HFA350 From phenotypes to breeding values

This course is aimed at giving students all the skills needed to work in the animal breeding and genetics industry, going from modern phenomics methods to advances quantitative genetic models. The course emphasizes real world analysis and problem solving on datasets provided in partnership by Norwegian breeding companies and organisations. This practical experience allows students to apply their skills to a complex, multidisciplinary problem, culminating in the delivery of a breeding strategy or product proposal tailored to the company’s needs.

This course equips students with the theoretical and practical skills to drive genetic innovation through the integration of the latest tools and models in phenomics, linear mixed models for breeding value estimation, including genomics selection and mapping, and lastly an introduction to simulation. Starting with the generation of novel phenotypes, students will work with diverse digital data streams—such as images, sensor data, and vibrational spectroscopy—to develop new, precise measurements of traits critical to modern breeding programs. Through hands-on exercises using R, students will gain proficiency in data analysis, and interpretation for practical applications in precision breeding.

The course then transitions into advanced linear mixed modeling techniques, variance component estimation, and breeding value prediction to enable effective selection decisions. Students will explore various statistical models, including univariate animal models, multivariate models, and models incorporating genotype-environment interactions. Prediction errors and model comparisons will be covered to reinforce understanding and application of these statistical methods.

Students expand their linear mixed model understanding to state of the art genomic selection models, relationship matrices and genome wide association studies.

Student are introduced to simulation tools and index selection to ensure breeding strategies that align with sustainable and precision breeding objectives across a variety of species.

Knowledge:

Students will gain a theoretical understanding of how to evaluate:

• The merit of new phenotypes from different digital technologies.
• The model fit of linear mixed models including BLUP, SNPBLUP, GBLUP, GWAS and multitrait, variance components
• How traits can be included in a selection index for genetic gain

Skills:

• Data analysis skills to include diverse data types like image/video, vibrational spectroscopy and sensors.
• Construct mixed model equations for models with random and fixed effects
• The abilities to estimate variance components and predict breeding values with commercial software (ASReml)
• Simulate a breeding program with the addition of new strategies
• Contribute in a team to presenting a multidisciplinary project
• Write, report, discuss and defend viewpoints in an individual produced report

Competence.

Students will be confident when exposed to new datasets and multidisciplinary breeding problems and have the ability to find optimal solution for genetic gain and socially responsible animal breeding.