A marginal likelihood approach for estimating penetrance from kin-cohort designs.
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.
Chatterjee
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Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland 20892, USA. chattern@mail.nih.gov
Wacholder
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D000465:Algorithms; D024682:BRCA2 Protein; D015982:Bias; D001699:Biometry; D001943:Breast Neoplasms / Q000453:epidemiology / Q000235:genetics; D015331:Cohort Studies; D004219:District of Columbia / Q000453:epidemiology; D004812:Epidemiologic Methods; D005260:Female; D019398:Genes, BRCA1; D005820:Genetic Testing; D006801:Humans; D007585:Jews / Q000235:genetics; D016013:Likelihood Functions; D015233:Models, Statistical; D009154:Mutation; D009363:Neoplasm Proteins / Q000235:genetics; D010641:Phenotype; D012307:Risk Factors; D014157:Transcription Factors / Q000235:genetics
D024682:BRCA2 Protein; D009363:Neoplasm Proteins; D014157:Transcription Factors