Haplotype association analysis of discrete and continuous traits using mixture of regression models.

We present a regression-based method of haplotype association analysis for quantitative and dichotomous traits in samples consisting of unrelated individuals. The method takes account of uncertain phase by initially estimating haplotype frequencies and obtaining the posterior probabilities of all possible haplotype combinations in each individual, then using these as weights in a finite mixture of regression models. Using this method, different combinations of marker loci can be modeled, to find a parsimonious set of marker loci that are most predictive and therefore most likely to be closely associated with the a quantitative trait locus. The method has the additional advantage of being able to use individuals with some missing genotype data, by considering all possible genotypes at the missing markers. We have implemented this method using the SNPHAP and Mx programs and illustrated its use on published data on idiopathic generalized epilepsy.