This PhD level course provides an in-depth exploration of simulation methods applied to key areas of animal science, animal breeding and genetics. Students will learn how to simulate and analyse different types of data, ranging from production and performance traits to molecular and epidemiological data, within animal populations. The course integrates core statistical frameworks — including linear models for both univariate and longitudinal observations — with advanced simulations of breeding programmes in species such as cattle, pigs, broilers and fish. The course also considers the simulation of complex data structures, such as biomarkers and RNA-Seq datasets, and addresses the challenges of molecular phenotyping and multi-level data integration. The final module explores behavioural and epidemiological modelling, including the use of social network data and stochastic transmission models to simulate disease dynamics and interactions between individuals. Throughout the course, students will engage with realistic scenarios and datasets, gaining hands-on experience of contemporary tools and approaches currently used and demanded in animal breeding research and industry.
Extent:
1,5 credits
Prerequisites
Admitted as PhD student or resident. MSc in Animal Science, Biology,
Genetics, Veterinary Science or related subjects or equivalent. In
addition, previous knowledge and experience of using R or Python will be
necessary for the student to fully understand the course content (e.g.
PVG0025 or PVG0045), as will a basic knowledge of statistics or similar
(e.g. linear regression models).