Identification of expression quantitative trait loci associated with schizophrenia and affective disorders in normal brain tissue

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 11 genome-wide association 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)). Utilizing stringent multi-region meta-analyses, we identified 2,224 cis-eQTLs associated with expression of 40 genes, including 11 non-coding RNAs. One cis-eQTL, rs16969968, results in a functionally disruptive missense mutation in CHRNA5, a schizophrenia-implicated gene. Importantly, comparing across tissues, we find that blood eQTLs capture 30% of brain-associated eQTLs are significant in tibial nerve. This study identifies putatively causal genes whose expression in region-specific tissue may contribute to the risk of schizophrenia and affective disorders.Author summary: An estimated 21 million people live worldwide with schizophrenia, 60 million with bipolar disorder, and 400 million with major depressive disorder. Recent genome-wide association studies have shed light on the genetic variants linked to these disorders, and increasing evidence suggests that their genetic architectures may overlap. However, understanding the roles of these variants in disease biology remains limited. Here we questioned whether genetic variation associated with these disorders is correlated with the expression of genes that are proximally located within the genome. Importantly, we evaluate this in two large and independent human brain tissue datasets. We subsequently identify, with high confidence, >2,200 disease-associated variants as putative regulators of expression for nearby genes. The identification of these regulated genes provides new insights into disease biology and will help prioritise associations for future mechanistic follow-up studies.