Fine-scale biogeographical ancestry inference in Southeast and East Asians via high-efficiency markers and machine learning approaches Article Swipe
Qingxin Yang
,
Jing Chen
,
Shengjie Nie
,
Chao Liu
,
Deng Hong
,
Guanglin He
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fevo.2025.1572596
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fevo.2025.1572596
Biogeographical ancestry inference offers valuable clues for forensic cold cases, but limited information is typically obtained from substructured populations within continental East Asian and Southeast groups. This study presents an integrative genomic dataset of 3,461 individuals from East Asia and Southeast Asia to elucidate the fine-scale population substructure and its role in precision forensic medicine. Six nested panels were developed with increasing ancestry-informative marker (AIM) density (ranging from 50 to 2,000 SNPs) to distinguish fine genetic differences between the six language groups and populations within the Sino-Tibetan language family. We found that the 2000 AIM panel exhibited differentiation efficiency in PCA comparable to that of all loci. Additionally, we constructed a classification machine learning model with an average prediction accuracy of 84%, highlighting the critical role of geographical information in improving model accuracy. Furthermore, we validated the accuracy of the deep learning method Locator in predicting geographical coordinates solely based on genetic information. This work highlights the power of integrating genetic and geographic data with artificial intelligence to refine fine-scale biogeographical ancestry inference, offering more profound insights into population structure in East Asia and Southeast Asia, with significant implications for forensic applications.
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- Language
- en
- Landing Page
- https://doi.org/10.3389/fevo.2025.1572596
- OA Status
- gold
- References
- 49
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- 10
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https://openalex.org/W4409112377Canonical identifier for this work in OpenAlex
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https://doi.org/10.3389/fevo.2025.1572596Digital Object Identifier
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Fine-scale biogeographical ancestry inference in Southeast and East Asians via high-efficiency markers and machine learning approachesWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-04-03Full publication date if available
- Authors
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Qingxin Yang, Jing Chen, Shengjie Nie, Chao Liu, Deng Hong, Guanglin HeList of authors in order
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https://doi.org/10.3389/fevo.2025.1572596Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3389/fevo.2025.1572596Direct OA link when available
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Inference, Scale (ratio), Evolutionary biology, Geography, Biology, Ecology, Cartography, Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.medicine. | 54 |
| abstract_inverted_index.precision | 52 |
| abstract_inverted_index.structure | 179 |
| abstract_inverted_index.typically | 14 |
| abstract_inverted_index.validated | 135 |
| abstract_inverted_index.artificial | 165 |
| abstract_inverted_index.comparable | 101 |
| abstract_inverted_index.efficiency | 98 |
| abstract_inverted_index.fine-scale | 45, 169 |
| abstract_inverted_index.geographic | 162 |
| abstract_inverted_index.highlights | 155 |
| abstract_inverted_index.increasing | 61 |
| abstract_inverted_index.inference, | 172 |
| abstract_inverted_index.population | 46, 178 |
| abstract_inverted_index.predicting | 145 |
| abstract_inverted_index.prediction | 118 |
| abstract_inverted_index.constructed | 109 |
| abstract_inverted_index.continental | 20 |
| abstract_inverted_index.coordinates | 147 |
| abstract_inverted_index.differences | 76 |
| abstract_inverted_index.distinguish | 73 |
| abstract_inverted_index.individuals | 35 |
| abstract_inverted_index.information | 12, 128 |
| abstract_inverted_index.integrating | 159 |
| abstract_inverted_index.integrative | 30 |
| abstract_inverted_index.populations | 18, 83 |
| abstract_inverted_index.significant | 187 |
| abstract_inverted_index.Furthermore, | 133 |
| abstract_inverted_index.Sino-Tibetan | 86 |
| abstract_inverted_index.geographical | 127, 146 |
| abstract_inverted_index.highlighting | 122 |
| abstract_inverted_index.implications | 188 |
| abstract_inverted_index.information. | 152 |
| abstract_inverted_index.intelligence | 166 |
| abstract_inverted_index.substructure | 47 |
| abstract_inverted_index.Additionally, | 107 |
| abstract_inverted_index.applications. | 191 |
| abstract_inverted_index.substructured | 17 |
| abstract_inverted_index.classification | 111 |
| abstract_inverted_index.Biogeographical | 0 |
| abstract_inverted_index.biogeographical | 170 |
| abstract_inverted_index.differentiation | 97 |
| abstract_inverted_index.ancestry-informative | 62 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.12785687 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |