The PS4-likelihood ratio calculator: flexible allocation of evidence weighting for case-control data in variant classification.
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Embargo End Date
Authors
Rowlands, CF
Garrett, A
Allen, S
Durkie, M
Burghel, GJ
Robinson, R
Callaway, A
Field, J
Frugtniet, B
Palmer-Smith, S
Grant, J
Pagan, J
McDevitt, T
McVeigh, TP
Hanson, H
Whiffin, N
Jones, M
Turnbull, C
CanVIG-UK,
Garrett, A
Allen, S
Durkie, M
Burghel, GJ
Robinson, R
Callaway, A
Field, J
Frugtniet, B
Palmer-Smith, S
Grant, J
Pagan, J
McDevitt, T
McVeigh, TP
Hanson, H
Whiffin, N
Jones, M
Turnbull, C
CanVIG-UK,
Document Type
Journal Article
Date
2024-09-24
Date Accepted
2024-08-05
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.
Citation
Journal of Medical Genetics,
DOI
Source Title
Journal of Medical Genetics
Publisher
BMJ PUBLISHING GROUP
ISSN
0022-2593
eISSN
1468-6244
1468-6244
1468-6244
Collections
Research Team
Translational Genetics