Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

PROJECT TITLE

Causes, risk factors, potential treatments, and trajectory of rare cardiovascular diseases

SUPERVISORS

DESCRIPTION OF PROJECT

Cardiovascular diseases remain a significant global health challenge, encompassing a wide spectrum of disorders that vary in prevalence and clinical presentation. This DPhil project will focus specifically on rare cardiovascular diseases—a subset of CVDs that pose unique diagnostic and therapeutic challenges due to their limited occurrence and distinct genetic underpinnings. By leveraging cutting-edge data analysis methods, the project aims to uncover novel insights into these rare conditions, thereby contributing to more effective and targeted healthcare strategies.

Understanding the genetic risk factors underlying rare cardiovascular diseases is crucial for advancing cardiovascular medicine. The successful candidate will have the opportunity to conduct state-of-the-art genetic epidemiology research, employing methods such as drug target Mendelian randomisation and genome-wide association studies (GWAS). These approaches will aid in identifying causal relationships between genetic variants and disease outcomes, helping to pinpoint potential targets for intervention and clarify the biological pathways involved in these conditions.

Electronic health records (EHRs) have emerged as a valuable resource for medical research, providing longitudinal patient data that offer rich insights into disease progression, treatment outcomes, and real-world clinical practices. This DPhil project will utilise large-scale EHR datasets to conduct robust observational studies, aiming to characterise the clinical trajectories, risk factors, and outcomes associated with rare cardiovascular diseases. Through advanced statistical analysis, the candidate will work to reveal the heterogeneity inherent in these diseases and identify patterns that can inform early diagnosis, risk stratification, and improved patient care.

This unique combination of genetic epidemiology and observational analysis aims to deepen our understanding of rare cardiovascular diseases, with the ultimate goal of enhancing the ability of healthcare systems to diagnose, treat, and manage these challenging conditions more effectively. The successful applicant will gain experience in advanced analytical methods, work within an interdisciplinary research environment, and contribute significantly to the evolving landscape of cardiovascular research.

The ideal candidate should have an academic background or MSc-level experience in a relevant field, such as epidemiology, biostatistics, genetics, bioinformatics, cardiovascular medicine, or related disciplines. Proficiency in statistical programming (R, Python, Stata) and data analysis are preferred. Basic knowledge of epidemiological study design, electronic health records, or large-scale data manipulation and analysis is also desirable. Excellent communication skills and the ability to work collaboratively in a multidisciplinary research environment are required.

TRAINING OPPORTUNITIES

As a DPhil student within the DeepMedicine Research Group, you will have access to state-of-the-art facilities and resources at the University of Oxford, a globally renowned institution for pioneering research. You will be part of a vibrant academic community, collaborating with experts in genetics, epidemiology, clinical medicine and AI. The NDWRH provides a supportive and inclusive research environment that fosters intellectual growth and interdisciplinary collaboration.

The project offers training in a wide range of topics, including those applicable to both clinical research and general personal development. The DPhil candidate will receive training in clinical trial execution, design, and analysis; IPD data meta-analysis methods; analysis of OMICs dataset; as well as causal inference approaches using observational big data.

Funding Information

The position is not currently funded; therefore, the candidate will need to secure funding. The University of Oxford provides some competitive DPhil scholarships that students can apply for in parallel. Additionally, the supervision team can guide and support the candidate in preparing and applying for DPhil fellowships from external funding bodies.

HOW TO APPLY

To apply for this research degree, please click here.