从遗传数据到亲属关系清晰度:使用机器学习来检测近亲关系
Dejan Šorgić1, Aleksandra Stefanović1, Mladen Popović2
1Department of Paternity Identification, Biological and Other Traces, Institute of Forensic Medicine, Niš, Serbia.
Frontiers in genetics
|June 18, 2025
概括
使用短串重复 (STR) 配置文件的新预测模型可以准确地检测出乱伦概念. 这种DNA分析工具即使数据有限,也能达到很高的准确性,有助于法医调查.
科学领域:
- 法医遗传学 法医遗传学
- 计算生物学 计算生物学
- 人口遗传学 人口遗传学
背景情况:
- 在法医遗传学中,区分正常的父权和乱伦关系至关重要.
- 短串重复 (STR) 分析是人类识别的标准方法.
- 为复杂的亲属情景开发强大的预测模型仍然是一个持续的挑战.
研究的目的:
- 开发和评估一个预测模型,用于检测使用母婴STR资料的近亲怀孕.
- 评估模型在不同种群和不同条件下的性能,包括突变模型和有限的数据.
主要方法:
- 基于来自美国和沙特阿拉伯的基因频率来生成模拟后代STR概况,用于正常的父亲和乱伦情景 (父亲-孩子,兄弟-姐妹).
- 机器学习算法,包括CatBoost算法,使用母婴STR配置文件进行训练和测试.
- 模型的表现被评估为二元分类 (正常亲属关系与乱伦亲属关系),使用不同数量的STR标志物,并考虑一阶段突变模型.
主要成果:
- CatBoost算法在分类近亲关系方面取得了很高的准确性,在美国人口中达到96.94%的29个标记和95%的21个标记.
- 该模型保持了很高的准确性 (90.37%),即使仅使用孩子的STR资料来预测美国人口.
- 使用沙特阿拉伯等位基因频率也实现了准确的预测 (94%),证明了跨人群适用性.
结论:
- 基于STR配置文件开发的预测模型有效地检测出近亲关系的怀孕.
- 该模型的准确性不受人口结构的显著影响,因此可以在多样化和孤立的人口中应用.
- 这种基于STR的预测工具为法医调查和遗传关系测试提供了宝贵的进步.
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