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Predict Variant Diagnosis Probability Using Structure, Function, and In Silico Features (KCNH2 and LQT2)

Brett Kroncke

October 01, 2020

  • Introduction
  • Part 1: Calculate probability of LQT2 diagnosis and LQT2 Probability Density using Various Subsets of the Literature and Cohort Data
    • All Literature Variants
      • LQT2 empirical LQTS diagnosis probability prior
      • Calculate LQTS probability densities
      • Calculate Weighted Spearman Correlation Coefficients
      • Scale all covariates
      • Calculate EM priors and posteriors for all variants
    • Literature Variants Where N = 1 Variants are removed
      • LQT2 empirical diagnosis probability prior
      • Calculate LQTS probability densities
      • Scale all covariates
      • Calculate EM priors and posteriors for all variants
    • Literature and Cohort Combined (for final predictions)
      • LQT2 empirical diagnosis probability prior
      • Calculate LQTS probability densities and annotate function and structural location
      • Calculate EM priors and posteriors for all variants
    • LQT2 diagnosis probability in Cohort Data for validation
  • Part 2: Coverage plots
    • Bootstrap and get the coverage rate
      • Bootstrap function
    • Plot coverage
      • Observed diagnosis probability as the “true” diagnosis probability
  • Part 3: Variance explained
    • Pearson R^2 and Spearman Rho Against EM Posterior from Cohort
    • Variance explained from literature dataset
  • Part 4: ROC and AUC plots