Evidence map›Paper›PMID 42791391›Full record

ArticleInternational journal of legal medicine2026

Moderate-density SNPs combined with machine learning method driven kinship identification and East Asian subpopulations discrimination analysis.

Xiaolian Wu, Qinglin Liu, Lisiteng Luo, Qiong Lan, Bofeng Zhu

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Article in International journal of legal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiaolian WuGuangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Qinglin LiuGuangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Lisiteng LuoGuangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Qiong LanGuangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, China.
Bofeng ZhuGuangzhou Key Laboratory of Forensic Multi-Omics for Precision Identification, School of Forensic Medicine, Southern Medical University, Guangzhou, Guangdong, China. zhubofeng7372@126.com.ORCID https://orcid.org/0000-0002-9038-2342

Funding

Guangdong Provincial Applied Science and Technology Research and Development Program 2024A1111120006National Natural Science Foundation of China No. 82293652
6 · The paper itself

Abstract

Moderate-density single nucleotide polymorphisms (SNPs) (nearly 2000 SNPs) have shown great forensic application value in Chinese Han. However, the forensic efficacy of these SNPs still requires validation in more populations. In this study, we evaluated the performance of these moderate-density SNPs combined with machine learning (ML) method for identifying first- to fourth-degree kinships in Chinese Yunnan Zhuang (CYZ) group. Moreover, we integrated genetic data from 135 populations spanning nine geographic regions obtained from public datasets to conduct comprehensive population genetic analyses. The present results showed that these SNPs were effective in identifying first- to third-degree kinships from unrelated individuals. The density curves of Log

Indexed as

Forensic geneticsKinship identificationMachine learning methodNext generation sequencingPopulation genetic analysis

Identifiers

PMID42791391

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.