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

eQTLGen Consortium, Rick Jansen, Matthias Nauck, Markus Perola, Natalia Pervjakova, Brandon Pierce, Joseph Powell, Holger Prokisch, Bruce Psaty, Olli Raitakari, Susan Ring, Samuli Ripatti, Olaf Rotzschke, Sina Ruëger, Ashis Saha, Markus Scholz, Katharina Schramm, Ilkka Seppälä, Michael Stumvoll, Patrick SullivanAlexander Teumer, Joachim Thiery, Lin Tong, Anke Tönjes, Joyce Van Meurs, Joost Verlouw, Uwe Völker, Urmo Võsa, Hanieh Yaghootkar, Biao Zeng, Riccardo E. Marioni, Grant W. Montgomery, Ian J. Deary, Naomi R. Wray, Peter M. Visscher, Allan F. McRae, Jian Yang

Research output: Contribution to journalArticleAcademicpeer-review

197 Citations (Scopus)

Abstract

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 (r b ). 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.

Original languageEnglish
Article number2282
JournalNature communications
Volume9
Issue number1
DOIs
Publication statusPublished - 1 Dec 2018

Cohort Studies

  • Netherlands Twin Register (NTR)

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