Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential

文献类型： 外文期刊

第一作者： Su, Jing

作者： Su, Jing;Xu, Kai;Li, Zirong;Hu, Yuan;Li, Lanzhi;Hu, Zhongli;Zheng, Xingfei;Song, Shufeng;Tang, Zhonghai

作者机构：

期刊名称：SCIENTIFIC REPORTS （ 影响因子：3.998； 五年影响因子：4.576 ）

ISSN： 2045-2322

年卷期： 2021 年 11 卷 1 期

页码：

收录情况： SCI

摘要： Rice yield per plant has a complex genetic architecture, which is mainly determined by its three component traits: the number of grains per panicle (GPP), kilo-grain weight (KGW), and tillers per plant (TP). Exploring ideotype breeding based on selection for genetically less complex component traits is an alternative route for further improving rice production. To understand the genetic basis of the relationship between rice yield and component traits, we investigated the four traits of two rice hybrid populations (575+1495 F-1) in different environments and conducted meta-analyses of genome-wide association study (meta-GWAS). In total, 3589 significant loci for three components traits were detected, while only 3 loci for yield were detected. It indicated that rice yield is mainly controlled by minor-effect loci and hardly to be identified. Selecting quantitative trait locus/gene affected component traits to further enhance yield is recommended. Mendelian randomization design is adopted to investigate the genetic effects of loci on yield through component traits and estimate the genetic relationship between rice yield and its component traits by these loci. The loci for GPP or TP mainly had a positive genetic effect on yield, but the loci for KGW with different direction effects (positive effect or negative effect). Additionally, TP (Beta=1.865) has a greater effect on yield than KGW (Beta=1.016) and GPP (Beta=0.086). Five significant loci for component traits that had an indirect effect on yield were identified. Pyramiding superior alleles of the five loci revealed improved yield. A combination of direct and indirect effects may better contribute to the yield potential of rice. Our findings provided a rationale for using component traits as indirect indices to enhanced rice yield, which will be helpful for further understanding the genetic basis of yield and provide valuable information for improving rice yield potential.

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