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Colocalization of GWAS and eQTL Signals Detects Target Genes
Open accessPublished version of a preprint
Abstract
The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.
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“One colocalization method originally proposed by Plagnol et al 112 frames shared causality as the null hypothesis and rejection of this would indicate violation of IV condition 2, i.e. that there are no common causes of the instrument and the outcome.112–114”
From Mendelian randomization (2022) · cites this paper as [114]
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“Modern human genetics methods, such as genetic colocalization163 and Mendelian randomization164, can also be used to test causality in ageing clock models.”
From Measuring biological age using omics data (2022) · cites this paper as [163]
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Metadata
- Authors
- Hormozdiari, F., van de Bunt, M., Segrè, A. V., Li, X., Joo, J. W. J., Bilow, M., Sul, J. H., Sankararaman, S., Pasaniuc, B., & Eskin, E.
- Affiliations
- University of California, Los Angeles (US)
- Broad Institute (US)
- Corresponding author
- Eskin, Eleazar
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 99
- Issue
- 6
- Pages
- 1245-1260
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5142122
- OpenAlex
- W2555614465
- Cited by
- 697
- References
- 50
- Citations by year
- 2026: 69 · 2025: 76 · 2024: 75 · 2023: 90 · 2022: 81 · 2021: 100 · 2020: 78 · 2019: 59 · 2018: 46 · 2017: 22 · 2016: 1
- FWCI
- 34.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (K25-HL080079, P01-HL28481, P01-HL30568, R01 -ES022282, R01-ES021801, R01-GM083198, R01-MH101782, U01-DA024417, U54EB020403)
- Broad Institute (R00-GM 111744-03)
- Novo Nordisk (HHSN268201000029C)
- National Science Foundation (0513612, 0729049, 0731455, 0916676, 1065276, 1302448, 1320589, 1331176)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- expression quantitative trait loci, GWAS risk loci, colocalization, causal variants, linkage disequilibrium, tissue-specific gene expression, target gene identification, summary statistics, non-coding variants
- MeSH terms
- Gene Expression Regulation, Genotype, Glucose, Humans, Insulin, Models, Genetic, Organ Specificity, Probability, Models, Statistical, Linkage Disequilibrium, Sample Size, Genetic Predisposition to Disease, Quantitative Trait Loci, Genome-Wide Association Study, Datasets as Topic
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