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使用自主开发的DIP系统和用于法医祖先推断的机器学习进行西北中国人口的遗传结构分析

Meiming Cai¹, Ruonan Shen¹, Xin Shi¹

¹Guangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, China.

|February 17, 2026

概括

在混合人群中,法医祖先推断使用60-DIP面板和机器学习得到了改进. 这种方法提高了分类东亚祖先的准确性,包括中国吉尔吉斯人 (CNK) 群体.

科学领域:

法医遗传学法医遗传学
人口遗传学人口遗传学
生物信息学是一种生物信息学.

背景情况:

法医祖先推断对于在法律调查中识别个人至关重要.
混合种群对准确的生物地理祖先估计提出了独特的挑战.
需要优化遗传面板和先进的分析方法来提高精度.

研究的目的:

评估一个新的60-DIP (移位互聚合酶) 面板,用于在混合种群中进行法医祖先推断.
将机器学习算法与传统祖先建模方法进行比较.
评估小组的表现和分类东亚亚亚种群的方法,特别是中国吉尔吉斯人 (CNK) 群体.

主要方法:

来自中国西北部 (CNK) 的吉尔吉斯斯坦样本和26个参考种群的基因型鉴定,使用60个DIP小组.
人口遗传分析包括主要成分分析 (PCA),ADMIXTURE和TreeMix.
机器学习算法的应用 (XGBoost,随机森林) 和祖先建模的传统方法.

主要成果:

60-DIP小组展示了高的法医效率 (累计的歧视概率>0.99999999999).
机器学习算法,特别是XGBoost,在大陆级祖先分类中显著超过传统方法 (准确率为0.919).
随机森林获得了最佳的东亚亚分区分类准确度 (0.709),对CNK集团来说准确度高.

结论:

这项研究支持CNK种群的东亚遗传背景.
机器学习与优化的DIP面板相结合,在混合群体中大大改善了法医祖先推断.
这种方法为法医生物地理祖先估计提供了一个有希望和实用的工具.

关键词:

法医学祖先推断的推断.法医遗传学法医遗传学遗传结构遗传结构插入/删除多态.机器学习机器学习

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