Joint Modeling of Linkage and Association Using Affected Sib-Pair Data


Show simple item record Chen, Ming-Huei en_US Cui, Jing en_US Guo, Chao-Yu en_US Cupples, L Adrienne en_US Van Eerdewegh, Paul en_US Dupuis, Josée en_US Yang, Qiong en_US 2012-01-11T15:51:13Z 2012-01-11T15:51:13Z 2007 en_US 2007-12-18 en_US
dc.identifier.citation Chen, 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.issn 1753-6561 en_US
dc.description.abstract There 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.sponsorship National 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.iso en en_US
dc.publisher BioMed Central en_US
dc.rights 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 (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.rights.uri en_US
dc.title Joint Modeling of Linkage and Association Using Affected Sib-Pair Data en_US
dc.type article en_US
dc.identifier.pubmedid 18466536 en_US
dc.identifier.pmcid 2367481 en_US

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