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Title: Integrative Modeling of eQTLs and Cis-Regulatory Elements Suggests Mechanisms Underlying Cell Type Specificity of eQTLs}
Article:
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.
Introduction:
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]–[3]. Gene expression is controlled by regulatory proteins, RNAs, and the cell type specific cis-regulatory elements with which they interact. Genetic variation within cis-regulatory elements( CREs, e. g., transcription promoters, enhancers, or insulators) can affect gene expression in a cell type specific manner. An extensive body of work, performed in a variety of cell types in both humans and model organisms, has demonstrated that genetic variants that impact gene expression, or expression quantitative trait loci( eQTLs), are common and exist in both cis( local) and trans( over long genetic distances)[3]–[6]. Over of genotype-phenotype associations found in genome-wide association studies( GWAS) are with non-coding single nucleotide polymorphisms( SNPs), making their mechanistic interpretation more challenging. It is possible that these associated SNPs tag causal coding SNPs; however, numerous compelling lines of evidence[2],[7]–[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]. Although eQTLs are increasingly used to provide mechanistic interpretations for human disease associations, the cell type specificity of eQTLs presents a problem. Because the cell type from which a given physiological phenotype arises may not be known, and because eQTL data exist for a limited number of cell types, it is critical to quantify and understand the mechanisms generating cell type specific eQTLs. For example, if a GWAS identifies a set of SNPs associated with risk of type II diabetes, the researcher must choose a target cell type to develop a mechanistic model of the molecular phenotype that causes the gross physiological change. One can imagine that the relevant cell type might be adipose tissue, liver, pancreas, or another hormone-regulating tissue. Furthermore, if the GWAS SNP produces a molecular phenotype( i. e., is an eQTL) in lymphoblastoid cell lines( LCLs), it is not necessarily the case that the SNP will generate a similar molecular phenotype in the cell type of interest. Furthermore, there are many examples of cell types with particular relevance to common diseases, for example dopaminergic neurons and Parkinson's disease, that lack comprehensive eQTL data or catalogs of CREs. The utility of eQTLs for complex trait interpretation will therefore be improved by a more thorough annotation of their cell type specificity. While several studies have quantified the reproducibility of eQTLs within or between cell types derived from the same or different individuals[17]–[28] the determinants of eQTL cell specificity are still largely unknown. We address this need in this study by analyzing cell specific eQTLs collected from eleven studies performed in seven different cell types and by integrating these data with cell specific CRE data to mechanistically interpret cell specific eQTLs. We used Bayesian regression models to identify all cis-linked SNPs that are independently associated with each gene expression trait in each study. In an effort to identify the functional determinants of eQTL cell specificity, we quantified the associations between eQTL SNPs and CRE data sets, many of which were derived from the cell types used in eQTL discovery and are known to function in a cell type specific manner( e. g., transcription factor binding sites( TFBSs), DHS sites). We further considered the relationship between eQTL SNP-CRE overlap and the cell type specificity of eQTL replication. Lastly, we built a series of classifiers to predict the cell type specificity of eQTLs in the absence of additional gene expression data and to predict the function of GWAS SNPs with phenotypes relevant to cell types lacking eQTL data. We believe these predictive models will facilitate more substantial mechanistic analyses of individual SNPs by enabling the integration of disease genetics and regulatory SNPs with the thousands of genomic data sets that have been produced by projects like ENCODE[29],[30].
Discussion:
The integrative analyses presented here provide new insights into the patterns of cis-eQTL replication within and between cell types, while controlling for biological and technical variation. Several notable results emerge from our analyses. We demonstrate that eQTLs are more likely to overlap activating CREs and less likely to overlap repressive CREs when they are ascertained from the same cell type versus different cell types. Cis-eQTL SNPs overlapping most classes of activating cis-regulatory elements are significantly more likely to replicate in independent studies. Conversely, eQTL SNPs that overlap repetitive or repressive chromatin states and eQTL SNP-gene pairs that are intersected by insulators are significantly less reproducible. Cis-eQTL SNP-CRE overlap is also significantly more predictive of eQTL reproducibility when the CRE data are derived from the same cell type as the gene expression data. Furthermore, eQTL SNPs that overlap cell type specific CREs are significantly enriched for cell type specific eQTLs, suggesting specific regulatory mechanisms for those cell type specific eQTL associations. The observed relationship between eQTL SNP reproducibility and CRE overlap led us to test the hypothesis that SNP-CRE overlap could be used to predict the cell type specificity of eQTLs in the absence of additional gene expression or genotype data. While we see room for substantial improvement, we believe that the successful validation of this hypothesis with a random forest classifier will enable improved interpretation of genome-wide association study results. After a GWAS is performed, it is now common practice to search eQTL databases to determine whether SNPs of interest are eQTLs for the cell type relevant to the study phenotype. When the SNPs are not known eQTLs in the cell type of interest, typically the line of reasoning is dropped; however, it is possible that the specific cell type was not tested, that the relevant SNP-gene pair was not interrogated, or that the sample size was too small for a cell type specific eQTL study to substantiate the SNP as an eQTL. Instead, if the researcher finds that the SNP is an eQTL in an alternative cell type, our classifier can be applied to determine the likelihood of the SNP being an eQTL in the cell type of interest when there are CRE data available for the relevant( or related) cell type. Furthermore, the genomic location can be scrutinized relative to known CREs to identify specific CRE types that may explain the mechanism by which gene transcription and downstream phenotype are regulated. Conversely, given those same GWAS hits, using these predictions one might be able to identify the physiologically relevant cell type based on overlap with( predicted or known) cell specific eQTLs.
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 . One functional mechanism is that the genetic variant may influence gene transcription via a co-localized genomic regulatory element , such as a transcription factor binding site within an open chromatin region . Often this type of regulation occurs in some cell types but not others . In this study , we look across eleven gene expression studies with seven cell types and consider how genetic transcription regulators , or eQTLs , replicate within and between cell types . We identify pervasive allelic heterogeneity , or transcriptional control of a single gene by multiple , independent eQTLs . We integrate extensive data on cell type specific regulatory elements from ENCODE to identify general methods of transcription regulation through enrichment of eQTLs within regulatory elements . We also build a classifier to predict eQTL replication across cell types . The results in this paper present a path to an integrative , predictive approach to improve our ability to understand the mechanistic basis of human phenotypic variation .