在涉及暴力和谋杀的犯罪现场经常遇到含血混合物。然而,血的存在,以及这些混合物中血液与特定供体的关联在法医分析中提出了重大挑战。鉴于这些挑战,这项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,提出了一种新的复合遗传标记,称为“血液特异性甲基化-微单倍型”。该标记物被设计用于检测血液和测定含血液混合物中的献血者。根据材料和方法部分中提到的选择标准,我们选择了10个血液特异性甲基化-微单倍型基因座纳入本研究.在这些基因座中,八个人表现出血液特异性低甲基化,而其余两个显示血液特异性超甲基化。根据我们研究中从124个样本中获得的数据,这10个成功测序的基因座的综合辨别力(CPD)为0.999999298。从大规模平行测序(MPS)获得样品等位基因甲基化率(Ram),其被定义为甲基化读取与被基因分型为特定等位基因的总聚类读取的比例。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。使用个体样品的Ram分布和模拟共享等位基因的Ram分布来训练和评估该模型。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,最终使我们能够确定血液和献血者在含有血液的混合物中的存在。我们的发现表明,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,而且能够通过DNA混合物解释方法以1:29、1:19、1:9、1:4、1:2、2:1、7:1、8:1、31:1和36:1(血液:非血液)此外,在含有四种体液(血液:阴道液:精液:唾液=1:1:1)的混合物中可以识别血液和真正的献血者的存在。重要的是要注意,虽然这些基因座表现出巨大的潜力,在解释结果时,必须考虑等位基因缺失和等位基因错误鉴定的影响.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已建立的遗传标记(STR,SNP,微单倍型,等。)用于分析含有血液的混合物。
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.