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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2021/2022

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DRPS : Course Catalogue : School of Biological Sciences : Postgraduate

Postgraduate Course: Quantitative Genetic Models (PGBI11085)

Course Outline
SchoolSchool of Biological Sciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites Students MUST also take: Population Genetics (PGBI11124) AND Quantitative Genetics (PGBI11125)
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesPGBI11124
PGBI11125
High Demand Course? Yes
Course Delivery Information
Academic year 2021/22, 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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
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:
  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsQGM,statistics,animal model,mixed model,genetic evaluation,genomic evaluation
Contacts
Course organiserDr Sara Knott
Tel: (0131 6)50 5444
Email: s.knott@ed.ac.uk
Course secretaryMiss Zofia Bekas
Tel: (0131 6)50 5513
Email: zofia.bekas@ed.ac.uk
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