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 Genetjournal_title
PLoS geneticsauthors
Turchin MC,Stephens Mdoi
10.1371/journal.pgen.1008431subject
Has Abstractpub_date
2019-10-09 00:00:00pages
e1008431issue
10eissn
1553-7390issn
1553-7404pii
PGENETICS-D-19-00888journal_volume
15pub_type
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