Fig 1.
Algorithm modules of multifactorial model analysis for variant prediction and classification.
(A) Modules of Bayesian multifactorial model analysis for variant prediction and classification. SLR = stepwise logistic regression. (B) 5-tiered variant classification scheme based on the estimated 95% probability credible interval (PCI) of variant pathogenicity.
Fig 2.
Outcome of 5-tiered predicted classes in MGPT data.
(A) The proportions of predicted classes from gene-specific IVP model analysis in which each prediction was evaluated from a subset of 16 in silico predictors. The analysis was based on 1,161 class known missense variants in 10 genes using LOOCV. (B) The proportions of predicted classes from MVP model analysis in which each prediction aggregated a prior distribution from IVP model with the available evidence predictors. The analysis was based on 1,016 variants with any available evidence statistics.
Table 1.
In silico variant prediction in MGPT data.
Fig 3.
Comparison of AUC statistics of standalone and meta in silico predictors in MGPT data.
(A) AUC statistics of top 10 standalone in silico predictors. (B) AUC statistics of 7 meta in silico predictors. The analysis models in legend were listed in descending order of AUC values. Abbreviations: AUC = area under the receiver operating characteristic curve; IVP = in silico variant prediction; MGPT = multigene panel test.
Table 2.
Multifactorial variant prediction in MGPT data.