A more accurate method for colocalisation analysis allowing for multiple causal variants

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 th...

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Bibliographic Details
Published in:PLoS genetics Vol. 17; no. 9; p. e1009440
Main Author: Wallace, Chris
Format: Journal Article
Language:English
Published: United States Public Library of Science 29.09.2021
Public Library of Science (PLoS)
Subjects:
Adaptation
Analysis
Bayes Theorem
Bayesian statistical decision theory
Biology and Life Sciences
Causality
Computational biology
Datasets
Gene mapping
Genetic Predisposition to Disease
Genetic variation
Genome-wide association studies
Genome-Wide Association Study
Genomes
Genomics
Haplotypes
Humans
Hypotheses
Linkage Disequilibrium
Medicine and Health Sciences
Methods
Research and Analysis Methods
Statistics
United Kingdom
ISSN:1553-7404, 1553-7390, 1553-7404
Online Access:Get full text
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Summary: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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The authors have declared that no competing interests exist.
ISSN:1553-7404
1553-7390
1553-7404
DOI:10.1371/journal.pgen.1009440