Show simple item record

dc.contributor.authorIoannidis, NMen_US
dc.contributor.authorRothstein, JHen_US
dc.contributor.authorPejaver, Ven_US
dc.contributor.authorMiddha, Sen_US
dc.contributor.authorMcDonnell, SKen_US
dc.contributor.authorBaheti, Sen_US
dc.contributor.authorMusolf, Aen_US
dc.contributor.authorLi, Qen_US
dc.contributor.authorHolzinger, Een_US
dc.contributor.authorKaryadi, Den_US
dc.contributor.authorCannon-Albright, LAen_US
dc.contributor.authorTeerlink, CCen_US
dc.contributor.authorStanford, JLen_US
dc.contributor.authorIsaacs, WBen_US
dc.contributor.authorXu, Jen_US
dc.contributor.authorCooney, KAen_US
dc.contributor.authorLange, EMen_US
dc.contributor.authorSchleutker, Jen_US
dc.contributor.authorCarpten, JDen_US
dc.contributor.authorPowell, IJen_US
dc.contributor.authorCussenot, Oen_US
dc.contributor.authorCancel-Tassin, Gen_US
dc.contributor.authorGiles, GGen_US
dc.contributor.authorMacInnis, RJen_US
dc.contributor.authorMaier, Cen_US
dc.contributor.authorHsieh, C-Len_US
dc.contributor.authorWiklund, Fen_US
dc.contributor.authorCatalona, WJen_US
dc.contributor.authorFoulkes, WDen_US
dc.contributor.authorMandal, Den_US
dc.contributor.authorEeles, RAen_US
dc.contributor.authorKote-Jarai, Zen_US
dc.contributor.authorBustamante, CDen_US
dc.contributor.authorSchaid, DJen_US
dc.contributor.authorHastie, Ten_US
dc.contributor.authorOstrander, EAen_US
dc.contributor.authorBailey-Wilson, JEen_US
dc.contributor.authorRadivojac, Pen_US
dc.contributor.authorThibodeau, SNen_US
dc.contributor.authorWhittemore, ASen_US
dc.contributor.authorSieh, Wen_US
dc.date.accessioned2016-11-14T11:22:29Z
dc.date.issued2016-10en_US
dc.identifier.citationAmerican journal of human genetics, 2016, 99 (4), pp. 877 - 885en_US
dc.identifier.issn0002-9297en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/201
dc.identifier.eissn1537-6605en_US
dc.identifier.doi10.1016/j.ajhg.2016.08.016en_US
dc.description.abstractThe vast majority of coding variants are rare, and assessment of the contribution of rare variants to complex traits is hampered by low statistical power and limited functional data. Improved methods for predicting the pathogenicity of rare coding variants are needed to facilitate the discovery of disease variants from exome sequencing studies. We developed REVEL (rare exome variant ensemble learner), an ensemble method for predicting the pathogenicity of missense variants on the basis of individual tools: MutPred, FATHMM, VEST, PolyPhen, SIFT, PROVEAN, MutationAssessor, MutationTaster, LRT, GERP, SiPhy, phyloP, and phastCons. REVEL was trained with recently discovered pathogenic and rare neutral missense variants, excluding those previously used to train its constituent tools. When applied to two independent test sets, REVEL had the best overall performance (p < 10<sup>-12</sup>) as compared to any individual tool and seven ensemble methods: MetaSVM, MetaLR, KGGSeq, Condel, CADD, DANN, and Eigen. Importantly, REVEL also had the best performance for distinguishing pathogenic from rare neutral variants with allele frequencies <0.5%. The area under the receiver operating characteristic curve (AUC) for REVEL was 0.046-0.182 higher in an independent test set of 935 recent SwissVar disease variants and 123,935 putatively neutral exome sequencing variants and 0.027-0.143 higher in an independent test set of 1,953 pathogenic and 2,406 benign variants recently reported in ClinVar than the AUCs for other ensemble methods. We provide pre-computed REVEL scores for all possible human missense variants to facilitate the identification of pathogenic variants in the sea of rare variants discovered as sequencing studies expand in scale.en_US
dc.formatPrint-Electronicen_US
dc.format.extent877 - 885en_US
dc.languageengen_US
dc.language.isoengen_US
dc.subjectHumansen_US
dc.subjectDiseaseen_US
dc.subjectArea Under Curveen_US
dc.subjectROC Curveen_US
dc.subjectDNA Mutational Analysisen_US
dc.subjectGene Frequencyen_US
dc.subjectMutation, Missenseen_US
dc.subjectSoftwareen_US
dc.subjectExomeen_US
dc.titleREVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants.en_US
dc.typeJournal Article
dcterms.dateAccepted2016-08-23en_US
rioxxterms.versionofrecord10.1016/j.ajhg.2016.08.016en_US
rioxxterms.licenseref.startdate2016-10en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfAmerican journal of human geneticsen_US
pubs.issue4en_US
pubs.notesNot knownen_US
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Genetics and Epidemiology/Oncogenetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Radiotherapy and Imaging/Oncogenetics
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Structural Biology
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Structural Biology/Structural Electron Microscopy
pubs.publication-statusPublisheden_US
pubs.volume99en_US
pubs.embargo.termsNot knownen_US
icr.researchteamOncogeneticsen_US
icr.researchteamStructural Electron Microscopyen_US
dc.contributor.icrauthorEeles, Rosalinden_US
dc.contributor.icrauthorKote-Jarai, Zsofiaen_US
dc.contributor.icrauthorLi, Qiuhongen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record