通过大规模基因组数据和机器学习进行综合遗传和地理祖先预测
Jing Chen1,2,3, Yuguo Huang2, Haoliang Fan4,5
1School of Forensic Medicine, Shanxi Medical University, Jinzhong, 030600, China.
Human genomics
|October 29, 2025
概括
准确的遗传和地理祖先在东亚和东南亚现在可以实现使用祖先信息单核酸多态 (SNP) 面板和机器学习. 这个框架为人口遗传学和法医科学提供了精确的见解.
科学领域:
- 人口遗传学 人口遗传学
- 基因组祖先推理推理
- 机器学习应用 机器学习应用
背景情况:
- 东亚和东南亚的复杂的人口历史阻碍了细微的基因和地理祖先确定.
- 有限的高分辨率基因组数据进一步复杂化了该地区准确的祖先分析.
研究的目的:
- 开发和验证一个全面的框架,共同确定东亚和东南亚人口的遗传祖先和地理起源.
- 评估各种祖先信息单核酸多态化 (SNP) 面板和机器学习算法用于祖先推断的有效性.
主要方法:
- 开发了七个嵌套的祖先信息单核酸多态 (SNP) 面板 (502,000 SNPs).
- 评估了六种机器学习算法,包括 eXtreme Gradient Boosting (XGBoost),用于遗传祖先分类.
- 利用Locator深度神经网络模型从基因型直接进行地理定位 (度和经度预测).
主要成果:
- 使用2000个祖先信息SNP的优化XGBoost模型实现了95.6%的准确性和0.999.99的AUC.
- 在2000个祖先信息型SNP上训练的定位模型,表现出与使用高密度基因组数据 (597,569个SNP) 的模型相比的性能.
- 该框架成功分析了67个东亚和东南亚群体中的1,703个人.
结论:
- 精心设计的祖先信息SNP面板与机器学习相结合,提供高度准确和高效的遗传祖先推断.
- 这种方法为东亚和东南亚的人口遗传学,法医科学和生物地理学提供了有价值的高分辨率见解.
- 开发的框架表明了针对基因组祖先研究的SNP面板和机器学习的潜力.
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