In the absence of a suspect the forensic aim is investigative, and the focus is one of discerning what genotypes best explain the evidence. In traditional systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor’s contribution and, as a result, possibly overlooked or inefficient investigative leads. Recent developments in single-cell analysis offer a way forward, by producing data capable of discriminating genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster’s content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes’ rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least $1-\alpha$. This is the credible genotype set and is used to inform database search criteria. Within this work we demonstrate the salience of single-cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most probable genotype was the true genotype for 97% of the loci when the number of cells in a cluster was at least two. Since efficient investigative leads will be borne by posterior mass distributions that are narrow and concentrated at the true genotype, we report that, for this test set, 47,900 (86%) loci returned only one credible genotype and of these 47,551 (99%) were the true genotype. When determining the LR for true contributors, 91% of the clusters rendered LR>10¹⁸, showing the potential of single-cell data to positively affect investigative reporting.

在没有嫌疑人的情况下，法医的目的是调查，重点是辨别哪种基因型最能解释证据。在传统系统中，如果样本包含来自许多捐赠者的 DNA，或者来自次要贡献者的信息被主要贡献者的信息淹没，则候选基因型列表可能会变得庞大，从而导致真正捐赠者贡献的证据价值降低，从而导致，可能被忽视或效率低下的调查线索。单细胞分析的最新发展通过产生能够区分基因型的数据提供了一条前进的道路。这是通过首先根据相似性对单细胞数据进行聚类来实现的，而不参考已知的基因型。通过良好的集群，可以合理地假设集群中的 scEPG 属于单个贡献者。有了这个假设，我们就可以确定每个基因座上每个可能的基因型的簇内容的概率，然后通过应用贝叶斯规则来确定所有基因型的后验概率质量分布。然后应用决策标准，使得落在该集合中的所有基因型的排序概率之和至少为$1-\alpha$。这是可信的基因型集，用于告知数据库搜索标准。在这项工作中，我们通过性能测试一组 630 个先前构建的混合物（包含最多 5 个平衡和不平衡贡献的供体）来证明单细胞分析的显着性。我们使用的 scEPG 是通过分离单细胞、采用直接 PCR 提取处理、扩增符合现有国家数据库的 STR 以及应用 PCR 后处理（可得出一个 DNA 拷贝的检测限）而生成的。我们确定，对于这些测试数据，99.3% 的真实基因型包含在 99.8% 可信的集合中，无论组成混合物的捐赠者数量如何。我们还确定，当簇中的细胞数量至少为两个时，最可能的基因型是 97% 基因座的真实基因型。由于有效的调查线索将由狭窄且集中于真实基因型的后部质量分布承担，因此我们报告说，对于该测试集，47,900 (86%) 个基因座仅返回一种可信的基因型，而其中 47,551 个 (99%) 是真正的基因型。在确定真正贡献者的 LR 时，91% 的簇呈现 LR>10 ¹⁸，这表明单细胞数据有可能对调查报告产生积极影响。