Postgraduate Course: Quantitative Genetic Models (PGBI11085)
Course Outline
| School | School of Biological Sciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course builds on lectures in quantitative genetics (in PGBI11001) and statistics (PGBI11003) and provides tools to analyse quantitative genetic data and interpret the results. Students will be introduced to statistical mixed models and the use of molecular data in quantitative genetic models. |
| Course description |
The course consists of 10 lectures and associated computer practicals.
1 Introduction to mixed models for genetic problems
2 Generalising to the animal model incorporating information from all relatives
3 Extending the simple linear model to include repeat records, common environment and maternal effects. Estimating variance components
4 Multivariate models. Genetic evaluations
5 Shrinkage, fixed versus random effects
6 Estimating effects of loci
7 Genomic relationships and their use in genetic evaluations
8 Genomic evaluation
9 Variances and Bayes models
10 Accuracy of genomic evaluation
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Information for Visiting Students
| Pre-requisites | PGBI11124
PGBI11125 |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
|
Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
58 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Quizzes (10%), in-course assessment and class test (90%)
The class test is a mix of questions and computer-based data analysis |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Knowledge and Understanding. The student should know and understand: (i) the key assumptions underlying random effects; (ii) how to use and fit random effects in a range of mixed models to yield information on genetic variation and account for other sources of non-genetic variance, using either pedigree or genomic information, and including the use of relationship matrices; (iii) how to assess the quality of predictions made from the models; and (iv) factors influencing the accuracy of genomic prediction and performance relative to pedigree.
- Practice: Applied knowledge, skills and understanding. The student should have practical knowledge and skills enabling the student: (i) to use REML to estimate genetic parameters from a range of genetic models which include other sources of shared variance, both in a univariate and multivariate context, using pedigree or genomic data; (ii) to carry out the appropriate hypothesis testing for fixed and random effects; (iii) to assess accuracy and bias of predictions; (iv) to obtain estimates from MCMC methods of estimation.
- Generic cognitive skills. The student should recognise how different data structures, different sources of genetic information and different objectives lead to the need for different models and parameterisations and how these can be formulated for analysis.
- Communication, IT and numeracy skills. The student should be able to: (i) write out the terms and assumptions in a mixed linear model in a form that is generic and does not depend on specific software packages; (ii) fit models, carry out statistical testing, and validation as described in (2) on a computer package such as ASReml in R; (iii) write down out hypotheses for statistical tests, describe their outcomes, and explain the inferences made from the outcomes.
- Autonomy, accountability and working with others. The student should be able to use the cognitive skills in (3), knowledge and understanding in (1), the practical skills in (2), and the communications skills in (4) to take unseen data suitable for fitting standard models and autonomously report on a full analysis, describing models, statistical tests, parameter estimates, validation and conclusions.
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | QGM,statistics,animal model,mixed model,genetic evaluation,genomic evaluation |
Contacts
| Course organiser | Dr Sara Knott
Tel: (0131 6)50 5444
Email: |
Course secretary | Miss Zofia Bekas
Tel: (0131 6)50 5513
Email: |
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