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Qi, T.* ; Wu, Y.* ; Zeng, J.* ; Zhang, F.* ; Xue, A.* ; Jiang, L.* ; Zhu, Z.* ; Kemper, K.* ; Yengo, L.* ; Zheng, Z.* ; eQTLGen Consortium* ; Marioni, R.E.* ; Montgomery, G.W.* ; Deary, I.J.* ; Wray, N.R.* ; Visscher, P.M.* ; McRae, A.F.* ; Yang, J.* ; eQTLGen Consortium (Müller-Nurasyid, M. ; Prokisch, H. ; Schramm, K.)

Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood.

Nat. Commun. 9:2282 (2018)
Publ. Version/Full Text Research data DOI
Open Access Gold
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Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes for brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top-associated cis-expression or -DNA methylation (DNAm) quantitative trait loci (cis-eQTLs or cis-mQTLs) between brain and blood (rb). Using publicly available data, we find that genetic effects at the top cis-eQTLs or mQTLs are highly correlated between independent brain and blood samples (r b = 0.70 for cis-eQTLs and r ^ b = 0.78 for cis-mQTLs). Using meta-analyzed brain cis-eQTL/mQTL data (n = 526 to 1194), we identify 61 genes and 167 DNAm sites associated with four brain-related phenotypes, most of which are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1980 to 14,115). Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL/mQTL data with large sample sizes.
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Publication type Article: Journal article
Document type Scientific Article
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Quellenangaben Volume: 9, Issue: 1, Pages: , Article Number: 2282 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed