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<p>Inferring the biogeographic origin of unknown individual is crucial in forensic practice. To enhance the efficacy of biogeographic origin inference, we previously developed a novel panel containing 56 ancestry-informative insertion/deletions (AI-InDels), three Y-InDels, and the Amelogenin gene, all with amplicons less than 200&#xa0;bp, facilitating DNA analysis of degraded samples. In this research, we investigated the forensic performance of the InDel panel in the Kazakh group in China, elucidated the genetic structure of Kazakh group, and verified the panel’s efficacy in assigning unknown individuals to the appropriate intercontinental regions. The findings demonstrated that the novel panel showed relatively high genetic polymorphisms of autosomal InDels and could serve as an efficient tool for forensic individual identification in Kazakh group. Furthermore, the multiple results of population genetic analyses indicated that the Kazakh group has a mixture of ancestral components from East Asian and European populations, with East Asian ancestry predominating. Using different machine learning models, we found that the novel panel assigned unknown individuals to their intercontinental regions with at least 99% accuracy at the three-continental level and at least 90% accuracy at the five-continental level. In conclusion, we demonstrated that the novel panel can effectively reveal the genetic structure of the group with mixed genetic background and infer the biogeographic origins of unknown individuals with high accuracy.</p>

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Efficiency of Ancestral Inference and Characterization of Genetic Structure of Chinese Kazakh Group with a Novel InDel Panel

  • 期刊论文

  • 发表日期：

    2025年10月27日

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  • Qinglin Liu,
  • Lisiteng Luo,
  • Qinglin Liang,
  • Xiaolian Wu,
  • Ming Zhao,
  • Xi Yuan,
  • Yifeng Lin,
  • Chunmei Shen,
  • Bofeng Zhu

摘要

Inferring the biogeographic origin of unknown individual is crucial in forensic practice. To enhance the efficacy of biogeographic origin inference, we previously developed a novel panel containing 56 ancestry-informative insertion/deletions (AI-InDels), three Y-InDels, and the Amelogenin gene, all with amplicons less than 200 bp, facilitating DNA analysis of degraded samples. In this research, we investigated the forensic performance of the InDel panel in the Kazakh group in China, elucidated the genetic structure of Kazakh group, and verified the panel’s efficacy in assigning unknown individuals to the appropriate intercontinental regions. The findings demonstrated that the novel panel showed relatively high genetic polymorphisms of autosomal InDels and could serve as an efficient tool for forensic individual identification in Kazakh group. Furthermore, the multiple results of population genetic analyses indicated that the Kazakh group has a mixture of ancestral components from East Asian and European populations, with East Asian ancestry predominating. Using different machine learning models, we found that the novel panel assigned unknown individuals to their intercontinental regions with at least 99% accuracy at the three-continental level and at least 90% accuracy at the five-continental level. In conclusion, we demonstrated that the novel panel can effectively reveal the genetic structure of the group with mixed genetic background and infer the biogeographic origins of unknown individuals with high accuracy.