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Ancestry estimation: Methodological evolution and forensic implications
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AI
DOI：10.1007/s12024-026-01230-9.png)
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Abstract
En 中文
Ancestry estimation has been significantly enhanced through interdisciplinary advances in genomics, bioinformatics, artificial intelligence and anthropology. However, persistent reliance on outdated typological methods necessitates a transition toward validated, contemporary techniques. This review aims to synthesize methodological evolution, current technical advancements and establish a forward-looking research agenda. The novelty of this study lies in its specialized forensic focus and critical analysis of the intersection between high-resolution genomic data and the requirements for legal admissibility under the Daubert and Frye standards. A literature search was conducted across SCOPUS, Web of Science, and PubMed databases (2000-2025), spanning the fields of genetics, forensic anthropology, and bioinformatics. Articles were selected and evaluated based on their relevance to methodological evolution, biological foundations, and the integration of advanced computational tools. The analysis identifies a paradigm shift toward statistically robust, population-based models. However, critical analysis reveals that while advanced genomic panels and machine learning algorithms achieve high precision, they face significant hurdles regarding legal admissibility due to a lack of technical transparency. Furthermore, current methods are hampered by databases bias and profound ethical concerns, including the potential for reinforcing racial profiling and absence of a global regulatory consensus in forensic DNA phenotyping. Ancestry estimation forensic utility depends on bridging the gap between technical precision and judicial reliability. We propose a research agenda focused on refining methodologies through machine learning, expanding globally diverse reference panels, and establishing harmonized legal frameworks to safeguard genetic privacy while maintaining the investigative utility of these tools in forensic casework. Key Points 1. Interdisciplinary approaches are crucial for advancing ancestry estimation. 2. Genetic, anthropological, and computational methodologies enhance accuracy and contextual understanding. 3. Forensic applications benefit from refined ancestry estimation techniques. 4. Limitations include data bias, complexity of ancestry, and ethical concerns. 5. Future directions emphasize inclusivity, collaboration, and ethical guidelines.
Keywords:
Anthropology
Geometric morphometrics
Genetics
Machine learning
Legal admissibility
DNA phenotyping
SNPs
Journal
IF:
1.4
Papers:
182
Citations:
1.9K