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已经证明了一个强大的遗传贡献，以广泛的心血管疾病相关的性状。全基因组关联研究（GWAS）已经确定了数千种与CVD相关性状（如体重指数、血脂水平和高血压）相关的遗传变异。总的来说，这些相关的遗传变异只解释了对这些性状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可能在解释这些缺失的性状遗传性方面发挥重要作用。GWAS的研究在很大程度上忽略了这一点，尽管越来越多的经验证据表明这种相互作用是重要的。一个主要的限制是，统计能力，以检测这些相互作用是严重限制了研究这种相互作用的复杂性和维度。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，并且研究队列的样本量非常大。在本研究中， 我们提出了一种新的方法来减少这种相互作用问题的维数。在测试特定的相互作用之前，我们建议首先确定一组减少的变体， 基于基因型效应的异方差性，证明了一些受相互作用影响的证据。我们还建议利用我们对公共NHGRI遗传数据库dbGaP的独特知识，在大型相关遗传流行病学研究中识别、协调和联合收割机遗传、表型和环境暴露数据，以提高我们的统计能力。本研究的目的是确定影响CVD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.