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How good are pathogenicity predictors in detecting benign variants?

Abhishek Niroula1, Mauno Vihinen1

  • 1Protein Structure and Bioinformatics, Department of Experimental Medical Science, Lund University, Lund, Sweden.

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|February 12, 2019
PubMed
Summary
This summary is machine-generated.

Selecting reliable computational tools is crucial for interpreting genetic variants in precision medicine. This study assessed 10 tools, finding PON-P2, FATHMM, and VEST performed best at identifying benign variants.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Computational tools are essential for interpreting genetic variants from sequencing data, impacting precision medicine.
  • The reliability of variant interpretation depends on systematic performance assessment of these tools.
  • Existing assessments show variable performance, often due to differences in test datasets.

Purpose of the Study:

  • To systematically evaluate the specificity of 10 variant interpretation tools.
  • To assess tool performance using a large dataset of common amino acid substitutions.
  • To analyze tool performance across different geographical populations.

Main Methods:

  • Utilized 63,160 common amino acid substitutions (allele frequency 1-25%) from the Exome Aggregation Consortium (ExAC) database.
  • Evaluated the specificity (ability to correctly identify benign variants) of 10 computational interpretation tools.
  • Tested tool performance on variants from six distinct geographical populations.

Main Results:

  • PON-P2 demonstrated the highest specificity (95.5%), followed by FATHMM (86.4%) and VEST (83.5%).
  • Significant performance variation was observed among the 10 evaluated tools.
  • The least effective tool incorrectly classified over one-third of benign variants as disease-causing.

Conclusions:

  • The study provides a benchmark for selecting reliable tools for benign variant interpretation in research and clinical settings.
  • PON-P2, FATHMM, and VEST are recommended for accurate benign variant classification.
  • Results offer valuable insights for developers aiming to improve variant interpretation tool performance.