Simulating Breeding Programmes with AlphaSimR

Designing efficient breeding programs is crucial for sustainable agriculture, but real-world testing takes years. Simulation allows you to model generations of genetic improvement in seconds, testing strategies before field implementation. In this text-only course, you will transition from understanding basic quantitative genetics to building and analyzing your own multi-generation breeding program simulations in R. What you will learn: Understand the core terminology of quantitative genetics and simulation modeling; Configure founder populations with realistic genome structures in AlphaSimR; Simulate selection, crossing, and advanced breeding schemes over multiple generations; Analyze simulation outputs using modern R data workflows and tidy principles; Evaluate genetic gain, inbreeding rates, and diversity metrics of your virtual populations; Compare different breeding strategies to optimize genetic progress and resource allocation. This course begins with essential terminology and foundational genetic definitions before guiding you step-by-step through writing simulation scripts and interpreting results. It is designed for agricultural scientists, animal breeders, and R enthusiasts who want to learn genetic simulation from the ground up, requiring only basic familiarity with R. Start reading today to design and optimize your own virtual breeding programs.