Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases

Identification of therapeutic targets from genome-wide association studies (GWAS) requires insights into downstream functional consequences. We harmonized 8,613 RNA-sequencing samples from 14 brain datasets to create the MetaBrain resource and performed cis - and trans -expression quantitative trait locus (eQTL) meta-analyses in multiple brain region- and ancestry-specific datasets ( n ≤ 2,759). Many of the 16,169 cortex cis -eQTLs were tissue-dependent when compared with blood cis -eQTLs. We inferred brain cell types for 3,549 cis -eQTLs by interaction analysis. We prioritized 186 cis -eQTLs for 31 brain-related traits using Mendelian randomization and co-localization including 40 cis -eQTLs with an inferred cell type, such as a neuron-specific cis -eQTL ( CYP24A1 ) for multiple sclerosis. We further describe 737 trans -eQTLs for 526 unique variants and 108 unique genes. We used brain-specific gene-co-regulation networks to link GWAS loci and prioritize additional genes for five central nervous system diseases. This study represents a valuable resource for post-GWAS research on central nervous system diseases. Brain expression quantitative trait locus (eQTL) and network analyses encompassing 8,613 RNA-sequencing samples from 14 eQTL datasets reveal downstream effects and putative drivers for brain-related diseases.