ratio. Moreover, since these disease-causing variants are shared among different populations, the discovery GWAS and target datasets do not have to be well-matched and the large-scale EA GWAS can be used to increase the overall discovery GWAS sample size. Based on these observations, we propose a novel gene-based PRS framework aimed at enhancing the performance of PRS in admixed populations. We first used an AA GWAS and an EA GWAS to identify genes that were associated with AUD in both populations, then used variants located within these genes’ boundaries to calculate PRS (PRSgene). We compared the performance of PRSgene with PRS calculated using variants located in intergenic regions (PRSintergenic) and all available variants (PRSall). Furthermore, for genes included in gene-based PRS calculations, we performed gene enrichment analysis using Gene Ontology (http://geneontology.org/) to test whether they were enriched in AUD or other biological processes that could provide novel insight into AUD mechanism. In addition, we tested in which tissues these genes were enriched. We also searched a publically available drug target database [24] to evaluate whether these genes were potential drug targets for AUD treatment, or drug targets for the treatment of other diseases but may be repurposed to treat AUD.
