Citation

Khan, Shumaila and Alam, Mahmood and Qasim, Iqbal and Khan, Shahnaz and Khan, Wahab and Mamyrbayev, Orken and Akhmediyarova, Ainur and Mukazhanov, Nurzhan and Alibiyeva,, Zhibek (2025) Genetic Diversity and Mutation Frequency Databases in Ethnic Populations: Systematic Review. JMIR Bioinformatics and Biotechnology, 6. e69454-e69454. ISSN 2563-3570

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Abstract

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study is to systematically evaluate 42 NEMDBs to (1) quantify gaps in standardization (70% nonstandard formats, 50% outdated data), (2) propose artificial intelligence/linked open data solutions for interoperability, and (3) highlight clinical implications for precision medicine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notable gaps in the cross-comparison of genetic variations, and 50% (21/42) of the databases contained incomplete or outdated data, limiting their clinical utility.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platforms and linked open data frameworks to address critical gaps in standardization (70% of databases) and outdated data (50%) alongside artificial intelligence–driven models for improved interoperability.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.

Item Type:	Article
Uncontrolled Keywords:	Ethnic-specific mutation frequency databases
Subjects:	Q Science > QA Mathematics > QA299.6-433 Analysis
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	27 Aug 2025 04:40
Last Modified:	27 Aug 2025 04:40
URII:	http://shdl.mmu.edu.my/id/eprint/14453

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