Bayesian multivariate reanalysis of large genetic studies identifies many new associations

• Published in: PLoS genetics Vol. 15; no. 10; p. e1008431
• Main Authors: Turchin, Michael C, Stephens, Matthew
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 09.10.2019; Public Library of Science (PLoS)
• Subjects: Analysis; Bayes Theorem; Bayesian analysis; Biology and Life Sciences; Data Interpretation, Statistical; Disease; Erythrocytes; Gene loci; Genome-wide association studies; Genome-Wide Association Study - methods; Genome-Wide Association Study - statistics & numerical data; Genomes; Genomics; Genotype & phenotype; Health; Homeostasis; Medicine and Health Sciences; Models, Genetic; Multivariate Analysis; Phenotypes; Physical Sciences; Polymorphism, Single Nucleotide; Research and Analysis Methods; Science Policy; Software; United States > US; Illinois; Chicago Illinois
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1008431

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.