Postgraduate Course: Genomics and Phenomics in Breeding (VESC11279)
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 fundamentals and current status of genomics and phenomics applications in agricultural species. This course will introduce genomic data, their quality control, and their use to estimate relationships and explore population structure, as well as cover topics such as genome-wide association studies -including imputation and meta-analysis-, genomic evaluation and marker assisted selection. We will also showcase advances in phenomics and illustrate how new technologies contribute to enhance sustainable breeding in a range of production systems and Global contexts.
The course will have a focus on practical implementation of the theoretical concepts, covering a wide range of case studies and examples for key plant and animal species, including aquaculture, and with practical sessions aiming for the students to develop strong statistical, programming and data analysis skills. |
| Course description |
This course aims to develop the students' understanding of genomics and phenomics technologies and their role in genetics and data-driven breeding. The students will become familiar with genomic data and, by the end of this course, will be able to assess the quality of these data and use them in a broad range of applications, including genome-wide association studies and genomic evaluation, in terrestrial livestock and aquaculture species, and plants.
The course will not only advance the students' knowledge and understanding of current technologies and their applications but also their statistical and computational skills by focusing on application of concepts to hands-on data analysis through practical sessions, and the use of appropriate and current general and specialised software solutions. Further, the course will discuss the contribution of these new technologies and their uses to tackle Global sustainability challenges affecting food security and sustainable agricultural production of feed, heat, power, and raw materials.
Key concepts will be introduced and explored in a directed framework with pre-recorded lectures and invited seminars and case studies, and in discussion boards and weekly online activities. Their practical applications will be worked through in asynchronous practical sessions mainly within the R statistical package framework, using current general and specialised software. There will also be an independent learning component, including recommended resources for self-directed learning and reflective activities, leading to the assessments.
We strive to facilitate an authentic learning experience, centred on authentic, relevant, real-world challenges, through the use case studies and real-world examples in our teaching, including, where appropriate real data and using state-of-the-art computational pipelines throughout the practical sessions. Discussion board interactions are designed to support the development of a community of practice with the potential to support the students beyond the course.
Themes covered during this 5-week course will include:
*Introduction to genomic data*
We will introduce genetic markers, and their current uses in genetics and breeding of agricultural species. We will learn how to handle, quality control and analyse genomic data in relevant contexts including association analysis and selective breeding. We will perform real life hands -on analysis using state-of-the-art computational pipelines.
*Using genomic data to estimate relationships, understand population structure and estimate genetic parameters*
We will use genomic data to estimate relatedness/ similarity between individuals and construct genomic relationship matrices. We will exploit these relationships to explore population structure, diversity and for estimation of genetic parameters in a variance component framework.
*Using genomic data in evaluation and selection*
We will use genomic data in a selective breeding context, introducing various genomic evaluation methodologies including genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (HBLUP).
*Using genomic data to uncover genetic associations*
We will use genomic data to uncover genetic associations and map loci that affect agriculturally relevant traits. We will learn concepts relevant to genome-wide association studies (GWAS), imputation and meta-analysis, and apply these to the analysis of appropriate datasets using state-of-the-art software pipelines.
*Introduction to phenomic technologies*
We will give an overview of how modern technologies have revolutionised phenotyping processes, allowing the recording of a wide range of novel phenotypes and environmental data at scale, and discuss how this information can be used in a selective breeding context.
Throughout the course, there will be opportunities to enhance professional skills, such as communication and presentation, through hands on activities.
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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
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
MVM Online Learning Block 3 |
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 %
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| Additional Information (Assessment) |
Formative: Data practicals and Multiple-Choice Questions (MCQs) to assess knowledge and understanding of genomics and phenomics and critical analysis of data analysis practicals. Discussion boards will also provide opportunities for feedback.
Summative assessments:
1. MCQs in relation to genomics data analysis and interpretation (30%)
2. Data Analysis, poster, and R Markdown, in relation to a chosen course theme (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 understanding of the principal theories, concepts, and principles of genomics and phenomics.
- Apply knowledge, skills and understanding using skills, techniques, or materials associated with the field of genomics and phenomics.
- Apply critical analysis, evaluation and synthesis to issues/scenarios/datasets which are informed by developments/challenges/situations in the field of genomics and phenomics.
- Undertake critical evaluation of numerical data, using ICT applications, in order to communicate to a specified audience about genomics and phenomics.
- Take responsibility for a range of complex data resources and make informed professional decisions in relation to current research problems in genomics.
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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. G Simm, G Pollott, R Mrode, R Houston, K Marshall. 2021. Genetic improvement of farmed animals. Most relevant chapters: 5-13. CABI.
2. G Acquaah. 2012. Principles of Plant Genetics and Breeding. Most relevant section: 7. John Wiley & Sons, Ltd.
3. M Ganal, et al. 2014. High-throughput SNP Profiling of Genetic Resources in Crop Plants Using Genotyping Arrays. In: R Tuberosa, A Graner, E Frison (eds) Genomics of Plant Genetic Resources. Springer, Dordrecht. [https://doi.org/10.1007/978-94-007-7572-5_6]
4. Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives. Molecular Plant, Review Article. [https://doi.org/10.1016/j.molp.2020.01.008]
5. Neethirajan S, Kemp B. Digital Phenotyping in Livestock Farming. Animals. 2021. [https://doi.org/10.3390/ani11072009]
6. Livestock phenomics and genetic evaluation approaches in Africa: current state and future perspectives, Front. Genet., 2023. [https://doi.org/10.3389/fgene.2023.1115973] |
Additional Information
| Graduate Attributes and Skills |
Enquiry and lifelong learning: The University of Edinburgh graduate seeks personal and academic learning in order to inform, guide, or make a positive difference to knowledge-creation, others, or themselves. Inspired by their exposure to world-leading research and innovative practices, they continue their own journey of life-long learning.
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 |
| Keywords | Genomics,Genotype,Gene,Sequence,Diversity,GWAS,Phenomics,Genomic evaluation and selection |
Contacts
| Course organiser | Dr Pau Navarro
Tel:
Email: Pau.Navarro@ed.ac.uk |
Course secretary | Miss Stavriana Manti
Tel: (0131 6)50 5310
Email: stavriana.manti@ed.ac.uk |
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