Li Hongzhe, Zhong Xiaoyun
Rowe Program in Human Genetics, School of Medicine, University of California, Davis, CA 95616, USA.
Biostatistics. 2002 Mar;3(1):57-75. doi: 10.1093/biostatistics/3.1.57.
We derive a multivariate survival model for age of onset data of a sibship from an additive genetic gamma frailty model constructed basing on the inheritance vectors, and investigate the properties of this model. Based on this model, we propose a retrospective likelihood approach for genetic linkage analysis using sibship data. This test is an allele-sharing-based test, and does not require specification of genetic models or the penetrance functions. This new approach can incorporate both affected and unaffected sibs, environmental covariates and age of onset or age at censoring information and, therefore, provides a practical solution for mapping genes for complex diseases with variable age of onset. Small simulation study indicates that the proposed method performs better than the commonly used allele-sharing-based methods for linkage analysis, especially when the population disease rate is high. We applied this method to a type 1 diabetes sib pair data set and a small breast cancer data set. Both simulated and real data sets also indicate that the method is relatively robust to the misspecification to the baseline hazard function.
我们从基于遗传向量构建的加性遗传伽马脆弱模型中推导出一个用于同胞组发病年龄数据的多变量生存模型，并研究该模型的性质。基于此模型，我们提出了一种使用同胞组数据进行遗传连锁分析的回顾性似然方法。该检验是基于等位基因共享的检验，不需要指定遗传模型或外显率函数。这种新方法可以纳入受影响和未受影响的同胞、环境协变量以及发病年龄或审查年龄信息，因此为定位发病年龄可变的复杂疾病基因提供了一个实际的解决方案。小型模拟研究表明，所提出的方法在连锁分析中比常用的基于等位基因共享的方法表现更好，特别是当人群疾病率较高时。我们将此方法应用于1型糖尿病同胞对数据集和一个小型乳腺癌数据集。模拟数据集和真实数据集均表明，该方法对基线风险函数的错误设定相对稳健。
