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dc.contributor.authorChen, Ming-Hueien_US
dc.contributor.authorCui, Jingen_US
dc.contributor.authorGuo, Chao-Yuen_US
dc.contributor.authorCupples, L Adrienneen_US
dc.contributor.authorVan Eerdewegh, Paulen_US
dc.contributor.authorDupuis, Joséeen_US
dc.contributor.authorYang, Qiongen_US
dc.date.accessioned2012-01-11T15:51:13Z
dc.date.available2012-01-11T15:51:13Z
dc.date.copyright2007en_US
dc.date.issued2007-12-18en_US
dc.identifier.citationChen, Ming-Huei, Jing Cui, Chao-Yu Guo, L Adrienne Cupples, Paul Van Eerdewegh, Josée Dupuis, Qiong Yang. "Joint modeling of linkage and association using affected sib-pair data" BMC Proceedings 1(Suppl 1):S38. (2007)en_US
dc.identifier.issn1753-6561en_US
dc.identifier.urihttp://hdl.handle.net/2144/3077
dc.description.abstractThere has been a growing interest in developing strategies for identifying single-nucleotide polymorphisms (SNPs) that explain a linkage signal by joint modeling of linkage and association. We compare several existing methods and propose a new method called the homozygote sharing transmission-disequilibrium test (HSTDT) to detect linkage and association or to identify SNPs explaining the linkage signal on chromosome 6 for rheumatoid arthritis using 100 replicates of the Genetic Analysis Workshop (GAW) 15 simulated affected sib-pair data. Existing methods considered included the family-based tests of association implemented in FBAT, a transmission-disequilibrium test, a conditional logistic regression approach, a likelihood-based approach implemented in LAMP, and the homozygote sharing test (HST). We compared the type I error rates and power for tests classified into three categories according to their null hypotheses: 1) no association in the presence of linkage (i.e., a SNP explains none of the linkage evidence), 2) no linkage adjusting for the association (i.e., a SNP explains all linkage evidence), and 3) no linkage and no association. For testing association in the presence of linkage, we found similar power among all tests except for the homozygote sharing test that had lower power. When testing linkage adjusting for association, similar power was observed between LAMP and HST, but lower power for the conditional logistic regression method. When testing linkage or association, the conditional logistic regression method was more powerful than FBAT.en_US
dc.description.sponsorshipNational Heart, Lung and Blood Institute's Framingham Heart Study (NO1-HC-25195); Millennium Phramaceuticals Bringham Rheumatoid Arthritis Sequential Study; National Institutes of Health Linux Cluster for Genetic Analysis Shared Instrumentation Grant (1S10 RR163736-01A1)en_US
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rightsCopyright 2007 Chen et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/2.0en_US
dc.titleJoint Modeling of Linkage and Association Using Affected Sib-Pair Dataen_US
dc.typearticleen_US
dc.identifier.pubmedid18466536en_US
dc.identifier.pmcid2367481en_US


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Copyright 2007 Chen et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as Copyright 2007 Chen et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.