Scholarlink

<p>Understanding fine-scale genetic and geographic ancestry in East and Southeast Asia is difficult due to complex population histories and limited high-resolution genomic data. Here, we introduce a comprehensive framework that combines ancestry-informative single nucleotide polymorphism (AISNP) panels with machine learning to jointly determine genetic ancestry and geographic origins in 1,703 individuals from 67 East and Southeast Asian groups. We developed seven nested AISNP panels, from 50 to 2,000 SNPs, and tested six classification algorithms: logistic regression, support vector machines, k-nearest neighbors, random forest, convolutional neural networks, and eXtreme Gradient Boosting (XGBoost). The best results came from the optimized XGBoost model, which achieved 95.6% accuracy and an AUC of 0.999 with 2,000 AISNPs. For geographic localization, we used the Locator model, a deep neural network that predicts latitude and longitude directly from unphased genotypes. Notably, Locator trained on just 2,000 AISNPs performed nearly as well as models built on high-density genomic data (597,569 SNPs). Overall, these findings show that carefully designed AISNP panels combined with suitable machine learning techniques can provide highly accurate and efficient ancestry inference, offering valuable insights for population genetics, forensic science, and biogeography in East and Southeast Asia.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrated genetic and geographic ancestry prediction via large-scale genomic data and machine learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

查看全文
  • Jing Chen,
  • Yuguo Huang,
  • Haoliang Fan,
  • Mengge Wang,
  • Guanglin He,
  • Jiangwei Yan

摘要

Understanding fine-scale genetic and geographic ancestry in East and Southeast Asia is difficult due to complex population histories and limited high-resolution genomic data. Here, we introduce a comprehensive framework that combines ancestry-informative single nucleotide polymorphism (AISNP) panels with machine learning to jointly determine genetic ancestry and geographic origins in 1,703 individuals from 67 East and Southeast Asian groups. We developed seven nested AISNP panels, from 50 to 2,000 SNPs, and tested six classification algorithms: logistic regression, support vector machines, k-nearest neighbors, random forest, convolutional neural networks, and eXtreme Gradient Boosting (XGBoost). The best results came from the optimized XGBoost model, which achieved 95.6% accuracy and an AUC of 0.999 with 2,000 AISNPs. For geographic localization, we used the Locator model, a deep neural network that predicts latitude and longitude directly from unphased genotypes. Notably, Locator trained on just 2,000 AISNPs performed nearly as well as models built on high-density genomic data (597,569 SNPs). Overall, these findings show that carefully designed AISNP panels combined with suitable machine learning techniques can provide highly accurate and efficient ancestry inference, offering valuable insights for population genetics, forensic science, and biogeography in East and Southeast Asia.