This article is part of the supplement: Genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional Loci

Open Access Proceedings

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

Ming-Huei Chen1*, Jing Cui2, Chao-Yu Guo1, L Adrienne Cupples3, Paul Van Eerdewegh4, Josée Dupuis3 and Qiong Yang3

  • * Corresponding author: Ming-Huei Chen

  • † Equal contributors

Author Affiliations

1 Department of Mathematics and Statistics, Boston University, 111 Cummington Street, Boston, Massachusetts 02115, USA

2 Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 221 Longwood Avenue 341G, Boston, Massachusetts 02115, USA

3 Department of Biostatistics, Boston University School of Public Health, 715 Albany Street, Boston, Massachusetts 02118, USA

4 Genizon BioSciences Inc., 880 McCaffrey, Montreal, Quebec H4T 2C7, Canada

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BMC Proceedings 2007, 1(Suppl 1):S38  doi:

Published: 18 December 2007


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.