A marginal likelihood approach for estimating penetrance from kin-cohort designs

Biometrics. 2001 Mar;57(1):245-52. doi: 10.1111/j.0006-341x.2001.00245.x.


The kin-cohort design is a promising alternative to traditional cohort or case-control designs for estimating penetrance of an identified rare autosomal mutation. In this design, a suitably selected sample of participants provides genotype and detailed family history information on the disease of interest. To estimate penetrance of the mutation, we consider a marginal likelihood approach that is computationally simple to implement, more flexible than the original analytic approach proposed by Wacholder et al. (1998, American Journal of Epidemiology 148, 623-629), and more robust than the likelihood approach considered by Gail et al. (1999, Genetic Epidemiology 16, 15-39) to presence of residual familial correlation. We study the trade-off between robustness and efficiency using simulation experiments. The method is illustrated by analysis of the data from the Washington Ashkenazi Study.

MeSH terms

  • Algorithms
  • BRCA2 Protein
  • Bias
  • Biometry*
  • Breast Neoplasms / epidemiology
  • Breast Neoplasms / genetics
  • Cohort Studies
  • District of Columbia / epidemiology
  • Epidemiologic Methods
  • Female
  • Genes, BRCA1
  • Genetic Testing
  • Humans
  • Jews / genetics
  • Likelihood Functions*
  • Models, Statistical
  • Mutation
  • Neoplasm Proteins / genetics
  • Phenotype
  • Risk Factors
  • Transcription Factors / genetics


  • BRCA2 Protein
  • Neoplasm Proteins
  • Transcription Factors