Re: On the use of familial aggregation in population-based case probands for calculating penetrance.
Re: On the use of familial aggregation in population-based case probands for calculating penetrance.
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回复：关于在基于人群的先证者中使用家族聚集来计算外显率。
DOI：
10.1093/jnci/95.1.74
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Amos,ChristopherI
中科院分区：
文献类型：
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作者：
Amos,ChristopherI
在最近一期的杂志（1）中，Begg描述了我们（2）和其他基于人群的病例筛选研究中的潜在偏差，这些研究通过亲属队列方法（3）估计突变率。Begg提出的主要问题是，一个病例系列代表了被选择具有使他们处于过度疾病风险的风险因素的个体。在某种程度上，任何危险因素在病例亲属中都被过度代表--因为遗传或家族原因--携带者和非携带者先证者的家庭成员都将显示出过度的疾病发生率，因此突变率将被高估。我们同意这一理论论点，但质疑Begg提到的乳腺癌和卵巢癌以及BRCA 1或BRCA 2突变的已发表研究中实际存在的偏倚程度（1）。特别是，我们基于卵巢癌病例系列对乳腺癌的预测率估计不太可能有偏差（2）。通过将携带者和非携带者亲属作为一个队列并对其进行考克斯回归分析，发现携带者和非携带者亲属中给定年龄的疾病风险，诊断时的年龄，死亡或随访结束作为时间变量，先证者突变状态作为暴露。这种方法产生的相对风险（RR）的疾病与先证者突变状态的亲属之间的估计，以及非携带者亲属（S 0）的生存函数的乘积限估计。估计的突变频率为1+ S 0 -2（S 0）RR。这种方法是强大的，在通常情况下，一个（有时两个）受影响的一级亲属每个家庭，但广义估计方程方法也可以使用。正如Begg所指出的（1），估计的RR不受偏倚影响;只有S 0受偏倚影响。事实上，1+ S 0 -2（S 0）RR估计了RR适用的任何类似基础人群的概率。因此，对于像我们（2）这样使用这种方法的研究，以及在人群中突变频率较低的非常常见的情况下，观察到的RR值肯定可以使用
In a recent issue of the Journal (1), Begg describes potential biases in our (2) and other population-based casescreening studies that estimate mutation penetrance through kin–cohort methods (3). The main issue raised by Begg is that a case series represents individuals selected to have risk factors that place them at excess disease risk. To the extent that any risk factors are overrepresented among case relatives—because of either genetic or familial reasons—family members of both carrier and noncarrier probands will show excess disease incidence, and thus mutation penetrance will be overestimated. We agree with this theoretical argument but question the degree of bias actually present in the published studies of breast and ovarian cancer and mutations in BRCA1 or BRCA2 to which Begg refers (1). In particular, our penetrance estimates of breast cancer based on an ovarian cancer case series are unlikely to be biased (2). Disease risk to a given age among carrier and noncarrier relatives is found by treating the relatives as a cohort and performing a Cox regression analysis on it, with age at diagnosis, death, or end of follow-up as the time variable and with proband mutation status as the exposure. This method yields an estimate of the relative risk (RR) of disease among relatives associated with proband mutation status, as well as a product–limit estimate of the survivor function for the noncarrier relatives (S0). The estimated mutation penetrance is 1+ S0–2 (S0) RR. This method is robust in the usual circumstances of one (or sometimes two) affected first-degree relatives per family, but generalized estimating equation methods can also be used. As Begg notes (1), the estimated RR is not subject to the bias; only S0 is subject to bias. In fact, 1+ S0–2 (S0) RR estimates the penetrance for any similar base population to which the RR would apply. Therefore, for studies such as ours (2) that use this method, and in the very usual circumstances where mutation frequency is low in the population, the observed RR values can certainly be used