Blood-containing mixtures are frequently encountered at crime scenes involving violence and murder. However, the presence of blood, and the association of blood with a specific donor within these mixtures present significant challenges in forensic analysis. In light of these challenges, this study sought to address these issues by leveraging blood-specific methylation sites and closely linked microhaplotype sites, proposing a novel composite genetic marker known as “blood-specific methylation-microhaplotype”. This marker was designed to the detection of blood and the determination of blood donor within blood-containing mixtures. According to the selection criteria mentioned in the Materials and Methods section, we selected 10 blood-specific methylation-microhaplotype loci for inclusion in this study. Among these loci, eight exhibited blood-specific hypomethylation, while the remaining two displayed blood-specific hypermethylation. Based on data obtained from 124 individual samples in our study, the combined discrimination power (CPD) of these 10 successfully sequenced loci was 0.999999298. The sample allele methylation rate (Ram) was obtained from massive parallel sequencing (MPS), which was defined as the proportion of methylated reads to the total clustered reads that were genotyped to a specific allele. To develop an allele type classification model capable of identifying the presence of blood and the blood donor, we used the Random Forest algorithm. This model was trained and evaluated using the Ram distribution of individual samples and the Ram distribution of simulated shared alleles. Subsequently, we applied the developed allele type classification model to predict alleles within actual mixtures, trying to exclude non-blood-specific alleles, ultimately allowing us to identify the presence of blood and the blood donor in the blood-containing mixtures. Our findings demonstrate that these blood-specific methylation-microhaplotype loci have the capability to not only detect the presence of blood but also accurately associate blood with the true donor in blood-containing mixtures with the mixing ratios of 1:29, 1:19, 1:9, 1:4, 1:2, 2:1, 7:1, 8:1, 31:1 and 36:1 (blood:non-blood) by DNA mixture interpretation methods. In addition, the presence of blood and the true blood donor could be identified in a mixture containing four body fluids (blood:vaginal fluid:semen:saliva = 1:1:1:1). It is important to note that while these loci exhibit great potential, the impact of allele dropouts and alleles misidentification must be considered when interpreting the results. This is a preliminary study utilising blood-specific methylation-microhaplotype as a complementary tool to other well-established genetic markers (STR, SNP, microhaplotype, etc.) for the analysis in blood-containing mixtures.

中文翻译：

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新型复合遗传标记血液特异性甲基化微单倍型识别体液混合物中献血者的初步研究

在涉及暴力和谋杀的犯罪现场经常会遇到含血混合物。然而，这些混合物中血液的存在以及血液与特定供体的关联给法医分析带来了重大挑战。鉴于这些挑战，本研究试图通过利用血液特异性甲基化位点和紧密相连的微单倍型位点来解决这些问题，提出了一种称为“血液特异性甲基化-微单倍型”的新型复合遗传标记。该标记物设计用于检测血液并确定含血液混合物中的献血者。根据材料和方法部分提到的选择标准，我们选择了10个血液特异性甲基化微单倍型位点纳入本研究。在这些基因座中，八个表现出血液特异性低甲基化，而其余两个表现出血液特异性高甲基化。根据我们研究中 124 个个体样本获得的数据，这 10 个成功测序的位点的综合辨别力 (CPD) 为 0.999999298。样本等位基因甲基化率（Ram）是通过大规模平行测序（MPS）获得的，其定义为甲基化读数占对特定等位基因进行基因分型的总聚类读数的比例。为了开发能够识别血液和献血者存在的等位基因类型分类模型，我们使用了随机森林算法。该模型使用单个样本的 Ram 分布和模拟共享等位基因的 Ram 分布进行训练和评估。随后，我们应用开发的等位基因类型分类模型来预测实际混合物中的等位基因，试图排除非血液特异性等位基因，最终使我们能够识别含血混合物中是否存在血液和献血者。我们的研究结果表明，这些血液特异性甲基化微单倍型基因座不仅能够检测血液的存在，而且能够在混合比例为 1:29、1:19、通过 DNA 混合物解释方法得出 1:9、1:4、1:2、2:1、7:1、8:1、31:1 和 36:1（血液：非血液）。此外，可以在含有四种体液（血液：阴道液：精液：唾液= 1：1：1：1）的混合物中识别血液和真正的献血者的存在。值得注意的是，虽然这些位点表现出巨大的潜力，但在解释结果时必须考虑等位基因丢失和等位基因错误识别的影响。这是一项初步研究，利用血液特异性甲基化微单倍型作为其他成熟遗传标记（STR、SNP、微单倍型等）的补充工具，用于分析含血液混合物。