Vicard P, Dawid A P, Mortera J, Lauritzen S L
Dipartimento di Economia, Università Roma Tre, Via Silvio D'Amico 77, Roma 00145, Italy.
Forensic Sci Int Genet. 2008 Jan;2(1):9-18. doi: 10.1016/j.fsigen.2007.07.002. Epub 2007 Oct 24.
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.
我们提出了一种用于从亲子鉴定案例中推断突变率的统计方法。该方法考虑了许多潜在偏差来源，包括隐藏突变、不完整的三联体家庭、不确定的父系身份以及不同的母系和父系突变率，同时允许使用多种突变模型。使用面向对象的贝叶斯网络来促进突变参数似然函数的计算。这可以处理来自完整的推定父子 - 母子三联体以及仅观察到孩子及其父母之一的缺陷案例的完整或汇总基因型信息。我们使用亲子鉴定案例数据集来说明各种偏差类型和假设的突变模型对突变参数推断的影响。特别是，我们表明在未确认父系身份的案例中可能存在相关信息，并且像通常那样排除这些案例可能会导致有偏差的结论。
