Ordered‐subset analysis (OSA) for family‐based association mapping of complex traits
Identifieur interne : 002605 ( Main/Corpus ); précédent : 002604; suivant : 002606Ordered‐subset analysis (OSA) for family‐based association mapping of complex traits
Auteurs : Ren-Hua Chung ; Silke Schmidt ; Eden R. Martin ; Elizabeth R. HauserSource :
- Genetic Epidemiology [ 0741-0395 ] ; 2008-11.
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- KwdEn :
Abstract
Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered‐subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait‐related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family‐based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL‐OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL‐OSA statistic under the null hypothesis that there is no relationship between the family‐specific covariate and the family‐specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL‐OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL‐OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL‐OSA to a family study of age‐related macular degeneration, where cigarette smoking was used as a covariate. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.
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DOI: 10.1002/gepi.20340
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However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered‐subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait‐related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family‐based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL‐OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL‐OSA statistic under the null hypothesis that there is no relationship between the family‐specific covariate and the family‐specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL‐OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL‐OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL‐OSA to a family study of age‐related macular degeneration, where cigarette smoking was used as a covariate. Genet. 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A permutation procedure is used to approximate the distribution of the APL‐OSA statistic under the null hypothesis that there is no relationship between the family‐specific covariate and the family‐specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL‐OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL‐OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL‐OSA to a family study of age‐related macular degeneration, where cigarette smoking was used as a covariate. Genet. 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A permutation procedure is used to approximate the distribution of the APL‐OSA statistic under the null hypothesis that there is no relationship between the family‐specific covariate and the family‐specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL‐OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL‐OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL‐OSA to a family study of age‐related macular degeneration, where cigarette smoking was used as a covariate. Genet. 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However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered‐subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait‐related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family‐based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL‐OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL‐OSA statistic under the null hypothesis that there is no relationship between the family‐specific covariate and the family‐specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL‐OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL‐OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL‐OSA to a family study of age‐related macular degeneration, where cigarette smoking was used as a covariate. <i>Genet. Epidemiol</i>. 2008. © 2008 Wiley‐Liss, Inc.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Ordered‐subset analysis (OSA) for family‐based association mapping of complex traits</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>OSA for Family‐Based Association Mapping</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Ordered‐subset analysis (OSA) for family‐based association mapping of complex traits</title></titleInfo><name type="personal"><namePart type="given">Ren‐Hua</namePart><namePart type="family">Chung</namePart><affiliation>Center for Human Genetics, Duke University Medical Center, Durham, North Carolina</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Silke</namePart><namePart type="family">Schmidt</namePart><affiliation>Center for Human Genetics, Duke University Medical Center, Durham, North Carolina</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Eden R.</namePart><namePart type="family">Martin</namePart><affiliation>Center for Genetic Epidemiology and Statistical Genetics, Miami Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, Florida</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Elizabeth R.</namePart><namePart type="family">Hauser</namePart><affiliation>Center for Human Genetics, Duke University Medical Center, Durham, North Carolina</affiliation><description>Correspondence: Center for Human Genetics, Duke University Medical Center, Box 3445, Durham, NC 27710===</description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2008-11</dateIssued><dateCaptured encoding="w3cdtf">2008-01-17</dateCaptured><dateValid encoding="w3cdtf">2008-03-16</dateValid><copyrightDate encoding="w3cdtf">2008</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">4</extent><extent unit="tables">4</extent><extent unit="references">39</extent></physicalDescription><abstract lang="en">Association analysis provides a powerful tool for complex disease gene mapping. However, in the presence of genetic heterogeneity, the power for association analysis can be low since only a fraction of the collected families may carry a specific disease susceptibility allele. Ordered‐subset analysis (OSA) is a linkage test that can be powerful in the presence of genetic heterogeneity. OSA uses trait‐related covariates to identify a subset of families that provide the most evidence for linkage. A similar strategy applied to genetic association analysis would likely result in increased power to detect association. Association in the presence of linkage (APL) is a family‐based association test (FBAT) for nuclear families with multiple affected siblings that properly infers missing parental genotypes when linkage is present. We propose here APL‐OSA, which applies the OSA method to the APL statistic to identify a subset of families that provide the most evidence for association. A permutation procedure is used to approximate the distribution of the APL‐OSA statistic under the null hypothesis that there is no relationship between the family‐specific covariate and the family‐specific evidence for allelic association. We performed a comprehensive simulation study to verify that APL‐OSA has the correct type I error rate under the null hypothesis. This simulation study also showed that APL‐OSA can increase power relative to other commonly used association tests (APL, FBAT and FBAT with covariate adjustment) in the presence of genetic heterogeneity. Finally, we applied APL‐OSA to a family study of age‐related macular degeneration, where cigarette smoking was used as a covariate. Genet. Epidemiol. 2008. © 2008 Wiley‐Liss, Inc.</abstract><note type="funding">National Institutes of Health (NIMH) - No. R01 MH595228; </note><note type="funding">Neurosciences Education and Research Foundation</note><subject lang="en"><genre>Keywords</genre><topic>family‐based association analysis</topic><topic>linkage</topic><topic>ordered‐subset analysis</topic><topic>covariate</topic><topic>genetic heterogeneity</topic></subject><relatedItem type="host"><titleInfo><title>Genetic Epidemiology</title><subTitle>The Official Publication of the International Genetic Epidemiology Society</subTitle></titleInfo><titleInfo type="abbreviated"><title>Genet. Epidemiol.</title></titleInfo><genre type="Journal">journal</genre><subject><genre>article category</genre><topic>Original Article</topic></subject><identifier type="ISSN">0741-0395</identifier><identifier type="eISSN">1098-2272</identifier><identifier type="DOI">10.1002/(ISSN)1098-2272</identifier><identifier type="PublisherID">GEPI</identifier><part><date>2008</date><detail type="volume"><caption>vol.</caption><number>32</number></detail><detail type="issue"><caption>no.</caption><number>7</number></detail><extent unit="pages"><start>627</start><end>637</end><total>12</total></extent></part></relatedItem><identifier type="istex">3D20476BBA5B3223966E298661D4E9E274FA55FC</identifier><identifier type="DOI">10.1002/gepi.20340</identifier><identifier type="ArticleID">GEPI20340</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2008 Wiley‐Liss, Inc.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Wiley Subscription Services, Inc., A Wiley Company</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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