We used the data in the brain resource to identify QTLs affecting gene expression and chromatin activity. We calculated expression, splicing-isoform, chromatin, and cell fraction QTLs (eQTLs, isoQTLs, cQTLs, and fQTLs, respectively). For eQTLs, we adopted a standard approach, closely adhering to the GTEx pipeline for maximal compatibility (figs. S31 to S33) (35). (However, for maximal utility of the resource, we also provide alternate lists, filtered more conservatively.) In the PFC, we identified ~2.5 million cis-eQTLs involving ~33,000 eGenes (expressed genes) [~17,000 noncoding and ~16,000 coding, with a false discovery rate of <0.05] (Fig. 4A). We found 1,341,182 eQTL single-nucleotide polymorphisms (SNPs) from ~5.3 million total SNPs tested in 1-Mb windows around genes, constituting 238,194 independent SNPs after linkage-disequilibrium (LD) pruning. This estimate identified substantially more eQTLs and associated eGenes than previous studies, reflecting our large sample size (8, 11, 21). The number of eGenes, in fact, approaches the total number of genes estimated to be expressed in the brain. That said, a very large fraction of the smaller GTEx and CMC brain eQTL sets was contained within our set
