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

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

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Title: Joint Modeling of Linkage and Association Using Affected Sib-pair Data
Author: Chen, Ming-Huei; Cupples, L Adrienne; Van Eerdewegh, Paul; Dupuis, Josée; Yang, Qiong; Cui, Jing; Guo, Chao-Yu

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Citation: Chen, Ming-Huei, Jing Cui, Chao-Yu Guo, L Adrienne Cupples, Paul Van Eerdewegh, Josée Dupuis, and Qiong Yang. 2007. Joint modeling of linkage and association using affected sib-pair data. BMC Proceedings 1(Suppl 1): S38.
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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.
Published Version: http://www.biomedcentral.com/1753-6561/1/S1/S38
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367481/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4872954

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