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My work uses genetics to understand the biology of Neurodegenerative and Psychiatric illnesses. In Neurodegeneration we aim to identify genetic variation that contributes to susceptibility and behavioural phenotypes (psychosis, depression) in late onset sporadic Alzheimer’s disease (AD) and to identify genetic variation that contributes to susceptibility and endophenotypes (age of onset, survival and site of onset) of sporadic Amyotrophic lateral sclerosis (ALS).
In Psychiatric Disease our goals are to identify genetic variation that contributes to susceptibility for major psychiatric disorders in particular schizophrenia and psychosis and to identify genetic variation that contributes to endophenotypes associated with these diseases. In addition to array based genotyping, we have used whole genome, exome and targeted exome sequencing approaches for the identification of genetic variation.
All of our studies employ robust statistical techniques and we have used Mendelian randomisation and structural equation modelling as an approach to infer causality.
2014 Professor of Genetics Institute of Psychiatry, Psychology and Neuroscience, London, UK
1998 Reader in Genetics Institute of Psychiatry, London, UK
1992 Senior Lecturer Institute of Psychiatry, London, UK
1989 Lecturer, Institute of Psychiatry, London, UK
1986 MRC staff scientist Clinical Research Centre
1984 Postdoctoral Fellow Neurobiologie Cellulaire, CNRS, Gif-sur-Yvette, France
1978 Postdoctoral research assistant Department of Pharmacology, University of Oxford, UK
DPhil Genetics, University of Oxford, UK
My team is part of the Biological Markers and Clinical Informatics cross cutting research theme of the Biomedical Research Unit for Dementia (BRU-D) and our aim is to contribute to the core facility and infrastructure for the standardised measurement of biological markers in late life dementia. This helps to support and facilitate research strategies across the different BRU-D disease and experimental medicine research areas.
We have been developing novel biomarker modalities in particular metabolomic lipid biomarkers identified through ultra-performance liquid chromatography/mass spectrometry analysis of plasma in collaboration with colleagues from KCL Institute of Pharmaceutical Science. The aim of this study was to use metabolomics to identify a battery of plasma metabolite molecules that could predict AD patients from controls using multivariate analysis methods and expands previous work that identified three lipid phosphatidylcholine (PC) molecules that were progressively diminished in subjects with mild cognitive impairment and AD patients.
In the latest study, we identified a combination of 10 metabolites, which predicted AD with near 80% accuracy. We subsequently identified six of the metabolites to be cholesterol esters, molecules not previously implicated in AD. The newly identified molecules were reduced in AD patients compared with controls. These metabolites are connected with the previously identified PCs through a one-step enzymatic reaction.