Estimating gene penetrance from family data.
Estimating gene penetrance from family data.
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DOI：
10.1002/gepi.20493
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发表时间：
2010-05
影响因子：
2.1
通讯作者：
Whittemore, Alice S.
中科院分区：
文献类型：
--
作者：
Gong, Gail;Hannon, Nathan;Whittemore, Alice S.
关键词：
家庭数据对于估计特定基因型携带者的疾病风险是有用的。外显率通常是假设亲属的表型是独立的，考虑到他们的基因型感兴趣的基因估计。当多种共同的风险因素导致疾病风险时，这种假设是不现实的。在这种情况下，即使在调整任何一个基因的基因型后，亲属的表型也是相关的（残差相关）。已经提出了许多方法来解决这个问题，但它们的性能还没有得到系统的评估。在模拟中，我们产生了一个罕见的（频率0.35%）等位基因的中度等位基因，和一个常见的（频率15%）等位基因的低等位基因，然后产生相关的疾病生存时间使用克莱顿-奥克斯copula模型。我们确定家庭使用人口和诊所设计。然后，我们将几种方法的估计值与从用于生成数据的模型中获得的最佳估计值进行了比较。我们发现，常见的低风险基因型比罕见的，中等风险的基因型更强大的模型误指定的概率估计。对于后者，忽略剩余疾病相关性获得的估计值有很大的偏差。仅基于分离风险等位基因的家庭的估计也有偏差。相反，通过假设存在遗传异质性来适应表型相关性的方法几乎是最佳的，即使生存数据被编码为二元结果。我们的结论是，容纳剩余表型相关性（即使只是近似）的估计比那些忽略它，编码截尾生存结果作为二进制不会大幅增加估计的均方误差，提供审查是不广泛的。
Family data are useful for estimating disease risk in carriers of specific genotypes of a given gene (penetrance). Penetrance is frequently estimated assuming that relatives' phenotypes are independent, given their genotypes for the gene of interest. This assumption is unrealistic when multiple shared risk factors contribute to disease risk. In this setting, the phenotypes of relatives are correlated even after adjustment for the genotypes of any one gene (residual correlation). Many methods have been proposed to address this problem, but their performance has not been evaluated systematically. In simulations we generated genotypes for a rare (frequency 0.35%) allele of moderate penetrance, and a common (frequency 15%) allele of low penetrance, and then generated correlated disease survival times using the Clayton-Oakes copula model. We ascertained families using both population and clinic designs. We then compared the estimates of several methods to the optimal ones obtained from the model used to generate the data. We found that penetrance estimates for common low-risk genotypes were more robust to model misspecification than those for rare, moderate-risk genotypes. For the latter, penetrance estimates obtained ignoring residual disease correlation had large biases. Also biased were estimates based only on families that segregate the risk allele. In contrast, a method for accommodating phenotype correlation by assuming the presence of genetic heterogeneity performed nearly optimally, even when the survival data were coded as binary outcomes. We conclude that penetrance estimates that accommodate residual phenotype correlation (even only approximately) outperform those that ignore it, and that coding censored survival outcomes as binary does not substantially increase the mean-square errror of the estimates, provided the censoring is not extensive.
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影响因子：
3.7
作者：
OAKES, D
通讯作者：
OAKES, D
影响因子：
11.2
作者：
Poynter, Jenny N.;Figueiredo, Jane C.;Le Marchand, Loic
通讯作者：
Le Marchand, Loic
影响因子：
2.1
作者：
Gong, G;Whittemore, AS
通讯作者：
Whittemore, AS
DOI：
10.1016/s0378-3758(00)00333-5
发表时间：
2001-06-01
影响因子：
0.9
作者：
Gail, MH;Pee, D;Carroll, R
通讯作者：
Carroll, R
影响因子：
2
作者：
KALBFLEISCH, JD;LAWLESS, JF
通讯作者：
LAWLESS, JF