Accelerating Development of OpenMx for Interoperability and Cloud Use

项目摘要 该项目旨在开发和应用新的统计方法，以解决主要问题 关于药物使用、滥用和依赖的因果问题。这个 开发将侧重于将孟德尔随机化方法论扩展到 测试其关键假设的新数据类型。这些发展包括： 非随机配对引起的偏差的估计和控制.数据分析 来自无关但基因分型的个体；纵向数据的扩展； 多变量网络模型；以及多组分析，以测试性别、年龄和 其他群体差异。这些新方法将应用于独特的纵向 中国吸烟者和不吸烟者的表型、基因和甲基化数据 荷兰双胞胎注册表和英国生物库的数据。精准识别 因果路径将使循证预防和治疗方法能够 将被设计和实施。

Project Summary This project aims to develop and apply novel statistical approaches to address key causal questions in the of substance use, abuse and dependence. The development will focus on extending Mendelian Randomization methodology to new data types that test its key assumptions. These developments include: estimating and controlling for biases due to non- random mating; analyses of data from unrelated but genotyped individuals; extension to longitudinal data; multivariate network models; and multiple-group analyses to test for sex, age and other group differences. These new methods will be applied to unique longitudinal phenotype, genotype and methylation data on smokers and non- smokers from the Netherlands Twin Register, and data from the UK biobank. Precise identification of causal pathways will enable evidence- based prevention and treatment methods to be devised and implemented.