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JNCI Monographs 1999 1999(26):55-60;
© 1999 by Oxford University Press
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Journal of the National Cancer Institute Monographs, No. 26, 55-60, 1999
© 1999 Oxford University Press

II. GENE CHARACTERIZATION PANEL

Kin-Cohort Designs for Gene Characterization

Mitchell H. Gail, David Pee, Raymond Carroll

Affiliations of authors: M. H. Gail, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD; D. Pee, Information Management Services, Rockville, MD; R. Carroll, Texas A & M University, College Station.

Correspondence to: Mitchell H. Gail, M.D., Ph.D., National Cancer Institute, EPS-8032, Bethesda, MD 20892 (e-mail: gailm{at}exchange.nih.gov).

BACKGROUND: In the kin-cohort design, a volunteer with or without disease (the proband) agrees to be genotyped, and one obtains information on the history of a disease in first-degree relatives of the proband. From these data, one can estimate the penetrance of an autosomal dominant gene, and this technique has been used to estimate the probability that Ashkenazi Jewish women with specific mutations of BRCA1 or BRCA2 will develop breast cancer. METHODS: We review the advantages and disadvantages of the kin-cohort design and focus on dichotomous outcomes, although a few results on time-to-disease onset are presented. We also examine the effects of violations of assumptions on estimates of penetrance. We consider selection bias from preferential sampling of probands with heavily affected families, misclassification of the disease status of relatives, violation of Hardy-Weinberg equilibrium, violation of the assumption that family members' phenotypes are conditionally independent given their genotypes, and samples that are too small to ensure validity of asymptotic methods. RESULTS and CONCLUSIONS: The kin-cohort design has several practical advantages, including comparatively rapid execution, modest reductions in required sample sizes compared with cohort or case-control designs, and the ability to study the effects of an autosomal dominant mutation on several disease outcomes. The design is, however, subject to several biases, including the following: selection bias that arises if a proband's tendency to participate depends on the disease status of relatives, information bias from inability of the proband to recall the disease histories of relatives accurately, and biases that arise in the analysis if the conditional independence assumption is invalid or if samples are too small to justify standard asymptotic approaches.


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