Incorporating single-locus tests into haplotype cladistic analysis in case-control studies

Abstract

In case-control studies, genetic associations for complex diseases may be probed either with single-locus tests or with haplotype-based tests. Although there are different views on the relative merits and preferences of the two test strategies, haplotype-based analyses are generally believed to be more powerful to detect genes with modest effects. However, a main drawback of haplotype-based association tests is the large number of distinct haplotypes, which increases the degrees of freedom for corresponding test statistics and thus reduces the statistical power. To decrease the degrees of freedom and enhance the efficiency and power of haplotype analysis, we propose an improved haplotype clustering method that is based on the haplotype cladistic analysis developed by Durrant et al. In our method, we attempt to combine the strengths of single-locus analysis and haplotype-based analysis into one single test framework. Novel in our method is that we develop a more informative haplotype similarity measurement by using p-values obtained from single-locus association tests to construct a measure of weight, which to some extent incorporates the information of disease outcomes. The weights are then used in computation of similarity measures to construct distance metrics between haplotype pairs in haplotype cladistic analysis. To assess our proposed new method, we performed simulation analyses to compare the relative performances of (1) conventional haplotype-based analysis using original haplotype, (2) single-locus allele-based analysis, (3) original haplotype cladistic analysis (CLADHC) by Durrant et al., and (4) our weighted haplotype cladistic analysis method, under different scenarios. Our weighted cladistic analysis method shows an increased statistical power and robustness, compared with the methods of haplotype cladistic analysis, single-locus test, and the traditional haplotype-based analyses. The real data analyses also show that our proposed method has practical significance in the human genetics field.

Figure 1. Examples of Distributions of Clusters at the Best Partition (T[best]) in 2,000 Simulations with the Two Clustering Methods under Different Settings

A-1 and A-2 present the distributions of clusters at T[best] using our weighted cladistic analysis method and CLADHC, respectively, when original haplotypes have a high diversity (the number of distinct haplotypes is 12); B-1 and B-2 display the distributions of clusters at T[best] corresponding to using our proposed method and CLADHC when original haplotypes have a low diversity (the number of distinct haplotypes is eight). Other parameters considered in the simulations are the same for A-1, A-2, B-1, and B-2, which include: q = 0.1, r = 1.75, and assuming an additive model.