Combining an Evolution-guided Clustering Algorithm and Haplotype-based LRT in Family Association Studies

BMC Genetics

Methodology Article

Mei-Hsien Lee¹  Chuhsing Kate Hsiao²  Jung-Ying Tzeng³  Su-Yun Huang⁴ 

[1] Department of Mathematics and Computer Science Education, Taipei Municipal University of Education, 10048, Taipei, Taiwan;Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, 10055, Taipei, Taiwan;Bioinformatics and Biostatistics Core, NTU Center for Genomic Medicine, National Taiwan University, 10055, Taipei, Taiwan;Research Center for Gene, Environment, and Human Health, College of Public Health, National Taiwan University, 10055, Taipei, Taiwan;Department of Statistics and Bioinformatics Research Center, North Carolina State University, 27695, Raleigh, North Carolina, USA;Institute of Statistical Science, Academia Sinica, 11529, Taipei, Taiwan;

关键词: Likelihood Ratio Test;    Haplotype Frequency;    Recessive Model;    Family Data;    Phase Ambiguity;   

DOI  :  10.1186/1471-2156-12-48

 received in 2010-10-03, accepted in 2011-05-19,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundWith the completion of the international HapMap project, many studies have been conducted to investigate the association between complex diseases and haplotype variants. Such haplotype-based association studies, however, often face two difficulties; one is the large number of haplotype configurations in the chromosome region under study, and the other is the ambiguity in haplotype phase when only genotype data are observed. The latter complexity may be handled based on an EM algorithm with family data incorporated, whereas the former can be more problematic, especially when haplotypes of rare frequencies are involved. Here based on family data we propose to cluster long haplotypes of linked SNPs in a biological sense, so that the number of haplotypes can be reduced and the power of statistical tests of association can be increased.ResultsIn this paper we employ family genotype data and combine a clustering scheme with a likelihood ratio statistic to test the association between quantitative phenotypes and haplotype variants. Haplotypes are first grouped based on their evolutionary closeness to establish a set containing core haplotypes. Then, we construct for each family the transmission and non-transmission phase in terms of these core haplotypes, taking into account simultaneously the phase ambiguity as weights. The likelihood ratio test (LRT) is next conducted with these weighted and clustered haplotypes to test for association with disease. This combination of evolution-guided haplotype clustering and weighted assignment in LRT is able, via its core-coding system, to incorporate into analysis both haplotype phase ambiguity and transmission uncertainty. Simulation studies show that this proposed procedure is more informative and powerful than three family-based association tests, FAMHAP, FBAT, and an LRT with a group consisting exclusively of rare haplotypes.ConclusionsThe proposed procedure takes into account the uncertainty in phase determination and in transmission, utilizes the evolutionary information contained in haplotypes, reduces the dimension in haplotype space and the degrees of freedom in tests, and performs better in association studies. This evolution-guided clustering procedure is particularly useful for long haplotypes containing linked SNPs, and is applicable to other haplotype-based association tests. This procedure is now implemented in R and is free for download.

【 授权许可】

Unknown   
© Lee et al; licensee BioMed Central Ltd. 2011. This article is published under license to 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.