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A framework for automated gene selection in genomic applications.

L Lazo de la Vega1,2,3,4, W Yu1, K Machini1,2,3

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Genetics in Medicine : Official Journal of the American College of Medical Genetics
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A new framework efficiently identifies disease-associated genes for genomic analysis. This approach saves time by creating dynamic, highly sensitive gene lists for various applications.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Medical Genetics

Background:

  • Genomic data evaluation requires efficient identification of disease-associated genes.
  • Individuals with unknown disease etiology and those undergoing genomic screening benefit from such tools.

Purpose of the Study:

  • To propose a framework for gene selection in genomic analyses.
  • To support applications using genes with established or emerging disease evidence.

Main Methods:

  • Compiled a comprehensive gene list (6,145 genes) from Human Gene Mutation Database, OMIM, and ClinVar.
  • Applied stringent filters and computationally curated evidence (DisGeNET) to create a refined list (3,929 genes).

Main Results:

  • The generated gene lists show high inclusion of genes with strong disease associations compared to manual curation.
  • Limited-evidence genes were largely excluded, enhancing specificity.
  • The framework successfully identified pathogenic variants in 45 genomes.

Conclusions:

  • The developed approach efficiently generates highly sensitive gene lists for genomic applications.
  • The framework is dynamic and updatable, offering significant time savings.
  • This method aids in evaluating genomic data for disease association.