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The recent explosion of genetic and high dimensional biobank and 'omic' datahas provided researchers with the opportunity to investigate the shared geneticorigin (pleiotropy) of hundreds to thousands of related phenotypes. However,existing methods for multi-phenotype genome-wide association studies (GWAS) donot model pleiotropy, are only applicable to a small number of phenotypes, orprovide no way to perform inference. To add further complication, raw geneticand phenotype data are rarely observed, meaning analyses must be performed onGWAS summary statistics whose statistical properties in high dimensions arepoorly understood. We therefore developed a novel model, theoretical framework,and set of methods to perform Bayesian inference in GWAS of high dimensionalphenotypes using summary statistics that explicitly model pleiotropy, begetfast computation, and facilitate the use of biologically informed priors. Wedemonstrate the utility of our procedure by applying it to metabolite GWAS,where we develop new nonparametric priors for genetic effects on metabolitelevels that use known metabolic pathway information and foster interpretableinference at the pathway level.

A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS