Ciske D J, Rich S S, King R A, Anderson V E, Bartow S, Vachon C, McGovern P G, Kushi L H, Zheng W, Sellers T A
Division of Epidemiology, School of Public Health, University of Minnesota, Minneapolis, USA.
Genet Epidemiol. 1996;13(4):317-28. 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 log(e) likelihood for the data under the specified hypothesis (recessive, no major gene, etc.) and the log(e) 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 [< or = 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.
大多数乳腺癌易感性的分离分析都对主要基因对发病年龄分布的影响进行了建模。然而,在与BRCA1或BRCA2相关的家族中,基因携带者的发病年龄存在很大差异。我们使用两种方式对假定的主要基因效应进行参数化的模型,对544个明尼苏达乳腺癌家族进行了分离分析:一是发病年龄较早,易感性水平相同(模型I),二是易感性较高,平均发病年龄相同(模型II)。对数据拟合了五种假设的遗传模式和一个无限制的一般假设。在指定假设(隐性、无主要基因等)下数据的对数似然与一般假设下的对数似然之差的两倍,渐近地服从自由度等于估计参数数量之差的卡方统计量分布。将此差异与卡方分布的临界值进行比较,以评估拟合优度。在模型I下,孟德尔和非孟德尔假设均被拒绝。当使用模型II时,非孟德尔假设被拒绝,而所有孟德尔假设均未被拒绝。一个常见等位基因(qA = 0.11)的孟德尔隐性遗传,外显率较高(87%),对数据的拟合效果最佳。然后,我们根据先证者的诊断年龄将家族分为两个子集[≤55岁(n = 265)与>55岁(n = 279)];在任一模型(I或II)下均没有异质性的证据。这些数据表明,在一些乳腺癌家族中,假定的易感基因的作用更好地表现为增加对乳腺癌的总体易感性,而不是发病年龄分布的改变。