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Abdominal fat has been shown to be an important risk factor for many chronic conditions, including diabetes, heart disease, and breast cancer. The objective of this study was to provide evidence for a major gene influence on the ratio of waist to hip circumference (WHR), a measurement commonly used in large scale studies to indicate the presence of abdominal fat. Segregation analysis was conducted on three subsets of families from the Minnesota Breast Cancer Family Study. One analysis was conducted among families with WHR measurements on all women. Two additional analyses were conducted on subsets of women stratified on menopausal status. Multiple regression analysis was used to identify factors associated with WHR expressed as a continuous trait. Complex segregation analyses were performed on the continuous trait of WHR and the covariates identified in the regression analysis. In the analysis of all women, all hypotheses were rejected. Among premenopausal women, the environmental hypothesis with no heterogeneity between generations fit the data best (P = 0.85). However, among postmenopausal women, the requirements for conclusion of the presence of a major gene were met. All non-Mendelian hypotheses were rejected (P < 0.0001), but the additive hypothesis was not rejected (P = 0.19) and provided the best fit to the data. The putative major gene identified by this model accounted for 42% of total phenotypic variance in WHR among these postmenopausal women. The allele for high WHR had a frequency of 27%. These findings support the hypothesis that the distribution of abdominal fat in postmenopausal women is under genetic control., (Copyright 2001 Wiley-Liss, Inc.)

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.