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dc.contributor.authorLarson, NBen_US
dc.contributor.authorMcDonnell, Sen_US
dc.contributor.authorAlbright, LCen_US
dc.contributor.authorTeerlink, Cen_US
dc.contributor.authorStanford, Jen_US
dc.contributor.authorOstrander, EAen_US
dc.contributor.authorIsaacs, WBen_US
dc.contributor.authorXu, Jen_US
dc.contributor.authorCooney, KAen_US
dc.contributor.authorLange, Een_US
dc.contributor.authorSchleutker, Jen_US
dc.contributor.authorCarpten, JDen_US
dc.contributor.authorPowell, Ien_US
dc.contributor.authorBailey-Wilson, Jen_US
dc.contributor.authorCussenot, Oen_US
dc.contributor.authorCancel-Tassin, Gen_US
dc.contributor.authorGiles, Gen_US
dc.contributor.authorMacInnis, Ren_US
dc.contributor.authorMaier, Cen_US
dc.contributor.authorWhittemore, ASen_US
dc.contributor.authorHsieh, C-Len_US
dc.contributor.authorWiklund, Fen_US
dc.contributor.authorCatalona, WJen_US
dc.contributor.authorFoulkes, Wen_US
dc.contributor.authorMandal, Den_US
dc.contributor.authorEeles, Ren_US
dc.contributor.authorKote-Jarai, Zen_US
dc.contributor.authorAckerman, MJen_US
dc.contributor.authorOlson, TMen_US
dc.contributor.authorKlein, CJen_US
dc.contributor.authorThibodeau, SNen_US
dc.contributor.authorSchaid, DJen_US
dc.date.accessioned2017-11-22T10:43:05Z
dc.date.issued2016-09en_US
dc.identifier.citationGenetic epidemiology, 2016, 40 (6), pp. 461 - 469en_US
dc.identifier.issn0741-0395en_US
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/919
dc.identifier.eissn1098-2272en_US
dc.identifier.doi10.1002/gepi.21983en_US
dc.description.abstractRare variants (RVs) have been shown to be significant contributors to complex disease risk. By definition, these variants have very low minor allele frequencies and traditional single-marker methods for statistical analysis are underpowered for typical sequencing study sample sizes. Multimarker burden-type approaches attempt to identify aggregation of RVs across case-control status by analyzing relatively small partitions of the genome, such as genes. However, it is generally the case that the aggregative measure would be a mixture of causal and neutral variants, and these omnibus tests do not directly provide any indication of which RVs may be driving a given association. Recently, Bayesian variable selection approaches have been proposed to identify RV associations from a large set of RVs under consideration. Although these approaches have been shown to be powerful at detecting associations at the RV level, there are often computational limitations on the total quantity of RVs under consideration and compromises are necessary for large-scale application. Here, we propose a computationally efficient alternative formulation of this method using a probit regression approach specifically capable of simultaneously analyzing hundreds to thousands of RVs. We evaluate our approach to detect causal variation on simulated data and examine sensitivity and specificity in instances of high RV dimensionality as well as apply it to pathway-level RV analysis results from a prostate cancer (PC) risk case-control sequencing study. Finally, we discuss potential extensions and future directions of this work.en_US
dc.formatPrint-Electronicen_US
dc.format.extent461 - 469en_US
dc.languageengen_US
dc.language.isoengen_US
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserveden_US
dc.subjectHumansen_US
dc.subjectBayes Theoremen_US
dc.subjectCase-Control Studiesen_US
dc.subjectSequence Analysis, DNAen_US
dc.subjectModels, Geneticen_US
dc.subjectGenetic Variationen_US
dc.subjectHigh-Throughput Nucleotide Sequencingen_US
dc.titlePost hoc Analysis for Detecting Individual Rare Variant Risk Associations Using Probit Regression Bayesian Variable Selection Methods in Case-Control Sequencing Studies.en_US
dc.typeJournal Article
dcterms.dateAccepted2016-04-27en_US
rioxxterms.versionofrecord10.1002/gepi.21983en_US
rioxxterms.licenseref.startdate2016-09en_US
rioxxterms.typeJournal Article/Reviewen_US
dc.relation.isPartOfGenetic epidemiologyen_US
pubs.issue6en_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.publication-statusPublisheden_US
pubs.volume40en_US
pubs.embargo.termsNot knownen_US
icr.researchteamOncogeneticsen_US
dc.contributor.icrauthorEeles, Rosalinden_US
dc.contributor.icrauthorKote-Jarai, Zsofiaen_US


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