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The PS4-likelihood ratio calculator: flexible allocation of evidence weighting for case-control data in variant classification

  • CanVIG-UK
  • Institute of Cancer Research
  • St George's University Hospitals NHS Foundation Trust
  • Sheffield Children's NHS Foundation Trust
  • Manchester University NHS Foundation Trust
  • Leeds Teaching Hospitals NHS Trust
  • University Hospital Southampton NHS Foundation Trust
  • Nottingham University Hospitals NHS Trust
  • Cardiff & Vale University Health Board
  • NHS Greater Glasgow and Clyde
  • NHS Lothian
  • CHI Crumlin
  • Royal Marsden NHS Foundation Trust
  • University of Exeter
  • University of Oxford
  • Broad Institute

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
6 Downloads (Pure)

Abstract

Background The 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework specifies that case-control observations can be scored as 'strong' evidence (PS4) towards pathogenicity. Methods We developed the PS4-likelihood ratio calculator (PS4-LRCalc) for quantitative evidence assignment based on the observed variant frequencies in cases and controls. Binomial likelihoods are computed for two models, each defined by prespecified OR thresholds. Model 1 represents the hypothesis of association between variant and phenotype (eg, OR≥5) and model 2 represents the hypothesis of non-association (eg, OR≤1). Results PS4-LRCalc enables continuous quantitation of evidence for variant classification expressed as a likelihood ratio (LR), which can be log-converted into log LR (evidence points). Using PS4-LRCalc, observed data can be used to quantify evidence towards either pathogenicity or benignity. Variants can also be evaluated against models of different penetrance. The approach is applicable to balanced data sets generated for more common phenotypes and smaller data sets more typical in very rare disease variant evaluation. Conclusion PS4-LRCalc enables flexible evidence quantitation on a continuous scale for observed case-control data. The converted LR is amenable to incorporation into the now widely used 2018 updated Bayesian ACMG/AMP framework.

Original languageEnglish
Pages (from-to)983-991
Number of pages9
JournalJournal of Medical Genetics
Volume61
Issue number10
DOIs
Publication statusPublished - 24 Sept 2024

Bibliographical note

Publisher Copyright:
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.

Keywords

  • Genetic Testing
  • Genetic Variation
  • Genetics
  • Genetics, Population

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