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dc.contributor.authorAbraham, Gad
dc.contributor.authorTye-Din, Jason A
dc.contributor.authorBhalala, Oneil G
dc.contributor.authorKowalczyk, Adam
dc.contributor.authorZobel, Justin
dc.contributor.authorInouye, Michael
dc.date.accessioned2018-11-09T00:30:18Z
dc.date.available2018-11-09T00:30:18Z
dc.date.issued2014-02
dc.identifier.issn1553-7390
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284874
dc.description.abstractPractical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.
dc.format.mediumElectronic-eCollection
dc.languageeng
dc.publisherPublic Library of Science (PLoS)
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHumans
dc.subjectCeliac Disease
dc.subjectGenetic Predisposition to Disease
dc.subjectHLA Antigens
dc.subjectRisk
dc.subjectGenomics
dc.subjectBiometry
dc.subjectHaplotypes
dc.subjectPolymorphism, Single Nucleotide
dc.subjectAlleles
dc.subjectGenome, Human
dc.subjectFemale
dc.titleAccurate and robust genomic prediction of celiac disease using statistical learning.
dc.typeArticle
prism.issueIdentifier2
prism.publicationDate2014
prism.publicationNamePLoS Genet
prism.startingPagee1004137
prism.volume10
dc.identifier.doi10.17863/CAM.32247
dcterms.dateAccepted2013-12-08
rioxxterms.versionofrecord10.1371/journal.pgen.1004137
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2014-02-13
dc.contributor.orcidInouye, Michael [0000-0001-9413-6520]
dc.identifier.eissn1553-7404
rioxxterms.typeJournal Article/Review
cam.issuedOnline2014-02-13


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