GENE-ENVIRONMENT INTERACTIONS IN THE LONGITUDINAL FRAMINGHAM HEART STUDY

纵向弗雷明汉心脏研究中的基因-环境相互作用

• 批准号: 8444475
• 负责人: DABEERU C RAO
• 金额: $ 32.56万
• 依托单位: WASHINGTON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8444475
• 关键词: Accounting; Age; Alcohol consumption; Area; Blood Pressure; Cardiovascular Diseases; Cardiovascular system; Coupled; Data; Data Set; Disease; Failure; Family; Framingham Heart Study; Generations; Genes; Genetic; Genetic Markers; Goals; Heritability; Investigation; Knowledge; Lead; Life Style; Meta-Analysis; Metabolic; Metabolic Diseases; Methods; Obesity; Phenotype; Physical activity; Public Health; Publishing; Reliance; Research; Risk; Signal Transduction; Smoking; Socioeconomic Status; Therapeutic Intervention; Visit; Weight; aging gene; base; cohort; gene discovery; gene environment interaction; genetic analysis; genome wide association study; genome-wide; high risk; improved; longitudinal analysis; novel; novel diagnostics; novel therapeutics; offspring; sex; tool; trait

DESCRIPTION (provided by applicant): Cardiovascular (CVD) and metabolic (MetS) diseases constitute a major public-health burden and, therefore, understanding the genetic basis of these traits is very important. The recent Genome-Wide Association Studies (GWAS) made good progress, but not enough. For example, two large consortia (CHARGE and ICBP) identified 13 loci for blood pressure (BP) which collectively explain less than 2% of BP variance (with most of the heritability still "missing"). The near-exclusive reliance on main effects of single genetic markers has been one of the major barriers to identifying more of the genes underlying these disease traits. Against this background, Gene-Environment Interaction (GEI) methods are known to vastly increase the statistical power for gene discovery (preliminary studies). Also, analysis of longitudinal data can be much more powerful than that of cross-sectional data. Combining GEI and longitudinal data should yield even more power. Despite the knowledge that GEI and longitudinal data both vastly improve the statistical power for gene discovery, this knowledge has not been leveraged yet, thus missing an opportunity to find (hopefully many) novel disease loci from large volumes of existing GWAS data. Thusly motivated, we propose to harness the large existing longitudinal Framingham Heart Study (FHS) data using our GEI methods including gene-age, gene-sex, gene-obesity, and gene-lifestyle interactions. This study will use FHS data on the 7 visits of the 'Offspring Cohort', the corresponding 7 visits of the 'Original Cohort', and the 'Generation 3 Cohort' (G3), to a total of 9,168 discovery subjects with GWAS data. Data include GWAS data, cardiovascular and metabolic phenotypes, and lifestyle data on physical activity, alcohol consumption, smoking, and socio-economic status (SES) in 2 to 3 generation families. All significant results will be replicated in four external studies involving 22,824 replication subjects. Completion of this project has the potential to discover (hopefully many) novel disease loci, which could effectively motivate others to re-analyze large volumes of existing GWAS data using GEI methods, ultimately leading to new diagnostic tools and therapeutic interventions.

描述（由申请人提供）：心血管（CVD）和代谢（MetS）疾病构成了主要的公共卫生负担，因此，了解这些特征的遗传基础非常重要。最近的全基因组关联研究（GWAS）取得了很好的进展，但还不够。例如，两个大的联盟（CHARGE和ICBP）确定了13个血压（BP）基因座，这些基因座共同解释了不到2%的BP方差（大部分遗传力仍然“缺失”）。几乎完全依赖于单一遗传标记的主效应已经成为识别这些疾病特征的更多基因的主要障碍之一。在这种背景下，基因-环境相互作用（GEI）方法被认为大大增加了基因发现（初步研究）的统计能力。此外，纵向数据的分析可能比横截面数据更强大。结合GEI和纵向数据应该会产生更大的力量。 尽管GEI和纵向数据都极大地提高了基因发现的统计能力，但这些知识尚未得到利用，因此错过了从大量现有GWAS数据中发现（希望是许多）新疾病位点的机会。因此，出于动机，我们建议利用现有的大型纵向心脏研究（FHS）数据，使用我们的GEI方法，包括基因-年龄，基因-性别，基因-肥胖和基因-生活方式的相互作用。本研究将使用“后代队列”的7次访视、“原始队列”和“第3代队列”（G3）的相应7次访视的FHS数据，共计9，168例具有GWAS数据的发现受试者。数据包括GWAS数据，心血管和代谢表型，以及2至3代家庭的体力活动，饮酒，吸烟和社会经济地位（SES）的生活方式数据。所有重要结果将在涉及22，824名复制受试者的四项外部研究中复制。该项目的完成有可能发现（希望是许多）新的疾病位点，这可以有效地激励其他人使用GEI方法重新分析大量现有的GWAS数据，最终导致新的诊断工具和治疗干预措施。