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, Riccardo E. Marioni, Grant W. Montgomery, Ian J. Deary, Naomi R. Wray, Peter M. Visscher, Allan F. McRae and Jian Yang ()
Additional contact information
Ting Qi: The University of Queensland
Yang Wu: The University of Queensland
Jian Zeng: The University of Queensland
Futao Zhang: The University of Queensland
Angli Xue: The University of Queensland
Longda Jiang: The University of Queensland
Zhihong Zhu: The University of Queensland
Kathryn Kemper: The University of Queensland
Loic Yengo: The University of Queensland
Zhili Zheng: The University of Queensland
Riccardo E. Marioni: University of Edinburgh
Grant W. Montgomery: The University of Queensland
Ian J. Deary: University of Edinburgh
Naomi R. Wray: The University of Queensland
Peter M. Visscher: The University of Queensland
Allan F. McRae: The University of Queensland
Jian Yang: The University of Queensland

Nature Communications, 2018, vol. 9, issue 1, 1-12

Abstract: 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 ( $$\hat r_b = 0.70$$ r ^ b = 0.70 for cis-eQTLs and $$\hat r_ b = 0.78$$ 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.

Date: 2018
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DOI: 10.1038/s41467-018-04558-1

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