We compared biological annotations from each of the GWAS using a previously established pipeline [17]. First, we used FUMA [20] v1.2.8 to identify independent SNPs (LD threshold of r2 < 0.1) and conducted competitive gene-set, tissue and pathway analysis using MAGMA v1.08 [21]. Next, we used an extension of MAGMA, Hi-C coupled MAGMA (H-MAGMA) [22], to assign non-coding (intergenic and intronic) SNPs to genes based on their chromatin interactions. Lastly, we used S-PrediXcan v0.6.2 [23] to predict transcript abundance in 13 brain tissues, and to test whether the predicted transcripts showed divergent correlation patterns with each of the genetic factors. Supplementary Note 3 contains detailed information on the bioannotations.
