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dc.contributor.authorNewcombe, Paulen
dc.contributor.authorNelson, Christopher Pen
dc.contributor.authorSamani, Nilesh Jen
dc.contributor.authorDudbridge, Franken
dc.date.accessioned2019-07-16T23:31:16Z
dc.date.available2019-07-16T23:31:16Z
dc.date.issued2019-10en
dc.identifier.issn0741-0395
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/294707
dc.description.abstractThe heritability of most complex traits is driven by variants throughout the genome. Consequently, polygenic risk scores, which combine information on multiple variants genome-wide, have demonstrated improved accuracy in genetic risk prediction. We present a new two-step approach to constructing genome-wide polygenic risk scores from meta- GWAS summary statistics. Local linkage disequilibrium (LD) is adjusted for in step 1, followed by, uniquely, long-range LD in step 2. Our algorithm is highly parallelisable since block-wise analyses in step 1 can be distributed across a high performance computing cluster, and flexible, since sparsity and heritability is estimated within each block. Inference is obtained through a formal Bayesian variable selection framework, meaning final risk predictions are averaged over competing models. We compared our method to two alternative approaches: LDPred and lassosum using all 7 traits in the WTCCC as well as meta-GWAS summaries for Type 1 Diabetes, Coronary Artery Disease, and Schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for Type 1 Diabetes, for which there are multiple heterogeneous signals in regions of both short and long range LD. With sufficient compute resources, our method also allows the fastest runtimes.
dc.format.mediumPrint-Electronicen
dc.languageengen
dc.publisherJohn Wiley & Sons Inc.
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHumansen
dc.subjectDiabetes Mellitus, Type 1en
dc.subjectGenetic Predisposition to Diseaseen
dc.subjectArea Under Curveen
dc.subjectRisk Factorsen
dc.subjectCase-Control Studiesen
dc.subjectROC Curveen
dc.subjectSchizophreniaen
dc.subjectMultifactorial Inheritanceen
dc.subjectPolymorphism, Single Nucleotideen
dc.subjectModels, Geneticen
dc.subjectCoronary Artery Diseaseen
dc.subjectGenome-Wide Association Studyen
dc.titleA flexible and parallelizable approach to genome-wide polygenic risk scores.en
dc.typeArticle
prism.endingPage741
prism.issueIdentifier7en
prism.publicationDate2019en
prism.publicationNameGenetic epidemiologyen
prism.startingPage730
prism.volume43en
dc.identifier.doi10.17863/CAM.41812
dcterms.dateAccepted2019-05-30en
rioxxterms.versionofrecord10.1002/gepi.22245en
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2019-10en
dc.contributor.orcidNewcombe, Paul [0000-0002-5611-6702]
dc.contributor.orcidDudbridge, Frank [0000-0002-8817-8908]
dc.identifier.eissn1098-2272
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idMRC (1185)
pubs.funder-project-idWellcome Trust (076113/C/04/Z)
pubs.funder-project-idWellcome Trust (061858/Z/00/E)


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