The interplay between genes and environment on cardiovascular disease

描述（由申请人提供）：心血管疾病（CVD）是美国死亡的主要原因。双胞胎和基于家庭的研究表明，遗传因素对大量cvd相关特征有很强的影响。全基因组关联研究（GWAS）已经确定了数千种与cvd相关特征（如体重指数、脂质水平和高血压）相关的遗传变异。总的来说，这些相关的遗传变异只能解释这些性状的一小部分遗传贡献。基因和环境暴露之间的相互作用可能在解释这些缺失的性状遗传能力方面发挥了重要作用。尽管越来越多的经验证据表明这种相互作用很重要，但GWAS研究在很大程度上忽略了这一考虑。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复杂性和维度的严重限制。研究成对相互作用的高多重检验校正显著性阈值要求相互作用效应相当大，研究队列的样本量非常大。在这项研究中，

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.