Genomic selection is currently the method of choice for improving the genetic potential of selection candidates in livestock breeding programs. This lecture introduces the reason why regression cannot be used in genomic selection. Alternatives to regression analysis that are suitable for genomic selection are presented. The concepts introduced are illustrated by excersises in R.
Learning objective
The students are familiar with the properties of multiple linear regression and they are able to analyse simple data sets using regression methods. The students know why multiple linear regression cannot be used for genomic selection. The students know the statistical methods used in genomic selection, such as BLUP-based approaches, Bayesian procedures and LASSO. The students are able to solve simple exercise problems using the statistical framework R.
Content
- Introduction to multiple linear regression - Problem n << p when using least squares in genomic selection - BLUP based approaches of solving problem of n << p - LASSO (Least Absolute Shrinkage and Selection Operator) as an alternative to approaches used in animal breeding - Introduction to Bayesian Statistics and parameter estimation - Application of Bayesian methods in genomic selection (BayesA, BayesB, BayesC, BayesN)
Lecture notes
Course notes in the form of a monograph, copies of the slides and solutions to the exercise questions are available on the net.
Literature
To be announced in the lectures.
Competencies
Subject-specific Competencies
Concepts and Theories
fostered
Techniques and Technologies
assessed
Method-specific Competencies
Analytical Competencies
assessed
Decision-making
fostered
Media and Digital Technologies
fostered
Problem-solving
assessed
Social Competencies
Communication
fostered
Cooperation and Teamwork
fostered
Personal Competencies
Adaptability and Flexibility
fostered
Creative Thinking
assessed
Critical Thinking
assessed
Self-direction and Self-management
fostered
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course