Inclusion of risk factor covariates in a segregation analysis of a population-based sample of 426 breast cancer families.

Although many segregation analyses of breast cancer have been published, few have included risk factor covariates. Maximum likelihood segregation analyses examining age-at-onset (model 1) and susceptibility (model 2) models of breast cancer were performed on 426 four-generation families originally ascertained between 1944 and 1952 through a breast cancer proband. Cancer status and risk factor data were collected through interviews of participants or surrogates. When segregation analyses were performed on 10,791 women, without estimation of any covariates, all hypotheses under both models were rejected. Model 1, which required estimation of fewer parameters than model 2, provided a better fit to the data according to Akaike's Information Criterion. Further segregation analyses were performed under model 1 on a subset of women with complete data on education, age at first birth (nulliparous women included), and alcohol use, covariates that were found to significantly (P<0.05) improve the fit over the addition of exam age alone in logistic regression models. All three covariates improved the fit of the models, as did year of birth, but at all stages of model building, all of the hypotheses were still rejected. After the allele frequency was fixed at 0.0033, a subset of families appeared to fit a dominant model. Using this model, risk estimates were calculated based on inferred genotype, age, and covariate values. The penetrance was estimated to be 0.15, much lower than previous estimates based on families ascertained through breast cancer probands with early onset. Moreover, the estimates of penetrance were not greatly influenced by incorporation of the measured risk factors.