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Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures

机译:通过使用DNA混合物中的定量短串联重复数据评估证据的权重

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摘要

The evaluation of results from mixtures of deoxyribonucleic acid (DNA) from two or more people in crime case investigations may be improved by taking not only the qualitative but also the quantitative part of the results into consideration. We present a statistical likelihood approach to assess the probability of observed peak heights and peak areas information for a pair of profiles matching the DNA mixture. Furthermore, we demonstrate how to incorporate this probability in the evaluation of the weight of the evidence by a likelihood ratio approach. Our model is based on a multivariate normal distribution of peak areas for assessing the weight of the evidence. On the basis of data from analyses of controlled experiments with mixed DNA samples, we exploited the linear relationship between peak heights and peak areas, and the linear relationships of the means and variances of the measurements. Furthermore, the contri-bution from one individual's allele to the mean area of this allele is assumed to be proportional to the average of peak height measurements of alleles, where the individual is the only contributor. For shared alleles in mixed DNA samples, it is possible to observe only the cumulative peak heights and areas. Complying with this latent structure, we used the EM algorithm to impute the missing variables on the basis of a compound symmetry model. The measurements were subject to intralocus and interlocus correlations not depending on the actual alleles of the DNA profiles. Owing to factorization of the likelihood, properties of the normal distribution and use of auxiliary variables, an ordinary implementation of the EM algorithm solved the missing data problem.
机译:通过不仅考虑定性而且考虑定量的结果,可以改善对来自两个或多个人的脱氧核糖核酸(DNA)混合物的犯罪结果调查结果的评估。我们提出了一种统计似然方法,用于评估观察到的峰高和峰面积信息的概率,该信息用于匹配DNA混合物的一对轮廓。此外,我们展示了如何通过似然比方法将这种概率纳入证据权重的评估中。我们的模型基于峰面积的多元正态分布,用于评估证据的权重。基于对混合DNA样品进行的对照实验分析得出的数据,我们利用了峰高和峰面积之间的线性关系,以及均值和测量值方差的线性关系。此外,假设从一个人的等位基因到该等位基因的平均面积的贡献与等位基因的峰高测量值的平均值成比例,而该个体是唯一的贡献者。对于混合DNA样品中的共享等位基因,可能仅观察到累积峰高和峰面积。遵循这种潜在的结构,我们使用EM算法在复合对称模型的基础上估算缺失变量。测量结果受到基因座内和基因座间的相关性的影响,而不取决于DNA谱的实际等位基因。由于可能性的分解,正态分布的属性和辅助变量的使用,EM算法的常规实现解决了丢失数据的问题。

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