The assignment of individuals to a population can be of importance for the identification of mass disaster victims or criminal offenders in the field of forensic genetics. This assignment is based on biostatistical methods that process data of ancestry informative markers (AIMs), which are selected based on large allele frequency differences between the populations of interest.

However, population assignments of individuals with an admixed genetic background are challenging. Admixed individuals are genetic mosaics of chromosomal segments from the parental populations, which may lead to ambiguous or no population assignment. This is problematic since admixture events are a substantial part of human history.

In this study, we present challenges of interpreting the evidential weight of population assignments. We used Genogeographer for likelihood ratio (LR) calculations and Brazilians as examples of admixed individuals. Brazilians are a very heterogenous population representing a three-way admixture between Native Americans, Europeans, and Africans. Ancestry informative markers were typed in a total of 589 individuals from Brazil using the Precision ID Ancestry Panel. The Brazilians were assigned to six metapopulations (East Asia, Europe, Middle East, North Africa, South-Central Asia, Sub-Saharan Africa) defined in the Genogeographer software and LRs were calculated if the AIM profile was not an outlier in all metapopulations and simulated two-way (1:1) admixtures of the six metapopulations.

Population assignments failed for 55% of the samples. These samples had significantly higher genetic contributions from East Asia, South-Central Asia and Sub-Saharan Africa, and significantly lower genetic contributions from Europe. Most of the individuals with population assignments were assigned to the metapopulations of Middle East (58%) or North Africa (36%), followed by Europe (4%), South-Central Asia (1%), and Sub-Saharan Africa (1%). For 8% of the samples, population assignments were only possible when assignments to simulated two-way (1:1) admixtures of the six metapopulations were considered. Most of these individuals were assigned to two-way admixtures of North Africa, South-Central Asia, or Sub-Saharan Africa.

Relatively low median likelihood ratios (LRs<1000) were observed when comparing population likelihoods for Europe, Middle East, North Africa, South-Central Asia, or simulated 1:1 admixtures of these metapopulations. Comparisons including East Asian or Sub-Saharan African populations resulted in larger median LRs (LR>10¹⁰).

The results suggested that the Precision ID Ancestry Panel provided too little information and that additional markers specifically selected for sub-continental differentiation may be required for accurate population assignment of admixed individuals. Furthermore, a Genogeographer database with additional populations including admixed populations would be advantageous for interpretation of admixed AIM profiles. It would likely increase the number of population assignments and illustrate alternatives to the most likely population, which would be valuable information for the case officer when writing the case report.

将个体分配到群体对于法医遗传学领域中大规模灾难受害者或刑事犯罪者的识别非常重要。该分配基于处理祖先信息标记 (AIM) 数据的生物统计方法，这些标记是根据感兴趣群体之间的较大等位基因频率差异进行选择的。

然而，具有混合遗传背景的个体的群体分配具有挑战性。混合个体是来自亲代群体的染色体片段的遗传镶嵌，这可能导致群体分配不明确或没有群体分配。这是有问题的，因为混合事件是人类历史的重要组成部分。

在这项研究中，我们提出了解释人口分配的证据权重的挑战。我们使用 Genogeographer 进行似然比 (LR) 计算，并使用巴西人作为混合个体的示例。巴西人是一个非常异质的群体，代表着美洲原住民、欧洲人和非洲人的三向混合体。使用 Precision ID Ancestry Panel 对来自巴西的总共 589 名个体进行了祖先信息标记的分类。巴西人被分配到 Genogeographer 软件中定义的六个综合种群（东亚、欧洲、中东、北非、中南亚、撒哈拉以南非洲），如果 AIM 配置文件不是所有综合种群中的异常值，则计算 LR模拟六种复合种群的双向（1:1）混合。

55% 的样本的总体分配失败。这些样本来自东亚、中南亚和撒哈拉以南非洲的遗传贡献明显较高，来自欧洲的遗传贡献明显较低。大多数具有种群分配的个体被分配到中东（58％）或北非（36％）的集合种群，其次是欧洲（4％），中南亚（1％）和撒哈拉以南非洲（ 1%）。对于 8% 的样本，只有在考虑分配到 6 个集合种群的模拟双向 (1:1) 混合物时才可能进行种群分配。这些人中的大多数被分配到北非、中南亚或撒哈拉以南非洲的双向混合体。

在比较欧洲、中东、北非、中南亚或这些集合种群的模拟 1:1 混合的种群可能性时，观察到相对较低的中位似然比 (LRs<1000)。包括东亚或撒哈拉以南非洲人口的比较导致较大的 LR 中位数 (LR>10 ¹⁰ )。

结果表明，Precision ID Ancestry Panel 提供的信息太少，并且可能需要专门为次大陆分化选择的额外标记才能准确地分配混合个体。此外，具有其他群体（包括混合群体）的基因地理学家数据库将有利于解释混合 AIM 概况。它可能会增加人口分配的数量，并说明最可能人口的替代方案，这对于案件官员撰写案件报告时将是有价值的信息。