Haplotype analysis is attracting greater interest in genetic studies of human diseases because of its ability to identify unique chromosomal segments which can harbor disease-predisposing genes. The study of haplotypes is also used to investigate many population processes, such as migration and immigration rates, linkage-disequilibrium strength, and the relatedness of populations.

Haplotype Frequency Viewer

Genetic data for HelixTree is assumed to be phase-ambiguous - that is, for each patient and marker locus, the two alleles are known, but it is not known which allele belongs to which chromosome.

However, what are often of interest are patterns of alleles over different loci on the same chromosome, that is, haplotypes. The module view shown here allows estimation of haplotype frequencies for selected loci using the Expectation/Maximization (EM) algorithm. Starting with either random or composite haplotype method estimated probabilities, each iteration of the EM algorithm computes would-be probabilities for the multi-locus genotypes based on the probabilities of their corresponding haplotypes as previously estimated or as initialized, assuming random mating (the "Expectation" step). The iteration then finishes by re-estimating the haplotype probabilities based on the ratios of the computed genotype probabilities to the actual ones (the "Maximization" step).

We used the approached outlined in: Excoffier L., Slatkin M. (1995) Maximum-likelihood estimation of molecular haplotype frequencies in a diploid population. Molecular Biology and Evolution 12: 921-927. Our haplotype frequency estimation routine handles missing values and is one of the fastest haplotype estimation software implementations available.