HAPLOTYPE INFERENCE AND BLOCK PARTITIONING IN MIXED POPULATION SAMPLES

摘要

Multi-population haplotype inference and block partitioning is a difficult task when dealing with mixed genotype samples A number of studies have shown that the haplotype block structures, as well as the collections of common haplotypes and their frequencies, vary significantly among world populations These differences are more extreme when the geographical locations for the populations are more distant Some of the previous studies performed haplotype inference in multi-population samples with known population assignment Others developed algorithms for clustering of the mixed haplotype or genotype samples with different block structures or genetic marker profiles We present a new algorithm that performs haplotype inference and block partitioning in a mixed sample of genotypes from two populations when the population assignments are not known Given a mixed genotype sample, the proposed algorithm (HAPLOCLUST) extracts two clusters of genotypes with different block structures in addition to performing haplotyp