A Deterministic Method for Haplotype Inference

Haplotypes are widely used in the analysis of relationship between genetics and diseases. Due to the cost of obtaining exact haplotype pairs, genotypes which contain the un-phased information corresponding to the haplotype pairs in the test subjects are used. Various haplotype inference algorithms have been proposed to resolve the un-phased information. In this paper, we propose a deterministic sequential Monte Carlo (DSMC)-based haplotype inference algorithm which allows for large datasets in terms of number of single nucleotide polymorphisms (SNP) and number of subjects, while providing similar or better performance for datasets under various conditions.