Benschop Corina C G, Sijen Titia
Department of Human Biological Traces, Netherlands Forensic Institute, P.O. Box 24044, 2490 AA The Hague, The Netherlands.
Forensic Sci Int Genet. 2014 Jul;11:154-65. doi: 10.1016/j.fsigen.2014.03.012. Epub 2014 Mar 30.
When dealing with mixed DNA profiles where contributors have donated DNA in unequal amounts, it is often useful to deduce the genotype of the major contributor. Inference of a major contributor's genotype empowers storage of the DNA profile in a DNA database (DDB), which is especially of interest in cases without a suspect. When a major contributor's genotype cannot be inferred straightforwardly, for instance because low level components are present, replicate analyses can be prepared and combined into a consensus profile. Here we describe an automated and freely available tool to deduce the major component's alleles in mixed consensus DNA profiles. In these consensus profiles, theoretical peak heights (PHs) are assigned to the alleles using the sum of the PHs in the individual amplifications. The LoCIM-tool (Locus Classification & Inference of the Major-tool) uses these PHs plus parameters on the stochastic threshold, heterozygote balance (HB) and major to minor(s) ratio to classify every locus as a type 1, type 2 or type 3 locus, which represent classes of increasing complexity. Based on the type of locus, the LoCIM-tool applies an inclusion percentage to deduce the alleles for the major contributor. Using the LoCIM-tool, 99.9% of all type 1 loci and 96.7% of all type 2 loci were inferred correctly from a large set of consensus DNA profiles that were generated from mixtures varying for the mixture ratio, amount of DNA per contributor, number of contributors, quality of DNA, and allele sharing among the contributors. For type 3 loci, we aimed at inferring the major contributor's alleles and possibly extra alleles, which occurred for 87.2% of all type 3 loci analysed using the LoCIM-tool. When compared to the overall results of manual inference by a group of forensic scientists, the LoCIM-tool obtains a higher percentage of correctly inferred loci. From our results, we conclude that the LoCIM-tool presents an objective, uniform and fast method to reliably deduce alleles of a major component.
在处理混合DNA图谱时,如果贡献者捐赠的DNA量不相等,推断主要贡献者的基因型通常很有用。推断主要贡献者的基因型有助于将DNA图谱存储在DNA数据库(DDB)中,这在没有嫌疑人的案件中尤为重要。当无法直接推断主要贡献者的基因型时,例如因为存在低水平成分,可以进行重复分析并合并成一个共识图谱。在这里,我们描述了一种自动化的免费工具,用于推断混合共识DNA图谱中主要成分的等位基因。在这些共识图谱中,使用个体扩增中等位基因峰高(PH)的总和为等位基因分配理论峰高。LoCIM工具(基因座分类与主要成分推断工具)使用这些峰高以及随机阈值、杂合子平衡(HB)和主次比例等参数,将每个基因座分类为1型、2型或3型基因座,它们代表了复杂性递增的类别。根据基因座的类型,LoCIM工具应用包含百分比来推断主要贡献者的等位基因。使用LoCIM工具,从大量因混合比例、每个贡献者的DNA量、贡献者数量、DNA质量以及贡献者之间的等位基因共享而不同的混合物生成的共识DNA图谱中,99.9%的1型基因座和96.7%的2型基因座被正确推断。对于3型基因座,我们旨在推断主要贡献者的等位基因以及可能的额外等位基因,使用LoCIM工具分析的所有3型基因座中有87.2%出现了这种情况。与一组法医科学家手动推断的总体结果相比,LoCIM工具正确推断基因座的百分比更高。从我们的结果中,我们得出结论,LoCIM工具提供了一种客观、统一且快速的方法来可靠地推断主要成分的等位基因。