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This project focuses on characterising the determinants of heart rhythm and cardiac arrhythmias such as atrial fibrillation (AF). Cardiac arrhythmias can indicate significant underlying cardiac disease, directly impact morbidity and mortality, and be important risk factors for cardio-embolic events. AF is the most commonly sustained cardiac arrhythmia and a major cause of death, stroke and disability. Improved approaches to prevent, diagnose, and treat arrhythmias are needed to avoid related threats to healthy ageing. Examining determinants of heart rate profiles and specific cardiac arrhythmias may improve our understanding of heart rhythm biology and contribute toward healthcare strategies.

UK Biobank, a prospective study involving 500,000 individuals, provides unique data on heart rhythm and extensive data on individual characteristics, laboratory biomarkers, genetic variants and disease outcomes. This project will focus on ~100K individuals who underwent a 4-lead electrocardiogram recording (whilst undergoing a short bicycle exercise test and recovery time and/or at rest). The UK Biobank data and heart rate profiles (and the computational algorithm-based phenotyping of arrhythmias already underway) will enable us to examine potential predictors of heart rhythm and the impact on disease.

During your DPhil, you will undertake a systematic investigation in UK Biobank participants to identify determinants of heart rhythm such as lifestyle and environmental factors, physical measures, and laboratory biomarkers. You will examine the relevance of candidate genes as well as undertake hypothesis-free genetic analyses in order to understand the genetic determinants of exercise heart rate dynamics and heart rhythm. Subsequently, you will evaluate the impact on health outcomes, such as stroke, and relevant imaging outcomes when available to assess impact on cardiovascular structure and function. This project will help to inform the development of effective strategies for arrhythmia prevention and treatment.

Relevant references

  • UK Biobank‎
  • Olesen et al. Atrial fibrillation: the role of common and rare genetic variants. European Journal of Human Genetics. 2014; 22:297–306.
  • Jouven et al. Heart-rate profile during exercise as a predictor of sudden death. New England Journal of Medicine. 2005; 352(19):1951-8.

Research Experience, Research Methods and Training

You will gain experience of statistical and genetic epidemiology and handling large datasets, learn about heart rhythm phenotypes and biology, and develop research planning and design skills. You will learn from an experienced multi-disciplinary team of statisticians, cardiologists, statistical geneticists and programmers.

Field Work, Secondments, Industry Placements and Training

Additional training in statistical programming and cardiovascular epidemiology will be provided. The successful applicant will have opportunities to attend and present work at relevant meetings.


In addition to the supervisor noted above, this project will be co-supervised by Professor Barbara Casadei of the Radcliffe Department of Medicine.

Prospective Candidate

The project involves statistical analysis of big data, including electronic records and genetic data, and requires previous statistical programming training/experience (e.g. R, SAS) and an interest in developing these skills. Examples of desirable prior qualifications are an MMath or MSc in Medical/Applied Statistics/Bioinformatics. An understanding of genetic epidemiology and biology would be a considerable advantage.


  • Jemma Hopewell
    Jemma Hopewell

    Associate Professor & Senior Scientist in Genetic Epidemiology and Clinical Trials