Biomaterial Database

Estimating mutation rates from paternity casework. 2008

P Vicard, and A P Dawid, and J Mortera, and S L Lauritzen

Dipartimento di Economia, Università Roma Tre, Via Silvio D'Amico 77, Roma 00145, Italy. vicard@uniroma3.it

We present a statistical methodology for making inferences about mutation rates from paternity casework. This takes account of a number of sources of potential bias, including hidden mutation, incomplete family triplets, uncertain paternity status and differing maternal and paternal mutation rates, while allowing a wide variety of mutation models. An object-oriented Bayesian network is used to facilitate computation of the likelihood function for the mutation parameters. This can process either full or summary genotypic information, both from complete putative father-mother-child triplets and from defective cases where only the child and one of its parents are observed. We use a dataset from paternity casework to illustrate the effects on inferences about mutation parameters of various types of biases and the mutation model assumed. In particular, we show that there can be relevant information in cases of unconfirmed paternity, and that excluding these, as has generally been done, can lead to biased conclusions.

UI	MeSH Term	Description	Entries
D008297	Male		Males
D008957	Models, Genetic	Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.	Genetic Models,Genetic Model,Model, Genetic
D009154	Mutation	Any detectable and heritable change in the genetic material that causes a change in the GENOTYPE and which is transmitted to daughter cells and to succeeding generations.	Mutations
D010334	Paternity	Establishing the father relationship of a man and a child.	Paternities
D002648	Child	A person 6 to 12 years of age. An individual 2 to 5 years old is CHILD, PRESCHOOL.	Children
D005819	Genetic Markers	A phenotypically recognizable genetic trait which can be used to identify a genetic locus, a linkage group, or a recombination event.	Chromosome Markers,DNA Markers,Markers, DNA,Markers, Genetic,Genetic Marker,Marker, Genetic,Chromosome Marker,DNA Marker,Marker, Chromosome,Marker, DNA,Markers, Chromosome
D005838	Genotype	The genetic constitution of the individual, comprising the ALLELES present at each GENETIC LOCUS.	Genogroup,Genogroups,Genotypes
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D001499	Bayes Theorem	A theorem in probability theory named for Thomas Bayes (1702-1761). In epidemiology, it is used to obtain the probability of disease in a group of people with some characteristic on the basis of the overall rate of that disease and of the likelihood of that characteristic in healthy and diseased individuals. The most familiar application is in clinical decision analysis where it is used for estimating the probability of a particular diagnosis given the appearance of some symptoms or test result.	Bayesian Analysis,Bayesian Estimation,Bayesian Forecast,Bayesian Method,Bayesian Prediction,Analysis, Bayesian,Bayesian Approach,Approach, Bayesian,Approachs, Bayesian,Bayesian Approachs,Estimation, Bayesian,Forecast, Bayesian,Method, Bayesian,Prediction, Bayesian,Theorem, Bayes
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model