Application of permanents of square matrices for DNA identification in multiple-fatality cases.
Application of permanents of square matrices for DNA identification in multiple-fatality cases.
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DOI：
10.1186/1471-2156-14-72
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发表时间：
2013-08-21
期刊：
影响因子：
2.9
通讯作者：
Yamada R
中科院分区：
文献类型：
--
作者：
Narahara M;Tamaki K;Yamada R
DNA图谱对于个人身份识别是必不可少的。在法医学中，通常使用似然比(LR)来识别个体。通过比较样本DNA的两个假设来计算LR：样本DNA与参考DNA相同或相关，以及它是从总体中随机抽样的。然而，对于多人死亡的情况，识别应该被视为一个分配问题，因此应该将特定的样本和参考对与整个数据集条件下的其他可能性进行比较。我们发展了一种新的方法，通过非负条目的方阵的恒等式来计算概率。由于确切的永久数被称为#P-完全问题，我们应用Huber定律算法来逼近永久数。在闭合事件的假设下，通过与LR相比，通过分析接收机工作特性曲线，我们进行了计算机模拟来评估我们方法的性能。当参考文献既没有提供专有等位基因也没有提供不可能的等位基因时，这两种方法之间的差异得到了很好的证明。新方法的灵敏度较高(0.188比0.055)，阈值为0.999，特异度为1，受试者工作特征曲线下面积较大(0.990比0.959，P = 9.6E-15)。因此，我们的方法为计算密集型分配问题提供了解决方案，并且可能是闭合事件多死亡案例中基于LR的识别的一种可行的选择。
DNA profiling is essential for individual identification. In forensic medicine, the likelihood ratio (LR) is commonly used to identify individuals. The LR is calculated by comparing two hypotheses for the sample DNA: that the sample DNA is identical or related to a reference DNA, and that it is randomly sampled from a population. For multiple-fatality cases, however, identification should be considered as an assignment problem, and a particular sample and reference pair should therefore be compared with other possibilities conditional on the entire dataset. We developed a new method to compute the probability via permanents of square matrices of nonnegative entries. As the exact permanent is known as a #P-complete problem, we applied the Huber–Law algorithm to approximate the permanents. We performed a computer simulation to evaluate the performance of our method via receiver operating characteristic curve analysis compared with LR under the assumption of a closed incident. Differences between the two methods were well demonstrated when references provided neither obligate alleles nor impossible alleles. The new method exhibited higher sensitivity (0.188 vs. 0.055) at a threshold value of 0.999, at which specificity was 1, and it exhibited higher area under a receiver operating characteristic curve (0.990 vs. 0.959, P = 9.6E-15). Our method therefore offers a solution for a computationally intensive assignment problem and may be a viable alternative to LR-based identification for closed-incident multiple-fatality cases.
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