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, fe w have included risk factor covariates. Maximum likelihood segregation anal yses examining age-at-onset (model 1) and susceptibility (model 2) models o f breast cancer were performed on 426 four-generation families originally a scertained between 1944 and 1952 through a breast cancer proband. Cancer st atus and risk factor data were collected through interviews of participants or surrogates. When segregation analyses were performed on 10,791 women, w ithout estimation of any covariates, all hypotheses under both models were rejected. Model I, which required estimation of fewer parameters than model 2, provided a better fit to the data according to Akaike's Information Cri terion. Further segregation analyses were performed under model 1 on a subs et of women with complete data on education, age at first birth (nulliparou s women included), and alcohol use, covariates that were found to significa ntly (P < 0.05) improve the fit over the addition of exam age alone in logi stic 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 hyp otheses 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 covaria te values. The penetrance was estimated to be 0.15, much lower than previou s estimates based on families ascertained through breast cancer probands wi th early onset. Moreover, the estimates of penetrance were not greatly infl uenced by incorporation of the measured risk factors. (C) 1999 Wiley-Liss, Inc.