Improving Mendelian randomisation based on meta-GWAS summary statistics.

观察性研究是在个体样本中测量各种疾病特征和感兴趣的特征的数据，在流行病学中被广泛用于寻找潜在的疾病驱动因素。理想情况下，这将导致新的治疗或公共卫生干预措施，以针对任何已确定的可改变的风险因素。然而，由于这些研究的观察性，通常不清楚确定的风险因素是否真正是因果关系(以便干预将是有效的)，或者是否未测量的过程真的推动了观察到的与疾病的关联(使干预无效)。一个经典的例子是黄牙和肺癌；在观察性研究中，这两者似乎是相关的，但与两者相关的真正驱动因素是吸烟。牙齿美白不会降低个人患癌症的风险。传统上，确定因果关系的黄金标准方法是对利益干预的随机试验。干预措施的随机分配打破了已知和未知的任何潜在过程的关联，允许对直接和公正的影响进行评估。然而，临床试验非常昂贵，可能不切实际，而且对于一些风险因素，如吸烟，是不道德的。孟德尔随机化(MR)是一个相对较新的想法，它提供了一个框架，通过利用有性繁殖过程中发生的遗传随机化，使用观察数据来评估暴露的因果关系。调节感兴趣暴露的遗传变异，而不是观察到的风险因素本身，被用来测试疾病关联。这些在受孕时是随机的，因此，如果生物学被很好地理解，可以被认为类似于针对暴露的随机临床试验中的干预。最近磁共振分析的激增是由一些大型“Meta-Gwas”(基因组广泛关联研究)的结果发表推动的，在这些研究中，多项研究将数万个个体结合成强大的联合分析。这些已经揭示了各种暴露和感兴趣的风险因素的强大基因特征，这些在MR下被用来在现有的观测数据中提出新的因果问题。利用这些强大的联盟Meta-Gwas的结果将是许多MR努力的关键。然而，META-GWAS通常只以简化的摘要形式发布结果，现有的用于整合这些摘要统计数据的MR框架存在一些缺陷。例如，相互关联的遗传变异，其中有许多，不能包括在内。此外，需要更好的方法来搜索这些巨大的和全基因组的摘要数据存储库，以便确定用于MR分析的最佳遗传特征。对于给定的MR分析，我们需要足够的变量来预测目标暴露，但必须避免包括通过其他方法影响结果的变量，如替代遗传路径。在这个项目中，我们的目标是推进当前的MR框架，以更好地利用来自大型Meta-Gwas的摘要数据。我们将探索重新利用和建立统计遗传学中其他领域开发的汇总统计方法，并对最近提出的MR-Egger汇总统计方法进行关键扩展，该方法提供对MR所需的生物学假设中的违规行为的稳健性。所有方法都将被开发和测试，以探索2型糖尿病、阿尔茨海默病和一系列癌症的因果疾病驱动因素，由我们在MRC流行病学部门的同事分享的四个引人注目的案例研究中分享。

Observational studies, in which data are measured in a sample of individuals for various disease traits and characteristics of interest, are widely used in epidemiology to seek out potential drivers of disease. Ideally this would lead to new treatments or public health interventions to target any modifiable risk factors identified. However, due to the observational nature of these studies it is often unclear whether the risk factors identified are truly causal (so that intervening would be effective), or whether an unmeasured process is truly driving the observed association with disease (making intervention ineffective). A classic example is yellow teeth and lung cancer; these would appear associated in an observational study but the true driver, which associates with both, is smoking. Teeth whitening would not reduce an individual's cancer risk. Traditionally, the gold-standard method for determining causality is a randomised trial for the intervention of interest. Random assignment of the intervention breaks any associations underlying processes, both known and unknown, allowing an evaluation of the direct and unbiased effect. However, clinical trials are very costly, may be impractical, and for some risk factors, such as smoking, are unethical.Mendelian randomization (MR), a relatively recent idea, offers a framework for assessing the causality of exposures using observational data by exploiting genetic randomisation that occurs during sexual reproduction. Genetic variants which modulate the exposure of interest, rather than the observed risk factor itself, are used to test for disease associations. These are randomised at conception and so, providing the biology is well understood, may be considered analogous to an intervention in a randomised clinical trial for the exposure. A recent surge in MR analyses has been driven by the publication of results from a number of large 'meta-GWAS' (Genome wide association studies), in which multiple studies combine tens of thousands of individuals into powerful joint analyses. These have revealed powerful genetic signatures for a variety of exposures and risk factors of interest, which are being used under MR to ask new causal questions in existing observational datasets.Leveraging results from these powerful consortium meta-GWAS will be key to many MR efforts. However, meta-GWAS typically only publish results in a reduced summarised form, and the existing MR frameworks for integrating summary statistics such as these have a number of shortcomings. For example, genetic variants that are correlated with one another, of which there are many, cannot be included. Furthermore, better performing methods are required to search these huge and genome-wide summary data repositories in order to identify optimal genetic signatures for use in MR. For a given MR analysis, we require enough variants to predict the target exposure but must avoid including variants which influence the outcome through other means, such as alternative genetic pathways.In this project we aim to advance current MR frameworks to make better use of summary data from large meta-GWAS. We will explore re-purposing and building upon summary statistics methods developed elsewhere in statistical genetics, and developing critical extensions to the recently proposed MR-Egger summary statistic approach, which offers robustness to violations in the biological assumptions necessary for MR. All methods will be developed and tested to explore causal disease drivers for type 2 Diabetes, Alzheimer's disease and a range of cancers in four compelling case studies shared by our colleagues at the MRC Epidemiology unit.