Department of Chemistry, Rutgers University, Camden, NJ, 08102, USA; Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
Center for Computational and Integrative Biology, Rutgers University, Camden, NJ, 08102, USA.
Forensic Sci Int Genet. 2020 Jul;47:102296. doi: 10.1016/j.fsigen.2020.102296. Epub 2020 Apr 11.
Forensic DNA signal is notoriously challenging to interpret and requires the implementation of computational tools that support its interpretation. While data from high-copy, low-contributor samples result in electropherogram signal that is readily interpreted by probabilistic methods, electropherogram signal from forensic stains is often garnered from low-copy, high-contributor-number samples and is frequently obfuscated by allele sharing, allele drop-out, stutter and noise. Since forensic DNA profiles are too complicated to quantitatively assess by manual methods, continuous, probabilistic frameworks that draw inferences on the Number of Contributors (NOC) and compute the Likelihood Ratio (LR) given the prosecution's and defense's hypotheses have been developed. In the current paper, we validate a new version of the NOCIt inference platform that determines an A Posteriori Probability (APP) distribution of the number of contributors given an electropherogram. NOCIt is a continuous inference system that incorporates models of peak height (including degradation and differential degradation), forward and reverse stutter, noise and allelic drop-out while taking into account allele frequencies in a reference population. We established the algorithm's performance by conducting tests on samples that were representative of types often encountered in practice. In total, we tested NOCIt's performance on 815 degraded, UV-damaged, inhibited, differentially degraded, or uncompromised DNA mixture samples containing up to 5 contributors. We found that the model makes accurate, repeatable and reliable inferences about the NOCs and significantly outperformed methods that rely on signal filtering. By leveraging recent theoretical results of Slooten and Caliebe (FSI:G, 2018) that, under suitable assumptions, establish the NOC can be treated as a nuisance variable, we demonstrated that when NOCIt's APP is used in conjunction with a downstream likelihood ratio (LR) inference system that employs the same probabilistic model, a full evaluation across multiple contributor numbers is rendered. This work, therefore, illustrates the power of modern probabilistic systems to report holistic and interpretable weights-of-evidence to the trier-of-fact without assigning a specified number of contributors or filtering signal.
法医 DNA 信号的解释极具挑战性，需要采用支持其解释的计算工具。虽然高拷贝、低贡献样本的数据会产生易于通过概率方法解释的电泳图谱信号，但法医痕迹的电泳图谱信号通常来自低拷贝、高贡献数样本，并且经常因等位基因共享、等位基因丢失、重峰和噪声而变得模糊不清。由于法医 DNA 谱图过于复杂，无法通过手动方法进行定量评估，因此开发了连续的概率框架，这些框架可以根据检方和辩方的假设对贡献者数量 (NOC) 进行推断，并计算似然比 (LR)。在当前的论文中，我们验证了一种新版本的 NOCIt 推断平台，该平台可根据电泳图谱确定贡献者数量的后验概率 (APP) 分布。NOCIt 是一种连续的推断系统，它结合了峰高模型（包括降解和差异降解）、正向和反向重峰、噪声和等位基因丢失，同时考虑了参考人群中的等位基因频率。我们通过对代表实践中常见类型的样本进行测试来确定算法的性能。总共，我们在包含多达 5 个贡献者的 815 个降解、紫外线损伤、抑制、差异降解或未受损 DNA 混合物样本上测试了 NOCIt 的性能。我们发现，该模型对 NOC 做出了准确、可重复和可靠的推断，并且明显优于依赖信号过滤的方法。通过利用 Slooten 和 Caliebe（FSI：G，2018）的最新理论结果，在适当的假设下，确定 NOC 可以被视为一种干扰变量，我们证明了当 NOCIt 的 APP 与采用相同概率模型的下游似然比 (LR) 推断系统一起使用时，可以对多个贡献者数量进行全面评估。因此，这项工作说明了现代概率系统的强大功能，可以向事实裁决者报告整体和可解释的证据权重，而无需指定贡献者数量或过滤信号。
