Haplotype inference for population data with genotyping errors.

Inference of haplotypes is important in genetic epidemiology studies. However, all large genotype data sets have errors due to the use of inexpensive genotyping machines that are fallible and shortcomings in genotyping scoring softwares, which can have an enormous impact on haplotype inference. In this article, we propose two novel strategies to reduce the impact induced by genotyping errors in haplotype inference. The first method makes use of double sampling. For each individual, the "GenoSpectrum" that consists of all possible genotypes and their corresponding likelihoods are computed. The second method is a genotype clustering algorithm based on multi-genotyping data, which also assigns a "GenoSpectrum" for each individual. We then describe two hybrid EM algorithms (called DS-EM and MG-EM) that perform haplotype inference based on "GenoSpectrum" of each individual obtained by double sampling and multi-genotyping data. Both simulated data sets and a quasi real-data set demonstrate that our proposed methods perform well in different situations and outperform the conventional EM algorithm and the HMM algorithm proposed by Sun, Greenwood, and Neal (2007, Genetic Epidemiology 31, 937-948) when the genotype data sets have errors.