A New Hybrid Haplotype Inference Method based-on Maximum Likelihood EstimationHo-Youl Jung1, Gil-Mi Ryu2, Jee-Yeon Heo, Ju-Young Lee, Hyo-Mi Kim, Jong-Keuk Lee, Chan Park, Bermseok Oh, and Kuchan Kimm
email@example.com, National Genome Research Institute; firstname.lastname@example.org, 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.