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dc.contributor.authorMacInnis, RJ
dc.contributor.authorSchmidt, DF
dc.contributor.authorMakalic, E
dc.contributor.authorSeveri, G
dc.contributor.authorFitzGerald, LM
dc.contributor.authorReumann, M
dc.contributor.authorKapuscinski, MK
dc.contributor.authorKowalczyk, A
dc.contributor.authorZhou, Z
dc.contributor.authorGoudey, B
dc.contributor.authorQian, G
dc.contributor.authorBui, QM
dc.contributor.authorPark, DJ
dc.contributor.authorFreeman, A
dc.contributor.authorSouthey, MC
dc.contributor.authorAl Olama, AA
dc.contributor.authorKote-Jarai, Z
dc.contributor.authorEeles, RA
dc.contributor.authorHopper, JL
dc.contributor.authorGiles, GG
dc.contributor.authorUK Genetic Prostate Cancer Study Collaborators
dc.date.accessioned2017-11-29T12:03:55Z
dc.date.issued2016-12
dc.identifier.citationCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2016, 25 (12), pp. 1619 - 1624
dc.identifier.issn1055-9965
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/959
dc.identifier.eissn1538-7755
dc.identifier.doi10.1158/1055-9965.epi-16-0301
dc.description.abstractBackground We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis.Methods We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array.Results From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions.Conclusions DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.Impact This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619-24. ©2016 AACR.
dc.formatPrint-Electronic
dc.format.extent1619 - 1624
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://www.rioxx.net/licenses/all-rights-reserved
dc.subjectUK Genetic Prostate Cancer Study Collaborators
dc.subjectHumans
dc.subjectProstatic Neoplasms
dc.subjectGenetic Predisposition to Disease
dc.subjectPolymorphism, Single Nucleotide
dc.subjectMiddle Aged
dc.subjectAustralia
dc.subjectMale
dc.subjectGenome-Wide Association Study
dc.subjectMachine Learning
dc.subjectUnited Kingdom
dc.titleUse of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.
dc.typeJournal Article
dcterms.dateAccepted2016-08-04
rioxxterms.versionofrecord10.1158/1055-9965.epi-16-0301
rioxxterms.licenseref.urihttps://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2016-12
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfCancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
pubs.issue12
pubs.notesNot known
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
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-statusPublished
pubs.volume25
pubs.embargo.termsNot known
icr.researchteamOncogeneticsen_US
dc.contributor.icrauthorEeles, Rosalinden
dc.contributor.icrauthorKote-Jarai, Zsofiaen


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