Aims and background: The project will utilise the power of ‘Big data’ from 500,000 participants of the UK Biobank cohort to identify personalised prevention factors for dementia. Using a precision medicine approach , the study participants will be first risk-stratified based on their genetic risk of dementia and group specific prevention factors will then be identified by investigating the interaction effects between the modifiable risk factors and genetic risk.
There is no cure for dementia and available treatments for managing the symptoms are largely ineffective. Around 35% of dementia cases are believed to be preventable through targeting modifiable risk factors, and therefore, identifying modifiable risk factors for preventing or delaying dementia onset is a major research pursuit. However, research until now has largely ignored the genetic influence, which can make people more, or less, amenable to the preventive measures.
• Validate and confirm the already reported modifiable risk factors by taking advantage of the bigger and more powerful sample of the UK Biobank cohort.
• Identify further novel modifiable factors from a wide range of measures collected by the UK Biobank cohort.
• Calculate person-specific genetic risk of dementia including the APOE genotype status and polygenic risk score (PRS).
• Assess the extent to which genetic susceptibility moderates the effects of prevention to identify personalised prevention factors for dementia.
Methods: Statistical modelling on an extensive range of modifiable risk factors, genotypic and cognitive function data from the UK Biobank, and incident dementia from the linked hospital admissions and primary care records. The effects of modifiable factors on the dementia risk and their interaction with polygenic risk will use Cox regression models.
Impacts: The results will provide valuable information regarding personalised prevention factors for dementia. These will inform health and social care policy to improve management of individuals at risk of developing dementia.
More information on the supervisor for this project: https://people.uea.ac.uk/en/persons/m-khondoker
Type of programme: PhD
Start date: October 2020
Mode of study: Full-time
Studentship length: 3 years
Applicants should have a minimum of an upper second class Honours degree, master’s degree, or equivalent in a relevant subject. Degree in statistics, applied mathematics, data science, computing, quantitative biology, bioinformatics, statistical genetics, physics or similar. Alternatively, degree in life sciences, social sciences, medicine with substantial component and/or experience in quantitative methods. The applicants should have an interest in ageing and dementia research and motivation for learning statistical modelling and analysing genome wide single nucleotide polymorphism (SNP) data. Experience in statistical modelling and analysing genotype data are not essential (can be trained) but would be beneficial. A numerate background would also be beneficial. Applicants whose first language is not English are normally expected to meet the minimum University requirements (e.g. 6.5 IELTS).
 Livingston, G., Sommerlad, A., Orgeta, V., Costafreda, S. G., Huntley, J., Ames, D., ... & Cooper, C. (2017). Dementia prevention, intervention, and care. The Lancet, 390(10113), 2673-2734.
 Dudbridge, F., Power and predictive accuracy of polygenic risk scores. PLoS genetics, 2013. 9(3): p. e1003348-e1003348.
 Andrews, Shea, et al. Interactive effect of APOE genotype and blood pressure on cognitive decline: the PATH through life study. Journal of Alzheimer's Disease 44, 4 (2015): 1087-1098.
 Licher, S., et al., Genetic predisposition, modifiable-risk-factor profile and long-term dementia risk in the general population. Nature Medicine, 2019.
 Lourida, I., et al., Association of Lifestyle and Genetic Risk With Incidence of DementiaAssociation of Lifestyle and Genetic Risk With Incidence of DementiaAssociation of Lifestyle and Genetic Risk With Incidence of Dementia. JAMA, 2019. 322(5): p. 430-437.