A new implementation of a semi-continuous method for DNA mixture interpretation

Q3 Medicine
Jacob Alfieri , Michael D. Coble , Carole Conroy , Angela Dahl , Douglas R. Hares , Bruce S. Weir , Charles Wolock , Edward Zhao , Hanley Kingston , Timothy W. Zolandz
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引用次数: 0

Abstract

A new calculation module within the PopStats module of the CODIS software package, based on the underlying mathematics presented in the MixKin software package, has been developed for assigning the Likelihood Ratio (LR) of DNA mixture profiles. This module uses a semi-continuous model that allows for population structure and allelic drop-out and drop-in but does not require allelic peak heights or other laboratory-specific parameters. This new implementation (named SC Mixture), like MixKin, does not specify or estimate a probability of drop-out. Instead, each contributor to a mixture has an independent drop-out rate, and the probability of the mixture profile for a specified proposition concerning the contributors is integrated over the range of possible drop-out rates. The allelic drop-in rate and the population structure parameter, theta, used by the software are specified by the user. The user can examine up to five contributors to a mixture, however, conditioning on assumed contributors and limiting the number of unknowns in both numerator and denominator hypotheses greatly improves performance. We report results from an extensive validation study performed for ten mixtures with each of one (single source), two, three, four, or five contributors, with four combinations of drop-in rate and a population structure parameter. Each mixture was run as a complete profile or with the random removal of alleles to simulate drop-out. All 1620 combinations were evaluated with PopStats, MixKin, and LRmix and considerable consistency was found among the results with all three packages.

一种半连续DNA混合解释方法的新实现
基于MixKin软件包提供的基础数学,在CODIS软件包的PopStats模块中开发了一个新的计算模块,用于分配DNA混合谱的似然比(LR)。该模块使用半连续模型,允许种群结构和等位基因的缺失和缺失,但不需要等位基因峰值高度或其他实验室特定参数。这个新的实现(名为SC混合物),像MixKin一样,不指定或估计退出的概率。相反,混合物的每个贡献者都有一个独立的退出率,并且关于贡献者的特定命题的混合物轮廓的概率在可能的退出率范围内进行集成。该软件使用的等位基因滴入率和种群结构参数theta由用户指定。用户最多可以检查混合物的五个贡献者,然而,根据假设的贡献者进行调节并限制分子和分母假设中的未知数数量大大提高了性能。我们报告了一项广泛的验证研究的结果,该研究对10种混合物进行了验证,每种混合物有一个(单一来源)、两个、三个、四个或五个贡献者,有四种跌落率和种群结构参数的组合。每个混合物作为一个完整的档案运行或随机去除等位基因来模拟辍学。使用PopStats、MixKin和LRmix对所有1620种组合进行了评估,并且在所有三种包装的结果中发现了相当大的一致性。
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来源期刊
Forensic Science International: Reports
Forensic Science International: Reports Medicine-Pathology and Forensic Medicine
CiteScore
2.40
自引率
0.00%
发文量
47
审稿时长
57 days
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