Bayesian multivariate re-analysis of large genetic studies identifies many novel associations

Genome-wide association studies (GWAS) are now a common tool to identify genetic variants that affect traits of interest. To date, the NHGRI GWAS Catalog has over 24,000 SNP-phenotype associations. However, the vast majority of these GWAS are conducted in univariate frameworks, ie when genetic variants are only tested against a single phenotype one at a time. This is in contrast to multivariate frameworks where genetic variants are tested against different combinations of traits simultaneously. Under many biological scenarios, taking into account the context of multiple phenotypes drastically increases power. Additionally, by testing combinations of traits, multivariate frameworks allow researchers to investigate a greater level of biological complexity. Despite these clear advantages, multivariate analyses are seldom implemented. Univariate GWAS already involve a large computational and statistical burden; performing an additional, exponentially greater number of tests is highly deterring. Furthermore, it is often unclear how to properly compare different multivariate models even when they can be efficiently conducted.