali Jauhar, Xu Jianlong, Li Zhikang
Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences Beijing, China.
Agricultural Genomics Institute, Chinese Academy of Agricultural Sciences Shenzhen, China.
Front Plant Sci. 2017 Jan 4;7:1998. doi: 10.3389/fpls.2016.01998. eCollection 2016.
Appearance and milling quality are two crucial properties of rice grains affecting its market acceptability. Understanding the genetic base of rice grain quality could considerably improve the high quality breeding. Here, we carried out an association analysis to identify QTL affecting nine rice grain appearance and milling quality traits using a diverse panel of 258 accessions selected from 3K Rice Genome Project and evaluated in two environments Sanya and Shenzhen. Genome-wide association analyses using 22,488 high quality SNPs identified 72 QTL affecting the nine traits. Combined gene-based association and haplotype analyses plus functional annotation allowed us to shortlist 19 candidate genes for seven important QTL regions affecting the grain quality traits, including two cloned genes ( and ), two fine mapped QTL ( and ) and three newly identified QTL (, and ). The most likely candidate gene(s) for each important QTL were also discussed. This research demonstrated the superior power to shortlist candidate genes affecting complex phenotypes by the strategy of combined GWAS, gene-based association and haplotype analyses. The identified candidate genes provided valuable sources for future functional characterization and genetic improvement of rice appearance and milling quality.
外观和碾磨品质是影响水稻籽粒市场接受度的两个关键特性。了解水稻籽粒品质的遗传基础能够显著改善优质育种。在此,我们利用从3000份水稻基因组计划中选取的258份不同材料组成的群体进行关联分析,以鉴定影响9个水稻籽粒外观和碾磨品质性状的QTL,并在三亚和深圳两个环境中进行了评估。使用22488个高质量单核苷酸多态性(SNP)进行全基因组关联分析,鉴定出72个影响这9个性状的QTL。基于基因的关联分析与单倍型分析以及功能注释相结合,使我们能够筛选出影响籽粒品质性状的7个重要QTL区域的19个候选基因,包括两个已克隆基因(和)、两个精细定位的QTL(和)以及三个新鉴定的QTL(、和)。还讨论了每个重要QTL最可能的候选基因。本研究证明了通过全基因组关联研究(GWAS)、基于基因的关联分析和单倍型分析相结合的策略筛选影响复杂表型的候选基因的强大能力。所鉴定的候选基因为未来水稻外观和碾磨品质的功能表征和遗传改良提供了宝贵资源。