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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Genetic Evidence and Integration of Various Data Sources for Classifying Uncertain Variants Into a Single Model</title><author><name sortKey="Goldgar, David E" sort="Goldgar, David E" uniqKey="Goldgar D" first="David E." last="Goldgar">David E. Goldgar</name><affiliation><nlm:aff id="A1">Department of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah</nlm:aff></affiliation></author><author><name sortKey="Easton, Douglas F" sort="Easton, Douglas F" uniqKey="Easton D" first="Douglas F." last="Easton">Douglas F. Easton</name><affiliation><nlm:aff id="A2">Genetic Epidemiology Unit, Strangeways Research Laboratories, University of Cambridge, Cambridge, United Kingdom</nlm:aff></affiliation></author><author><name sortKey="Byrnes, Graham B" sort="Byrnes, Graham B" uniqKey="Byrnes G" first="Graham B." last="Byrnes">Graham B. Byrnes</name><affiliation><nlm:aff id="A3">Epidemiology Methods and Support Group, International Agency for Research on Cancer (IARC), Lyon, France</nlm:aff></affiliation></author><author><name sortKey="Spurdle, Amanda B" sort="Spurdle, Amanda B" uniqKey="Spurdle A" first="Amanda B." last="Spurdle">Amanda B. Spurdle</name><affiliation><nlm:aff id="A4">Molecular Cancer Epidemiology Laboratory, Queensland Institute for Medical Research, Brisbane, Australia</nlm:aff></affiliation></author><author><name sortKey="Iversen, Edwin S" sort="Iversen, Edwin S" uniqKey="Iversen E" first="Edwin S." last="Iversen">Edwin S. Iversen</name><affiliation><nlm:aff id="A5">Department of Statistical Science, Duke University, Durham, North Carolina</nlm:aff></affiliation></author><author><name sortKey="Greenblatt, Marc S" sort="Greenblatt, Marc S" uniqKey="Greenblatt M" first="Marc S." last="Greenblatt">Marc S. Greenblatt</name><affiliation><nlm:aff id="A6">Department of Medicine and Vermont Cancer Center, University of Vermont College of Medicine, Burlington, Vermont</nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">18951437</idno><idno type="pmc">2936773</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2936773</idno><idno type="RBID">PMC:2936773</idno><idno type="doi">10.1002/humu.20897</idno><date when="2008">2008</date><idno type="wicri:Area/Pmc/Corpus">001477</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">001477</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Genetic Evidence and Integration of Various Data Sources for Classifying Uncertain Variants Into a Single Model</title><author><name sortKey="Goldgar, David E" sort="Goldgar, David E" uniqKey="Goldgar D" first="David E." last="Goldgar">David E. Goldgar</name><affiliation><nlm:aff id="A1">Department of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah</nlm:aff></affiliation></author><author><name sortKey="Easton, Douglas F" sort="Easton, Douglas F" uniqKey="Easton D" first="Douglas F." last="Easton">Douglas F. Easton</name><affiliation><nlm:aff id="A2">Genetic Epidemiology Unit, Strangeways Research Laboratories, University of Cambridge, Cambridge, United Kingdom</nlm:aff></affiliation></author><author><name sortKey="Byrnes, Graham B" sort="Byrnes, Graham B" uniqKey="Byrnes G" first="Graham B." last="Byrnes">Graham B. Byrnes</name><affiliation><nlm:aff id="A3">Epidemiology Methods and Support Group, International Agency for Research on Cancer (IARC), Lyon, France</nlm:aff></affiliation></author><author><name sortKey="Spurdle, Amanda B" sort="Spurdle, Amanda B" uniqKey="Spurdle A" first="Amanda B." last="Spurdle">Amanda B. Spurdle</name><affiliation><nlm:aff id="A4">Molecular Cancer Epidemiology Laboratory, Queensland Institute for Medical Research, Brisbane, Australia</nlm:aff></affiliation></author><author><name sortKey="Iversen, Edwin S" sort="Iversen, Edwin S" uniqKey="Iversen E" first="Edwin S." last="Iversen">Edwin S. Iversen</name><affiliation><nlm:aff id="A5">Department of Statistical Science, Duke University, Durham, North Carolina</nlm:aff></affiliation></author><author><name sortKey="Greenblatt, Marc S" sort="Greenblatt, Marc S" uniqKey="Greenblatt M" first="Marc S." last="Greenblatt">Marc S. Greenblatt</name><affiliation><nlm:aff id="A6">Department of Medicine and Vermont Cancer Center, University of Vermont College of Medicine, Burlington, Vermont</nlm:aff></affiliation></author></analytic><series><title level="j">Human mutation</title><idno type="ISSN">1059-7794</idno><idno type="eISSN">1098-1004</idno><imprint><date when="2008">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p id="P2">Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process.</p></div></front></TEI><pmc article-type="research-article" xml:lang="EN"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<pmc-dir>properties manuscript</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9215429</journal-id><journal-id journal-id-type="pubmed-jr-id">2408</journal-id><journal-id journal-id-type="nlm-ta">Hum Mutat</journal-id><journal-title>Human mutation</journal-title><issn pub-type="ppub">1059-7794</issn><issn pub-type="epub">1098-1004</issn></journal-meta><article-meta><article-id pub-id-type="pmid">18951437</article-id><article-id pub-id-type="pmc">2936773</article-id><article-id pub-id-type="doi">10.1002/humu.20897</article-id><article-id pub-id-type="manuscript">NIHMS230627</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title-group><article-title>Genetic Evidence and Integration of Various Data Sources for Classifying Uncertain Variants Into a Single Model</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Goldgar</surname><given-names>David E.</given-names></name><xref ref-type="aff" rid="A1">1</xref><xref ref-type="corresp" rid="CR1">*</xref></contrib><contrib contrib-type="author"><name><surname>Easton</surname><given-names>Douglas F.</given-names></name><xref ref-type="aff" rid="A2">2</xref></contrib><contrib contrib-type="author"><name><surname>Byrnes</surname><given-names>Graham B.</given-names></name><xref ref-type="aff" rid="A3">3</xref></contrib><contrib contrib-type="author"><name><surname>Spurdle</surname><given-names>Amanda B.</given-names></name><xref ref-type="aff" rid="A4">4</xref></contrib><contrib contrib-type="author"><name><surname>Iversen</surname><given-names>Edwin S.</given-names></name><xref ref-type="aff" rid="A5">5</xref></contrib><contrib contrib-type="author"><name><surname>Greenblatt</surname><given-names>Marc S.</given-names></name><xref ref-type="aff" rid="A6">6</xref></contrib><contrib contrib-type="author"><collab>IARC Unclassified Genetic Variants Working Group</collab><xref ref-type="author-notes" rid="FN1">†</xref></contrib></contrib-group><aff id="A1"><label>1</label>Department of Dermatology, University of Utah School of Medicine, Salt Lake City, Utah</aff><aff id="A2"><label>2</label>Genetic Epidemiology Unit, Strangeways Research Laboratories, University of Cambridge, Cambridge, United Kingdom</aff><aff id="A3"><label>3</label>Epidemiology Methods and Support Group, International Agency for Research on Cancer (IARC), Lyon, France</aff><aff id="A4"><label>4</label>Molecular Cancer Epidemiology Laboratory, Queensland Institute for Medical Research, Brisbane, Australia</aff><aff id="A5"><label>5</label>Department of Statistical Science, Duke University, Durham, North Carolina</aff><aff id="A6"><label>6</label>Department of Medicine and Vermont Cancer Center, University of Vermont College of Medicine, Burlington, Vermont</aff><author-notes><corresp id="CR1"><label>*</label>Correspondence to: David E. Goldgar, Department of Dermatology, University of Utah School of Medicine, 30 North 1900 East, SOM A330, Salt Lake City, UT 84132-2409., <email>david.goldgar@hsc.utah.edu</email></corresp><fn id="FN1"><label>†</label><p id="P1">The members of the IARC Working Group are listed in the <xref ref-type="app" rid="APP1">Appendix</xref>.</p></fn></author-notes><pub-date pub-type="nihms-submitted"><day>30</day><month>8</month><year>2010</year></pub-date><pub-date pub-type="ppub"><month>11</month><year>2008</year></pub-date><pub-date pub-type="pmc-release"><day>10</day><month>9</month><year>2010</year></pub-date><volume>29</volume><issue>11</issue><fpage>1265</fpage><lpage>1272</lpage><permissions><copyright-statement>© 2008 Wiley-Liss, Inc.</copyright-statement><copyright-year>2008</copyright-year></permissions><abstract><p id="P2">Genetic testing often results in the finding of a variant whose clinical significance is unknown. A number of different approaches have been employed in the attempt to classify such variants. For some variants, case-control, segregation, family history, or other statistical studies can provide strong evidence of direct association with cancer risk. For most variants, other evidence is available that relates to properties of the protein or gene sequence. In this work we propose a Bayesian method for assessing the likelihood that a variant is pathogenic. We discuss the assessment of prior probability, and how to combine the various sources of data into a statistically valid integrated assessment with a posterior probability of pathogenicity. In particular, we propose the use of a two-component mixture model to integrate these various sources of data and to estimate the parameters related to sensitivity and specificity of specific kinds of evidence. Further, we discuss some of the issues involved in this process and the assumptions that underpin many of the methods used in the evaluation process.</p></abstract><kwd-group><kwd>likelihood models</kwd><kwd>unclassified variants</kwd><kwd>integrated model</kwd><kwd>classification</kwd></kwd-group><contract-num rid="CA1">R01 CA116167-01A1
||CA</contract-num><contract-num rid="CA1">R01 CA096536-01
||CA</contract-num><contract-num rid="CA1">P50 CA116201-010002
||CA</contract-num><contract-sponsor id="CA1">National Cancer Institute : NCI</contract-sponsor></article-meta></front></pmc></record>Pour manipuler ce document sous Unix (Dilib)
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