To examine exploratory associations between PRSs and hundreds of medical diagnoses, we used the PRS-CS method (28) described above to compute Consumption and Problems PRSs for each of the 66,915 unrelated genotyped individuals of European ancestry from the Vanderbilt University Medical Center biobank (BioVU)(31). Using electronic health record data in BioVU, we performed phenome-wide association studies (PheWASs) for Consumption and Problems PRSs using the PheWAS (32) v0.12 package in R. Specifically, we fit a logistic regression model to each of the 1,335 case/control phenotypes in BioVU (“phecodes”; Supplementary Material 7.3) in order to estimate the effect of a given PRS on each diagnosis. Sex, median age of the longitudinal electronic health record measurements, and the first 10 principal components were included as covariates. We then repeated the PheWAS analyses using AUD diagnoses (phecodes 317, 317.1) as additional covariates. A standard Benjamini–Hochberg false discovery rate (FDR 5%) correction was applied to account for multiple testing.
