Skip to main content

Bayesian Modeling for Genetic Anticipation in Presence of Mutational Heterogeneity: A Case Study in Lynch Syndrome

Buy Article:

$43.00 plus tax (Refund Policy)

Summary Genetic 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 subtypes known to be associated with the disease phenotype. In this article, 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 (MMR) genes known to cause hereditary nonpolyposis colorectal cancer, also called Lynch syndrome (LS). We find evidence for a decrease in AOO between generations in this article. Our model predicts family‐level anticipation effects that are potentially useful in genetic counseling clinics for high‐risk families.
No References
No Citations
No Supplementary Data
No Data/Media
No Metrics

Document Type: Research Article

Affiliations: 1: Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A. 2: Clinical Research Centre, Copenhagen University Hospital, 2650 Hvidovre, Denmark 3: Departments of Internal Medicine, Epidemiology and Human Genetics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.

Publication date: 2011-12-01

  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more