In:
Scientific Data, Springer Science and Business Media LLC, Vol. 7, No. 1 ( 2020-10-12)
Abstract:
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).
Type of Medium:
Online Resource
ISSN:
2052-4463
DOI:
10.1038/s41597-020-00642-8
Language:
English
Publisher:
Springer Science and Business Media LLC
Publication Date:
2020
detail.hit.zdb_id:
2775191-0
