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.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland 20892, USA. chattern@mail.nih.gov
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Chatterjee
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Wacholder
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