Genetics of Parkinson’s disease progression

Background to the project

Parkinson’s disease (PD) is a complex condition, arising from the interplay of genetics, lifestyle, and environmental risk factors. It is characterised by heterogeneity in age at onset, rate of progression, treatment response, and combinations of motor and non-motor symptoms. 

No disease-modifying treatments currently exist that can stop or slow the progression of PD. As the phenotypic heterogeneity in PD likely reflects underlying genetic and molecular heterogeneity, it is important to uncover the genetic and molecular factors associated with these symptoms. This will help identify novel therapeutic targets to stop or slow disease progression. It will also help stratify individuals who might experience faster motor and cognitive progression for inclusion in clinical trials. Finally, since most research to date has been predominantly conducted in participants of European ancestry, it is imperative that future studies include participants from diverse ethnic backgrounds to improve health equity.

What the studentship will encompass

The main aim of this PhD project is to investigate shared genetic pathways between PD progression phenotypes, such as motor, cognitive, and neuropsychiatric phenotypes. The specific objectives of the PhD project are to: 1) investigate shared genetic pathways between PD phenotypes of progression; 2) integrate genetic data with gene expression data to further characterise the genetic pathways associated with PD progression phenotypes; and 3) explore genetic pathways in underrepresented populations/ethnic groups.

This project

will use large-scale genetic data from the Global Parkinson’s Genetics Program

(GP2, https://gp2.org/). GP2 is an ambitious resource program with the aim of

genotyping over 200,000 individuals with and without PD around the world to

further understand the genetic architecture of PD and make this knowledge

globally relevant. Currently, genetic data from >54,000 samples from 63

unique locations have been included in the latest data release (GP2 release 8).

The project will also use summary statistics data from existing PD progression

Genome-Wide Association Studies (GWASs) (Alejandro Martínez Carrasco et al.

2023, Manuela M. X. Tan et al. 2024) and genotype-tissue expression summary

statistics from different brain and blood panels through GTEx, MetaBrain, or eQTLGen.

The candidate will join the GP2 Trainee Network, which consists of over 260 members globally, and gain access to the GP2 Learning Platform for online training (https://gp2.org/training/). This platform provides an introduction to various topics related to PD genetics and bioinformatics. The candidate will also have the opportunity to attend in-person GP2 bioinformatics training workshops and hackathons to enhance their data analysis skills.

In particular for this role, they will be trained in cutting-edge statistical genetics methods, including GWAS, statistical colocalization, transcriptome-wide association studies (TWAs), genomic structural equation modelling (Genomic SEM), Polygenic Risk Scores (PRS) analyses, and Mendelian Randomization (MR), including drug-target MR.

 

Findings from this exciting PhD project could point to genes and pathways that could be ultimately targeted to slow down or stop the progression of different Parkinson’s progression phenotypes. Some of these genes and pathways will be shared, while others will be distinct between different phenotypes. This research could also help us identify Parkinson’s patients with different progression trajectories, who can be ultimately recruited for clinical trials. In this way, we will be able to design better trials. Over time, this information can be used to select the right treatments for the right patients.

Details of supervision

This project will be supervised by Dr. Petroula Proitsi (p.proitsi@qmul.ac.uk), Dr. Maria Teresa Periñan Tocino (t.perinantocino@qmul.ac.uk), and Dr. Kajsa Atterling Brolin (k.brolin@qmul.ac.uk) at the Centre for Preventive Neurology, Wolfson Institute of Population Health. Dr. Periñan Tocino and Dr. Atterling Brolin are based at University of Seville and Lund University, respectively, and hold honorary contracts with QMUL, visiting on a regular basis. The student will also interact with collaborators, including Prof. Alastair Noyce, a consultant neurologist and renowned expert in PD epidemiology at QMUL with a leading role within GP2, along with other experts in Parkinson’s progression genetics.

The Centre for Preventive Neurology (CPN), at the Wolfson Institute for Population Health (WIPH) at the Queen Mary University of London (QMUL), has a distinctive research programme focused on the early detection, diagnosis, and prevention of neurological diseases, such as PD, with a strong emphasis on underrepresented populations and addressing health inequalities. QMUL provides an exceptional environment for this project. Its research outputs, impact performance, and research environment were highly rated in the 2021 Research Excellence Framework (REF), where QMUL was ranked 7th in the UK for research quality. QMUL is one of the most diverse research-led universities globally, with equality, diversity, and inclusion (EDI) principles embedded throughout all strategic and day-to-day activities.[1] 

Requirements:

Candidates are required to have a good Bachelor’s degree (minimum 2:1) or Master’s degree in a relevant data science field, such as bioinformatics, epidemiology, medical statistics, or computational biology, demonstrating their proficiency in applying statistical methods to complex biological and health-related datasets. Basic knowledge of R, Python, or other related programming languages commonly used in bioinformatics, along with basic programming experience is required. Prior experience with PD or other neurodegenerative diseases is beneficial.

How to apply

The deadline for applications is 12:00 (GMT) 22nd November 2024. Late applications will not be considered. Interviews will be scheduled for the 2nd week of December 2024.

To be considered for this PhD, please submit an application our application site here.

1)     A CV (maximum 2 pages)

2)     A Personal statement (maximum 1 page) that should include: why you are interested in undertaking this project and what relevant existing skills, training, and knowledge you would bring to the project.

Shortlisted applicants will also be required to provide transcripts and two references