Haplotyping approaches such as those implemented in MaCH and IMPUTE2 proceed through a series of iterative steps. In each step, a new pair of haplotypes is sampled for each individual as an imperfect mosaic22 of the estimated haplotypes (“templates”) for other individuals in the dataset. After a number of iterations, “best-guess” haplotypes are constructed for each individual by combining information across the sampled haplotype configurations; both MaCH and IMPUTE2 perform this step by minimizing the expected switch error rate23. The computational cost of phasing with these methods depends on the number of iterations performed and the number of template haplotypes that are used in each update. For the experiments described here, we used 20 iterations and 200 – 400 templates for MaCH and 30 iterations (first 10 discarded as burn-in) and 80 templates for IMPUTE2. These methods differ in various details, such as how they fit the parameters of their models and how they choose templates for each haplotype sampling step; further information is provided in the original papers6,12.
