Chris Wallace (Theme Lead), Stephen Burgess, and John Whittaker
We aim to make impacts on aetiological understanding and consequently on defining disease taxonomy, on the selection and ranking of drug targets, on the choice of target populations and endpoints most appropriate to medicines, and on development of biomarkers to enhance mechanistic understanding and facilitate drug development and clinical decision making.
Key research themes are:
Joint analysis of traits, genes or variants to uncover mechanisms that are common to different diseases, and hence establish a set of disease definitions based on mechanism to supplement or replace the current reliance on observed pathophysiology. It also increases statistical power by borrowing information across traits, genes or variants. These approaches will be applied at multiple levels, from traditional disease definitions to molecular traits such as proteomics or transcriptomics, for instance to borrow information across cell type or tissues.
Longitudinal analyses: The majority of previous genetic/genomic analyses have ignored time, focusing particularly on contrasts between cases and controls. We will move beyond this to methods to model biomarker or disease trajectories over time, identify variants associated with specific disease courses and therapeutic responses and to investigate how genetic associations that vary over time can be exploited to assess the time-varying nature of causal effects. Again, there is a natural extension to multivariate phenotypes, either on the biomarker or disease level (eg, to investigate the causal mechanisms underlying multi-morbidity).
Informing drug discovery and development: The methods described above have natural application to drug discovery, in terms of identifying and validating drug targets, and determining the groups of individuals most likely to benefit from intervention on those targets. We will pursue both methodological development, eg further development of methods for non-linear Mendelian randomization and application to specific examples of high interest, alongside benchmarking of the usefulness of these approaches, eg by assessment of the ability of MR approaches to predict the size and direction of effect obtained by pharmaceutical intervention on a given protein.