Bayesian modeling to predict malignant hyperthermia susceptibility and pathogenicity of RYR1, CACNA1S and STAC3 variants

Abstract

Aim: Identify variants in RYR1, CACNA1S and STAC3, and predict malignant hyperthermia (MH) pathogenicity using Bayesian statistics in individuals clinically treated as MH susceptible (MHS).

Materials & methods: Whole exome sequencing including RYR1, CACNA1S and STAC3 performed on 64 subjects with: MHS; suspected MH event or first-degree relative; and MH negative. Variant pathogenicity was estimated using in silico analysis, allele frequency and prior data to calculate Bayesian posterior probabilities.

Results: Bayesian statistics predicted CACNA1S variant p.Thr1009Lys and RYR1 variants p.Ser1728Phe and p.Leu4824Pro are likely pathogenic, and novel STAC3 variant p.Met187Thr has uncertain significance. Nearly a third of MHS subjects had only benign variants.

Conclusion: Bayesian method provides new approach to predict MH pathogenicity of genetic variants.