Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 08102, USA.
Department of Computer Science, Rutgers University, Camden, NJ 08102, USA.
Forensic Sci Int Genet. 2016 May;22:149-160. doi: 10.1016/j.fsigen.2016.02.005. Epub 2016 Feb 23.
In forensic DNA interpretation, the likelihood ratio (LR) is often used to convey the strength of a match. Expanding on binary and semi-continuous methods that do not use all of the quantitative data contained in an electropherogram, fully continuous methods to calculate the LR have been created. These fully continuous methods utilize all of the information captured in the electropherogram, including the peak heights. Recently, methods that calculate the distribution of the LR using semi-continuous methods have also been developed. The LR distribution has been proposed as a way of studying the robustness of the LR, which varies depending on the probabilistic model used for its calculation. For example, the LR distribution can be used to calculate the p-value, which is the probability that a randomly chosen individual results in a LR greater than the LR obtained from the person-of-interest (POI). Hence, the p-value is a statistic that is different from, but related to, the LR; and it may be interpreted as the false positive rate resulting from a binary hypothesis test between the prosecution and defense hypotheses. Here, we present CEESIt, a method that combines the twin features of a fully continuous model to calculate the LR and its distribution, conditioned on the defense hypothesis, along with an associated p-value. CEESIt incorporates dropout, noise and stutter (reverse and forward) in its calculation. As calibration data, CEESIt uses single source samples with known genotypes and calculates a LR for a specified POI on a question sample, along with the LR distribution and a p-value. The method was tested on 303 files representing 1-, 2- and 3-person samples injected using three injection times containing between 0.016 and 1 ng of template DNA. Our data allows us to evaluate changes in the LR and p-value with respect to the complexity of the sample and to facilitate discussions regarding complex DNA mixture interpretation. We observed that the amount of template DNA from the contributor impacted the LR--small LRs resulted from contributors with low template masses. Moreover, as expected, we observed a decrease of p-values as the LR increased. A p-value of 10(-9) or lower was achieved in all the cases where the LR was greater than 10(8). We tested the repeatability of CEESIt by running all samples in duplicate and found the results to be repeatable.
在法医 DNA 分析中，似然比 (LR) 常用于表示匹配的强度。扩展了不使用电泳图中包含的所有定量数据的二进制和半连续方法，创建了用于计算 LR 的全连续方法。这些全连续方法利用电泳图中捕获的所有信息，包括峰高。最近，还开发了使用半连续方法计算 LR 分布的方法。LR 分布已被提出作为研究 LR 稳健性的一种方法，LR 稳健性取决于用于计算其的概率模型。例如，LR 分布可用于计算 p 值，即随机选择的个体产生的 LR 大于从感兴趣的人 (POI) 获得的 LR 的概率。因此，p 值是一种与 LR 不同但相关的统计量；它可以解释为起诉方和辩护方之间二元假设检验的假阳性率。在这里，我们提出了 CEESIt，这是一种结合全连续模型的两个特征的方法，用于在辩护假设的条件下计算 LR 及其分布，以及相关的 p 值。CEESIt 在其计算中纳入了辍学、噪声和口吃（反转和前进）。作为校准数据，CEESIt 使用具有已知基因型的单一来源样本，并计算指定 POI 在问题样本上的 LR，以及 LR 分布和 p 值。该方法在 303 个文件上进行了测试，这些文件代表了使用三种注入时间注入的 1、2 和 3 人样本，其中包含 0.016 到 1ng 的模板 DNA。我们的数据使我们能够评估 LR 和 p 值随样本复杂性的变化，并促进有关复杂 DNA 混合物解释的讨论。我们观察到供体的模板 DNA 量影响 LR--来自模板质量低的供体的小 LR。此外，正如预期的那样，我们观察到随着 LR 的增加，p 值降低。当 LR 大于 10(8)时，所有情况下都达到了 10(-9)或更低的 p 值。我们通过重复运行所有样本来测试 CEESIt 的可重复性，发现结果是可重复的。
