We performed multiple in-silico analyses to compare the results from each of the AUDIT latent genetic factors. First, we used FUMA (24) v1.2.8 to identify independent SNPs and study their functional consequences, which included ANNOVAR categories, Combined Annotation Dependent Depletion scores, RegulomeDB scores. Second, we used MAGMA v1.08 (24, 25) to conduct competitive gene-set and pathway analyses for each of the AUDIT genetic latent factors. SNPs were mapped to 18,546 protein-coding genes from Ensembl build 85. Gene-sets were obtained from Msigdb v7.0 (“Curated gene sets”, “GO terms”). We also used an extension of this method, Hi-C coupled MAGMA (H-MAGMA)(26), to assign non-coding (intergenic and intronic) SNPs to genes based on their chromatin interactions. Exonic and promoter SNPs are assigned to genes based on physical position. We used four Hi-C datasets, which were derived from fetal brain, adult brain, iPSC-derived neurons and and iPSC-derived astrocytes (https://github.com/thewonlab/H-MAGMA). Lastly, we used S-PrediXcan v0.6.2 (27) to predict gene expression levels in 13 brain tissues, and to test whether the predicted gene expression showed divergent correlation patterns with each of the AUDIT latent genetic factors.
