Postgraduate Course: Principles of Genetic Evaluation (VESC11281)
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 fundamental principles of genetic evaluations in animal and plant populations, including the theoretical statistical foundations required to perform genetic evaluations and assess the outcomes. This course will cover various topics, including introduction to matrix algebra and linear models, data recording, best linear unbiased prediction methodology, accuracy of prediction, validation methods, and introduction to estimating variance components.
The course will have a strong practical element demonstrating in practice the statistical methods taught and the students will develop their statistical and programming skills to perform operations commonly required in this field of work and research. |
| Course description |
This online postgraduate course aims to develop the students' understanding of key principles and concepts in genetic evaluations in animals and plants. The students will develop an understanding of the statistical foundations, and by the end of this course, they will be able to set up the basic evaluation models to calculate key genetic parameters essential in the implementation of modern breeding programmes.
The course will advance the students' statistical and programming skills by emphasising on the practical implementations and considerations in the field, 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, alongside self-directed learning and reading lists.
Themes covered during this 5-week course will include:
*Introduction to Matrix Algebra*
Will review matrix algebra and will examine linear models, including regression, random effects, fixed effects, and covariates, with practical examples using R.
*Foundations of genetic evaluation*
Will discuss data records available, how genetic evaluations were developed and what methodologies have been used.
*Main evaluation models*
Best Linear Unbiased Prediction methodology will be introduced, relationship matrices and main models used in evaluations.
*Assessing the outcomes*
Will examine examples from animals and plants, assessing the outcomes and implementations of genetic evaluations, including estimation of accuracy of predictions, and validation strategies using AlphaSimR.
*Introduction to variance component analysis*
Will present a general introduction to estimating variance components, ANOVA, REML, accompanied by practical sessions.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2025/26, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
MVM Online Learning Block 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Online Activities 33,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
65 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Formative: There will be a number of Multiple-Choice Questions (MCQs) to assess knowledge and understanding genetic evaluation and data analysis. Discussion boards will also provide opportunities for feedback.
Summative assessments:
1. MCQs in relation to knowledge and understanding of genetic evaluation and data analysis (10%)
2. MCQs in relation to knowledge and understanding of genetic evaluation and data analysis (10%)
3. MCQs in relation to knowledge and understanding of genetic evaluation and data analysis (10%)
4. Data analysis, R Markdown and recommendations in relation to genetic evaluation (70%) |
| Feedback |
The formative assessments will allow students to learn from feedback on these before embarking on the final summative assessments. Feedback will be in accordance with policy and regulations to ensure it is timely, consist of tangible suggestions such that it is actionable and relevant to the question being asked as well as the course and the programme going forward. Students are encouraged to reflect on their feedback and discuss with course leads if they need clarification of feedback received. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical knowledge and understanding of the principal 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, and approaches in genetic evaluation.
- Undertake critical evaluations of a range of numerical data, using appropriate software/ICT applications, which are appropriate for genetic evaluation.
|
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. R Workbook [https://jillymackay.github.io/RatRDSVS/]
2. 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]
3. G Acquaah. 2012. Principles of Plant Genetics and Breeding. John Wiley & Sons, Ltd.
4. Selection index and introduction to mixed model methods. Van Vleck, L. Dale. 1993. |
Additional Information
| Graduate Attributes and Skills |
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 and intellectual autonomy: graduates use their personal and intellectual autonomy to think independently, exercise personal judgement, and analyse facts and data in order to develop appropriate solutions.
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 | Genetic evaluation and selection,selective breeding,agriculture,aquaculture,sustainable breeding |
Contacts
| Course organiser | Dr Ivan Pocrnic
Tel:
Email: ivan.pocrnic@roslin.ed.ac.uk |
Course secretary | Miss Stavriana Manti
Tel: (0131 6)50 5310
Email: stavriana.manti@ed.ac.uk |
|
|
University Homepage