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

Ting Qi; Yang Wu; Jian Zeng; Futao Zhang; Angli Xue; Longda Jiang; Zhihong Zhu; Kathryn Kemper; Loic Yengo; Zhili Zheng; J. van Dongen; eQTLGen Consortium

doi:10.1038/s41467-018-04558-1

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

Ting Qi, Yang Wu, Jian Zeng, Futao Zhang, Angli Xue, Longda Jiang, Zhihong Zhu, Kathryn Kemper, Loic Yengo, Zhili Zheng, J. van Dongen, eQTLGen Consortium

Research output: Contribution to Journal › Article › Academic › peer-review

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 language	English
Article number	2282
Journal	Nature Communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-04558-1
Publication status	Published - 1 Dec 2018

Funding

This research was supported by the Australian Research Council (DP160101343, DP160101056, DP160103860, and DP160102400), the Australian National Health and Medical Research Council (1113400, 1107258, 1083656, 1078037, and 1078901), the US National Institutes of Health (GM099568, GM075091, and AG042568), and the Sylvia & Charles Viertel Charitable Foundation. This study makes use of data from dbGaP (accessions: phs000428 and phs000424), UK Biobank Resource (application number: 12514), UK10K project and CommonMind Consortium. A full list of acknowledgements to these data sets can be found in the Supplementary Note 3.

Funders	Funder number
Australian National Health and Medical Research Council	1078901, 1107258, 1083656, 1113400, 1078037
US National Institutes of Health	GM099568, GM075091, AG042568
National Institute of General Medical Sciences	R01GM075091
Sylvia and Charles Viertel Charitable Foundation
Australian Research Council	DP160101343, DP160102400, DP160101056, DP160103860

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Netherlands Twin Register (NTR)

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title = "Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood",

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.",

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AU - Kalnapenkis, Anette

AU - Kasela, Silva

AU - Kettunen, Johannes

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

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