| Literature DB >> 35058635 |
Biao Zeng1,2,3,4,5, Jaroslav Bendl1,2,3,4,5, Roman Kosoy1,2,3,4,5, John F Fullard1,2,3,4,5, Gabriel E Hoffman6,7,8,9,10, Panos Roussos11,12,13,14,15,16,17.
Abstract
While large-scale, genome-wide association studies (GWAS) have identified hundreds of loci associated with brain-related traits, identification of the variants, genes and molecular mechanisms underlying these traits remains challenging. Integration of GWAS with expression quantitative trait loci (eQTLs) and identification of shared genetic architecture have been widely adopted to nominate genes and candidate causal variants. However, this approach is limited by sample size, statistical power and linkage disequilibrium. We developed the multivariate multiple QTL approach and performed a large-scale, multi-ancestry eQTL meta-analysis to increase power and fine-mapping resolution. Analysis of 3,983 RNA-sequenced samples from 2,119 donors, including 474 non-European individuals, yielded an effective sample size of 3,154. Joint statistical fine-mapping of eQTL and GWAS identified 329 variant-trait pairs for 24 brain-related traits driven by 204 unique candidate causal variants for 189 unique genes. This integrative analysis identifies candidate causal variants and elucidates potential regulatory mechanisms for genes underlying schizophrenia, bipolar disorder and Alzheimer's disease.Entities:
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Year: 2022 PMID： 35058635 PMCID： PMC8852232 DOI： 10.1038/s41588-021-00987-9
Source DB: PubMed Journal: Nat Genet ISSN： 1061-4036 Impact factor: 41.307