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 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 (rb). Using publicly available data (n = 72 to 1,366), we find that the genetic effects of cis-eQTLs (PeQTL < 5´10 -8 ) or mQTLs (PmQTL < 1´10 -10 ) are highly correlated between independent brain and blood samples ( " = 0.70 with SE = 0.015 for cis-eQTL and " = 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.Charitable Foundation. This study makes use of data from dbGaP (accessions: phs000428.v1.p1 and phs000424.v6.p1), UK Biobank Resource (application number: 12514), UK10K project and CommonMind Consortium. A full list of acknowledgements to these data sets can be found in Supplementary Note. The members of the eQTLGen Consortium are (in alphabetical order):

Cross-disorder GWAS meta-analysis for Attention Deficit/Hyperactivity Disorder, Autism Spectrum Disorder, Obsessive Compulsive Disorder, and Tourette Syndrome

“…The SMR software was used and analysis was performed for each individual disorder as well as using results from our GWAS metaanalyses (48). We used GWAS summary statistics for each studied disorder (as described above), the LD structure from from 1000 Genomes European reference panel and summary statistics from brain expression quantitative trait loci (eQTL) analysis (49), which quantified the effect of SNPs over gene expression levels in brain tissue (36,50). Only variants showing a consistent allele frequency (pairwise MAF difference between datasets no more than 0.20) across all three datasets (GWAS summary statistic, 1000 Genome reference, and eQTL summary statistic) were included in the analysis.…”

A genome-wide genetic pleiotropy approach identified shared loci between multiple system atrophy and inflammatory bowel disease

“…Querying these three variants for eQTL status in the GTEx data (version 7) [25] revealed DENND1B (rs12740041), TTC33 (rs4957144) and PSMF1 (rs116843836) as potential target genes in various tissues ( Table 2). Additional scan of blood (N > 30,000) [26] and brain (N > 520) [27] eQTL summary statistics with substantially larger sample sizes than corresponding tissues in GTEx (Nblood<400, Nbrain<200) suggested rs12740041 as eQTL for DENND1B and rs4957144 as eQTL from TTC33 in both blood and brain, while rs4957144 was also highlighted as potential eQTL for RPL37 gene in brain ( Table 2). TTC33 is 158 kb downstream of rs4957144 ( Supplementary Figure 3), thus is not shown in Table 1, however, we decided to include it into consequent analyses together with genes listed in Table 1.…”

“…Additionally, eQTL status for lead variants identified in the conjFDR analysis was checked in brain eQTL summary statistics extracted from [27] and blood eQTL summary statistics from [26]. LocusCompare tool (see URLs) was then applied to check whether loci identified in conjFDR analysis colocalizes with eQTL signal.…”

“…Colocalization of association signals from conjFDR analysis and brain eQTL data[27] in the upstream region of DENND1B gene.…”

“…Significant eQTL functionality of lead variants of loci shared between MSA and IBD/CD. Only results passing multiple testing correction from GTEx (passing FDR<0.05), Qi et al[27] (passing Bonferroni correction p<0.05/N, where N is a number of genes tested for a given SNP) and Võsa et al[26] (passing Bonferroni correction p<0.05/N). The effect size was computed in a normalized space; thus, its magnitude does not have direct biological interpretation.…”