U.S. Army Criminal Investigation Laboratory, Defense Forensic Science Center, Forest Park, GA 30297, United States.
U.S. Army Criminal Investigation Laboratory, Defense Forensic Science Center, Forest Park, GA 30297, United States.
Forensic Sci Int Genet. 2018 Jul;35:50-56. doi: 10.1016/j.fsigen.2018.03.016. Epub 2018 Mar 31.
Modern probabilistic genotyping (PG) software is capable of modeling stutter as part of the profile weighting statistic. This allows for peaks in stutter positions to be considered as allelic or stutter or both. However, prior to running any sample through a PG calculator, the examiner must first interpret the sample, considering such things as artifacts and number of contributors (NOC or N). Stutter can play a major role both during the assignment of the number of contributors, and the assessment of inclusion and exclusion. If stutter peaks are not filtered when they should be, it can lead to the assignment of an additional contributor, causing N contributors to be assigned as N + 1. If peaks in the stutter position of a major contributor are filtered using a threshold that is too high, true alleles of minor contributors can be lost. Until now, the software used to view the electropherogram stutter filters are based on a locus specific model. Combined stutter peaks occur when a peak could be the result of both back stutter (stutter one repeat shorter than the allele) and forward stutter (stutter one repeat unit larger than the allele). This can challenge existing filters. We present here a novel stutter filter model in the ArmedXpert™ software package that uses a linear model based on allele for back stutter and applies an additive filter for combined stutter. We term this the allele specific stutter model (AM). We compared AM with a traditional model based on locus specific stutter filters (termed LM). This improved stutter model has the benefit of: Instances of over filtering were reduced 78% from 101 for a traditional model (LM) to 22 for the allele specific model (AM) when scored against each other. Instances of under filtering were reduced 80% from 85 (LM) to 17 (AM) when scored against ground truth mixtures.
现代概率基因分型 (PG) 软件能够将重峰建模为谱权重统计的一部分。这使得重峰位置的峰可以被视为等位基因或重峰或两者兼而有之。然而，在将任何样本输入 PG 计算器之前，检查者必须首先解释样本，考虑到伪影和贡献者数量 (NOC 或 N) 等因素。重峰在分配贡献者数量以及评估包含和排除时都可以发挥重要作用。如果在应该过滤重峰峰时没有过滤它们，可能会导致分配额外的贡献者，从而导致将 N 个贡献者分配为 N+1。如果使用过高的阈值过滤主要贡献者的重峰位置的峰，则可能会丢失次要贡献者的真实等位基因。到目前为止，用于查看电泳图谱重峰的软件过滤器是基于特定基因座的模型。当一个峰可能是反向重峰（重峰比等位基因短一个重复）和正向重峰（重峰比等位基因大一个重复单元）的结果时，就会出现组合重峰。这可能会对现有过滤器构成挑战。我们在这里提出了一种新的重峰过滤模型，该模型使用基于等位基因的线性模型来处理反向重峰，并对组合重峰应用加性过滤器。我们将其称为等位基因特异性重峰模型 (AM)。我们将 AM 与基于基因座特定重峰过滤器的传统模型 (LM) 进行了比较。与传统模型相比，改进后的重峰模型具有以下优势：在相互比较时，传统模型（LM）中过度过滤的实例从 101 减少到 22，而等位基因特异性模型（AM）中过度过滤的实例从 85 减少到 17。在与地面真实混合物评分时，传统模型中欠过滤的实例从 85 减少到 17，而等位基因特异性模型中欠过滤的实例从 101 减少到 22。
