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Comprehensive evaluation of ACMG/AMP-based variant classification tools.

Tohid Ghasemnejad1, Yuheng Liang1, Khadijeh Hoda Jahanian2

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Summary
This summary is machine-generated.

This study compares ACMG/AMP variant interpretation tools for Mendelian disorders. Phenotype-driven tools like LIRICAL and Franklin show superior accuracy in variant prioritization.

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Area of Science:

  • Genomic Medicine
  • Clinical Bioinformatics
  • Computational Biology

Background:

  • The American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) guidelines are standard for clinical variant interpretation.
  • A lack of comparative studies on ACMG/AMP software tools hinders clinical adoption.
  • Clinicians need evidence-based guidance for selecting variant interpretation tools.

Purpose of the Study:

  • To benchmark and compare the performance of ACMG/AMP-based variant interpretation software.
  • To identify top-performing tools for variant prioritization in Mendelian disorders.
  • To provide guidance for clinicians on selecting appropriate tools for variant analysis.

Main Methods:

  • Benchmarked four ACMG/AMP tools (Franklin, InterVar, TAPES, Genebe) against LIRICAL using 151 expert-curated Mendelian disorder datasets.
  • Evaluated tools based on top-N accuracy, retention rates, precision, recall, F1 scores, and AUC.
  • Utilized bootstrap confidence intervals and Friedman tests for statistical validation.

Main Results:

  • LIRICAL (68.21%) and Franklin (61.59%) demonstrated superior top-10 variant prioritization accuracy in Mendelian disorders.
  • Tools with advanced phenotypic integration significantly outperformed those relying solely on genomic features.
  • Statistical analysis confirmed the significant outperformance of LIRICAL and Franklin (p<0.0000).

Conclusions:

  • Phenotype-driven variant interpretation tools, such as LIRICAL and Franklin, are more effective for Mendelian disorders.
  • The study provides crucial data for clinicians selecting variant interpretation software.
  • Open availability of data and code ensures reproducibility and facilitates further research.