Statistical performance of cladistic strategies for haplotype grouping in pharmacogenetics

Abstract

Haplotypes comprising multiple single nucleotide polymorphisms (SNPs) are popular covariates for capturing the key genetic variation present over a region of interest in the DNA sequence. Although haplotypes can provide a clearer assessment of genetic variation in a region than their component SNPs considered individually, the multi-allelic nature of haplotypes increases the complexity of the statistical models intended to discover association with outcomes of interest. Cladistic methods cluster haplotypes according to the estimates of their genealogical closeness and have been proposed recently as strategies for reducing model complexity and increasing power. Two examples are methods based on a haplotype nesting algorithm described by Templeton et al. (Genetics 1987; 117:343-351) and hierarchical clustering of haplotypes as described by Durrant et al. (Am. J. Hum. Genet. 2004; 75:35-43). In the context of assessing the pharmacogenetic effects of candidate genes, for which high-density SNP data have been gathered, we have conducted a simulation-based case study of the testing and estimation properties of two strategies based on Templeton's algorithm (TA), one being that described by Seltman et al. (Am. J. Hum. Genet. 2001; 68:1250-1263; Genet. Epidemiol. 2003; 25:48-58), as well as the method of Durrant et al. using data from a diabetes clinical trial. Even after adjusting for multiplicity, improvements in power can be realized using cladistic approaches with treatment group sizes in the range expected for standard trials, although these gains may be sensitive to the cladistic structure used. Differences in the relative performance of the cladistic approaches examined were observed with the clustering approach of Durrant et al. showing statistical properties superior to the methods based on TA.