We used FUSION (downloaded from http://gusevlab.org/projects/fusion on May 2, 2019)16 to estimate protein weights in the discovery and replication datasets, separately. Briefly, we estimated SNP-based heritability for each gene using protein data. For proteins with significant SNP-based heritability (p-value <0.01), we used FUSION to compute the effect of SNPs on protein abundance using multiple predictive models (top1, blup, lasso, enet, bslmm)16, and the most predictive model was selected. For mRNA with significant SNP-based heritability (p-value <0.01), we modified how FUSION estimates mRNA weights to accommodate individuals with more than one brain region with transcriptomic data. First, the flag -scale 1 was added to handle pre-scaled expression values, as expression was scaled across individual tissues before filtering for matched genotype and combining across all tissues. Second, the family ID (FID) in the plink FAM file was used to ensure that within cross validation, all samples from the same individual were always in the same fold, and that no fold differed in size by more than 5% from any other fold. Similar to protein weights, mRNA weights were estimated using all models
