The Social and Genetic Epidemiology of Health Behaviors: An Integrated Approach

健康行为的社会和遗传流行病学：综合方法

• 批准号: 8737918
• 负责人: Jason D Boardman
• 金额: $ 18.53万
• 依托单位: UNIVERSITY OF COLORADO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-19 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8737918
• 关键词: Accounting; Adolescent; Affect; Alcohol consumption; Body mass index; Candidate Disease Gene; Child; Cohort Studies; Communities; Complex; Data; Data Set; Data Sources; Demographer; Demographic Analyses; Development; Eating; Education; Environment; Environmental Risk Factor; Epidemiologist; Estimation Techniques; Family; Family member; Foundations; Framingham Heart Study; Generations; Genes; Genetic; Genetic Determinism; Genetic Risk; Genome; Goals; Health; Health behavior; Health behavior outcomes; Individual; Investigation; Literature; Longitudinal Studies; Mediator of activation protein; Methodology; Methods; Modeling; Obesity; Outcome; Outcome Measure; Parents; Persons; Phenotype; Play; Population; Recovery; Research; Research Design; Research Personnel; Respondent; Sampling; Science; Scientist; Series; Siblings; Smoking; Social Environment; Source; Statistical Methods; Stratification; Testing; Time; Tobacco use; Twin Multiple Birth; Twin Studies; United States National Academy of Sciences; United States National Institutes of Health; Wisconsin; Work; Youth; base; design; drinking; family structure; flexibility; gene environment interaction; genetic association; genetic epidemiology; genome-wide; interest; multilevel analysis; physical conditioning; programs; public health relevance; simulation; social; user-friendly

DESCRIPTION (provided by applicant): Most health behavior outcomes of interest to social demographers are affected by both environmental and genetic factors but current methods for handling these two predictors of interest tend to be restricted to specific (e.g., sibling or famil-based) study designs. In the proposed project, we will develop a multi-level model that can account for both types of predictors across multiple study designs and, perhaps even more importantly, can utilize any and all genetic information that is available (e.g., estimated genetic relationship between related AND unrelated pairs using genome-wide data or assumed relationship based on family structure). In this study, we first verify the proposed method via a series of detailed and extensive simulations. We will then demonstrate the usefulness of this method empirically using genetic and phenotypic data of interest to demographers (e.g., BMI and smoking) from two generations of family members and unrelated respondents in the Framingham Heart Study.

描述（由申请人提供）：社会人口统计学家感兴趣的大多数健康行为结果都受到环境和遗传因素的影响，但目前处理这两个感兴趣的预测因素的方法往往仅限于特定的（例如，兄弟姐妹或基于家庭的）研究设计。在拟议的项目中，我们将开发一个多级模型，该模型可以解释多个研究设计中的两种类型的预测因子，并且也许更重要的是，可以利用任何可用的遗传信息（例如，估计的遗传信息） 使用全基因组数据或基于家族结构的假设关系来确定相关和不相关对之间的关​​系）。在本研究中，我们首先通过一系列详细且广泛的模拟来验证所提出的方法。然后，我们将使用弗雷明汉心脏研究中两代家庭成员和无关受访者的人口统计学家感兴趣的遗传和表型数据（例如体重指数和吸烟），凭经验证明该方法的有用性。