Dissecting the genetics of the human transcriptome identifies novel trait-related trans-eQTLs and corroborates the regulatory relevance of non-protein coding loci†

Abstract

Genetics of gene expression (eQTLs or expression QTLs) has proved an indispensable tool for understanding biological pathways and pathomechanisms of trait-associated SNPs. However, power of most genome-wide eQTL studies is still limited. We performed a large eQTL study in peripheral blood mononuclear cells of 2112 individuals increasing the power to detect trans-effects genome-wide. Going beyond univariate SNP-transcript associations, we analyse relations of eQTLs to biological pathways, polygenetic effects of expression regulation, trans-clusters and enrichment of co-localized functional elements. We found eQTLs for about 85% of analysed genes, and 18% of genes were trans-regulated. Local eSNPs were enriched up to a distance of 5 Mb to the transcript challenging typically implemented ranges of cis-regulations. Pathway enrichment within regulated genes of GWAS-related eSNPs supported functional relevance of identified eQTLs. We demonstrate that nearest genes of GWAS-SNPs might frequently be misleading functional candidates. We identified novel trans-clusters of potential functional relevance for GWAS-SNPs of several phenotypes including obesity-related traits, HDL-cholesterol levels and haematological phenotypes. We used chromatin immunoprecipitation data for demonstrating biological effects. Yet, we show for strongly heritable transcripts that still little trans-chromosomal heritability is explained by all identified trans-eSNPs; however, our data suggest that most cis-heritability of these transcripts seems explained. Dissection of co-localized functional elements indicated a prominent role of SNPs in loci of pseudogenes and non-coding RNAs for the regulation of coding genes. In summary, our study substantially increases the catalogue of human eQTLs and improves our understanding of the complex genetic regulation of gene expression, pathways and disease-related processes.

Figure 1.

Distances between eSNPs and regulated genes. Histogram of the distance in kilobase between eSNPs and transcription start sites (show at the left side) and between eSNPs and transcription end sites of corresponding genes (shown at the right sight). Dark grey bars represent start and end of transcribed regions. Vertical lines and adjacent numbers are percentiles of all upstream and downstream distances found within 5 Mb 3′ from TSS and 5′ from TES. The upper panels show all eSNPs at FDR ≤ 5%, the lower panels are restricted to the strongest eSNP per regulated gene.

Figure 2.

Estimating the gap between explained and predicted heritability of gene expression. To estimate the gap between explained and predicted heritability of gene expression, we compared the explained variance of gene expression of combined eSNPs versus the genetic variance of gene-expression levels resulting from all imputed SNPs (CW-heritability). This is shown at the left side for all SNPs, in the middle for all SNPs found on the chromosome, where the regulated transcript is located (cis-regulation), and at the right side for all SNPs found on all chromosomes, where the regulated transcript is not located (trans-regulation). Triangles indicate transcripts with significant genome-wide CW-heritability (P ≤ 0.05). For each graph, a loess-estimator including confidence bounds is shown. Note that, for convenience, the ordinate in (C) is log₁₀-transformed. Transcripts with an explained variance of combined trans-eSNPs of zero are shown at the bottom of the graph.