对Y-STR配置文件应用的贡献者估计方法数量的不确定性
Shota Inokuchi1, Hiroaki Nakanishi2, Aya Takada3
1Department of Forensic Medicine, Graduate School of Medicine, Juntendo University, 2-1-1 Hongo, Bunkyo-ku, Tokyo, Japan; Forensic Science Laboratory, Tokyo Metropolitan Police Department, 3-35-21 Shakujiidai, Nerima-ku, Tokyo, Japan.
Forensic science international. Genetics
|September 17, 2024
概括
法医实验室可以准确地估计混合Y-short串联重复 (Y-STR) 配置文件中的贡献者数量 (NoC),使用最大基因单数 (MAC-single) 和总基因单数 (TAC) 方法. 这些方法为Y-STR混合物分析提供了可靠的性能.
科学领域:
- 法医科学 法医科学 法医科学
- 遗传学 是一个遗传学.
- 人口遗传学 人口遗传学
背景情况:
- 最大基数 (MAC) 和总基数 (TAC) 是已建立的方法来估计自体短并列重复 (STR) 档案中贡献者的数量 (NoC).
- 越来越多的Y-STR类型化工具包包括单复制和多复制的位点,因此需要适应Y-STR混合物中NOC估计的方法.
- 现有方法在Y-STR配置文件上的性能,特别是在多样化的群体中,需要进行彻底的评估.
研究的目的:
- 适应和评估MAC和TAC方法的性能,用于混合Y-STR配置文件中的NOC估计.
- 定义和评估特定的MAC变异 (MAC-单个和MAC-多个),以计算位置副本数.
- 为了比较不同人群 (美国和河南汉) 中这些方法的准确性和不确定性.
主要方法:
- 在美国和河南汉族人群中生成了12万个in silico混合Y-STR配置文件 (1-6个贡献者),使用美国和河南汉族人群的27个Y-STR位点的类型频率.
- 将数据集分为训练 (用于TAC曲线构造) 和测试 (用于绩效评估) 的数据集.
- 计算的性能指标 (准确性,精度,回忆,F1得分) 用于MAC单,MAC多,和TAC方法,评估NOC上限的影响.
主要成果:
- 单一的MAC方法实现了高的整体准确度:美国人口为0.7920,河南汉族人口为0.8207.
- TAC方法也表现出强的表现:美国人口为0.7877%,河南汉族人口为0.8385%.
- 马克多种方法的准确性显著降低 (0.4329美国,0.4609河南汉),这表明仅仅使用多副本位置的局限性.
结论:
- 单一MAC和TAC方法对于估计混合Y-STR配置文件中贡献者的数量是准确和可靠的.
- 这些经过验证的方法可以在法医实验室有效地用于Y-STR混合物分析.
- 在Y-STR分析中,区分单拷贝和多拷贝位点对于使用基于MAC的方法进行准确的NOC估计至关重要.
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