Statistical Analysis of Uniparental Disomy Data Using Hidden Markov Models |
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Authors: | H. Zhao J. Li W. P. Robinson |
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Affiliation: | Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut 06520, USA. hongyu.zhao@yale.edu |
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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. |
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Keywords: | Crossover interference Crossover process Hidden Markov model Nondisjunction Uniparental disomy |
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