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dc.contributor.authorStacey, David
dc.contributor.authorFauman, Eric B
dc.contributor.authorZiemek, Daniel
dc.contributor.authorSun, Ben
dc.contributor.authorHarshfield, Eric
dc.contributor.authorWood, Angela
dc.contributor.authorButterworth, Adam
dc.contributor.authorSuhre, Karsten
dc.contributor.authorPaul, Dirk
dc.date.accessioned2018-11-20T00:30:37Z
dc.date.available2018-11-20T00:30:37Z
dc.date.issued2019-01-10
dc.identifier.issn0305-1048
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/285444
dc.description.abstractQuantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the 'Prioritization of candidate causal Genes at Molecular QTLs' (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of 'true positive' causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.
dc.format.mediumPrint
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHumans
dc.subjectLipids
dc.subjectProteins
dc.subjectChromosome Mapping
dc.subjectPhenotype
dc.subjectQuantitative Trait Loci
dc.subjectGenome-Wide Association Study
dc.subjectGenetic Association Studies
dc.subjectMolecular Sequence Annotation
dc.titleProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci.
dc.typeArticle
prism.issueIdentifier1
prism.publicationDate2019
prism.publicationNameNucleic Acids Res
prism.startingPagee3
prism.volume47
dc.identifier.doi10.17863/CAM.32804
dcterms.dateAccepted2018-09-11
rioxxterms.versionofrecord10.1093/nar/gky837
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-01
dc.contributor.orcidSun, Ben [0000-0001-6347-2281]
dc.contributor.orcidHarshfield, Eric [0000-0001-8767-0928]
dc.contributor.orcidWood, Angela [0000-0002-7937-304X]
dc.contributor.orcidButterworth, Adam [0000-0002-6915-9015]
dc.contributor.orcidPaul, Dirk [0000-0002-8230-0116]
dc.identifier.eissn1362-4962
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMRC (1508647)
pubs.funder-project-idMedical Research Council (MR/L003120/1)
pubs.funder-project-idWellcome Trust (105602/Z/14/Z)
pubs.funder-project-idBritish Heart Foundation (None)
cam.issuedOnline2018-09-20
datacite.issourceof.doi10.1101/230094


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