Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.
Identifieur interne : 001546 ( PubMed/Corpus ); précédent : 001545; suivant : 001547Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.
Auteurs : Robert J. Macinnis ; Daniel F. Schmidt ; Enes Makalic ; Gianluca Severi ; Liesel M. Fitzgerald ; Matthias Reumann ; Miroslaw K. Kapuscinski ; Adam Kowalczyk ; Zeyu Zhou ; Benjamin Goudey ; Guoqi Qian ; Quang M. Bui ; Daniel J. Park ; Adam Freeman ; Melissa C. Southey ; Ali Amin Al Olama ; Zsofia Kote-Jarai ; Rosalind A. Eeles ; John L. Hopper ; Graham G. GilesSource :
- Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology [ 1538-7755 ] ; 2016.
Abstract
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.
DOI: 10.1158/1055-9965.EPI-16-0301
PubMed: 27539266
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Schmidt</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Makalic, Enes" sort="Makalic, Enes" uniqKey="Makalic E" first="Enes" last="Makalic">Enes Makalic</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Severi, Gianluca" sort="Severi, Gianluca" uniqKey="Severi G" first="Gianluca" last="Severi">Gianluca Severi</name><affiliation><nlm:affiliation>Human Genetics Foundation, Torino, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Fitzgerald, Liesel M" sort="Fitzgerald, Liesel M" uniqKey="Fitzgerald L" first="Liesel M" last="Fitzgerald">Liesel M. Fitzgerald</name><affiliation><nlm:affiliation>Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania.</nlm:affiliation></affiliation></author><author><name sortKey="Reumann, Matthias" sort="Reumann, Matthias" uniqKey="Reumann M" first="Matthias" last="Reumann">Matthias Reumann</name><affiliation><nlm:affiliation>IBM Research, Zurich, Switzerland.</nlm:affiliation></affiliation></author><author><name sortKey="Kapuscinski, Miroslaw K" sort="Kapuscinski, Miroslaw K" uniqKey="Kapuscinski M" first="Miroslaw K" last="Kapuscinski">Miroslaw K. Kapuscinski</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Kowalczyk, Adam" sort="Kowalczyk, Adam" uniqKey="Kowalczyk A" first="Adam" last="Kowalczyk">Adam Kowalczyk</name><affiliation><nlm:affiliation>Warsaw University of Technology, Warsaw, Poland.</nlm:affiliation></affiliation></author><author><name sortKey="Zhou, Zeyu" sort="Zhou, Zeyu" uniqKey="Zhou Z" first="Zeyu" last="Zhou">Zeyu Zhou</name><affiliation><nlm:affiliation>IBM Research - Australia, Carlton, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Goudey, Benjamin" sort="Goudey, Benjamin" uniqKey="Goudey B" first="Benjamin" last="Goudey">Benjamin Goudey</name><affiliation><nlm:affiliation>IBM Research - Australia, Carlton, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Qian, Guoqi" sort="Qian, Guoqi" uniqKey="Qian G" first="Guoqi" last="Qian">Guoqi Qian</name><affiliation><nlm:affiliation>School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Bui, Quang M" sort="Bui, Quang M" uniqKey="Bui Q" first="Quang M" last="Bui">Quang M. Bui</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Park, Daniel J" sort="Park, Daniel J" uniqKey="Park D" first="Daniel J" last="Park">Daniel J. Park</name><affiliation><nlm:affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Freeman, Adam" sort="Freeman, Adam" uniqKey="Freeman A" first="Adam" last="Freeman">Adam Freeman</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Southey, Melissa C" sort="Southey, Melissa C" uniqKey="Southey M" first="Melissa C" last="Southey">Melissa C. Southey</name><affiliation><nlm:affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Al Olama, Ali Amin" sort="Al Olama, Ali Amin" uniqKey="Al Olama A" first="Ali Amin" last="Al Olama">Ali Amin Al Olama</name><affiliation><nlm:affiliation>Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Kote Jarai, Zsofia" sort="Kote Jarai, Zsofia" uniqKey="Kote Jarai Z" first="Zsofia" last="Kote-Jarai">Zsofia Kote-Jarai</name><affiliation><nlm:affiliation>The Institute of Cancer Research, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Eeles, Rosalind A" sort="Eeles, Rosalind A" uniqKey="Eeles R" first="Rosalind A" last="Eeles">Rosalind A. Eeles</name></author><author><name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L" last="Hopper">John L. Hopper</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia. j.hopper@unimelb.edu.au.</nlm:affiliation></affiliation></author><author><name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G" last="Giles">Graham G. Giles</name><affiliation><nlm:affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</nlm:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2016">2016</date><idno type="RBID">pubmed:27539266</idno><idno type="pmid">27539266</idno><idno type="doi">10.1158/1055-9965.EPI-16-0301</idno><idno type="wicri:Area/PubMed/Corpus">001546</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">001546</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.</title><author><name sortKey="Macinnis, Robert J" sort="Macinnis, Robert J" uniqKey="Macinnis R" first="Robert J" last="Macinnis">Robert J. Macinnis</name><affiliation><nlm:affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Schmidt, Daniel F" sort="Schmidt, Daniel F" uniqKey="Schmidt D" first="Daniel F" last="Schmidt">Daniel F. Schmidt</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Makalic, Enes" sort="Makalic, Enes" uniqKey="Makalic E" first="Enes" last="Makalic">Enes Makalic</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Severi, Gianluca" sort="Severi, Gianluca" uniqKey="Severi G" first="Gianluca" last="Severi">Gianluca Severi</name><affiliation><nlm:affiliation>Human Genetics Foundation, Torino, Italy.</nlm:affiliation></affiliation></author><author><name sortKey="Fitzgerald, Liesel M" sort="Fitzgerald, Liesel M" uniqKey="Fitzgerald L" first="Liesel M" last="Fitzgerald">Liesel M. Fitzgerald</name><affiliation><nlm:affiliation>Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania.</nlm:affiliation></affiliation></author><author><name sortKey="Reumann, Matthias" sort="Reumann, Matthias" uniqKey="Reumann M" first="Matthias" last="Reumann">Matthias Reumann</name><affiliation><nlm:affiliation>IBM Research, Zurich, Switzerland.</nlm:affiliation></affiliation></author><author><name sortKey="Kapuscinski, Miroslaw K" sort="Kapuscinski, Miroslaw K" uniqKey="Kapuscinski M" first="Miroslaw K" last="Kapuscinski">Miroslaw K. Kapuscinski</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Kowalczyk, Adam" sort="Kowalczyk, Adam" uniqKey="Kowalczyk A" first="Adam" last="Kowalczyk">Adam Kowalczyk</name><affiliation><nlm:affiliation>Warsaw University of Technology, Warsaw, Poland.</nlm:affiliation></affiliation></author><author><name sortKey="Zhou, Zeyu" sort="Zhou, Zeyu" uniqKey="Zhou Z" first="Zeyu" last="Zhou">Zeyu Zhou</name><affiliation><nlm:affiliation>IBM Research - Australia, Carlton, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Goudey, Benjamin" sort="Goudey, Benjamin" uniqKey="Goudey B" first="Benjamin" last="Goudey">Benjamin Goudey</name><affiliation><nlm:affiliation>IBM Research - Australia, Carlton, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Qian, Guoqi" sort="Qian, Guoqi" uniqKey="Qian G" first="Guoqi" last="Qian">Guoqi Qian</name><affiliation><nlm:affiliation>School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Bui, Quang M" sort="Bui, Quang M" uniqKey="Bui Q" first="Quang M" last="Bui">Quang M. Bui</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Park, Daniel J" sort="Park, Daniel J" uniqKey="Park D" first="Daniel J" last="Park">Daniel J. Park</name><affiliation><nlm:affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Freeman, Adam" sort="Freeman, Adam" uniqKey="Freeman A" first="Adam" last="Freeman">Adam Freeman</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Southey, Melissa C" sort="Southey, Melissa C" uniqKey="Southey M" first="Melissa C" last="Southey">Melissa C. Southey</name><affiliation><nlm:affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.</nlm:affiliation></affiliation></author><author><name sortKey="Al Olama, Ali Amin" sort="Al Olama, Ali Amin" uniqKey="Al Olama A" first="Ali Amin" last="Al Olama">Ali Amin Al Olama</name><affiliation><nlm:affiliation>Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Kote Jarai, Zsofia" sort="Kote Jarai, Zsofia" uniqKey="Kote Jarai Z" first="Zsofia" last="Kote-Jarai">Zsofia Kote-Jarai</name><affiliation><nlm:affiliation>The Institute of Cancer Research, London, United Kingdom.</nlm:affiliation></affiliation></author><author><name sortKey="Eeles, Rosalind A" sort="Eeles, Rosalind A" uniqKey="Eeles R" first="Rosalind A" last="Eeles">Rosalind A. Eeles</name></author><author><name sortKey="Hopper, John L" sort="Hopper, John L" uniqKey="Hopper J" first="John L" last="Hopper">John L. Hopper</name><affiliation><nlm:affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia. j.hopper@unimelb.edu.au.</nlm:affiliation></affiliation></author><author><name sortKey="Giles, Graham G" sort="Giles, Graham G" uniqKey="Giles G" first="Graham G" last="Giles">Graham G. Giles</name><affiliation><nlm:affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology</title><idno type="eISSN">1538-7755</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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.</div></front></TEI><pubmed><MedlineCitation Status="In-Process" Owner="NLM"><PMID Version="1">27539266</PMID><DateCreated><Year>2016</Year><Month>08</Month><Day>19</Day></DateCreated><DateRevised><Year>2017</Year><Month>06</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1538-7755</ISSN><JournalIssue CitedMedium="Internet"><Volume>25</Volume><Issue>12</Issue><PubDate><Year>2016</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology</Title><ISOAbbreviation>Cancer Epidemiol. Biomarkers Prev.</ISOAbbreviation></Journal><ArticleTitle>Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.</ArticleTitle><Pagination><MedlinePgn>1619-1624</MedlinePgn></Pagination><Abstract><AbstractText Label="BACKGROUND" NlmCategory="BACKGROUND">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.</AbstractText><AbstractText Label="METHODS" NlmCategory="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.</AbstractText><AbstractText Label="RESULTS" NlmCategory="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.</AbstractText><AbstractText Label="CONCLUSIONS" NlmCategory="CONCLUSIONS">DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.</AbstractText><AbstractText Label="IMPACT" NlmCategory="CONCLUSIONS">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.</AbstractText><CopyrightInformation>©2016 American Association for Cancer Research.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>MacInnis</LastName><ForeName>Robert J</ForeName><Initials>RJ</Initials><AffiliationInfo><Affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Schmidt</LastName><ForeName>Daniel F</ForeName><Initials>DF</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Makalic</LastName><ForeName>Enes</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Severi</LastName><ForeName>Gianluca</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>Human Genetics Foundation, Torino, Italy.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Gustave Roussy, F-94805, Villejuif, France.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>FitzGerald</LastName><ForeName>Liesel M</ForeName><Initials>LM</Initials><AffiliationInfo><Affiliation>Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Reumann</LastName><ForeName>Matthias</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>IBM Research, Zurich, Switzerland.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>UNU-MERIT (United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology), Maastricht University, Maastricht, the Netherlands.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kapuscinski</LastName><ForeName>Miroslaw K</ForeName><Initials>MK</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kowalczyk</LastName><ForeName>Adam</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Warsaw University of Technology, Warsaw, Poland.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhou</LastName><ForeName>Zeyu</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>IBM Research - Australia, Carlton, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Goudey</LastName><ForeName>Benjamin</ForeName><Initials>B</Initials><AffiliationInfo><Affiliation>IBM Research - Australia, Carlton, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qian</LastName><ForeName>Guoqi</ForeName><Initials>G</Initials><AffiliationInfo><Affiliation>School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Bui</LastName><ForeName>Quang M</ForeName><Initials>QM</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Park</LastName><ForeName>Daniel J</ForeName><Initials>DJ</Initials><AffiliationInfo><Affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Melbourne Bioinformatics Platform, Victorian Life Sciences Computation Initiative, University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Freeman</LastName><ForeName>Adam</ForeName><Initials>A</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Southey</LastName><ForeName>Melissa C</ForeName><Initials>MC</Initials><AffiliationInfo><Affiliation>Genetic Epidemiology Laboratory, Department of Pathology, University of Melbourne, Victoria, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Al Olama</LastName><ForeName>Ali Amin</ForeName><Initials>AA</Initials><AffiliationInfo><Affiliation>Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Kote-Jarai</LastName><ForeName>Zsofia</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>The Institute of Cancer Research, London, United Kingdom.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Eeles</LastName><ForeName>Rosalind A</ForeName><Initials>RA</Initials></Author><Author ValidYN="Y"><LastName>Hopper</LastName><ForeName>John L</ForeName><Initials>JL</Initials><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia. j.hopper@unimelb.edu.au.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Department of Epidemiology, School of Public Health and Institute of Health and Environment, Seoul National University, Seoul, South Korea.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Giles</LastName><ForeName>Graham G</ForeName><Initials>GG</Initials><AffiliationInfo><Affiliation>Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, Australia.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Centre for Epidemiology and Biostatistics, University of Melbourne, Melbourne, Australia.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><CollectiveName>UK Genetic Prostate Cancer Study Collaborators</CollectiveName></Author></AuthorList><Language>eng</Language><GrantList CompleteYN="Y"><Grant><GrantID>U19 CA148537</GrantID><Acronym>CA</Acronym><Agency>NCI NIH HHS</Agency><Country>United States</Country></Grant></GrantList><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>08</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Cancer Epidemiol Biomarkers Prev</MedlineTA><NlmUniqueID>9200608</NlmUniqueID><ISSNLinking>1055-9965</ISSNLinking></MedlineJournalInfo><CommentsCorrectionsList><CommentsCorrections RefType="Cites"><RefSource>Nat Rev Urol. 2014 Jan;11(1):18-31</RefSource><PMID Version="1">24296704</PMID></CommentsCorrections><CommentsCorrections RefType="Cites"><RefSource>Comput 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Version="1">12783937</PMID></CommentsCorrections></CommentsCorrectionsList><CoiStatement>None of the authors declare any conflicts of interest.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2016</Year><Month>04</Month><Day>11</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2016</Year><Month>07</Month><Day>13</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>08</Month><Day>04</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>8</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>8</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>8</Month><Day>20</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId 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