AI-Powered Genomics for Climate-Resilient Livestock: From Cattle to Complex Traits
Supervisors:
Dr Richard L Mort, Lancaster University
Dr Davina Hill, University of Glasgow
Dr Barbara Shih, Lancaster University
Summary:
Genetic studies routinely link DNA variants to traits, but rarely explain how those variants actually work together, or point to a clear biological story. This PhD will build a new AI-driven framework that closes that gap, converting genetic association data into concrete, testable hypotheses by mapping how genes interact within relevant cell types, rather than just producing another long list of variants.
You'll test and refine the framework on a real-world problem with genuine stakes: predicting coat pigmentation in dairy cattle, a trait that affects heat tolerance and skin cancer risk as summers get hotter. Using a cohort of almost 900 genotyped cows, you'll apply machine learning to score coat patterns from photographs, then benchmark your AI framework against existing genetic studies. You'll work with industry partners Genus ABS and LIC New Zealand, both leaders in cattle genetics.
Co-supervised across Lancaster University and the University of Glasgow, you'll gain broad training in AI, bioinformatics, genetics and statistics. Because the framework is built to generalise, the skills and tools you develop will carry over directly to other traits, species and datasets well beyond this project, from human disease to crop science.