Bayesian multivariate reanalysis of large genetic studies identifies many new associations

English version

Autoři: Michael C. Turchin ^aff001; Matthew Stephens ^aff001
Působiště autorů: Department of Human Genetics, The University of Chicago, Chicago, Illinois, United States of America ^aff001; Department of Statistics, The University of Chicago, Chicago, Illinois, United States of America ^aff002
Vyšlo v časopise: Bayesian multivariate reanalysis of large genetic studies identifies many new associations. PLoS Genet 15(10): e32767. doi:10.1371/journal.pgen.1008431
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008431

Souhrn

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.

Klíčová slova:

Alleles – Blood pressure – Genetic polymorphism – Genome-wide association studies – Open data – Phenotypes

Zdroje

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