Understanding health conditions linked to psoriatic disease across diverse populations

About the Project

Psoriasis and psoriatic arthritis are systemic inflammatory diseases that affect more than the skin and joints. People with psoriatic disease commonly experience other long-term conditions, such as cardiovascular disease, metabolic disease and depression (“comorbidities”), which substantially affect quality of life, treatment decisions and long-term outcomes. Yet evidence is limited: most studies examine single comorbidities in isolation, without understanding how conditions cluster or accumulate; and most have been conducted in predominantly white European ancestry populations. This creates an evidence gap for diverse populations, despite known ethnic differences in multimorbidity.

This PhD will use linked electronic health record, questionnaire and genomic data from two of the world’s largest biobank resources: Our Future Health and All of Us. Together, these resources include more than two million participants and provide ethnic diversity, longitudinal follow-up and genomic data at exceptional scale. The project offers interdisciplinary training across epidemiology, statistical genetics, health data science and clinical psoriatic disease research.

The overall aim is to define the burden, temporal sequence and likely causal direction of comorbidities in psoriasis and psoriatic arthritis across diverse populations. The project comprises three complementary studies.

Study 1 will map multimorbidity in psoriatic disease. The student will develop and validate approaches to identify psoriasis and psoriatic arthritis using diagnosis codes, prescribing data and genetic information. They will estimate the prevalence of approximately 40 long-term conditions and use adjusted regression and clustering methods to identify multimorbidity profiles compared with matched controls, including analyses across ethnic groups.

Study 2 will examine how comorbidities develop over time. Using longitudinal data, the student will investigate which health conditions people with psoriatic disease are more likely to develop, when they arise, and how multimorbidity accumulates. Analyses will use time-to-event models to estimate incidence rates and relative risks, alongside trajectory modelling to identify distinct patterns of progression.

Study 3 will investigate causal relationships using genetics. The student will use genome-wide association data and statistical genetics approaches, including shared genetic architecture, pleiotropy analyses and Mendelian randomisation, to test whether comorbidities contribute causally to psoriatic disease, arise as consequences of it, or share common biological pathways.

The student will join a highly productive, interdisciplinary research environment spanning the Centre for Epidemiology and the Centre for Genetics and Genomics. They will be embedded within an active community of postgraduate researchers, clinical academics, geneticists and health data scientists. The supervisory team brings complementary expertise in psoriatic disease, epidemiology, dermatology, rheumatology, statistical genetics and large-scale biobank research, supported by multi-million-pound funding from major organisations including Arthritis UK, the Medical Research Council and the NIHR.

The student will receive close supervisory support through regular meetings, project-specific methodological guidance, research group seminars and wider doctoral development training. They will be supported to develop as an independent researcher through patient and public involvement, scientific writing, conference presentation, and engagement with national and international collaborative networks. This studentship will provide a strong platform for a career in academia, industry, precision medicine, health data science or clinically applied research using large-scale biomedical data.

Eligibility

Applicants should hold, or be close to completing, a strong Master’s degree in epidemiology, biostatistics, bioinformatics, data science, public health, genetics, computational biology, or a related quantitative discipline. Applicants from clinical, biomedical or population health backgrounds will also be considered where they can demonstrate strong quantitative skills.

The ideal candidate will be motivated to use large-scale health and genomic data to address clinically important questions in inflammatory disease, multimorbidity and health inequalities. Experience with statistical software, particularly R/Python, and familiarity with regression-based analytical methods are desirable. Evidence of research potential, such as a dissertation, preprint, publication, conference presentation, or substantial analytical project, would be advantageous.

We particularly welcome applicants who are intellectually curious, methodologically rigorous, comfortable working across disciplines, and keen to develop as independent researchers at the interface of epidemiology, genomics and precision medicine.

How to Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website. Interested candidates must first make contact with the Primary Supervisor prior to submitting a formal application, to discuss their interest and suitability for the project. On the online application form select PhD Epidemiology.