Estimation of mutation rates from paternity cases using a Bayesian network

We present a statistical model and methodology for making inferences about mutation rates from paternity casework. This takes proper 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. A Bayesian network is constructed to facilitate computation of the likelihood function for the mutation parameters. It can process both full and summary genotypic information, from both complete putative father-mother-child triplets and defective cases where only the child and one of its parents are observed. Detailed analysis of a specific dataset is used to illustrate the effects of the various types of biases, and of the assumed mutation model, on inferences about mutation parameters.We present a statistical model and methodology for making inferences about mutation rates from paternity casework. This takes proper 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. A Bayesian network is constructed to facilitate computation of the likelihood function for the mutation parameters. It can process both full and summary genotypic information, from both complete putative father-mother-child triplets and defective cases where only the child and one of its parents are observed. Detailed analysis of a specific dataset is used to illustrate the effects of the various types of biases, and of the assumed mutation model, on inferences about mutation parameters