Abstract
Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL) mapping has paralleled the adoption of genome-wide association studies (GWAS) for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE) data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human complex phenotypic variation.
Highlights
The precise spatial and temporal control of gene transcription is critical for biological processes, as evidenced by the causal role of gene expression perturbation in many human diseases [1,2,3]
This is further supported by the finding that genome-wide association studies (GWAS) associations are enriched within DNase I hypersensitive (DHS) sites [12] and expression quantitative trait locus (eQTL) single nucleotide polymorphisms (SNPs) [13,14], and by several elegant GWAS follow up studies that have mechanistically tied disease associations with SNPs that cause the misregulation of gene expression [15,16]
When interpreting genome-wide association studies showing that specific genetic variants are associated with disease risk, scientists look for a link between the genetic variant and a biological mechanism behind that disease
Summary
The precise spatial and temporal control of gene transcription is critical for biological processes, as evidenced by the causal role of gene expression perturbation in many human diseases [1,2,3]. It is possible that these associated SNPs tag causal coding SNPs; numerous compelling lines of evidence [2,7,8,9,10,11] demonstrate that regulatory SNPs have causal roles in many complex human phenotypes This is further supported by the finding that GWAS associations are enriched within DNase I hypersensitive (DHS) sites [12] and eQTL SNPs [13,14], and by several elegant GWAS follow up studies that have mechanistically tied disease associations with SNPs that cause the misregulation of gene expression [15,16]
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