Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures
Evaluating the weight of evidence by using quantitative short tandem repeat data in DNA mixtures
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DOI：
10.1111/j.1467-9876.2010.00722.x
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发表时间：
2010-01-01
影响因子：
1.6
通讯作者：
Morling, Niels
中科院分区：
文献类型：
--
作者：
Tvedebrink, Torben;Eriksen, Poul Svante;Morling, Niels
在犯罪案件调查中，通过不仅考虑结果的定性部分，而且考虑结果的定量部分，可以改进对来自两个或更多人的脱氧核糖核酸（DNA）混合物的结果的评估。我们提出了一种统计似然方法来评估观察到的与 DNA 混合物匹配的一对轮廓的峰高和峰面积信息的概率。此外，我们演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来评估证据的权重。根据混合 DNA 样品对照实验分析数据，我们利用了峰高和峰面积之间的线性关系，以及测量平均值和方差的线性关系。此外，假设一个个体的等位基因对该等位基因的平均面积的贡献与等位基因的峰高测量平均值成正比，其中该个体是唯一的贡献者。对于混合 DNA 样本中的共享等位基因，可以仅观察累积峰高和面积。遵循这种潜在结构，我们使用 EM 算法在复合对称模型的基础上对缺失变量进行插补。测量结果受到位点内和位点间相关性的影响，而不取决于 DNA 谱的实际等位基因。由于似然因式分解、正态分布的性质以及辅助变量的使用，EM 算法的普通实现解决了缺失数据问题。
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 contribution 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.