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 journalArticlepeer-review

    7 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

    Bibliographical note

    Funding Information:
    Cancer Prevention and Research Institute of Texas [RP130090]; National Institutes of Health [P01CA34936, P30 CA016672].

    Publisher Copyright:
    © 2018, © 2018 American Statistical Association.

    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

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