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Bayesian factor analysis for multi-trait fine-mapping of pleiotropic genetic variants

Abstract Number:

3648 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Weiqiong Huang (1), Christopher McKennan (1), Joshua Cape (2), Emily Hector (3)

Institutions:

(1) University of Pittsburgh, N/A, (2) University of Wisconsin–Madison, N/A, (3) North Carolina State University, N/A

Co-Author(s):

Christopher McKennan  
University of Pittsburgh
Joshua Cape  
University of Wisconsin–Madison
Emily Hector  
North Carolina State University

First Author:

Weiqiong Huang  
University of Pittsburgh

Presenting Author:

Weiqiong Huang  
N/A

Abstract Text:

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.

Keywords:

Multi-trait fine-mapping|Metabolite genome wide association study|Bayesian statistics|Factor analysis|Pleiotropic|Metabolomic analysis

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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