The course is aimed at tackling problems from practical modern genetic improvement of agricultural livestock and crops. Through the course you acquire theoretical insight in designing genetic improvement programs in general and experience in some practical examples. You will be able to design genetic improvement programs for simple situations. Recently developed methods, including the use of genomic data, are considered and their role in designing genetic improvement programs is evaluated.
Important topics in designing genetic improvement programs are to compare genetic progress with the increase in inbreeding, and to explain and compare risks and consequences of different genetic improvement programs using simulation tools. Topics to be covered in the course include modeling and simulation using deterministic and stochastic methods, use of molecular information, reproductive technologies, selection strategies and mating strategies.
Lectures from 8.30 until 12.00. Group work, assignments and presentations in the afternoon.
The participants will get several papers to read before the course week, and they will prepare a presentation of a selected topic.
- Compare realistic genetic improvement programs for important species of agricultural livestock.
- Compare realistic genetic improvement programs for important species of agricultural crops.
- Explain effects of changes in genetic improvement programs, including the use of reproductive technologies, selection strategies, mating strategies, and population size.
- Compare methods for the incorporation of DNA marker information and information about single genes with effects on quantitative traits on breeding decisions.
- Summarize the consequences of the use of DNA information in genetic improvement programs and compare these with the effects of conventional breeding.
The PhD students will present a topic for the fellow students during the course.
Lectures, theoretical exercises and assignments, and computer exercises. The lectures are based on short introductions to the subjects and subsequent discussions partly based on reading of scientific papers. The students are required to contribute actively to this discussion.
- 6 hours seminar
- 20 hours lecture
- 50 hours independent work
- 14 hours other (theoretical/computer exercises)
Prerequisite corresponding to a M.Sc. course in Quantitative Genetics.
Admission for NOVA courses is handled by the course organiser/ the NOVA member institution organising the course. Please see the links in the margin for more information.