Extensions of Mendelian Randomization Methodology for Combined Genomic and Methylomic Analysis

用于组合基因组和甲基组分析的孟德尔随机化方法的扩展

• 批准号: 10200739
• 负责人: MICHAEL CHURTON NEALE
• 金额: $ 35.59万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10200739
• 关键词: Address; Adult; Age; Alcohol consumption; Alcohols; Birth; Cardiovascular Diseases; Cigarette; Companions; Complex; Computer software; DNA Methylation; Data; Data Analyses; Databases; Dependence; Development; Disease; Educational workshop; Environment; Environmental Risk Factor; Etiology; Family; Genes; Genetic; Genetic Risk; Genomics; Genotype; Individual; International; Malignant Neoplasms; Marijuana; Marijuana Smoking; Measures; Mendelian randomization; Mental disorders; Methodology; Methods; Methylation; Modeling; Nature; Netherlands; Nicotine Dependence; Partner in relationship; Pathway interactions; Pattern; Phenotype; Policy Maker; Prevention; Process; Publications; Relapse; Research Design; Research Personnel; Risk Factors; Sampling; Sex Differences; Site; Smoker; Smoking; Smoking Behavior; Statistical Methods; Substance Addiction; Substance abuse problem; Symptoms; System; Teaching Method; Testing; Time; Tobacco smoking behavior; Twin Multiple Birth; Variant; Work; addiction; base; biobank; comorbidity; design; effective intervention; effective therapy; evidence base; genome wide association study; genomic data; handbook; improved; instrument; marijuana use; methylomics; network models; non-smoker; novel; online course; pleiotropism; prevent; psychiatric symptom; sex; substance use; trait; web site

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.

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。