DrFARM: Identification and inference for pleiotropic gene in multi-trait metabolomics GWAS

Music City Center 

Pleiotropic variants are often identified by running separate genome-wide association studies (GWAS) on each trait and then combining results, but this marginal-summary-statistics-based approach can lead to spurious findings by inflating each trait's residual variance. We propose a new statistical approach, Debiased-regularized Factor Analysis Regression Model (DrFARM), which employs a joint regression model to analyze high-dimensional genetic variants while accounting for multilevel trait dependencies. This joint modeling strategy permits comprehensive false discovery rate (FDR) control. DrFARM leverages debiasing techniques and the Cauchy combination test, both theoretically justified, to establish a valid post-selection inference on pleiotropic variants. Through extensive simulations, we demonstrate that DrFARM appropriately controls the overall FDR. Applying DrFARM to data on 1,031 metabolites measured in 6,135 men from the Metabolic Syndrome in Men (METSIM) study, we identify 288 new metabolite associations at loci that did not reach significance in prior METSIM metabolite GWAS analyses.

High-dimensional inference

debiasing

metabolomics

factor analysis model

post-selection inference