Dementia: translating the genetic and epidemiologic findings into drug targets.
Alzheimer disease (AD) is one of the major challenges in population health. The past decade has seen a rapid increase in knowledge of AD-pathogenesis. The picture emerging is that of a highly heritable but partly preventable (+/-30%) disease. Improvements in education (brain-reserve) and early-treatment of vascular disease may explain up to 30% of patients. The major hurdle to overcome to prevent AD in the remaining 70% is that of paucity of cellular and animal models that reflect the complex genetic architecture of dementia that involves the concerted effects of >35 known genes. Combining the NDPH expertise on genetics and epidemiology with that of MSD artificial intelligence (AI) and cellular drug target screening of ODDI, the aim of this DPhil project is to translate genetic and epidemiologic findings into drug targets.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
Multi-omics pathway analysis has brought to surface various pathways implicated in AD including (innate) immunity, lipid metabolism, tau-binding proteins, and amyloid metabolism. Beyond neurons, there is a key role of glia cells and astrocytes in AD. This project aims to:
- Extend our knowledge of the genes and pathways implicated in AD combing classical genetic and novel AI (neuronal network) analysis;
- Separate out genetic loci that are primarily involved in brain reserve and vascular pathology (i.e., relevant for epidemiological high-risk group definition) from those primarily involved in neurodegeneration.
- Use the wealth of epidemiological and multi-omics data on various cell types to identify the most likely causal genes and pathways relevant for translation;
- Cross-validate the interaction of genes in and across various degenerative pathways (immunity, lipid, tau, amyloid) in big data and cellular models, with the aim to identify drug targets.
The project will use data of the UK Biobank and major consortia studying AD (AD Sequencing Project (ADSP), European AD database (EADB)). The candidate will conduct epidemiologic analysis of education, neuroimaging data and vascular risk factors. LD-regression, Mendelian Randomisation and multi-omics data integration will be used to separate the genes that are primarily involved in brain reserve, vascular pathology and neurodegeneration.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The work will involve big data analyses and data integration in close collaboration with fellow DPhils and senior scientists working in NDPH, MSD and ODDI. The candidate will work in the Big Data Institute and TDI-ODDI lab, making use of unique facilities and an excellent multi-disciplinary research community.
The candidate should have a 2.1 or higher degree and should have a basic training and interest in: 1) statistics/big data 2) genetics and epidemiology 3) biology.