Li H, Thompson E A, Wijsman E M
Section of Biostatistics, Mayo Clinic/Foundation, Rochester, Minnesota, USA.
Genet Epidemiol. 1998;15(3):279-98. doi: 10.1002/(SICI)1098-2272(1998)15:3<279::AID-GEPI6>3.0.CO;2-#.
Analysis of age of onset is a key factor in the segregation and linkage analysis of some complex genetic traits. Previous work in the genetics literature has used parametric distributional assumptions on age of onset. In this paper, a Cox model with latent major gene effects is used: a semiparametric model with unspecified baseline hazard. A Monte Carlo EM procedure is used to obtain maximum likelihood estimates. Markov chain Monte Carlo is used to realize genotypic configurations from the posterior distribution given the current model and the observed data, and these genotypic configurations are used to estimate the expectations in the EM algorithm. Simulated data sets indicate that the parameters can be estimated well, and one real data set shows the practical applicability of the proposed method.
发病年龄分析是某些复杂遗传性状分离和连锁分析中的关键因素。遗传学文献中的先前工作对发病年龄使用了参数分布假设。在本文中，使用了具有潜在主基因效应的Cox模型：一种具有未指定基线风险的半参数模型。采用蒙特卡罗期望最大化（EM）程序来获得最大似然估计。马尔可夫链蒙特卡罗方法用于根据当前模型和观测数据从后验分布中实现基因型配置，并且这些基因型配置用于估计EM算法中的期望值。模拟数据集表明参数能够得到很好的估计，并且一个真实数据集展示了所提方法的实际适用性。
