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Joint modeling of linkage and association using affected sib-pair data

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-disequ...

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Autores principales: Chen, Ming-Huei, Cui, Jing, Guo, Chao-Yu, Cupples, L Adrienne, Van Eerdewegh, Paul, Dupuis, Josée, Yang, Qiong
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367481/
https://www.ncbi.nlm.nih.gov/pubmed/18466536
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author Chen, Ming-Huei
Cui, Jing
Guo, Chao-Yu
Cupples, L Adrienne
Van Eerdewegh, Paul
Dupuis, Josée
Yang, Qiong
author_facet Chen, Ming-Huei
Cui, Jing
Guo, Chao-Yu
Cupples, L Adrienne
Van Eerdewegh, Paul
Dupuis, Josée
Yang, Qiong
author_sort Chen, Ming-Huei
collection PubMed
description 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.
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spelling pubmed-23674812008-05-06 Joint modeling of linkage and association using affected sib-pair data Chen, Ming-Huei Cui, Jing Guo, Chao-Yu Cupples, L Adrienne Van Eerdewegh, Paul Dupuis, Josée Yang, Qiong BMC Proc Proceedings 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. BioMed Central 2007-12-18 /pmc/articles/PMC2367481/ /pubmed/18466536 Text en Copyright © 2007 Chen et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 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.
spellingShingle Proceedings
Chen, Ming-Huei
Cui, Jing
Guo, Chao-Yu
Cupples, L Adrienne
Van Eerdewegh, Paul
Dupuis, Josée
Yang, Qiong
Joint modeling of linkage and association using affected sib-pair data
title Joint modeling of linkage and association using affected sib-pair data
title_full Joint modeling of linkage and association using affected sib-pair data
title_fullStr Joint modeling of linkage and association using affected sib-pair data
title_full_unstemmed Joint modeling of linkage and association using affected sib-pair data
title_short Joint modeling of linkage and association using affected sib-pair data
title_sort joint modeling of linkage and association using affected sib-pair data
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367481/
https://www.ncbi.nlm.nih.gov/pubmed/18466536
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