Optimal designs for estimating penetrance of rare mutations of a disease-susceptibility gene
Optimal designs for estimating penetrance of rare mutations of a disease-susceptibility gene
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DOI：
10.1002/gepi.10219
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发表时间：
2003-04-01
影响因子：
2.1
通讯作者：
Whittemore, AS
中科院分区：
文献类型：
--
作者：
Gong, G;Whittemore, AS
许多临床决策需要准确估计与已知疾病易感基因突变相关的疾病风险。当突变很少的时候，这样的风险估计是困难的。我们使用计算机模拟来比较从基于家族数据的两种设计获得的估计的性能。在第一个(基于临床的设计)中，家庭是确定的，因为他们满足关于家庭成员中多个疾病发生的特定标准。在第二种(基于人口的设计)中，家庭通过称为先证者的基于人口的受影响个人登记进行抽样，对其家庭更有可能分离突变的先证者进行过度抽样。我们使用反映BRCA1/2基因突变频率和外显率的模型来生成家庭结构、基因类型和表型。我们研究了由于未测量的、共同的风险因素造成的风险异质性的影响，方法是包括因另一基因的未测量的基因类型而引起的风险差异。选择这些模拟是为了模拟两种类型的设计中常用的确定和选择过程。我们发现，在没有不可测量的共同风险因素的情况下，两种设计的外显率估计几乎是公正的，但在这些因素存在的情况下，两种设计的外显率估计都是向上偏向的。随着第二个基因的不同基因之间风险差异的增加，这种偏倚也会增加。然而，与估计的标准误差相比，它是很小的。基于人群的设计的标准误差大约是基于临床的设计的两倍，这些设计具有相同的家庭数量。使用均方根误差作为绩效的衡量标准，我们发现在所有情况下，基于临床的设计比具有相同家庭数量的基于总体的设计给出了更准确的估计。粗略的方差计算表明，基于临床的设计给出了更准确的估计，因为它们包括更多已识别的突变携带者。2003年。(C)2003年Wiley-Liss，Inc.
Many clinical decisions require accurate estimates of disease risks associated with mutations of known disease-susceptibility genes. Such risk estimation is difficult when the mutations are rare. We used computer simulations to compare the performance of estimates obtained from two types of designs based on family data. In the first (clinic-based designs), families are ascertained because they meet certain criteria concerning multiple disease occurrences among family members. In the second (population-based designs), families are sampled through a population-based registry of affected individuals called probands, with oversampling of probands whose families are more likely to segregate mutations. We generated family structures, genotypes, and phenotypes using models that reflect the frequencies and penetrances of mutations of the BRCA1/2 genes. We studied the effects of risk heterogeneity due to unmeasured, shared risk factors by including risk variation due to unmeasured genotypes of another gene. The simulations were chosen to mimic the ascertainment and selection processes commonly used in the two types of designs. We found that penetrance estimates from both designs are nearly unbiased in the absence of unmeasured shared risk factors, but are biased upward in the presence of such factors. The bias increases with increasing variation in risks across genotypes of the second gene. However, it is small compared to the standard error of the estimates. Standard errors from population-based designs are roughly twice those from clinic-based designs with the same number of families. Using the root-mean-square error as a measure of performance, we found that in all instances, the clinic-based designs gave more accurate estimates than did the population-based designs with the same numbers of families. Rough variance calculations suggest that clinic-based designs give more accurate estimates because they include more identified mutation carriers. 2003. (C) 2003 Wiley-Liss, Inc.