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Sieberts, S.K.* ; Perumal, T.M.* ; Carrasquillo, M.M.* ; Allen, M.* ; Reddy, J.S.* ; Hoffman, G.E.* ; Dang, K.K.* ; Calley, J.* ; Ebert, P.J.* ; Eddy, J.* ; Wang, X.* ; Greenwood, A.K.* ; Mostafavi, S.* ; Omberg, L.* ; Peters, M.A.* ; Logsdon, B.A.* ; de Jager, P.L.* ; Ertekin-Taner, N.* ; Mangravite, L.M.* ; The AMP-AD Consortium (Arnold, M.)

Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions.

Sci. Data 7:340 (2020)

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The availability of high-quality RNA-sequencing and genotyping data of post-mortem brain collections from consortia such as CommonMind Consortium (CMC) and the Accelerating Medicines Partnership for Alzheimer’s Disease (AMP-AD) Consortium enable the generation of a large-scale brain cis-eQTL meta-analysis. Here we generate cerebral cortical eQTL from 1433 samples available from four cohorts (identifying >4.1 million significant eQTL for >18,000 genes), as well as cerebellar eQTL from 261 samples (identifying 874,836 significant eQTL for >10,000 genes). We find substantially improved power in the meta-analysis over individual cohort analyses, particularly in comparison to the Genotype-Tissue Expression (GTEx) Project eQTL. Additionally, we observed differences in eQTL patterns between cerebral and cerebellar brain regions. We provide these brain eQTL as a resource for use by the research community. As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).

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