Genomic Prediction in Animals and Plants

The course focuses on the quantitative genetics and statistical background of models to predict traits based on genomic information (genomic prediction), with main focus on agricultural (plant and animal breeding) and some related biological applications. The course addresses prediction from high-dimensional data covering tools like mixed models, Bayesian shrinkage approaches and machine learning kernel methods and discusses use of kernel methods to handle interactions and non-linear regressions. All approached are trained in computer practicals with the objective that students obtain an understanding of the statistical principles of the different models, and can analyse data with a critical assessment of the results from different statistical approaches.

Course leader

Luc Janss

Target group

Master's Level