Professor Jack Bowden

# Causality@Exeter: Seminar Series - Combining Mendelian randomization with Pharmacogenetics

## Open to University of Exeter staff and students

An Institute for Data Science and Artificial Intelligence seminar
Date15 March 2021
Time14:00 to 16:00
PlaceOnline

Join us for the second in this series of seminars looking at Causality research at Exeter.

Professor Jack Bowden - Medical School: Combining Mendelian randomization with Pharmacogenetics

Understanding whether one thing causes another is a central goal of much of data science.  For example, understanding causal and effect relationships allows us to answer questions such as “Does this treatment harm or help patients?”  However, much of data science, machine learning and statistics is built on correlations.  This seminar series brings together researchers across the University working on and using causal analysis with the aims of understanding different approaches to causal analysis and developing new collaborations and methods.  Starting with University of Exeter experts in causal analysis, the seminars will expand to include external visitors.

## Abstract

Combining Mendelian randomization with Pharmacogenetics

Over the last 20 years the field of Epidemiology has embraced the exploitation of random genetic inheritance to help uncover causal mechanisms of disease using the technique of Mendelian randomization (MR). Provided the genetic variants satisfy the Instrumental Variable' assumptions, MR can consistently estimate the average causal effect of an intervention (e.g. lowering blood pressure) on a population's health. Genetic variants can also play an important role in helping to explain treatment effect heterogeneity, through the science of pharmaco-genetics.  A canonical example is Clopidogrel: the primary drug for stroke prevention in the UK and many other countries.  It requires CYP2C19 enzyme activation in order to be properly metabolised and thus work to its fullest extent.

In this talk I will review the general pharmaco-genetic approach to exploring treatment effect heterogeneity, which utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we refer to as the genetically mediated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In the special case of only partial violation, we then show that the optimal way to estimate the treatment effect is to combine the robust GMTE estimate with a statistically independent estimate derived from the principles of Mendelian-randomization.  A full decision framework is then described to decide when a particular estimation strategy is most appropriate. We illustrate these approaches by re-analysing UK Biobank-CPRD linked data relating to {\it CYP2C19} genetic variants, Clopidogrel use and stroke risk, and data relating to ApoE genetic variants, statin use and Coronary Artery Disease (CAD).