Bayesian Semiparametric Estimation of Cancer-Specific Age-at-Onset Penetrance With Application to Li-Fraumeni Syndrome

Seung Jun Shin, Ying Yuan, Louise C. Strong, Jasmina Bojadzieva, Wenyi Wang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration until individuals in a high-risk group develop different cancers, and accommodate family data using family-wise likelihoods. We tackle the ascertainment bias arising when family data are collected through probands in a high-risk population in which disease cases are more likely to be observed. We apply the proposed method to a cohort of 186 families with Li-Fraumeni syndrome identified through probands with sarcoma treated at MD Anderson Cancer Center from 1944 to 1982. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)541-552
Number of pages12
JournalJournal of the American Statistical Association
Volume114
Issue number526
DOIs
Publication statusPublished - 2019 Apr 3

Fingerprint

Semiparametric Estimation
Bayesian Estimation
Cancer
Competing Risks Model
Competing Risks
Semiparametric Model
Genotype
Phenotype
Likelihood
Proportion
Likely
Family
Semiparametric estimation
Estimate

Keywords

  • Cancer-specific age-at-onset penetrance
  • Competing risk
  • Family-wise likelihood
  • Gamma frailty model
  • Li-Fraumeni syndrome

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Cite this

Bayesian Semiparametric Estimation of Cancer-Specific Age-at-Onset Penetrance With Application to Li-Fraumeni Syndrome. / Shin, Seung Jun; Yuan, Ying; Strong, Louise C.; Bojadzieva, Jasmina; Wang, Wenyi.

In: Journal of the American Statistical Association, Vol. 114, No. 526, 03.04.2019, p. 541-552.

Research output: Contribution to journalArticle

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