法医遗传学中的推论对建立基因假设的敏感性
Sensitivity of inferences in forensic genetics to assumptions about founding genes
摘要：
许多法医遗传学问题可以使用离散变量的结构化系统来处理，贝叶斯网络提供了一个有吸引力的实用建模框架，并允许通过概率传播方法来计算推断。然而，当标准假设被违反时--例如，当等位基因频率未知时，存在血统上的同一性或群体是异质性的--在创始基因之间产生依赖性，这使得通过传播方法精确计算条件概率不那么直接。在这里，我们说明了不同的方法来评估敏感性的假设在法医遗传学问题的创始人。这些方法包括约束最速下降法、线性分式规划和用结构表示依赖。我们说明这些方法在几个法医遗传学的例子，涉及刑事鉴定，简单和复杂的有争议的亲子关系和DNA混合物。
英文摘要：
Many forensic genetics problems can be handled using structured systems of discrete variables, for which Bayesian networks offer an appealing practical modeling framework, and allow inferences to be computed by probability propagation methods. However, when standard assumptions are violated--for example, when allele frequencies are unknown, there is identity by descent or the population is heterogeneous--dependence is generated among founding genes, that makes exact calculation of conditional probabilities by propagation methods less straightforward. Here we illustrate different methodologies for assessing sensitivity to assumptions about founders in forensic genetics problems. These include constrained steepest descent, linear fractional programming and representing dependence by structure. We illustrate these methods on several forensic genetics examples involving criminal identification, simple and complex disputed paternity and DNA mixtures.
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