Using Mendelian Randomization to strengthen causal inference from observational data.

Using Mendelian Randomization to strengthen causal inference from observational data.
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This is a past event

Joint IAHS/RSS HLG talk: Derrick Bennett

Understanding the causal role of biomarkers in cardiovascular and other diseases is crucial in order to find effective approaches (including pharmacological therapies) for disease treatment and prevention. Classical observational studies provide naïve estimates of the likely role of biomarkers in disease development; however, such studies are prone to bias. This has direct relevance for drug development as if drug targets track to non-causal biomarkers, this can lead to expensive failure of these drugs in phase III randomised controlled trials. In an effort to provide a more reliable indication of the likely causal role of a biomarker in the development of disease, Mendelian randomisation studies are increasingly used, and this is facilitated by the availability of large-scale genetic data. This talk will give an overview of the rationale and provide a non-technical description of the methods and potential limitations of Mendelian randomisation. Examples will be given where Mendelian randomisation has provided pivotal information for drug discovery including predicting efficacy, informing on target-mediated adverse effects and providing potential new evidence for drug repurposing.

Speaker
Derrick Bennett, CTSU, Nuffield Department of Population Health, University of Oxford, Big Data Institute
Hosted by
Dr David McLernon
Venue
Room 1:029, Polwarth Building, Foresterhill, UoA, Aberdeen