Hunan Engineering & Technology Research Center for Agricultural Big Data Analysis & Decision-Making, Hunan Agricultural University, Changsha, 410128, China.
State Key Laboratory of Hybrid Rice, Wuhan University, Wuhan, 430072, China.
Sci Rep. 2021 Mar 25;11(1):6894. doi: 10.1038/s41598-021-86389-7.
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) 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.
单位面积产量由穗粒数（GPP）、千粒重（KGW）和单株分蘖数（TP）三个构成因子决定，具有复杂的遗传结构。基于选择遗传结构相对简单的构成因子的理想型选择，是进一步提高水稻产量的另一种途径。为了理解水稻产量与构成因子之间的关系的遗传基础，我们在不同环境下调查了两个水稻杂交群体（575+1495 F）的四个性状，并对全基因组关联研究（meta-GWAS）进行了荟萃分析。总共检测到三个构成因子性状的 3589 个显著位点，而产量的只有 3 个位点。这表明，水稻产量主要由微效位点控制，很难识别。建议选择影响构成因子的数量性状/基因来进一步提高产量。采用孟德尔随机化设计，通过构成因子研究位点对产量的遗传效应，并通过这些位点估计水稻产量与其构成因子之间的遗传关系。穗粒数或分蘖数的位点主要对产量有正向遗传效应，而千粒重的位点则具有不同的方向效应（正向效应或负向效应）。此外，TP（Beta=1.865）对产量的影响大于 KGW（Beta=1.016）和 GPP（Beta=0.086）。鉴定出五个对产量有间接影响的构成因子显著位点。聚合这五个位点的优良等位基因可提高产量。直接和间接效应的组合可能更有助于发挥水稻的产量潜力。我们的研究结果为利用构成因子作为提高水稻产量的间接指标提供了依据，这将有助于进一步理解产量的遗传基础，并为提高水稻产量潜力提供有价值的信息。
