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Genome-wide hierarchical mixed model association analysis

文献类型： 外文期刊

作者： Hao, Zhiyu 2 ; Gao, Jin 3 ; Song, Yuxin 3 ; Yang, Runqing 1 ; Liu, Di 2 ;

作者机构： 1.Chinese Acad Fishery Sci, Res Ctr Aquat Biotechnol, Beijing 100141, Peoples R China

2.Heilongjiang Acad Agr Sci, Inst Anim Husb, Harbin 150086, Peoples R China

3.Nanjing Agr Univ, Wuxi Fisheries Coll, Nanjing, Jiangsu, Peoples R China

关键词： genome-wide association analysis; genomic breeding value; hierarchical mixed model; joint association analysis; statistical power

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：13.994； 五年影响因子：12.784 ）

ISSN： 1467-5463

年卷期： 2021 年 22 卷 6 期

页码：

收录情况： SCI

摘要： In genome-wide mixed model association analysis, we stratified the genomic mixed model into two hierarchies to estimate genomic breeding values (GBVs) using the genomic best linear unbiased prediction and statistically infer the association of GBVs with each SNP using the generalized least square. The hierarchical mixed model (Hi-LMM) can correct confounders effectively with polygenic effects as residuals for association tests, preventing potential false-negative errors produced with genome-wide rapid association using mixed model and regression or an efficient mixed-model association expedited (EMMAX). Meanwhile, the Hi-LMM performs the same statistical power as the exact mixed model association and the same computing efficiency as EMMAX. When the GBVs have been estimated precisely, the Hi-LMM can detect more quantitative trait nucleotides (QTNs) than existing methods. Especially under the Hi-LMM framework, joint association analysis can be made straightforward to improve the statistical power of detecting QTNs.

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