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Mutation, Gene Flow, and Genetic Drift01:09

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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Genetic Diversity and Mutation Frequency Databases in Ethnic Populations: Systematic Review.

Shumaila Khan1, Mahmood Alam2, Iqbal Qasim3

  • 1Department of Computer Science, University of Science and Technology Bannu, Bannu Township, Bannu, 28100, Pakistan, 92 3329959220.

JMIR Bioinformatics and Biotechnology
|December 4, 2025
PubMed
Summary
This summary is machine-generated.

National and ethnic mutation frequency databases (NEMDBs) are vital for precision medicine but suffer from standardization and data quality issues. AI and linked open data solutions are proposed to improve interoperability and clinical utility.

Keywords:
ethnic-specific mutation frequency databasesgenetic diversityinherited diseasemutation disorder

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • National and ethnic mutation frequency databases (NEMDBs) are essential for gene variation research and precision medicine.
  • They provide critical insights into genetic diversity across populations and its health implications.

Purpose of the Study:

  • To systematically evaluate 42 NEMDBs for standardization gaps and data quality.
  • To propose AI and linked open data solutions for enhanced interoperability.
  • To highlight the clinical implications of NEMDBs for precision medicine.

Main Methods:

  • A systematic assessment of 42 NEMDBs was conducted.
  • Criteria included data collection, system design, querying, accessibility, and integration capabilities.
  • Standardization and data quality challenges were specifically addressed.

Main Results:

  • 70% of NEMDBs lack standardized formats, and 50% contain outdated data, limiting clinical utility.
  • 60% exhibit gaps in cross-comparing genetic variations.
  • Open-source platforms like LOVD showed a 40% increase in usability.

Conclusions:

  • Cloud-based platforms and linked open data frameworks are proposed to address standardization and data gaps.
  • AI-driven models can enhance interoperability among NEMDBs.
  • User-centric design is prioritized to benefit clinicians, researchers, and the public.