Linkage analysis with gene‐environment interaction: model illustration and performance of ordered subset analysis
Identifieur interne : 002D52 ( Main/Corpus ); précédent : 002D51; suivant : 002D53Linkage analysis with gene‐environment interaction: model illustration and performance of ordered subset analysis
Auteurs : Silke Schmidt ; Michael A. Schmidt ; Xuejun Qin ; Eden R. Martin ; Elizabeth R. HauserSource :
- Genetic Epidemiology [ 0741-0395 ] ; 2006-07.
English descriptors
Abstract
The ordered subset analysis (OSA) method allows for the incorporation of covariates into the linkage analysis of a dichotomous disease phenotype in order to reduce genetic heterogeneity. Complex human diseases may involve gene‐environment (G × E) interactions, which represent a special form of heterogeneity. Here, we present results of a simulation study to evaluate the performance of OSA when the disease‐generating mechanism includes G × E interaction, in the absence of main effects of gene and environment. First, the complex simulation models are illustrated graphically. Second, we show that OSA is underpowered to detect small to moderate interaction effects, consistent with previous evaluations of other linkage analysis methods. When interaction effects are large enough to produce substantial marginal effects, standard linkage methods have sufficient power to detect significant baseline linkage evidence in the entire dataset. The power of OSA to improve upon a high baseline lod score is then strongly dependent on the underlying genetic model, especially the susceptibility allele frequency. If significant, OSA identifies family subsets that are more efficient for follow‐up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E)=10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per‐genotype efficiency in the subset is 20–30% greater than in the entire dataset. Genet. Epidemiol. 2006. © 2006 Wiley‐Liss, Inc.
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DOI: 10.1002/gepi.20152
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Complex human diseases may involve gene‐environment (G × E) interactions, which represent a special form of heterogeneity. Here, we present results of a simulation study to evaluate the performance of OSA when the disease‐generating mechanism includes G × E interaction, in the absence of main effects of gene and environment. First, the complex simulation models are illustrated graphically. Second, we show that OSA is underpowered to detect small to moderate interaction effects, consistent with previous evaluations of other linkage analysis methods. When interaction effects are large enough to produce substantial marginal effects, standard linkage methods have sufficient power to detect significant baseline linkage evidence in the entire dataset. The power of OSA to improve upon a high baseline lod score is then strongly dependent on the underlying genetic model, especially the susceptibility allele frequency. If significant, OSA identifies family subsets that are more efficient for follow‐up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E)=10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per‐genotype efficiency in the subset is 20–30% greater than in the entire dataset. Genet. Epidemiol. 2006. © 2006 Wiley‐Liss, Inc.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Silke Schmidt</name><affiliations><json:string>Center for Human Genetics, Duke University Medical Center, Durham, North Carolina</json:string></affiliations></json:item><json:item><name>Michael A. 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If significant, OSA identifies family subsets that are more efficient for follow‐up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E)=10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per‐genotype efficiency in the subset is 20–30% greater than in the entire dataset. Genet. 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Complex human diseases may involve gene‐environment (G × E) interactions, which represent a special form of heterogeneity. Here, we present results of a simulation study to evaluate the performance of OSA when the disease‐generating mechanism includes G × E interaction, in the absence of main effects of gene and environment. First, the complex simulation models are illustrated graphically. Second, we show that OSA is underpowered to detect small to moderate interaction effects, consistent with previous evaluations of other linkage analysis methods. When interaction effects are large enough to produce substantial marginal effects, standard linkage methods have sufficient power to detect significant baseline linkage evidence in the entire dataset. The power of OSA to improve upon a high baseline lod score is then strongly dependent on the underlying genetic model, especially the susceptibility allele frequency. If significant, OSA identifies family subsets that are more efficient for follow‐up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E)=10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per‐genotype efficiency in the subset is 20–30% greater than in the entire dataset. <i>Genet. Epidemiol</i>. 2006. © 2006 Wiley‐Liss, Inc.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Linkage analysis with gene‐environment interaction: model illustration and performance of ordered subset analysis</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Linkage Analysis with Gene‐Environment Interaction</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Linkage analysis with gene‐environment interaction: model illustration and performance of ordered subset analysis</title></titleInfo><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><description>Correspondence: Center for Human Genetics, Duke University Medical Center, Box 3445, Durham, NC 27710===</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Michael A.</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">Xuejun</namePart><namePart type="family">Qin</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 Human Genetics, Duke University Medical Center, Durham, North Carolina</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><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">2006-07</dateIssued><dateCaptured encoding="w3cdtf">2005-11-07</dateCaptured><dateValid encoding="w3cdtf">2006-03-16</dateValid><copyrightDate encoding="w3cdtf">2006</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">9</extent><extent unit="tables">1</extent><extent unit="references">23</extent></physicalDescription><abstract lang="en">The ordered subset analysis (OSA) method allows for the incorporation of covariates into the linkage analysis of a dichotomous disease phenotype in order to reduce genetic heterogeneity. Complex human diseases may involve gene‐environment (G × E) interactions, which represent a special form of heterogeneity. Here, we present results of a simulation study to evaluate the performance of OSA when the disease‐generating mechanism includes G × E interaction, in the absence of main effects of gene and environment. First, the complex simulation models are illustrated graphically. Second, we show that OSA is underpowered to detect small to moderate interaction effects, consistent with previous evaluations of other linkage analysis methods. When interaction effects are large enough to produce substantial marginal effects, standard linkage methods have sufficient power to detect significant baseline linkage evidence in the entire dataset. The power of OSA to improve upon a high baseline lod score is then strongly dependent on the underlying genetic model, especially the susceptibility allele frequency. If significant, OSA identifies family subsets that are more efficient for follow‐up analysis than the entire dataset, in terms of the proportion of susceptible genotypes among generated marker genotypes. For example, when strong G × E interaction with RR(G × E)=10 is operating in at least 70% of families in the dataset, OSA has at least 70% power to detect a subset of families with significantly greater linkage evidence, the majority of linked families are captured in the OSA subset, and the per‐genotype efficiency in the subset is 20–30% greater than in the entire dataset. Genet. Epidemiol. 2006. © 2006 Wiley‐Liss, Inc.</abstract><note type="funding">National Institute of Health - No. NEI R03‐EY015216; No. NIMH R01‐MH595228; No. NIA R01‐AG20135; </note><note type="funding">Neurosciences Education and Research Foundation.</note><subject lang="en"><genre>Keywords</genre><topic>complex human disease</topic><topic>genetic heterogeneity</topic><topic>simulation</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>2006</date><detail type="volume"><caption>vol.</caption><number>30</number></detail><detail type="issue"><caption>no.</caption><number>5</number></detail><extent unit="pages"><start>409</start><end>422</end><total>14</total></extent></part></relatedItem><identifier type="istex">3E7B51924B9DF72FA7F4F54E0BA84E39ADCAE07A</identifier><identifier type="DOI">10.1002/gepi.20152</identifier><identifier type="ArticleID">GEPI20152</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2006 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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