Tuesday, Aug 6: 10:30 AM - 12:20 PM
3648
Contributed Posters
Oregon Convention Center
We introduce a Bayesian factor model to perform fast and interpretable fine-mapping on hundreds to thousands of traits simultaneously to identify causal genetic variants from genome wide association study (GWAS) summary statistics. Our model decomposes genetic effects into an indirect effect mediated by latent biological processes and a direct effect, where the indirect effect helps model the shared genetic origin of traits and the direct effect captures trait-specific genetic variation. Critically, our model and estimation pipeline facilitate the use of biologically informed priors, like metabolic pathway information in metabolomics or phylogenetic trees in microbiomics, which beget interpretable inference. We derive the statistical properties of our estimators by studying their asymptotic properties as the number of samples, traits, and genetic variants go to infinity, and apply our method to real metabolite GWAS summary statistics to jointly fine-map more than 700 metabolites. We show our method is powerful enough to recapitulate results from a study with 20 times our sample size, and is able to make inferences that would otherwise be impossible with current analysis pipelines.
Multi-trait fine-mapping
Metabolite genome wide association study
Bayesian statistics
Factor analysis
Pleiotropic
Metabolomic analysis
Main Sponsor
Section on Statistics in Genomics and Genetics