Why do some people escape genetic risk? Multi-biobank study of immune-mediated inflammatory diseases

About the Project

Most people at high genetic risk of immune-mediated inflammatory disease never develop disease, while some develop disease despite apparently low inherited risk. These “genetically discordant” individuals may reveal mechanisms of disease protection, resilience and susceptibility that are missed by conventional genetic risk prediction.

This PhD will use UK Biobank, with replication in All of Us, to study why genetic risk does or does not translate into clinical disease. The project will integrate polygenic risk, electronic health records, environmental exposures and sequencing data across rheumatoid arthritis, axial spondyloarthritis and psoriatic disease.

Study 1: Environmental modifiers of genetic risk

The student will identify individuals with high (common variant) polygenic risk who remain disease-free, and individuals with low genetic risk who develop disease. They will test whether lifestyle and other potentially modifiable factors help explain resilience or susceptibility.

Study 2: Rare variant discovery

The student will use sequencing data to identify rare coding variants, protective alleles and genetic modifiers associated with discordant disease status. This study will investigate whether genetic architecture beyond common polygenic risk helps explain why some individuals unexpectedly develop disease, while others remain protected.

Study 3: Pre-diagnostic health trajectories

The student will examine whether longitudinal health records before diagnosis reveal distinct trajectories into disease. This will include temporal patterns of comorbidity accumulation before clinical onset.

The supervisory team brings together clinical epidemiology, rheumatology, and internationally recognised leadership in immune-mediated disease genetics, providing an exceptional environment for training at the interface of genetic and traditional epidemiology.

The student will gain training in genetic epidemiology, longitudinal modelling, rare variant analysis, and biobank-scale health data science, preparing them for a career in academia, biotechnology, pharmaceutical research, precision medicine or applied health data science.

Eligibility

Candidates should hold, or be close to obtaining, a first-class or strong upper second-class honours degree, or equivalent, in a relevant quantitative, biomedical or population health discipline. Suitable backgrounds include epidemiology, biostatistics, bioinformatics, statistical genetics, data science, computational biology, public health, genetics, medicine or a related field.

We particularly encourage applications from candidates with strong quantitative aptitude, experience using statistical software such as R or Python, and an interest in applying large-scale health and genomic data to clinically important questions in immune-mediated disease. Prior experience with epidemiological analysis, regression modelling, electronic health record data, polygenic risk scores, sequencing data or longitudinal analysis would be advantageous. Candidates should be intellectually curious, methodologically rigorous, highly motivated, and keen to develop as independent researchers at the interface of epidemiology and genomics.