Schizophrenia and the affective disorders, here comprising bipolar disorder and major depressive disorder, are psychiatric illnesses that lead to significant morbidity and mortality worldwide. Whilst understanding of their pathobiology remains limited, large case-control studies have recently identified single nucleotide polymorphisms (SNPs) associated with these disorders. However, discerning the functional effects of these SNPs has been difficult as the associated causal genes are unknown. Here we evaluated whether schizophrenia and affective disorder associated-SNPs are correlated with gene expression within human brain tissue. Specifically, to identify expression quantitative trait loci (eQTLs), we leveraged disorder-associated SNPs identified from six Psychiatric Genomics Consortium and CONVERGE Consortium studies with gene expression levels in post-mortem, neurologically-normal tissue from two independent human brain tissue expression datasets (UK Brain Expression Consortium (UKBEC) and Genotype-Tissue Expression (GTEx)). We identified 6 188 and 16 720 cis-acting SNPs exceeding genome-wide significance (p<5x10 -8 ) in the UKBEC and GTEx datasets, respectively. 1 288 cis-eQTLs were significant in a metaanalysis leveraging overlapping brain regions and were associated with expression of 15 genes, including three non-coding RNAs. One cis-eQTL, rs16969968, results in a functionally disruptive missense mutation in CHRNA5, a schizophrenia-implicated gene.Meta-analysis identified 297 trans-eQTLs associated with 24 genes that were significant in a region-specific manner. Importantly, comparing across tissues, we find that blood eQTLs largely do not capture brain cis-eQTLs. This study identifies putatively causal genes whose expression in region-specific brain tissue may contribute to the risk of schizophrenia and affective disorders. downloaded from the PGC website (https://www.med.unc.edu/pgc). SNPs were collated from the following studies: PGC-SCZ2 4 for schizophrenia, PGC-BIP 5 and PGC-MooDs 6 for bipolar disorder, PGC-MDD 7 and CONVERGE 8 for major depressive disorder, and PGC-Cross Disorder Analysis 9 for multiple disorders (schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder and attention-deficit hyperactivity disorder).Of note, PGC-MooDs analysis included samples from the PGC-BIP study; PGC-Cross Disorders analysis included samples from the PGC-SCZ2, PGC-BIP and PGC-MDD studies.We included overlapping studies to maximize the number of disorder-associated SNPs in our analysis as there may be loci identified in one study but not in another.SNPs with a study p-value < 5 x 10 -5 were included in the analysis. For SNPs from all studies except PGC-SCZ2, we also obtained SNPs that are in moderate-high linkage disequilibrium (LD, R 2 ≥ 0.5) with the study-SNPs 31 using the web-based tool rAggr (http://raggr.usc.edu/). LD-analysis settings were as follows: CEU population from 1000 Genomes Phase 3 October 2014 release, build hg19, minimum minor allele frequency = 0.001, maximum distanc...