Association and aggregation analysis using kin-cohort designs with applications to genotype and family history data...

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.