Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data

文献类型： 外文期刊

第一作者： Xu, Haiming

作者： Xu, Haiming;Jiang, Beibei;Cao, Yujie;Xu, Haiming;Jiang, Beibei;Cao, Yujie;Zhang, Yingxin;Zhan, Xiaodeng;Shen, Xihong;Cheng, Shihua;Cao, Liyong;Zhang, Yingxin;Zhan, Xiaodeng;Shen, Xihong;Cheng, Shihua;Cao, Liyong;Lou, Xiangyang

作者机构：

期刊名称：BIOMED RESEARCH INTERNATIONAL （ 影响因子：3.411； 五年影响因子：3.62 ）

ISSN： 2314-6133

年卷期： 2015 年

页码：

收录情况： SCI

摘要： With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.

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