Clustering of haplotypes based on phylogeny: how good a strategy for association testing?

Haplotypes are now widely used in association studies between markers and disease susceptibility locus. However, when a large number of markers are considered, the number of possible haplotypes increases leading to two problems: an increased number of degrees of freedom that may result in a lack of power and the existence of rare haplotypes that may be difficult to take into account in the statistical analysis. In a recent paper, Durrant et al proposed a method, CLADHC, to group haplotypes based on distance matrices and showed that this could considerably increase the power of the association test as compared to either single-locus analysis or haplotype analysis without prior grouping. Although the authors considered different one-disease-locus susceptibility models in their simulations, they did not study the impact of the linkage disequilibrium (LD) pattern and of the susceptibility allele frequency on their conclusions. Here, we show, using haplotype data from five regions of the genome of different lengths and with different LD patterns, that, when a single disease susceptibility locus is simulated, the prior grouping of haplotypes based on the algorithm of Durrant et al does not increase the power of association testing except in very particular situations of LD patterns and allele frequencies.