A more accurate method for colocalisation analysis allowing for multiple causal variants
Open accessPublished version of a preprint
Abstract
In genome-wide association studies (GWAS) it is now common to search for, and find, multiple causal variants located in close proximity. It has also become standard to ask whether different traits share the same causal variants, but one of the popular methods to answer this question, coloc, makes the simplifying assumption that only a single causal variant exists for any given trait in any genomic region. Here, we examine the potential of the recently proposed Sum of Single Effects (SuSiE) regression framework, which can be used for fine-mapping genetic signals, for use with coloc. SuSiE is a novel approach that allows evidence for association at multiple causal variants to be evaluated simultaneously, whilst separating the statistical support for each variant conditional on the causal signal being considered. We show this results in more accurate coloc inference than other proposals to adapt coloc for multiple causal variants based on conditioning. We therefore recommend that coloc be used in combination with SuSiE to optimise accuracy of colocalisation analyses when multiple causal variants exist.
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After exclusion of pleiotropic regions, 32 proteins had QTL that colocalized with AD risk (PP.H4>0.8).
“Finally, we assessed colocalization48,49 between each of our 2,477 significant pQTL associations (cis and trans) and the AD GWAS. After exclusion of pleiotropic regions, 32 proteins had QTL that colocalized with AD risk (PP.H4>0.8, Fig. 4A, Supp. Tables S43&44).”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [49]
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- Authors
- Wallace, C.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Wallace, Chris
- ORCID
- Wallace, Chris
- Venue
- PLOS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2021-09-29
- Volume
- 17
- Issue
- 9
- Pages
- e1009440
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC8504726
- OpenAlex
- W3202212354
- Cited by
- 567
- References
- 6
- Citations by year
- 2026: 202 · 2025: 151 · 2024: 125 · 2023: 59 · 2022: 27 · 2021: 3
- FWCI
- 30.63
- Type
- journal article
- Language
- en
- Update notices
- new_version (2021-10-11)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo
- Apollo (cc-by)
- Funders
- Wellcome Trust (WT107881, WT220788)
- Medical Research Council (MC UU 00002/4)
- NIHR Cambridge BRC (BRC-1215-20014)
- 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
- COLOC, fine mapping, genome-wide association study, conditional inference
- MeSH terms
- Bayes Theorem, Humans, Linkage Disequilibrium, Causality, Genetic Predisposition to Disease, Genome-Wide Association Study
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