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Assessing performance of pathogenicity predictors using clinically relevant variant datasets.

Adam C Gunning1,2, Verity Fryer2, James Fasham1

  • 1College of Medicine and Health, University of Exeter Medical School Institute of Biomedical and Clinical Science, Exeter, Devon, UK.

Journal of Medical Genetics
|August 27, 2020
PubMed
Summary
This summary is machine-generated.

Pathogenicity predictors are crucial for genomic variant interpretation. Newer meta-predictors outperform older tools, but performance significantly drops on clinically relevant datasets, necessitating careful tool selection.

Keywords:
genetic testinggenetic variationgeneticsgenomicshuman genetics

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Area of Science:

  • Genomic variant interpretation
  • Bioinformatics
  • Clinical genetics

Background:

  • Pathogenicity predictors are widely used for genomic variant interpretation.
  • Independent validation using clinically relevant datasets is lacking.

Purpose of the Study:

  • To evaluate the performance of pathogenicity predictors on clinically relevant data.
  • To compare newer meta-predictors against traditional in silico tools.

Main Methods:

  • Derived two validation datasets: 'open' (public databases) and 'clinically representative' (exome/panel sequencing).
  • Evaluated REVEL, GAVIN, ClinPred, SIFT, and PolyPhen-2 performance.

Main Results:

  • Newer meta-predictors generally outperform older tools.
  • All predictors showed lower performance on the clinically representative dataset.
  • REVEL achieved the best performance (AUC 0.82) on the clinical dataset.
  • Consensus-based approaches reduced performance due to discordance and false concordance.

Conclusions:

  • Meta-predictors are preferred over traditional in silico tools.
  • A consensus-based approach is not recommended for current practices.