Dr. Alastair Noyce is a Clinical Senior Lecturer in the Preventive Neurology Unit at the Wolfson Institute of Preventive Medicine, Queen Mary University of London. His main research interests are Parkinson’s disease and related neurodegenerative disorders, particularly early identification and epidemiology, including environmental, clinical and genetic determinants. His other interests include objective measurement of motor and non-motor features of Parkinson’s.
Over the last 8 years he has led the PREDICT-PD study, which is a large cohort study that aims to identify those in the earliest stages of Parkinson’s, who may one day benefit from drugs that could slow or even halt the disease. Alongside PREDICT-PD he works on a variety of other observational studies, in the phases of Parkinson’s disease both pre-diagnosis and post-diagnosis. He has nurtured an interest in genetic epidemiology and the use of common genetic variation to estimate the nature of causal relationships between a variety of risk factors and Parkinson’s (so-called Mendelian randomisation).
Alastair graduated from Barts and the London School of Medicine and Dentistry in 2007 with distinction in all subjects and a 1st class degree in BMedSci Molecular Therapeutics. He then pursued integrated clinical-academic training via the Foundation Academic Programme (2007-2009) and an NIHR Academic Clinical Fellowship at UCL (2009-2012). In August 2012, he left clinical training to pursue a PhD in Neuroscience at UCL, funded by Parkinson’s UK, which he was awarded in February 2016. Between 2014-2016 he undertook an MSc in Epidemiology at the London School of Hygiene and Tropical Medicine, for which he was awarded distinction. He took up his appointment as Clinical Senior Lecturer in the Preventive Neurology Unit at the Wolfson Institute in August 2017 and is currently growing his team and the portfolio of work within the Unit.
Using Mendelian randomization to explore causal risk factors and their penetrance for Parkinson’s disease
Mendelian randomization (MR) involves using genetic variants as instruments to study causal relationships between exposures or risk factors and outcomes. MR has great potential application in the study of Parkinson’s disease (PD) and other degenerative neurological conditions, since they have long prodromal phases that may give rise to associations driven by reverse causation in observational studies. Furthermore, associations that arise in observational studies may frequently suffer from confounding, which may be in-part mitigated through a variety of MR methods.
In this talk, I will review the principles and practical applications of two-sample MR, and provide real examples of use to-date in PD. I will discuss how MR can be used to prioritise or de-prioritise intervention strategies, and suggest how use may be expanded in the future to further understanding of PD causation.