Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case-Study in Lynch Syndrome
Author(s)Boonstra, Philip S.
Taylor, Jeremy M. G.
Moreno, Victor M.
Gruber, Stephen B.
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AbstractGenetic anticipation, described by earlier age of onset (AOO) and more aggressive symptoms in successive generations, is a phenomenon noted in certain hereditary diseases. Its extent may vary between families and/or between mutation sub-types known to be associated with the disease phenotype. In this paper, we posit a Bayesian approach to infer genetic anticipation under flexible random effects models for censored data that capture the effect of successive generations on AOO. Primary interest lies in the random effects. Misspecifying the distribution of random effects may result in incorrect inferential conclusions. We compare the fit of four candidate random effects distributions via Bayesian model fit diagnostics. A related statistical issue here is isolating the confounding effect of changes in secular trends, screening and medical practices that may affect time to disease detection across birth cohorts. Using historic cancer registry data, we borrow from relative survival analysis methods to adjust for changes in age-specific incidence across birth cohorts. Our motivating case-study comes from a Danish cancer register of 124 families with mutations in mismatch repair genes known to cause hereditary non-polyposis colorectal cancer, also called Lynch syndrome. We find evidence for a decrease in AOO between generations in this study. Our model predicts family level anticipation effects which are potentially useful in genetic counseling clinics for high risk families.