Postgraduate Course: Advanced Modelling in Genetic Evaluation (VESC11274)
Course Outline
| School | Royal (Dick) School of Veterinary Studies |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This online postgraduate course covers advanced concepts and models used in genetic evaluation in animal and plant populations, with a strong focus on application. It follows from Principles of Genetic Evaluation and will cover more complex scenarios and data structures, including repeated records and multiple traits, direct and indirect effects, non-additive effects, and genotype by environment interactions. Alternative modelling strategies, statistical approaches and associated estimation methods, and state-of-the-art software implementations will be discussed. These concepts and methods are directly relevant and applicable to real-world breeding and genetics problems across production systems and species. The course will have a strong practical element where the students will apply statistical and programming skills to perform evaluations using the models and techniques showcased. |
| Course description |
This online postgraduate course aims to develop the students' understanding of advanced principles and concepts in genetic evaluation in animals and plants. The students will develop an understanding of the statistical foundations and by the end of this course, will be able to determine the appropriate models that underlie genetic evaluation in populations according to their structure, the structure of the phenotypic records and the genetic architecture of the traits in question, amongst other factors. They will have the skills to implement these models and perform evaluations using state-of-the-art analytical pipelines, relevant to breeding programmes across production systems and species.
The course will advance the students' statistical and analytical skills through application to real-life examples and consideration of practical uses of concepts such as model validation and assessment of accuracy.
The concepts will be introduced in pre-recorded lectures, and the students will apply their knowledge in real-world examples in asynchronous practical sessions, and will discuss and receive feedback via discussion boards and weekly online activities. The course will have a self-directed learning component and a resource list to support learning and teaching activities.
Themes covered during this 5-week course will include:
* Repeated measures and multiple traits, Direct and indirect effects, Non-additive effects, Environmental effects and Gene by Environment (GxE) interactions
* Foundations of genetic evaluation with appropriate models including assumptions about the expected values and covariance, estimation of model parameters, including fixed and random effects and hyper-parameters such as variance components, and the impact of alternative modelling on genetic evaluations and accuracy of breeding values.
* Statistical approaches with associated estimation methods, and state-of-the-art software implementations including open-source and commercial software, standalone software and various add-on packages.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical knowledge and understanding of advanced theories, concepts, and processes of genetic evaluation.
- Apply knowledge, skills and understanding in using a range of specialised skills, techniques, practices and/or software, that are at the forefront of genetic evaluation.
- Apply critical analysis, evaluation, and synthesis to forefront issues/examples/developments/approaches in genetic evaluation.
- Undertake critical evaluations of a range of numerical data, using software/ICT applications, which are appropriate for genetic evaluation.
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Reading List
The reading list will be provided electronically via Resource Lists. Essential/recommended and further reading and resources that align with the weekly content and course topics will be made available through the University Resource List platform. Here is an example of potential resources to be included:
1. Linear Models for the Prediction of the Genetic Merit of Animals, 4th Edition, Raphael A. Mrode, Ivan Pocrnic. [https://www.cabidigitallibrary.org/doi/book/10.1079/9781800620506.0000]
2. Gaynor, R.C., Gorjanc, G., Hickey, J.M. (2021), AlphaSimR: an R package for breeding program simulations, G3 Genes|Genomes|Genetics. [https://doi.org/10.1093/g3journal/jkaa017]
3. Gorjanc et al. (2015) Reliability of pedigree-based and genomic evaluations in selected populations
4. de Jong et al. (2023) Comparison of Genomic Prediction Models for General Combining Ability in Early Stages of Hybrid Breeding Programs [https://doi.org/10.1002/csc2.21105]
5. Werner et al. (2024) FieldSimR: an R package for simulating plot data in multi-environment field trials [https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1330574/full]
6. Bancic et al. (2024) A framework for simulating genotype by environment interaction using multiplicative models [https://doi.org/10.1007/s00122-024-04644-7]
7. G Simm, G Pollott, R Mrode, R Houston, K Marshall. 2021. Genetic improvement of farmed animals. CABI.
8. G Acquaah. 2012. Principles of Plant Genetics and Breeding. John Wiley & Sons, Ltd. |
Additional Information
| Graduate Attributes and Skills |
Outlook and engagement: The University of Edinburgh graduate draws on quality, experiences, and expertise of others to engage with the global community in a manner that is respectful, ethical, and positive.
Research and enquiry: The University of Edinburgh graduate develops their skills in research and enquiry, including problem-solving, analytical, and critical thinking, and digital literacies.
Personal effectiveness: The University of Edinburgh graduate will draw from their experiences and knowledge to ensure they can adapt to new, fluid, or complex situations/scenarios with sensitivity, integrity, and confidence. |
| Keywords | Breeding programme,genetic and genomic evaluation and selection,sustainable breeding |
Contacts
| Course organiser | Prof Gregor Gorjanc
Tel:
Email: Gregor.Gorjanc@roslin.ed.ac.uk |
Course secretary | Mr Gordon Littlejohn
Tel:
Email: Gordon.Littlejohn@ed.ac.uk |
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