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dc.contributor.authorNewcombe, Paul J
dc.contributor.authorNelson, Christopher P
dc.contributor.authorSamani, Nilesh J
dc.contributor.authorDudbridge, Frank
dc.date.accessioned2019-07-16T23:31:16Z
dc.date.available2019-07-16T23:31:16Z
dc.date.issued2019-10
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 parallelizable since block-wise analyses in Step 1 can be distributed across a high-performance computing cluster, and flexible, since sparsity and heritability are 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 seven traits in the Welcome Trust Case Control Consortium as well as meta-GWAS summaries for type 1 diabetes (T1D), coronary artery disease, and schizophrenia. Performance was generally similar across methods, although our framework provided more accurate predictions for T1D, 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-Electronic
dc.languageeng
dc.publisherWiley
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectHumans
dc.subjectDiabetes Mellitus, Type 1
dc.subjectGenetic Predisposition to Disease
dc.subjectArea Under Curve
dc.subjectRisk Factors
dc.subjectCase-Control Studies
dc.subjectROC Curve
dc.subjectSchizophrenia
dc.subjectMultifactorial Inheritance
dc.subjectPolymorphism, Single Nucleotide
dc.subjectModels, Genetic
dc.subjectCoronary Artery Disease
dc.subjectGenome-Wide Association Study
dc.titleA flexible and parallelizable approach to genome-wide polygenic risk scores.
dc.typeArticle
prism.endingPage741
prism.issueIdentifier7
prism.publicationDate2019
prism.publicationNameGenet Epidemiol
prism.startingPage730
prism.volume43
dc.identifier.doi10.17863/CAM.41812
dcterms.dateAccepted2019-05-30
rioxxterms.versionofrecord10.1002/gepi.22245
rioxxterms.versionAM
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-10
dc.contributor.orcidNewcombe, Paul J [0000-0002-5611-6702]
dc.contributor.orcidDudbridge, Frank [0000-0002-8817-8908]
dc.identifier.eissn1098-2272
rioxxterms.typeJournal Article/Review
pubs.funder-project-idMRC (1185)
pubs.funder-project-idWellcome Trust (076113/C/04/Z)
pubs.funder-project-idWellcome Trust (061858/Z/00/E)
cam.issuedOnline2019-07-22


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