Improving Mendelian randomisation based on meta-GWAS summary statistics.

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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.