title: Statistical analysis of uniparental disomy data using hidden Markov models.
abstract: Genetic studies of uniparental disomy (UPD) employing many markers have helped geneticists to gain a better understanding of the molecular mechanisms underlying nondisjunction. However, most existing methods cannot simultaneously analyze all genetic markers and consistently incorporate crossover interference; they thus fail to make the most use of genetic information in the data. In the present article, we describe a hidden Markov model for multilocus uniparental disomy data. This method is based on the chi-square model for the crossover process and can simultaneously incorporate all marker information including untyped and uninformative markers. We then apply this novel method to analyze a set of UPD15 data.
authors.affiliation: Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520, USA. hongyu.zhao@yale.edu
authors.forename: H
authors.initials: H
authors.lastname: Zhao
authors.forename: J
authors.initials: J
authors.lastname: Li
authors.forename: W P
authors.initials: WP
authors.lastname: Robinson
mesh_terms: D001699:Biometry; D016009:Chi-Square Distribution; D002884:Chromosomes, Human, Pair 15 / Q000235:genetics; D005260:Female; D005819:Genetic Markers; D006801:Humans; D008297:Male; D008390:Markov Chains; D008957:Models, Genetic; D015233:Models, Statistical; D024182:Uniparental Disomy
chemical_list: D005819:Genetic Markers