EASTBIO Genetic dissection of individual health trajectories and their role in disease transmission

Royal (Dick) School of Veterinary Studies / The Roslin Institute

Infectious disease remains the number 1 threat to sustainable livestock productions. Animals are known to differ widely in their response to infection. Whereas resilient individuals manage to recover quickly and fully, less resilient individuals often experience prolonged infection with long-term health damage, and some die. Resilience to infection is an important trait in all species, not only because it affects an individual’s own health, but also because an individual’s resilience is likely to affect its ability to transmit infection and thus impact on the health of the entire herd.

The recent explosion of biological data and innovative data-driven computational methods provide exciting new opportunities to monitor and analyse individuals’ resilience trajectory over time, and determine to what extent these are genetically regulated. However, adequate mathematical and computational methods analyse individuals’ resilience trajectories and integrate them into effective breeding programmes are currently lacking.

The aims of this studentship are to
(1) Apply mathematical Hidden Markov Models and computational Deep Learning methods to characterise resilience trajectories of individuals and identify different response types
(2) To determine to what extent resilience trajectories are genetically regulated and dissect their underlying genetic architecture
(3) To develop a genetic-epidemiological prediction model to assess the feasibility of integrating resilience trajectories into genetic selection, and its impact on individual and herd health.

We will use a large and unique existing dataset comprising longitudinal disease data together with ‘omics’ data from thousands of genotyped pigs that were experimentally challenged with a virulent strain of the Porcine Reproductive and Respiratory Syndrome virus (PRRSV) to construct and analyse resilience trajectories. To analyse the resilience trajectories, we will build upon previously developed mathematical methods (Lough et al. Proc. R. Soc B. 2015; Torres et al. 2016. Plos Biol. 14(6)), and complement these with Hidden Markov Models and Deep Learning methods to characterise trajectories and trajectory phenotypes of resilient animals. We will then use random regression models combined with genome wide association studies to determine the genetic architecture of resilience trajectories. Finally, we will use genomic prediction models coupled with epidemiological models to assess the use of resilience trajectories to breed healthier animals, and the requirements for data recording and beneficial consequences of doing so.

The PhD student will join a vibrant multi-disciplinary team at Roslin consisting of bio-mathematicians and geneticists, and will receive training in mathematical and computational modelling, as well as animal breeding and genetics, both through targeted course and close individual supervision. The project is a collaboration between the School of Statistics and the Roslin Institute and will provides the PhD student with training in advanced statistics. The successful candidate will have the opportunity to help resolve one of the most pressing livestock disease problems by mining unique datasets and integrating advanced statistical models into future breeding programmes.

This project builds upon and extents a long-standing international collaboration on animal health genetics between the Roslin Institute, Iowa State University (Prof. Jack Dekkers), and the industrial partner Genus. Prof. Dekkers will be involved in the genetic analysis in objective 2.

Eligibility:
All candidates should have or expect to have a minimum of an appropriate upper 2nd class degree. To qualify for full funding students must be UK or EU citizens who have been resident in the UK for 3 years prior to commencement.