DrFARM: Identification and inference for pleiotropic gene in GWAS

Abstract

In a standard analysis, pleiotropic variants are identified by running separate genome-wide association studies (GWAS) and combining results across traits. But such two-stage statistical approach may lead to spurious results. We propose a new statistical approach, Debiased-regularized Factor Analysis Regression Model (DrFARM), through a joint regression model for simultaneous analysis of high-dimensional genetic variants and multilevel dependencies. This joint modeling strategy controls overall error to permit universal false discovery rate (FDR) control. DrFARM uses the strengths of the debiasing technique and the Cauchy combination test, both being theoretically justified, to establish a valid post selection inference on pleiotropic variants. Through extensive simulations, we show that DrFARM appropriately controls overall FDR. Applying DrFARM to data on 1,031 metabolites measured on 6,135 men from the Metabolic Syndrome in Men (METSIM) study, we identify 288 new metabolite associations at loci that did not reach statistical significance in prior METSIM metabolite GWAS.