Genetic Evaluation and Breeding
Course Objectives and Course assessment
Description of course
Genetic improvement has proven to be a very cost effective and
sustainable option to achieve improvements in efficiency and product quality
in animal production systems. Current developments in information
technology, reproductive technology and molecular genetics have increased
the efficiency of animal breeding methods. The impact of such new technologies
can not be easily understood without knowledge of the basic theory of
This course is a sequel to the “Introduction to Quantitative Genetics” (GEST325). It assumes understanding of basic principles of quantitative genetics, selection theory and animal breeding programs. The course attempts to develop a deeper understanding of methods for genetic evaluation of animals (including the use of molecular markers), methods to define breeding objectives and conduct multiple trait selection, and it provides an understanding of the tools to design and evaluate breeding programs. The course notes provide most of the material required. the practicals are designed to understand the technical aspects, and to stimulate creative and critical thinking about animal genetic improvement programs.
Application in animal breeding industries are discussed. Computer simulation and exercises are used to illustrate the topics. A case portfolio is developed to integrate different topics that are covered and to project the theory on practical situations
The course objectives for GENE422/522 are:
1. Provide a deeper understanding of quantitative genetic principles as applied in animal breeding programs
2. An understanding of genetic evaluation methods
3. An understanding of the issues involved in breeding program design
4. An understanding of the potential influence of new reproductive and genetic technologies on animal breeding programs
5. To provide skills to independently solve common animal breeding problems.
For GENE522 there are additional requirements to understand more detail of animal breeding data analysis, multiple trait evaluation and genetic parameter estimation.
Assessment of the Unit:
Final Exam in November (open book) 40%
Practical Reports and Problem Sets 20%
Case Portfolio 40%