A Misclassification Model for the Non‐Disjunction Fraction in Meiosis I

• Published in: Biometrical journal Vol. 50; no. 6; pp. 940 - 953
• Main Authors: Loschi, Rosangela H., Monteiro, João V. D., Souto, Cristiane S.
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag 01.12.2008; Wiley-VCH
• Subjects: Applications; Bayes estimator; Bayes Theorem; Biology, psychology, social sciences; Classification errors; Computer Simulation; Distribution theory; Exact sciences and technology; General topics; Humans; Mathematics; Meiosis - genetics; Models, Genetic; Models, Statistical; Monte Carlo Method; Multinomial distribution; Non‐informative priors; Numerical analysis; Numerical analysis. Scientific computation; Numerical methods in probability and statistics; Probability and statistics; Sciences and techniques of general use; Statistics; Trisomy; Trisomy - genetics; Biometrics; Bayes estimation; Monte Carlo method; Trisomy; Statistical distribution; Error estimation; Statistical moment; Life science; Probability; Misclassification error; Probability distribution; Bad classification; Stochastic method; Non-informative priors; Statistical method; Numerical analysis; Bayes estimator; Classification errors; Approximation theory; Multinomial distribution; Posterior distribution
• ISSN: 0323-3847; 1521-4036; 1521-4036
• DOI: 10.1002/bimj.200810477

In this paper we introduce a misclassification model for the meiosis I non‐disjunction fraction in numerical chromosomal anomalies named trisomies. We obtain posteriors, and their moments, for the probability that a non‐disjunction occurs in the first division of meiosis and for the misclassification errors. We also extend previous works by providing the exact posterior, and its moments, for the probability that a non‐disjunction occurs in the first division of meiosis assuming the model proposed in the literature which does not consider that data are subject to misclassification. We perform Monte Carlo studies in order to compare Bayes estimates obtained by using both models. An application to Down Syndrome data is also presented. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)