Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions
Name
s41597-020-00642-8.pdf
Description
Published version
Size
1.5 MB
Format
Unknown
Checksum (MD5)
0a6bb04e42c3ee11331d81d1a8475774
Author(s)
Sieberts, Solveig K.
Perumal, Thanneer M.
Carrasquillo, Minerva M.
Allen, Mariet
Reddy, Joseph S.
Hoffman, Gabriel E.
Dang, Kristen K.
Calley, John
Ebert, Philip J.
Eddy, James
Date Issued
October 2020
Journal
Scientific Data
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). 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).
MIT Department
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/s41597-020-00642-8
