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Genetic analysis of grain yield conditioned on its component traits in rice (Oryza sativa L.)

机译:水稻籽粒产量性状的遗传分析

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摘要

Grain yield (GY) of rice is a complex trait consisting of several yield components. It is of great importance to reveal the genetic relationships between GY and its yield components at the QTL (quantitative trait loci) level for multi-trait improvement in rice. In the present study, GY per plant in rice and its 3 yield component traits, panicle number per plant (PN), grain number per panicle (GN), and 1000-grain weight (GW), were investigated using a doubled-haploid population derived from a cross of an indica variety IR64 and a japonica variety Azucena. The phenotypic values collected from 2 cropping seasons were analysed by QTLNetwork 2.0 for mapping QTLs with additive (a) and/or additive × environment interaction (ae) effects. Furthermore, conditional QTL analysis was conducted to detect QTLs for GY independent of yield components. The results showed that the general genetic variation in GY was largely influenced by GN with the contribution ratio of 29.2%, and PN and GN contributed 10.5% and 74.6% of the genotype × environment interaction variation in GY, respectively. Four QTLs were detected with additive and/or additive × environment interaction effects for GY by the unconditional mapping method. However, for GY conditioned on PN, GN, and GW, 6 additional loci were identified by the conditional mapping method. All of the detected QTLs affecting GY were associated with at least one of the 3 yield components. The results revealed that QTL expressions of GY were contributed differently by 3 yield component traits, and provide valuable information for effectively improving GY in rice.
机译:水稻的籽粒产量(GY)是一个复杂的性状,由几种产量构成。在水稻的多性状改良中,重要的是揭示QY(定量性状基因座)水平上GY及其产量构成之间的遗传关系。在本研究中,使用双倍单倍体群体调查了水稻中每株植物的甘蓝及其3个产量构成特征,每株穗数(PN),每穗粒数(GN)和1000粒重(GW)。衍生自印度品种IR64和粳稻品种Azucena的杂交。通过QTLNetwork 2.0分析了两个种植季节的表型值,以绘制具有加性(a)和/或加性×环境相互作用(ae)效应的QTL。此外,进行了条件性QTL分析,以检测GY的QTL,而与产量成分无关。结果表明,GY基因的一般遗传变异受GN的影响较大,占29.2%,而PN和GN分别占GY基因型×环境相互作用变异的10.5%和74.6%。通过无条件映射方法,检测到四个具有GY的加性和/或加性×环境相互作用效应的QTL。但是,对于以PN,GN和GW为条件的GY,通过条件映射方法还确定了6个额外的基因座。所有检测到的影响GY的QTL与3个产量构成要素中的至少一个相关。结果表明,3个产量组分性状对GY的QTL表达有不同的贡献,为有效提高水稻的GY提供有价值的信息。

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