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一种对概率基因分型中贡献者数量范围进行加权的方法的性能。

Performance of a method for weighting a range in the number of contributors in probabilistic genotyping.

机构信息

ESR, Private Bag 92019, Auckland, New Zealand.

ESR, Private Bag 92019, Auckland, New Zealand; Department of Statistics, University of Auckland, Auckland, New Zealand.

出版信息

Forensic Sci Int Genet. 2020 Sep;48:102352. doi: 10.1016/j.fsigen.2020.102352. Epub 2020 Jul 9.

Abstract

Uncertainty in the assignment of the number of contributors (NoC) can be encountered, particularly in higher-order mixtures, where alleles may be shared between contributors, may have dropped out, or may be masked by the stutter artefacts or allelic peaks of a more dominant contributor. Most probabilistic genotyping software requires the assignment of NoC prior to interpretation. NoC has been described as a nuisance parameter. Taylor et al. [1] describe a method to weigh the probability of the profile under different values of N and incorporate this into a likelihood ratio (LR). Within this paper we explore the performance of this variable number of contributors (varNoC) method programmed within the probabilistic genotyping software STRmix™. The desired combination of performance and runtime was obtained using the default STRmix™ version 2.7 MCMC settings in conjunction with a 2.5 % hyper-rectangle range, at least 10,000 naïve MC iterations and 8 MCMC chains. The varNoC LR demonstrated the typical sensitivity and specificity behaviour seen in previous studies, with a high level of reproducibility given repeat analyses. Profiles previously demonstrating ambiguity in the NoC assigned using conventional estimation methods, were able to be reliably interpreted and a varNoC LR assigned.

摘要

在分配贡献者数量(NoC)时可能会遇到不确定性,特别是在更高阶的混合物中,等位基因可能在贡献者之间共享,可能已经丢失,或者可能被更占主导地位的贡献者的颤振伪影或等位基因峰所掩盖。大多数概率基因分型软件都需要在解释之前分配 NoC。NoC 被描述为一个麻烦的参数。Taylor 等人 [1] 描述了一种方法,可以根据不同的 N 值对谱进行加权,并将其纳入似然比(LR)中。在本文中,我们探索了在概率基因分型软件 STRmix™ 中编程的可变数量贡献者(varNoC)方法的性能。使用默认的 STRmix™ 版本 2.7 MCMC 设置以及 2.5%的超矩形范围、至少 10000 次天真 MC 迭代和 8 个 MCMC 链,获得了所需的性能和运行时的组合。varNoC LR 表现出了与之前研究中所见的典型敏感性和特异性行为,并且具有高水平的可重复性。先前使用常规估计方法在 NoC 分配上表现出模糊性的谱,能够进行可靠的解释并分配 varNoC LR。

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