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dc.contributor.authorHernández, N
dc.contributor.authorSoenksen, J
dc.contributor.authorNewcombe, P
dc.contributor.authorSandhu, M
dc.contributor.authorBarroso, Ines
dc.contributor.authorWallace, Chris
dc.contributor.authorAsimit, Jennifer
dc.date.accessioned2022-01-06T11:50:18Z
dc.date.available2022-01-06T11:50:18Z
dc.date.issued2021-10-22
dc.identifier.issn2041-1723
dc.identifier.otherPMC8536717
dc.identifier.other34686674
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332152
dc.descriptionFunder: “Expanding excellence in England” award from Research England
dc.description.abstractJoint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Here we show flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.
dc.description.sponsorshipWellcome Trust [WT107881]
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceessn: 2041-1723
dc.sourcenlmid: 101528555
dc.titleThe flashfm approach for fine-mapping multiple quantitative traits
dc.typeArticle
dc.date.updated2022-01-06T11:50:17Z
prism.issueIdentifier1
prism.publicationNameNature Communications
prism.volume12
dc.identifier.doi10.17863/CAM.79598
dcterms.dateAccepted2021-10-04
rioxxterms.versionofrecord10.1038/s41467-021-26364-y
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidBarroso, Ines [0000-0001-5800-4520]
dc.contributor.orcidWallace, Chris [0000-0001-9755-1703]
dc.contributor.orcidAsimit, Jennifer [0000-0002-4857-2249]
dc.identifier.eissn2041-1723
pubs.funder-project-idMedical Research Council (MR/R021368/1)
pubs.funder-project-idWellcome Trust (107881/Z/15/Z)
pubs.funder-project-idMedical Research Council (MC_UU_00002/4)
pubs.funder-project-idNational Institute for Health Research (NIHRDH-IS-BRC-1215-20014)
cam.issuedOnline2021-10-22


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International