ArticleBMC genomics2025
Bioinformatic insights into five Chinese population substructures inferred from the East Asian-specific AISNP panel.
Article in BMC genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Integrated genetic and geographic ancestry prediction via large-scale genomic data and machine learning.Human genomics · 2025Article
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6 authors.
Funding
Abstract
backgroundRecent advances in population-specific high-quality reference databases have significantly improved the performance of forensic panel development for personal identification, parentage testing, and biogeographical ancestry inference. However, the discriminative power of previously developed AISNP panels remains limited in applications involving regional Chinese population substructures.
resultsWe used the high-quality Chinese population-specific genetic resource to develop six nested C5ClusterTag-50/100/250/500/1000/2000 ancestry-informative SNP panels focused on inferring population stratification among geographically and genetically distinct Chinese populations. We used comprehensive bioinformatics approaches and machine learning to validate the effectiveness of these panels in both the testing and training datasets. A total of 2,772 individuals were screened across different AISNP panels based on the I
conclusionsThese panels can differentiate ethnolinguistic Chinese populations into five subgroups based on geographical divisions or linguistic affiliations, achieving a high average accuracy rate in machine learning models. This work not only developed a robust ancestry inference panel and new tools for predicting the ancestry of ethnolinguistic Chinese populations but also created a comprehensive reference dataset and machine learning model applicable to population and forensic uses globally.
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