Genetic Diversity and Mutation Frequency Databases in Ethnic Populations: Systematic Review
Khan S. Alam M. Qasim I. Khan S. Khan W. Mamyrbayev O. Akhmediyarova A. Mukazhanov N. Alibiyeva Z.
2025JMIR Publications Inc.
JMIR Bioinformatics and Biotechnology
2025#6
Background: National and ethnic mutation frequency databases (NEMDBs) play a crucial role in documenting gene variations across populations, offering invaluable insights for gene mutation research and the advancement of precision medicine. These databases provide an essential resource for understanding genetic diversity and its implications for health and disease across different ethnic groups. Objective: The aim of this study is to systematically evaluate 42 NEMDBs to (1) quantify gaps in standardization (70% nonstandard formats, 50% outdated data), (2) propose artificial intelligence/linked open data solutions for interoperability, and (3) highlight clinical implications for precision medicine across NEMDBs. Methods: A systematic approach was used to assess the databases based on several criteria, including data collection methods, system design, and querying mechanisms. We analyzed the accessibility and user-centric features of each database, noting their ability to integrate with other systems and their role in advancing genetic disorder research. The review also addressed standardization and data quality challenges prevalent in current NEMDBs. Results: The analysis of 42 NEMDBs revealed significant issues, with 70% (29/42) lacking standardized data formats and 60% (25/42) having notable gaps in the cross-comparison of genetic variations, and 50% (21/42) of the databases contained incomplete or outdated data, limiting their clinical utility. However, databases developed on open-source platforms, such as LOVD, showed a 40% increase in usability for researchers, highlighting the benefits of using flexible, open-access systems. Conclusions: We propose cloud-based platforms and linked open data frameworks to address critical gaps in standardization (70% of databases) and outdated data (50%) alongside artificial intelligence–driven models for improved interoperability. These solutions prioritize user-centric design to effectively serve clinicians, researchers, and public stakeholders.
ethnic-specific mutation frequency databases , genetic diversity , inherited disease , mutation disorder
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Department of Computer Science, University of Science and Technology Bannu, Bannu, Pakistan
Faculty of Business, Law and Social Sciences, Birmingham City University, Birmingham, United Kingdom
Hertfordshire Business School, University of Hertfordshire, Hatfield, United Kingdom
Department of Chemistry, University of Science and Technology Bannu, Bannu, Pakistan
Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia
Institute of Information and Computational Technologies, Satbayev University, Almaty, Kazakhstan
Institute of Automation and Information Technologies, Satbayev University, Almaty, Kazakhstan
Department of Computer Science
Faculty of Business
Hertfordshire Business School
Department of Chemistry
Faculty of Computing and Informatics
Institute of Information and Computational Technologies
Institute of Automation and Information Technologies
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