Using genetic risk scores to improve diagnosis and outcomes in seronegative inflammatory arthritis

About the Project

Diagnosing inflammatory arthritis promptly and accurately can be challenging when antibody blood tests are normal (“seronegative”) and clinical features overlap. Seronegative rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis and polymyalgia rheumatica can present with similar symptoms but require different treatment strategies. Misclassification can lead to delayed effective treatment, prolonged glucocorticoid exposure and poorer outcomes.

This PhD will use large-scale linked clinical and genomic datasets, including biobank and disease-specific cohorts, to test whether polygenic risk scores for rheumatoid arthritis, psoriatic arthritis and axial spondyloarthritis can improve disease classification, prognosis and treatment stratification. The project comprises three complementary studies.

Study 1: Genetically informed stratification of axial spondyloarthritis

The student will examine whether axial spondyloarthritis and psoriatic arthritis genetic risk distinguishes clinically meaningful subgroups within axial spondyloarthritis. Analyses will focus on clinical phenotype, extra-musculoskeletal manifestations, disease severity, treatment response and drug persistence, with the aim of supporting more personalised management and treatment selection.

Study 2: Genetic classification of seronegative rheumatoid arthritis

Seronegative rheumatoid arthritis is an underserved patient group, often experiencing longer diagnostic delays and less intensive treatment than seropositive rheumatoid arthritis. The student will test whether polygenic risk scores for rheumatoid arthritis, psoriatic arthritis and axial spondyloarthritis distinguish subgroups with different disease severity, treatment outcomes or later diagnostic reclassification.

Study 3: Genetic risk and steroid outcomes in polymyalgia rheumatica

The student will identify people with polymyalgia rheumatica using linked primary care and prescribing data. They will test whether higher genetic liability to rheumatoid arthritis, psoriatic arthritis or axial spondyloarthritis is associated with prolonged glucocorticoid treatment or later diagnostic reclassification, potentially identifying patients whose apparent polymyalgia rheumatica reflects overlapping inflammatory arthritis biology.

The student will receive training in polygenic risk scores, electronic health record phenotyping, large-scale linked datasets, and longitudinal/prediction modelling. They will gain experience working across genetic epidemiology, clinical rheumatology and precision medicine, supported by an interdisciplinary supervisory team with expertise in inflammatory arthritis, genomics and real-world data. This project will prepare the student for a career in genetic epidemiology, precision medicine, rheumatology research, health data science, biotechnology or pharmaceutical research.

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 inflammatory arthritis. Prior experience with epidemiological analysis, regression modelling, electronic health record data, polygenic risk scores, longitudinal analysis, prediction modelling or pharmacoepidemiology would be advantageous. Candidates should be intellectually curious, methodologically rigorous, highly motivated, and keen to develop as independent researchers at the interface of epidemiology, genomics and precision medicine.