Evidence map›Paper›PMID 32310995›Full record

ArticlePLoS genetics2020

Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses.

Chris Wallace

Erratum issuedAbstract read
In one paragraph

Article in PLoS genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 259 papers, 17 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
259citing papers in PubMed, 17 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

259 citing papers in PubMed, 17 syntheses or guidelines pooled it.

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199 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Chris WallaceCambridge Institute for Therapeutic Immunology & Infectious Disease, and MRC Biostatistics Unit, University of Cambridge, Cambridge, United Kingdom.ORCID 0000-0001-9755-1703

Funding

GWAS CatalogU41HG007823 · NHGRI · EUROPEAN MOLECULAR BIOLOGY LABORATORY · PI PARKINSON, HELEN ELIZABETH · 2014 to 2021
$6.7M
Medical Research Council MC UU 00002/4Medical Research Council MC_UU_00002/4NHGRI NIH HHS U41 HG007823Wellcome TrustWellcome Trust WT107881
6 · The paper itself

Abstract

Horizontal integration of summary statistics from different GWAS traits can be used to evaluate evidence for their shared genetic causality. One popular method to do this is a Bayesian method, coloc, which is attractive in requiring only GWAS summary statistics and no linkage disequilibrium estimates and is now being used routinely to perform thousands of comparisons between traits. Here we show that while most users do not adjust default software values, misspecification of prior parameters can substantially alter posterior inference. We suggest data driven methods to derive sensible prior values, and demonstrate how sensitivity analysis can be used to assess robustness of posterior inference. The flexibility of coloc comes at the expense of an unrealistic assumption of a single causal variant per trait. This assumption can be relaxed by stepwise conditioning, but this requires external software and an LD matrix aligned to study alleles. We have now implemented conditioning within coloc, and propose a new alternative method, masking, that does not require LD and approximates conditioning when causal variants are independent. Importantly, masking can be used in combination with conditioning where allelically aligned LD estimates are available for only a single trait. We have implemented these developments in a new version of coloc which we hope will enable more informed choice of priors and overcome the restriction of the single causal variant assumptions in coloc analysis.

Indexed as

Linkage DisequilibriumGenome-Wide Association StudyHumansPolymorphism, Single NucleotideQuantitative Trait Loci

Identifiers

PMID32310995
PMCPMC7192519

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.