Journal of Medical GeneticsGenetic & Genomic MedicinePeer-reviewed journal article
The PS4-Likelihood Ratio Calculator: Flexible allocation of evidence weighting for case-control data in variant classification
Charlie F RowlandsiD, Alice GarrettiD, Sophie AlleniD, Miranda DurkieiD, George J. BurgheliD, Rachel RobinsoniD, Alison CallawayiD, Joanne Field, Bethan A. Frugtniet, Sheila Palmer-Smith, Jonathan GrantiD, Judith Pagan, Trudi McDevittiD, Terri Patricia McVeighiD, Helen HansoniD, Nicola WhiffiniD, Michael Emlyn JonesiD, Clare TurnbulliD, CanVIG-UK
Abstract
BackgroundWithin the 2015 American College of Medical Genetics/Association of Molecular Pathology (ACMG/AMP) variant classification framework, case-control observations can only be scored dichotomously as strong evidence (PS4) towards pathogenicity or nil. MethodsWe 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 pre-specified odds ratio (OR) thresholds. Model one represents the hypothesis of association between variant and phenotype (e.g. OR[≥]5) and model two represents the hypothesis of non-association (e.g. OR[≤]1). ResultsPS4-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 datasets generated for more common phenotypes and smaller datasets more typical in very rare disease variant evaluation. ConclusionPS4-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.
- Citations
- 8
- FWCI
- 2.12
- Field percentile
- top 12%
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