Bayesian multivariate reanalysis of large genetic studies identifies many new associations

被引：10

作者：

Turchin, Michael C. ^{[1
,3
]}

Stephens, Matthew ^{[1
,2
]}

机构：

[1] Univ Chicago, Dept Human Genet, Chicago, IL 60637 USA

[2] Univ Chicago, Dept Stat, Chicago, IL 60637 USA

[3] Brown Univ, Dept Ecol & Evolutionary Biol, Ctr Computat Mol Biol, Providence, RI 02912 USA

来源：

PLOS GENETICS | 2019年 / 15卷 / 10期

基金：

美国国家卫生研究院;

关键词：

GENOME-WIDE ASSOCIATION; LOCI; GWAS; EQTL; ARCHITECTURE; VARIANTS; PRESSURE; INSIGHTS; BIOLOGY; LINK;

D O I：

10.1371/journal.pgen.1008431

中图分类号：

Q3 [遗传学];

学科分类号：

071007 ; 090102 ;

摘要：

Genome-wide association studies (GWAS) have now been conducted for hundreds of phenotypes of relevance to human health. Many such GWAS involve multiple closely-related phenotypes collected on the same samples. However, the vast majority of these GWAS have been analyzed using simple univariate analyses, which consider one phenotype at a time. This is despite the fact that, at least in simulation experiments, multivariate analyses have been shown to be more powerful at detecting associations. Here, we conduct multivariate association analyses on 13 different publicly-available GWAS datasets that involve multiple closely-related phenotypes. These data include large studies of anthropometric traits (GIANT), plasma lipid traits (GlobalLipids), and red blood cell traits (HaemgenRBC). Our analyses identify many new associations (433 in total across the 13 studies), many of which replicate when follow-up samples are available. Overall, our results demonstrate that multivariate analyses can help make more effective use of data from both existing and future GWAS.

引用

页数：18

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