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

By Alexandra C. Nica, Stephen B. Montgomery, A. Dimas, Barbara E. Stranger, C. Beazley, I. Barroso, Emmanouil T. Dermitzakis. PLoS Genetics, 2010

461 citations recorded by the source corpus.

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.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4069d9202c53a67e18d2e7e8e746af8755928319

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Citations in the corpus

Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics (2016, background): “This class includes COLOC [18], Sherlock [17], and RTC [1], and more recently eCAVIAR [19], and ENLOC [20].”
Fine-mapping inflammatory bowel disease loci to single variant resolution (2017, methodology): “We next used conditional association to test for evidence of colocalization, as described in Nica et al.25.”
Fast and efficient QTL mapper for thousands of molecular phenotypes (2015, background): “Genome-wide association studies have shown that most common trait-associated variants fall into non-coding genomic regions and likely alter gene regulation (Maurano et al., 2012; Nica et al., 2010).”
Colocalization of GWAS and eQTL Signals Detects Target Genes (2016, methodology): “The conditional method for fine-mapping and RTC [13] have some similarity in nature.”
Cancer Genetics and Epigenetics: Two Sides of the Same Coin? (2012, background): “Further, several genome-wide expression quantitative trait loci studies in humans have demonstrated a link between genetic variation and changes in gene regulation (Nica et al., 2010; Nicolae et al., 2010).”
Joint analysis of functional genomic data and genome-wide association studies of 18 human traits (2013, unknown): “41;43–46 ) will be essential.”
Chromatin marks identify critical cell types for fine mapping complex trait variants (2012, unknown): “(4) np g © 2 01 3 N at ur e A m er ic a, In c.”