Title: Genetics and causality: towards more accessible and more reliable Mendelian randomization investigations
Funder: Medical Research Council

Abstract: Mendelian randomization is the use of genetic variants as proxy measurements for a modifiable exposure to judge whether interventions on the exposure are likely to reduce disease risk. Mendelian randomization has a history of correctly predicting the results of randomized trials of pharmacological interventions, and has wide applicability in a broad range of scientific fields for making the crucial distinction between causation and correlation. The approach is becoming more widely used, and many publications using the technique have appeared in high-profile medical journals. There are three general aims of this research: 1. Methods development: To develop innovative methods to extend the Mendelian randomization paradigm in response to the increasing availability of relevant data and to the needs of applied research. 2. Applied analysis: To partner with leading experts in different disease areas to validate and prioritize candidate risk factors as targets for intervention to improve clinical outcomes, with particular focus on cardiovascular disease. 3. Dissemination of methods: To bridge the gap between methodological researchers in instrumental variables methods and applied investigators wanting to conduct studies using Mendelian randomization. Specific research objectives include developing methods so that publicly-available data, in particular summarized association estimates with traits from large genetic consortia, can be leveraged to make causal inferences, particularly when the instrumental variable assumptions may be in doubt for some of the genetic variants. Additionally, understanding how the methods can be applied to large-scale population-based cohorts (“biobanks”). Further, investigating how methods can be used to triage large numbers of potential risk factors measured in high-throughput “omics” platforms. Other research objectives include: correcting for selection bias in instrumental variable analysis; investigating optimal approaches for Mendelian randomization using fine-mapping data; developing Bayesian model averaging approaches for instrument selection; and proposing methodological approaches for the analysis of matched data (both covariate matched and family-based data from recall-by-genotype studies). Novel findings will be fed into pharmaceutical pipelines for drug development through traditional routes of dissemination (journal articles, conference presentations) and collaborative links with the pharmaceutical industry. Translation and pathways to impact are maximized by close collaboration with epidemiological and clinical collaborators; to facilitate this Stephen Burgess has a joint affiliation with the Cardiovascular Epidemiology Unit at the University of Cambridge, as well as an honorary affiliation at the MRC Integrative Epidemiology Unit in Bristol.


Research activity codes: 1.4 METHODOLOGIES AND MEASUREMENTS;2.5 RESEARCH DESIGN AND METHODOLOGIES (AETIOLOGY);8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: CARDIOVASCULAR;GENERIC HEALTH RELEVANCE