Join us

About the role

We are looking for a motivated Research Associate to lead the genomic and proteomic analyses for STRAT-GLP1, a research programme funded by The Michael J. Fox Foundation for Parkinson’s Research (MJFF). This post will deliver the core analytical aims of the project, using human genetics, multi-cohort genome-wide association studies (GWAS), plasma proteomics, and drug-target Mendelian randomisation to define metabolically stratified Parkinson’s disease (PD) subtypes and identify which patients are most likely to respond to GLP-1 receptor agonists (GLP-1RAs).

What you would be doing

You will be working closely with Dr Cynthia Sandor and collaborators at Cardiff University (Prof Caleb Webber, Dr Samuel Neaves, and Dr Viola Volpato). The project aims to understand how metabolic factors — including insulin resistance, glycaemic dysregulation, adiposity, lipid metabolism, and renin–angiotensin system activity — influence PD risk, genetic architecture, and disease progression. Using large international datasets including UK Biobank, All of Us, Our Future Health, PPMI, and OPDC/Tracking, you will perform stratified GWAS and drug-target Mendelian randomisation to identify genetically defined PD subtypes, and translate these findings into clinically actionable blood-based proteomic biomarkers using Olink (PPMI and UK Biobank) and SomaScan (OPDC/Tracking) platforms.

You will be the primary analyst on the project, with access to established data pipelines and secure compute environments from day one (UK Biobank Application 69610; PPMI; All of Us; Our Future Health; OPDC/Tracking). Advanced statistical approaches will be used to integrate genetic and proteomic data to identify patterns of disease progression and potential therapeutic targets, including GLP1R-related pathways.

What we are looking for

You will hold a PhD in statistical genetics, computational biology, bioinformatics, epidemiology, or a closely related quantitative discipline. You should have demonstrable experience in GWAS, Mendelian randomisation, and large-scale biobank data analysis. Familiarity with proteomic datsets and Bayesian modelling is desirable but not essential.