A probability model for the meiosis I non-disjunction fraction in numerical chromosomal anomalies.
Numerical chromosome abnormalities (aneuploidies) are among the most common known causes of mental retardation and the leading cause of pregnancy loss in humans. They primarily arise by the process of meiotic non-disjunction. We still know very little about the contribution of genetic and environmental causes for non-disjunction in humans. In order to increase our understanding of the epidemiology of human trisomies, it is necessary to establish the proportion of cases occurring in the first or second division of meiosis. Trisomic patients will display, in study of microsatellite typed by the polymerase chain reaction (PCR), three fragment peaks of equal intensity, two fragments at an average 2:1 dosage or one individual fragment. In this work we describe a statistical approach for estimation of the fraction of meiosis I non-disjunctions (F) in the absence of the parental information. First we determine a probability model for the number of peaks in a polymorphic microsatellite locus, which is a function of F. Based on this model, we obtain a maximum likelihood estimator for F, using the observed proportion of one, two and three allele patterns in trisomic individuals. Relying on the properties of maximum likelihood theory, we also calculate the asymptotic variance and confidence intervals for F. Owing to the fact that the samples of trisomic patients are limited in number, the use of asymptotic theory may be compromised. Thus, we employ the bootstrap technique to build confidence intervals for F and compare the results with those obtained from the normal theory. This estimator that dispenses the need to study parents opens the possibility of using archival material for comparative epidemiological studies of Down's syndrome and other aneuploidies. In this paper we propose a probability model to estimate the fraction of meiosis I non-disjunction, F, by only using the proportion of allele patterns of trisomy individuals, while traditional methods require typing pericentromeric markers from those affected and their parents.
Franco
Glaura C
GC
Departamento de Estatística, ICEx, UFMG, Caixa Postal 702, Belo Horizonte, MG 31.270-901, Brazil. glaura@est.ufmg.br
Lucio
Paulo S
PS
Parra
Flávia C
FC
Pena
Sérgio D J
SD
D001938:Brazil; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D008297:Male; D008540:Meiosis / Q000235:genetics*; D018895:Microsatellite Repeats / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D009630:Nondisjunction, Genetic; D016133:Polymerase Chain Reaction; D014314:Trisomy / Q000235:genetics*