In this course, ways of calculating breeding values in domestic animal breeding programs will be explored. Focus will be put on understanding the methods, and limitations of the methods. Small practical calculation examples and matrix notation will be used. We will go through (chapter 26 in the textbook): The general mixed effect model. Estimation of fixed effects and prediction of random effects (blup). Estimability. Standard errors of estimators. Animal model. Reduced animal model. Calculation of relationship matrix and inverse relationship matrix. Breeding values in models with repeated measurements on individuals. Maternal effects models. There will not be focus on computer programmes that are specialised for breeding value calculations in practical domestic animal breeding situations, but the programme matlab will be used. (An alternative is R.) Regarding variance component estimation (chapter 27 in the textbook), an introduction to the underlying theoretical foundation and the principles for calculation techniques will be covered.

Students will learn what breeding values calculated as blup-values are, and will be able to calculate these values for example data sets. They will also be acquainted with the estimation of variance components that are required to find blup-values.

Depending on the number of participating students, the teaching method will be colloquia with teacher participation or lectures (if more than around 10 students). In addition there are datalab sessions. Exercises that are essential in order to understand the textbook will be given almost every week.

Teacher assistance linked to discussion groups/lectures and exercises.

Lynch and Walsh (1998). Genetics and analysis of quantitative traits. Sinauer. Chapter 26 and 27. Exercises, notes, programs, and other material will be made available on the course web site.

Breeding course. (HFA200, or AQB200.)

Matrix manipulation (linear algebra). Acquaintance with MS Excel.

Hand-in exercises may be considered in order to assure good study progression throughout the semester. Presentation of own term paper for the other students, and participation at such presentations.

Semester term paper.

300 hours, including individual study, exercises and participation and presentation in discussion groups/lectures.

Special requirements in Science

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Discussion groups/lectures: 2 hours per week. Datalab: 2 hours per week.

If less than 5 students teaching routines may be altered.

An external examiner assesses the semester assignment.