Department of Public Health and Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei 10055, Taiwan.
BMC Genet. 2011 May 19;12:48. doi: 10.1186/1471-2156-12-48.
With 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.
In 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.
The 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.
随着国际 HapMap 计划的完成，许多研究已经开展，以调查复杂疾病与单倍型变异之间的关联。然而，这种基于单倍型的关联研究通常面临两个困难；一个是研究染色体区域中单倍型构型的数量众多，另一个是当仅观察基因型数据时单倍型相位的不明确性。后者的复杂性可以基于包含家族数据的 EM 算法来处理，而前者则可能更加复杂，特别是当涉及罕见频率的单倍型时。在这里，我们基于家族数据提出了一种方法，即将连锁 SNPs 的长单倍型在生物学意义上进行聚类，从而减少单倍型的数量，并提高关联的统计检验的功效。
在本文中，我们使用家族基因型数据，并结合聚类方案和似然比统计量来检验数量性状与单倍型变异之间的关联。首先，根据单倍型的进化关系对其进行分组，以建立一组包含核心单倍型的集合。然后，我们根据这些核心单倍型为每个家族构建传递和非传递相位，同时考虑相位模糊性作为权重。接下来，我们使用这些加权和聚类的单倍型进行似然比检验（LRT），以检验与疾病的关联。这种基于进化的单倍型聚类和 LRT 中加权分配的组合，通过其核心编码系统，能够将单倍型相位模糊性和传递不确定性纳入分析。模拟研究表明，与三种基于家族的关联检验（FAMHAP、FBAT 和仅包含罕见单倍型的 LRT）相比，该方法更具信息量和更有效。
所提出的方法考虑了相位确定和传递的不确定性，利用了单倍型中包含的进化信息，减少了单倍型空间的维度和检验的自由度，并在关联研究中表现更好。这种基于进化的聚类程序对于包含连锁 SNPs 的长单倍型特别有用，并且适用于其他基于单倍型的关联检验。该程序现在已在 R 中实现，并可免费下载。
