Haplotype Inference and Haplotype Information

Abstract

Haplotypes have become increasingly popular because of the abundance of single nucleotide polymorphisms (SNPs) and the limited power of the single-locus analyses. To contend with some weaknesses of the existing haplotype inference methods, we propose new algorithms based on the partition-ligation idea. In particular, we first partition the whole haplotype into smaller segments. Then, we use either the Gibbs sampler or the EM algorithm to construct the partial haplotypes of each segment and to assemble all the segments together. Our algorithm can infer haplotype frequencies rapidly and accurately for a large number of linked SNPs and provides a robust estimate of their standard deviations. The algorithms are robust to the violation of Hardy-Weinberg equilibrium and can handle missing marker data easily. As a follow-up study, we also investigated two related questions: how much the haplotype information contributes to linkage disequilibrium (LD) mapping and whether an in silico haplotype construction preceding the LD analysis can help. For simple disease gene mapping our conclusions are as follows: (a) if a proper statistical model is employed, the loss of haplotype information for either control or disease data do not have a great impact on LD fine mapping, and (b) haplotype inference should be carried out jointly with LD analysis to achieve the most accurate location estimation.