Synthesis of 53 tissue and cell line expression QTL datasets reveals master eQTLs

Abstract

Background: Gene expression genetic studies in human tissues and cells identify cis- and trans-acting expression quantitative trait loci (eQTLs). These eQTLs provide insights into regulatory mechanisms underlying disease risk. However, few studies systematically characterized eQTL results across cell and tissues types. We synthesized eQTL results from >50 datasets, including new primary data from human brain, peripheral plaque and kidney samples, in order to discover features of human eQTLs.

Results: We find a substantial number of robust cis-eQTLs and far fewer trans-eQTLs consistent across tissues. Analysis of 45 full human GWAS scans indicates eQTLs are enriched overall, and above nSNPs, among positive statistical signals in genetic mapping studies, and account for a significant fraction of the strongest human trait effects. Expression QTLs are enriched for gene centricity, higher population allele frequencies, in housekeeping genes, and for coincidence with regulatory features, though there is little evidence of 5' or 3' positional bias. Several regulatory categories are not enriched including microRNAs and their predicted binding sites and long, intergenic non-coding RNAs. Among the most tissue-ubiquitous cis-eQTLs, there is enrichment for genes involved in xenobiotic metabolism and mitochondrial function, suggesting these eQTLs may have adaptive origins. Several strong eQTLs (CDK5RAP2, NBPFs) coincide with regions of reported human lineage selection. The intersection of new kidney and plaque eQTLs with related GWAS suggest possible gene prioritization. For example, butyrophilins are now linked to arterial pathogenesis via multiple genetic and expression studies. Expression QTL and GWAS results are made available as a community resource through the NHLBI GRASP database [http://apps.nhlbi.nih.gov/grasp/].

Conclusions: Expression QTLs inform the interpretation of human trait variability, and may account for a greater fraction of phenotypic variability than protein-coding variants. The synthesis of available tissue eQTL data highlights many strong cis-eQTLs that may have important biologic roles and could serve as positive controls in future studies. Our results indicate some strong tissue-ubiquitous eQTLs may have adaptive origins in humans. Efforts to expand the genetic, splicing and tissue coverage of known eQTLs will provide further insights into human gene regulation.

Figure 1

Frequency of eGenes and eQTLs across 53 datasets. A: Distribution of the occurrence of 19,038 unique eGenes across all 53 eQTL datasets. Inset: histogram of 1,784 genes found in > =15 eQTL datasets. B: Distribution of the occurrence of 56,089 unique, best cis-eQTLs across all 53 eQTL datasets. Inset: Histogram of 279 cis-eQTLs found in > =15 eQTL datasets. C: Distribution of the occurrence of 7,075 unique and best trans-eQTLs across all 53 eQTL datasets. Inset: Histogram of 37 trans-eQTLs found in ≥ 4 eQTL datasets. For each trans-eQTL, all proxy SNPs in perfect linkage disequilibrium (r^{^2} = 1 in CEU) are also included [42].

Figure 2

Hierarchical clustering shows robust eGenes with strong genetic influences across a majority of studies. eGenes present in >70% of datasets (>35/53 datasets). Individual datasets are indicated at bottom with eGenes listed to the right. Presence (black) or absence (white) of eGenes as eQTLs within individual datasets is shown.

Figure 3

eQTL-eGene distance distributions relative to datasets and tissue group. Common SNP and transcript annotations were used to re-annotate all datasets and eQTL location categorized as: in the eGene, cis (≤500 kb from eGene), trans (>500 kb but on the same chromosome), trans.diff.chr (eQTL and eGene map to different chromosomes).

Figure 4

Significance of eQTLs relative to distance from eGene boundaries. A: 116,563 best eQTLs per eGene per dataset are shown across all 53 eQTL datasets. eQTLs located in their eGenes are plotted at 0 on the x-axis, otherwise the x-axis indicates distance of each eQTL to its eGene (from 5′: -1 Mb to 3′: +1 Mb). Not shown are 393 eQTLs with P < 1 × 10^-150 which also display a highly central tendency. B: A histogram of the number of eQTLs per kb of distance from the 5′ transcription start sites (TSS) of eGenes.

Figure 5

Housekeeping genes are over-represented among eGenes common to many tissue datasets. A density plot of eGenes that are housekeeping versus non-housekeeping genes (as defined by [51]) across datasets. The eGene distributions differ significantly (P < 1.12 × 10^-11).