Statistical approaches for causal analysis in genetics data

孟德尔随机化(MR)分析使用人群中等位基因的随机分配来获得风险因素和结果之间的关联的“明确”估计。越来越多地，高维数据的基因研究，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。然而，这些数据的使用对传统方法提出了挑战；例如，许多变种是多效性的，因此可能不是适合MR的工具。已提出许多方法来消除这些影响，例如代理调解人方法，我将把它们应用于该部门现有的丰富的基因和表型数据，如区间研究，以提供更强大的因果推断。广泛的“组学”数据的可获得性将使我能够构建生物信息的遗传变异子集，作为磁共振的工具，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种的新发现，从而为发现潜在的因果关系提供更大的能力。我将开发和测试其他分析策略，如基于聚类的方法，以帮助识别跨越一个或多个基因的潜在路径，以促进对支撑观察到的数据结构的特定生物过程的理解，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.