Using nutrigenetic, nutrigenomic and machine learning approaches to investigate the risk of cardiometabolic disease-related traits in ethnically diverse populations.

About the Project

This PhD project represents an exciting opportunity to develop and conduct research at the forefront of nutritional and genetic epidemiology, contributing to the prediction, prevention, and better understanding of the development of non-communicable diseases such as obesity and diabetes, using datasets from multiple ethnic groups. We are seeking a highly motivated candidate for a PhD project that will focus on nutritional genomics and statistical genetics. The project will focus on identifying genetic determinants of cardiometabolic traits and investigating the effect of these genetic determinants and lifestyle factors on these traits in multiple ethnic groups using detailed epidemiological analyses and machine learning approaches.

The student will use cutting-edge genetic epidemiological methods to investigate gene-diet interactions in ethnically diverse populations – both across the UK and across multiple global settings. Specific emphasis will be placed on the development of novel analytical approaches to the analysis of hypotheses related to gene-diet interactions. The student will receive training in nutrigenetics, nutrigenomics, genetic epidemiology, statistical genetics and bioinformatics. The student will also have the opportunity to discuss the exact title and nature of the project to suit their interests and to incorporate recent advances in these exciting areas.

This project provides an opportunity to work with a group that has a strong track record in nutritional and genetic epidemiology and to develop links with experts nationally and internationally. The successful candidate will be expected to actively participate in group meetings such as journal clubs and department seminars.

The project will be carried out at the Hugh Sinclair Unit of Human Nutrition, University of Reading, UK. The project will be supervised by Prof Vimal Karani, Professor in Nutrigenetics and Nutrigenomics. For an informal discussion, contact Prof Vimal Karani S (v.karani@reading.ac.uk).

University of Reading:

The University of Reading, located west of London, England, is ranked at 172 globally, according to the QS World University Rankings 2025. 98% of research at the University is of international standing (REF 2021, combining the University’s world leading, internationally excellent and internationally recognised submissions). The University’s main Whiteknights Campus is set in 130 hectares of beautiful, award-winning parkland, less than a 30-minute train ride to London Paddington and is approximately 30 miles from London Heathrow airport.

During your PhD at the University of Reading, you will expand your research knowledge and skills, receiving supervision and training in a number of different forms. We also provide dedicated training in important transferable skills that will support your career aspirations. If you need to develop your academic English skills before you start your studies, then the University has an excellent International Study and Language Institute which can help with this.

Eligibility:

• Applicants should have a good bachelor’s degree (minimum of a UK Upper Second (2:1) or equivalent)/master’s degree in a relevant subject or a strongly-related discipline.
• International applicants will also need to meet the University’s English Language requirements.

The University of Reading is committed to a policy of equal opportunities and non-discriminatory treatment for all members of its community.

How to apply:

Submit an application for a PhD via our online application system.

Further information:

Insert link to department PhD webpage: https://www.reading.ac.uk/food/phd

Enquiries:

Prof Vimal Karani S, v.karani@reading.ac.uk

Funding Notes

BSc (grade 2:1 or 1) or MSc (merit or distinction) in a relevant subject

References

1. Zhu X, Ventura EF, Bansal S, Wijeyesekera A, Vimaleswaran KS. Integrating genetics, metabolites, and clinical characteristics in predicting cardiometabolic health outcomes using machine learning algorithms - A systematic review. Comput Biol Med. 2025;186:109661.
2. Wuni R, Curi-Quinto K, Liu L, Espinoza D, Aquino AI, Del Valle-Mendoza J, Aguilar-Luis MA, Murray C, Nunes R, Methven L, Lovegrove JA, Penny M, Favara M, Sánchez A, Vimaleswaran KS. Interaction between genetic risk score and dietary carbohydrate intake on high-density lipoprotein cholesterol levels: Findings from the study of obesity, nutrition, genes and social factors (SONGS). Clin Nutr ESPEN. 2025; 66: 83-92.
3. Wuni R, Amerah H, Ammache S, Cruvinel N, da Silva N, Kuhnle G, Horst M, Vimaleswaran KS. Interaction between genetic risk score and dietary fat intake on lipid-related traits in Brazilian young adults. British Journal of Nutrition, 2024, 132 (5); 575-589.