Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential.
Genome-wide association study and Mendelian randomization analysis provide insights for improving rice yield potential.
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全基因组关联分析和孟德尔随机化分析为提高水稻产量潜力提供了新的思路。
DOI：
10.1038/s41598-021-86389-7
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发表时间：
2021-03-25
影响因子：
4.6
通讯作者：
Li L
中科院分区：
文献类型：
--
作者：
Su J;Xu K;Li Z;Hu Y;Hu Z;Zheng X;Song S;Tang Z;Li L
水稻单株产量具有复杂的遗传结构，主要由每穗粒数（GPP）、千粒重（KGW）和单株分蘖数（TP）3个组成性状决定。通过选择遗传复杂度较低的组成性状，探索理想型育种是进一步提高水稻产量的另一条途径。为了解水稻产量与构成性状关系的遗传基础，对不同环境下的两个水稻杂交组合（575 + 1495 F1）的4个性状进行了分析，并进行了全基因组关联分析（meta-GWAS）。结果表明，3个性状共检测到3589个显著性位点，而产量仅检测到3个显著性位点。这说明水稻产量主要受微效位点控制，难以识别。建议选择数量性状位点/基因影响的构成性状，以进一步提高产量。采用孟德尔随机化设计，通过水稻产量的构成性状研究各基因座对产量的遗传效应，并利用这些基因座估计水稻产量与其构成性状之间的遗传关系。GPP和TP基因座对产量的遗传效应主要为正效应，而KGW基因座对产量的遗传效应方向不同（正效应或负效应）。此外，TP（Beta = 1.865）对产量的影响大于KGW（Beta = 1.016）和GPP（Beta = 0.086）。对产量有间接影响的组分性状的五个显着位点被确定。聚合这5个位点的上级等位基因显示了产量的提高。直接和间接效应的结合可能更有助于提高水稻的产量潜力。本研究结果为利用构成性状作为水稻产量的间接指标提供了理论依据，有助于进一步了解水稻产量的遗传基础，为提高水稻产量潜力提供有价值的信息。
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 F1) 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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