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

T Qi, Y Wu, J Zeng, F Zhang, A Xue, L Jiang… - Nature …, 2018 - nature.com
Nature communications, 2018nature.com
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 …
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 (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 ( for cis-eQTLs and 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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