Decoding chronic pain: a genetic roadmap to better prediction and treatment

Supervisors:

• Dr Richard Packer Richard.packer@leicester.ac.uk
• Professor Louise Wain

Project Description:

This PhD is funded by the Andrew Fry Memorial Award.

Chronic pain affects millions of people and is one of the most common and debilitating symptoms across a wide range of health conditions. It is also a marker of disease progression in its own right. Yet we still cannot explain why pain becomes chronic in some people and not others, why severity varies so widely between patients, or how pain shapes the course of the disease underneath it. The genetic factors behind all three remain poorly understood — and while they do, opportunities for earlier intervention and better-targeted treatment are being missed.

This PhD sets out to uncover the genetic basis of chronic pain progression, and to turn that understanding into candidate genes with real therapeutic potential.

What you will do

You will work through four connected stages, each building a distinct methodological skill set.

Evidence synthesis. You will critically appraise and collate the existing genetic studies of pain, establishing what is genuinely known and where the gaps lie.

Phenotype development. Working with clinical and data collaborators, you will build novel, data-driven definitions of pain progression from large-scale longitudinal electronic health records and prescription data. This is a substantial data linkage and curation challenge in its own right, and one of the most transferable skills you will take from the project.

Statistical genetics. You will test these new phenotypes and run genome-wide association studies (GWAS) to identify the genetic variants associated with how pain develops and persists.

Translational analysis. Using imputed and sequenced data, you will pinpoint candidate genes with therapeutic potential — a step that demands functional and clinical interpretation rather than statistics alone.

Why this project, and why now?

You will graduate with a combination that is in high demand across both academia and industry: data linkage and curation at scale, statistical genetics, and translational genomics. Few PhDs offer all three, and fewer still offer them on a question with this much clinical urgency behind it.

Just as importantly, this is a project you can realistically finish. The first supervisor's own published research on the genetics of pain provides the foundation, the collaborations, and the preliminary data, giving you a clear route through a varied project within 3.5 years. You will be supported throughout by a wider team of genetic epidemiologists, clinicians and statisticians, so there is expertise on hand at every stage rather than only at the start.

Who we are looking for

We welcome applications from students with a background in genetics, epidemiology, statistics, data science, medicine, or a related quantitative or biomedical discipline. Prior experience with genetic or health record data is welcome but not essential — what matters most is a strong quantitative aptitude, curiosity about the biology behind the numbers, and an interest in research that reaches patients.

If you would like an informal conversation about the project before applying, please get in touch.

References

1. Packer, R. et al. Genome-wide association study of neuropathic pain phenotypes implicates loci involved in neural cell adhesion, channels, collagen matrix formation, and immune regulation. Pain 167, 284–296 (2026).

2. Packer, R. J. et al. DeepPheWAS: an R package for phenotype generation and association analysis for phenome-wide association studies. Bioinformatics 39, btad073 (2023).

3. Eccleston, C. et al. The establishment, maintenance, and adaptation of high- and low-impact chronic pain: a framework for biopsychosocial pain research. Pain 164, 2143–2147 (2023).

4. Vitali, D. et al. How Well Can We Measure Chronic Pain Impact in Existing Longitudinal Cohort Studies? Lessons Learned. The Journal of Pain 26, 104679 (2025).