Bayesian multivariate reanalysis of large genetic studies identifies many new associations.

Abstract:

: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.

journal_name

PLoS Genet

journal_title

PLoS genetics

authors

Turchin MC,Stephens M

doi

10.1371/journal.pgen.1008431

subject

Has Abstract

pub_date

2019-10-09 00:00:00

pages

e1008431

issue

10

eissn

1553-7390

issn

1553-7404

pii

PGENETICS-D-19-00888

journal_volume

15

pub_type

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