Candidate Causal Regulatory Effects by Integration of Expression QTLs with Complex Trait Genetic Associations

Abstract

The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r2) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.

Genome‐wide association studies of multiple sclerosis

“…In attempts to overlap GWAS and eQTL data, the key issue is not just to identify eQTLs in a GWAS locus, but to identify those that appear to be driven by the same underlying genetic variant driving disease risk 59. This has proven a difficult challenge, as a result of linkage disequilibrium60. Practically, because eQTL are very common, and many variants show association to disease in a locus, it is likely that at least some variants associated with disease will also have eQTL evidence for a nearby gene 61.…”

Functional genetic screen of human diversity reveals that a methionine salvage enzyme regulates inflammatory cell death

“…Thus, although there was no evidence of systematic bias, there were no SNPs with P values lower than neutral expectation. However, previous work has shown the value in examining functional subsets of SNPs, specifically SNPs associated with expression levels of nearby genes [cis-expression quantitative trait loci (cis-eQTL)], to enhance discovery from GWAS (26)(27)(28). Doing so also makes biological sense, because SNPs that alter nearby gene expression are more likely to affect cellular function as well.…”

Progress and Promise of Genome-Wide Association Studies for Human Complex Trait Genetics

“…Several studies show that complex traitassociated variants overlap with eQTL variants (e.g., Emilsson et al 2008;Nica et al 2010), helping to prioritize a gene and mechanism for functional followup. Furthermore, a recent study of GWAS associations reported that on a global scale, GWAS-identified variants are significantly more likely to be eQTL than minor-allelefrequency-matched SNPs chosen from high-throughput GWAS platforms (Nicolae et al 2010).…”