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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Published on: October 11, 2018

Prioritization of positional candidate genes using multiple web-based software tools.

Tobias A Thornblad1, Kate S Elliott, Jeremy Jowett

  • 1Genetic Epidemiology, Queensland Institute of Medical Research, Brisbane, Australia. tobias.thornblad@gmail.com

Twin Research and Human Genetics : the Official Journal of the International Society for Twin Studies
|January 9, 2008
PubMed
Summary
This summary is machine-generated.

Prioritizing genes for complex traits is crucial. Combining multiple bioinformatics tools, like PosMed, GeneSniffer, and SUSPECTS, improves candidate gene identification compared to single tools.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Complex Trait Genetics

Background:

  • Identifying causal genes for complex traits relies on prioritizing genes within candidate genomic regions.
  • A lack of established bioinformatics tools for gene prioritization hinders this process.

Purpose of the Study:

  • To evaluate the performance of three positional candidate gene software tools: PosMed, GeneSniffer, and SUSPECTS.
  • To compare their effectiveness in prioritizing genes across different genomic region sizes (40, 20, and 10 Mb) for common diseases.

Main Methods:

  • The study compared PosMed, GeneSniffer, and SUSPECTS using human genome regions centered around known susceptibility genes for breast cancer, Crohn's disease, age-related macular degeneration, and schizophrenia.
  • Performance was assessed based on the ranking of known susceptibility genes within varying genomic region sizes.

Main Results:

  • No single tool consistently ranked the known susceptibility gene highly across all tested regions.
  • Significant variation existed in gene prioritization lists and rank order among the three tools.
  • Combining multiple tools demonstrated superior performance in ranking positional candidates, especially for narrower genomic regions.

Conclusions:

  • Relying on a single gene prioritization tool is not recommended due to performance variability.
  • Using multiple tools increases the likelihood of identifying true susceptibility genes.
  • Narrower genomic regions and consensus prioritization from multiple tools enhance the accuracy of candidate gene identification for complex traits.