Using GWAS Data for Enhanced Mendelian Randomization Studies

描述(由申请人提供)：这项建议解决了使用观察数据对治疗效果进行因果推断的困难。这一领域的方法学进步对确定干预措施的最有希望的杠杆点，从而优化影响行为、生物标记物或心理社会危险因素的一系列干预或治疗的临床建议具有巨大的影响。我们的建议发展了先前在孟德尔随机化(MR)设计方面的工作，这是使用遗传变异作为工具的工具变量的特例。我们通过利用已经在基因组广泛关联研究(GWAS)背景下收集的数据来扩展和加强MR的应用。将磁共振研究嵌入GWAS数据将导致更强大的研究和对这些研究的有效性的更好评估，从而产生更可信的磁共振效应估计。我们建议利用GWAS的数据，在MR研究中进行四项重要的创新。首先，Gwas允许对背景遗传特征进行全面的表征，以控制由于种群分层而可能产生的偏见。其次，关于多个候选遗传工具的信息可以用来创建多基因风险分数。这些将提供远比单基因更强大的工具，改善统计能力和机会，为有效的MR研究对假设进行批判性评估。第三，我们可以利用多种工具对每种工具的有效性进行过度识别测试。过度识别检验是评估工具变量的标准计量经济学工具，但到目前为止，它们还没有应用于MR研究。最后，我们展示了一种使用基因-环境和基因-基因交互作用来评估MR假设的新方法。我们以目前关于心理社会表型(抑郁、焦虑和社会融合的症状)对糖尿病和冠心病的影响的研究为例。心理社会对这两种疾病的影响在观察性研究中得到了很好的支持，但之前干预心理社会风险因素的随机试验结果令人失望。因此，这是实施分析观测数据的新方法的理想领域。我们的分析基于先前在护士健康研究和卫生专业人员随访研究两个队列中收集的GWAS数据。我们已经确定了一些可能影响每一种心理社会表型的候选基因，我们还将利用感兴趣的表型的Gwas研究来计算多基因或多基因风险分数，作为MR研究的工具。MR可以成为一种非常强大的工具来估计因果效应，这种方法的应用增长非常迅速。然而，他依赖的是强有力的假设，而这些假设很少经过严格检验；这在一定程度上是因为目前还没有评估这些假设的工具。我们的工作旨在解决这一差距，并促进MR的明智应用，以在观测数据中提供可信的效果估计。 公共卫生相关性：孟德尔随机化是一种新的方法，可以提供新的机会，从观测数据中得出可信的效果估计。这种方法使用影响暴露于特定生物标记物、行为或其他类型风险因素的基因类型。这种基因的变异可能会提供自然实验，以估计风险因素对后续健康结果的影响。我们使用新的工具来加强这项研究设计，并评估来自这项设计的效果估计是否公正。我们的方法利用了许多流行病学样本已经进行的大量投资，为全基因组关联研究收集数据。为了说明这些方法，我们侧重于评估心理社会困扰和社会融合对冠心病和糖尿病的影响。

DESCRIPTION (provided by applicant): This proposal addresses the difficulty of using observational data to drawing causal inferences on treatment effects. Methodological advances in this area have enormous implications for efforts to identify the most promising leverage points for interventions and thereby optimize clinical advice regarding a range of interventions or treatments affecting behavioral, biomarker, or psychosocial risk factors. Our proposal develops prior work on Mendelian Randomization (MR) designs, which are a special case of instrumental variables that use genetic variants as instruments. We extend and strengthen MR applications by taking advantage of data already collected in the context of Genome Wide Association Studies (GWAS). Embedding MR studies in GWAS data will result in more powerful studies and better assessments of the validity of those studies, leading to more credible MR effect estimates. We propose capitalizing on the GWAS data to allow four important innovations in MR studies. First, GWAS allows comprehensive characterization of background genetic characteristics to control for possible bias due to population stratification. Second, information on multiple candidate genetic instruments can be used to create multi-gene risk scores. These will provide far stronger instruments than single genes alone, improving both the statistical power and the opportunities to critically evaluate the assumptions for a valid MR study. Third, we can take advantage of the multiple instruments to conduct over-identification tests for the validity of each instrument. Over-identification tests are a standard econometric tool for evaluating instrumental variables but they have to date not been applied in MR studies. Finally, we demonstrate a novel approach to evaluating the assumptions of MR using gene- environment and gene-gene interactions. We use as an example current research on the effects of psychosocial phenotypes (symptoms of depression and anxiety and social integration) on diabetes and coronary heart disease. Psychosocial effects on both diseases are well-supported in observational studies, but previous randomized trials intervening on psychosocial risk factors had disappointing results. Thus, this is an ideal area in which to implement new approaches to analyzing observational data. Our analyses are based on previously collected GWAS data in two cohorts, the Nurses' Health Study and the Health Professionals Follow-up Study. We have identified a number of candidate genes likely to influence each of the psychosocial phenotypes and we will also take advantage of GWAS studies for the phenotypes of interest to calculate multi- gene or polygenic risk scores to use as instruments in MR studies. MR can be an extremely powerful tool to estimate causal effects, and applications of this approach have increased very rapidly. However, MR rests on strong assumptions that are rarely critically tested; in part this is because tools to evaluate these assumptions have not been available. Our work is intended to address this gap and foster judicious applications of MR to provide credible effect estimates in observational data. PUBLIC HEALTH RELEVANCE: Mendelian Randomization is a method that may offer new opportunities to derive credible effect estimates from observational data. This method uses genotypes that influence exposure to a particular biomarker, behavior, or other type of risk factor. Variations in this genotype may provide natural experiments to estimate the effect of the risk factor on subsequent health outcomes. We use new tools to make this study design stronger and to evaluate whether the effect estimates from this design are unbiased. Our methods take advantage of large investments already made by many epidemiologic samples in collecting data for genome wide association studies. To illustrate the methods, we focus on estimating the effects of psychosocial distress and social integration on coronary heart disease and diabetes.