Segregation analysis of breast cancer: A comparison of type-dependent age-at-onset versus type-dependent susceptibility models

• Published in: Genetic epidemiology Vol. 13; no. 4; pp. 317 - 328
• Main Authors: Ciske, David J., Rich, Stephen S., King, Richard A., Anderson, V. Elving, Bartow, Sue, Vachon, Celine, McGovern, Paul G., Kushi, Larry H., Zheng, Wei, Sellers, Thomas A.
• Format: Journal Article
• Language: English
• Published: New York Wiley Subscription Services, Inc., A Wiley Company 1996; Wiley-Liss
• Subjects: Adult; Age of Onset; age-at-onset; Aged; Aged, 80 and over; Biological and medical sciences; Breast Neoplasms - genetics; families; Female; Genetic Heterogeneity; Genetic Predisposition to Disease; Gynecology. Andrology. Obstetrics; Humans; Mammary gland diseases; Medical sciences; Middle Aged; Models, Genetic; segregation analysis; susceptibility; Tumors; Human; Mammary gland diseases; Segregation analysis; Genetic inheritance; Age of onset; Risk factor; Predisposition; Genetic parameter; Tumor; Mammary gland; Major gene
• ISSN: 0741-0395; 1098-2272
• DOI: 10.1002/(SICI)1098-2272(1996)13:4<317::AID-GEPI1>3.0.CO;2-1

Most segregation analyses of breast cancer susceptibility have modeled the effect of the major gene on the age‐at‐onset distribution. However, in families linked to BRCA1 or BRCA2, there is wide variation in the age‐at‐onset among gene carriers. We performed a segregation analysis of 544 Minnesota breast cancer families using models which parameterized the putative major gene effect in two ways: earlier age‐at‐onset, with a common level of susceptibility (model I), and greater susceptibility, with a common mean age‐at‐onset (model II). Five hypothetical modes of transmission and an unrestricted general hypothesis were fitted to the data. Twice the difference between the loge likelihood for the data under the specified hypothesis (recessive, no major gene, etc.) and the loge likelihood under the general hypothesis is distributed asymptotically as a chi‐square statistic with the degrees of freedom equal to the difference in the number of parameters estimated. This difference was compared to the critical value for the chi‐square distribution to assess goodness‐of‐fit. Under model I, both Mendelian and non‐Mendelian hypotheses were rejected. When model II was used, the non‐Mendelian hypotheses were rejected whereas all Mendelian hypotheses were not. Mendelian recessive inheritance of a common allele (qA = 0.11) with a high penetrance (87%) provided the best fit to the data. We then stratified the families into two subsets based on the age at diagnosis of the proband [≤55 years (n = 265) versus >55 years (n = 279)]; there was no evidence of heterogeneity under either model (I or II). These data suggest that, in some breast cancer families, the effect of the putative susceptibility gene is better represented as increasing overall susceptibility to breast cancer rather than as a shift in the age‐at‐onset distribution. © 1996 Wiley‐Liss, Inc.