A Fast and Powerful Empirical Bayes Method for Genome-Wide Association Studies

文献类型： 外文期刊

第一作者： Chang, Tianpeng

作者： Chang, Tianpeng;Liang, Mang;An, Bingxing;Wang, Xiaoqiao;Zhu, Bo;Xu, Lingyang;Zhang, Lupei;Gao, Xue;Chen, Yan;Li, Junya;Gao, Huijiang;Wei, Julong

作者机构：

关键词： empirical Bayes; genome-wide association study; kinship matrix; linear mixed model; proximal contamination

期刊名称：ANIMALS （ 影响因子：2.752； 五年影响因子：2.942 ）

ISSN： 2076-2615

年卷期： 2019 年 9 卷 6 期

页码：

收录情况： SCI

摘要： Linear mixed model (LMM) is an efficient method for GWAS. There are numerous forms of LMM-based GWAS methods. However, improving statistical power and computing efficiency have always been the research hotspots of the LMM-based GWAS methods. Here, we proposed a fast empirical Bayes method, which is based on linear mixed models. We call it Fast-EB-LMM in short. The novelty of this method is that it uses a modified kinship matrix accounting for individual relatedness to avoid competition between the locus of interest and its counterpart in the polygene. This property has increased statistical power. We adopted two special algorithms to ease the computational burden: Eigenvalue decomposition and Woodbury matrix identity. Simulation studies showed that Fast-EB-LMM has significantly increased statistical power of marker detection and improved computational efficiency compared with two widely used GWAS methods, EMMA and EB. Real data analyses for two carcass traits in a Chinese Simmental beef cattle population showed that the significant single-nucleotide polymorphisms (SNPs) and candidate genes identified by Fast-EB-LMM are highly consistent with results of previous studies. We therefore believe that the Fast-EB-LMM method is a reliable and efficient method for GWAS.

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