Statistical approaches for causal analysis in genetics data

孟德尔随机化（MR）分析使用等位基因在人群中的随机分配，以获得风险因素和结果之间关联的“无混杂”估计。越来越多的高维数据的遗传学研究，如蛋白质组学、代谢组学或其他“组学”，允许使用MR进行大规模的因果分析。例如许多变体是多效性的，因此可能不是MR的合适工具。已经提出了许多方法来解开这些效应，例如代理中介体方法，我将把它们应用于该部门丰富的基因型和表型数据，如INTERVAL研究，以提供更可靠的因果推断。广泛的“组学”数据的可用性将使我能够构建遗传变异的生物学信息子集，用作MR的工具，与不可知论方法相比，获得更可解释的结果。此外，我将通过进行全基因组关联分析来增加现有的已知相关变异与新发现，从而为发现潜在的因果效应提供更大的动力。我将开发和测试其他分析策略，如基于聚类的方法，以帮助确定一个或多个基因的潜在途径，以促进对特定生物过程的理解，这些过程是所观察到的数据结构的基础，并阐明潜在的治疗药物靶点。

Mendelian randomisation (MR) analysis uses the random allocation of alleles in the population to obtain an "unconfounded" estimate of the association between a risk factor and an outcome. Increasingly, genetic studies of high dimensional data, such as proteomics, metabolomics or other "omics", is allowing wide-scale causal analysis using MR. However, the use of such data raises challenges for traditional methods; for example, many variants are pleiotropic and may therefore not make suitable tools for MR. Many methods have been proposed to disentangle these effects, such as proxy mediator approaches, and I will apply them to the rich genotypic and phenotypic data available in the department, such as the INTERVAL study, to provide more robust causal inference. The availability of a wide range of "omics" data will allow me to construct biologically informed subsets of genetic variants to use as instruments for MR for more interpretable results compared with agnostic approaches. Furthermore, I will augment the existing set of known associated variants with novel findings by undertaking genome wide association analysis leading to greater power for discovery of potential causal effects. I will develop and test other analysis strategies such as clustering based methods to help identify potential pathways across one or more genes to advance the understanding of specific biological processes that underpin the observed data structure and elucidate potential therapeutic drug targets.