A New Hybrid Haplotype Inference Method based-on Maximum Likelihood Estimation

Ho-Youl Jung1, Gil-Mi Ryu2, Jee-Yeon Heo, Ju-Young Lee, Hyo-Mi Kim, Jong-Keuk Lee, Chan Park, Bermseok Oh, and Kuchan Kimm
1hyjung@ngri.re.kr, National Genome Research Institute; 2gmryu@ngri.re.kr, National Genome Research Institute

This article presents a hybrid method that can identify the individual's haplotype from the given genotypes. Because of the limitation of the conventional single-locus analysis, haplotypes have gained increasing attention in the mapping of complex-disease genes. Conventionally there are two approaches which resolve the individual's haplotypes. One is the molecular haplotypings which have many potential limitations in cost and convenience. The other is the in-silico haplotypings which phase the haplotypes from the diploid genotyped populations, and are cost effective and high-throughput method. In-silico haplotyping is divided into two sub-categories - statistical and computational method. The former computes the frequencies of the common haplotypes, and then resolves the individual's haplotypes. The latter directly resolves the individual's haplotypes using the perfect phylogeny model first proposed by Dan Gusfield. Our method combines two approaches in order to increase the accuracy. The individuals' haplotypes are resolved by considering the MLE (maximum likelihood estimation) in the process of computing the frequencies of the common haplotypes.