The interplay between genes and environment on cardiovascular disease

描述(申请人提供)：心血管疾病(CVD)是美国主要的死亡原因。双胞胎和以家庭为基础的研究表明，基因对一系列与心血管疾病相关的特征有很强的遗传贡献。全基因组关联研究已经确定了数千个与心血管疾病相关特征相关的遗传变异，如体重指数、血脂水平和高血压。总体而言，这些相关的遗传变异只解释了对这些特征的总体遗传贡献的一小部分。基因和环境暴露之间的相互作用很可能在解释一些缺失的特征遗传性方面发挥重要作用。尽管越来越多的经验证据表明这种相互作用是重要的，但全球气候变化研究在很大程度上忽视了这一考虑。一个主要的限制是，检测这些相互作用的统计能力受到研究这些相互作用的额外复杂性和维度的严重限制。来自研究配对交互作用的高多重检验校正显着阈值要求交互作用相当大，并且研究队列的样本量非常大。在这项研究中， 我们提出了一种新的方法来降低这个交互问题的维度。在测试特定的交互之前，我们建议首先确定一组简化的变体，这些变体 基于基因效应的异方差，证明了一些受交互作用影响的证据。我们还建议利用我们对公共NHGRI基因数据库DBGaP的独特知识来识别、协调和结合大型相关遗传流行病学研究中的遗传、表型和环境暴露数据，以增强我们的统计能力。本研究的目的是确定影响心血管疾病的重要GxE相互作用，以便更深入地了解疾病的分子机制，并促进更有针对性和更有效的干预策略。

DESCRIPTION (provided by applicant): Cardiovascular disease (CVD) is the leading cause of mortality in the United States. Twin and family-based studies have demonstrated a strong genetic contribution to a wide-array of CVD-related traits. Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with CVD-related traits such as body mass index, lipid levels and hypertension. In aggregate these associated genetic variants explain only a small proportion of the overall genetic contribution to these traits. The interplay between genes and environmental exposures is likely to play a substantial role in explaining away some of these missing trait heritabilities. GWAS studies have largely ignored this consideration, despite the growing empirical evidence that such interactions are important. One major limitation has been that statistical power to detect these interactions is severely limited by the added complexity and dimensionality of studying such interactions. High multiple-test corrected significance thresholds from studying pair-wise interactions require interaction effects to be considerable and the sample size of the study cohort to be very large. In this study, we propose to apply a novel approach to reduce the dimensionality of this interaction problem. Prior to testing specific interactions, we propose to first identify a reduced set of variants that demonstrate some evidence, based on heteroscedasticity of genotype effects, for being subject to interaction. We also propose to use our unique knowledge of the public NHGRI genetic database dbGaP to identify, harmonize and combine genetic, phenotypic and environmental exposure data across large relevant genetic-epidemiological studies to increase our statistical power. The goal of this study is to identify important GxE interactions that impact CVD in order to provide greater insight into the molecular mechanisms of the disease and facilitate more targeted and more effective intervention strategies.