Supervisors: 

Main supervisor: Dr Androniki Psifidi (apsifidi@rvc.ac.uk)

Co-supervisors: Prof Damer Blake (dblake@rvc.ac.uk)

Prof Georgios Banos (Scotland’s Rural College and University of Edinburgh) (georgios.banos@sruc.ac.uk)  

Dr. Ankit Hinsu (day to day supervisor) (ahinsu@rvc.ac.uk)

Department: Clinical Science and Services

Project Detail

Avian influenza remains one of the most pressing zoonotic threats worldwide, with chickens acting as a major reservoir for viruses that can spill over into wildlife, livestock, and humans. Recent years have seen an unprecedented rise in highly pathogenic avian influenza (HPAI) outbreaks, including expansion into new host species and repeated incursions into human populations. While environmental exposure shapes infection risk, growing evidence shows that host biology—particularly genetic variation and epigenetic regulation—plays a decisive role in determining whether individual birds become infected or resist viral colonisation. Understanding these biological determinants is essential for developing sustainable, long‑term disease‑control strategies. Selective breeding offers a promising route to enhance natural resistance in poultry, reducing viral circulation, mitigating zoonotic spillover, and strengthening One Health resilience. This project draws on a unique collection of genomic, epigenomic, and transcriptomic datasets generated through the GCRF‑UKRI One Health Poultry Hub (https://www.onehealthpoultry.org), providing an exceptional opportunity to dissect the host factors that influence susceptibility to avian influenza virus (AIV).

The project aims to uncover the genetic and epigenetic architecture underlying natural resistance to AIV by integrating multiple layers of biological information. Genome‑wide association studies will identify SNPs and genomic regions linked to infection status. In parallel, DNA methylation differences between infected and non‑infected birds will be characterised using Reduced Representation Bisulfite Sequencing (RRBS) to reveal epigenetic signatures associated with susceptibility or resistance. These findings will then be integrated with RNA‑seq data to determine whether genetic variants or methylation changes influence gene expression in pathways relevant to antiviral immunity, host–virus interactions, and epithelial barrier function. Functional enrichment and pathway analyses will help contextualise the results, while qRT‑PCR validation will confirm the most promising candidate genes and regulatory features. Finally, genomic breeding values for AIV resistance will be estimated to assess the feasibility of incorporating these insights into selective‑breeding programmes.

By combining genomics, epigenomics, transcriptomics, and wet‑lab validation, this project will generate a multi‑layered understanding of host resistance to avian influenza. The findings have the potential to inform breeding strategies that enhance disease resilience in poultry, identify molecular targets for vaccines and/or therapeutics, and contribute to broader One Health efforts to reduce zoonotic risk. The student will gain advanced skills in quantitative genetics, bioinformatics, and wet-lab techniques, preparing them for careers across health, genomics, and computational biology. The work is expected to lead to a peer‑reviewed publication and provide a foundation for future research into sustainable control of zoonotic diseases.

References:

1. W. Drobik-Czwarno, et al. Genetic basis of resistance to avian influenza in different commercial varieties of layer chickens. Poultry Sci. 2018; 97:3421-3428. DOI:10.3382/ps/pey233

2. G. Banos, et al. Integrating Genetic and Genomic Analyses of Combined Health Data Across Ecotypes to Improve Disease Resistance in Indigenous African Chickens. Front Genet. 2020; 11:543890. DOI:10.3389/fgene.2020.543890 

Requirements

Essential

• Must meet our standard MRes entry requirements
• Applicants should have a genuine interest in computational and data‑driven approaches, as the project involves working with bioinformatics tools and analytical workflows. An in‑depth background in programming or advanced computation is not necessary; enthusiasm for learning and engaging with these methods is far more important.

Desirable

• It is helpful if applicants have some previous wet-lab experience or prior exposure to data analysis, or working in a Linux environment, although this is not essential.

This will be a full-time project commencing in October 2026, based at RVC's Hawkshead campus. 

No work with animals will be required to complete this project.

Funding

Partially funded: The MRes student will be expected to meet their course fees and living expenses. All other project costs will be met by the supervisor. All the datasets have already been generated as part of the One Health Poultry Hub project  

Please note that EU/EEA and Swiss national students may no longer be eligible for the “Home” rate of tuition fees, dependent on personal circumstances (including immigration status and residence history in the UK) and UK government rules which are currently being developed. For up-to-date information on fees for EU/EEA and Swiss national students following Brexit please see our fees and funding page.

How to apply

Deadline: 8th May 2026

For more information on the application process and English Language requirements see How to Apply.

Interviews will take place remotely (Teams, Zoom etc) within 4 weeks of the closing date.We welcome informal enquiries - these should be directed to apsifidi@rvc.ac.uk