Association and aggregation analysis using kin‐cohort designs with applications to genotype and family history data from the Washington Ashkenazi Study

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Abstract
When a rare inherited mutation in a disease gene, such as BRCA1, is found through extensive study of high‐risk families, it is critical to estimate not only age‐specific penetrance of the disease associated with the mutation, but also the residual effect of family history once the mutation is taken into account. The kin‐cohort design, a cross‐sectional survey of a suitable population that collects DNA and family history data, provides an efficient alternative to cohort or case‐control designs for estimating age‐specific penetrance in a population not selected because of high familial risk. In this report, we develop a method for analyzing kin‐cohort data that simultaneously estimate the age‐specific cumulative risk of the disease among the carriers and non‐carriers of the mutations and the gene‐adjusted residual familial aggregation or correlation of the disease. We employ a semiparametric modeling approach, where the marginal cumulative risks corresponding to the carriers and non‐carriers are treated non‐parametrically and the residual familial aggregation is described parametrically by a class of bivariate failure time models known as copula models. A simple and robust two‐stage method is developed for estimation. We apply the method to data from the Washington Ashkenazi Study [Struewing et al., 1997, N Engl J Med 336:1401–1408] to study the residual effect of family history on the risk of breast cancer among non‐carriers and carriers of specific BRCA1/BRCA2 germline mutations. We find that positive history of a single first‐degree relative significantly increases risk of the non‐carriers (RR = 2.0, 95% CI = 1.6–2.6) but has little or no effect on the carriers. Genet. Epidemiol. 21:123–138, 2001.