Markov Chain Monte Carlo approaches to analysis of genetic and environmental components of human developmental change and G X E interaction
Markov Chain Monte Carlo approaches to analysis of genetic and environmental components of human developmental change and G X E interaction
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DOI：
10.1023/a:1023446524917
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发表时间：
2003-05-01
影响因子：
2.6
通讯作者：
Erkanli, A
中科院分区：
文献类型：
--
作者：
Eaves, L;Erkanli, A
线性结构模型提供了25年来双胞胎和家庭数据分析的统计支柱。新一代的问题不能轻易地被强制纳入当前建模和数据分析方法的框架，因为它们涉及非线性过程。最大化这种非线性模型的参数的可能性往往是繁琐的，并不容易产生目前的数值方法。应用马尔可夫链蒙特卡罗（MCMC）方法模拟非线性效应的基因和环境中的MZ和DZ双胞胎概述。在模拟双生子数据中，探讨了存在基因型-环境相关的非线性发育变化和基因型×环境互作。MCMC方法恢复模拟参数，并提供误差和潜在（缺失）性状值的估计。MCMC方法可能存在的局限性进行了讨论。进一步的研究是必要的，探讨的方法，可以扩大在发育遗传流行病学的研究视野的价值。
The linear structural model has provided the statistical backbone of the analysis of twin and family data for 25 years. A new generation of questions cannot easily be forced into the framework of current approaches to modeling and data analysis because they involve nonlinear processes. Maximizing the likelihood with respect to parameters of such nonlinear models is often cumbersome and does not yield easily to current numerical methods. The application of Markov Chain Monte Carlo (MCMC) methods to modeling the nonlinear effects of genes and environment in MZ and DZ twins is outlined. Nonlinear developmental change and genotype x environment interaction in the presence of genotype-environment correlation are explored in simulated twin data. The MCMC method recovers the simulated parameters and provides estimates of error and latent (missing) trait values. Possible limitations of MCMC methods are discussed. Further studies are necessary explore the value of an approach that could extend the horizons of research in developmental genetic epidemiology.