Identifying gene targets for brain-related traits using transcriptomic and methylomic data from blood
Riccardo E Marioni,
Grant W Montgomery,
Ian J Deary,
Naomi R. Wray,
Peter M Visscher,
Allan F McRae,
Posted 07 Mar 2018
bioRxiv DOI: 10.1101/274472 (published DOI: 10.1038/s41467-018-04558-1)
Posted 07 Mar 2018
Understanding the difference in genetic regulation of gene expression between brain and blood is important for discovering genes associated with brain-related traits and disorders. Here, we estimate the correlation of genetic effects at the top associated cis-expression (cis-eQTLs or cis-mQTLs) between brain and blood for genes expressed (or CpG sites methylated) in both tissues, while accounting for errors in their estimated effects (r_b). Using publicly available data (n = 72 to 1,366), we find that the genetic effects of cis-eQTLs (P_eQTL < 5e-8) or mQTLs (P_mQTL < 1e-10) are highly correlated between independent brain and blood samples (r_b = 0.70 with SE = 0.015 for cis-eQTL and r_b = 0.78 with SE = 0.006 for cis-mQTLs). Using meta-analyzed brain eQTL/mQTL data (n = 526 to 1,194), we identify 61 genes and 167 DNA methylation (DNAm) sites associated with 4 brain-related traits and disorders. Most of these associations are a subset of the discoveries (97 genes and 295 DNAm sites) using data from blood with larger sample sizes (n = 1,980 to 14,115). We further find that cis-eQTLs with tissue-specific effects are approximately uniformly distributed across all the functional annotation categories, and that mean difference in gene expression level between brain and blood is almost independent of the difference in the corresponding cis-eQTL effect. Our results demonstrate the gain of power in gene discovery for brain-related phenotypes using blood cis-eQTL or cis-mQTL data with large sample sizes.
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