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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) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis course builds on lectures in Quantitative Genetics (PGBI11125) and Statistics and Data Analysis (PGBI11003) and provides tools to analyse quantitative traits using genetic models and interpret the results. Students will be introduced to statistical mixed models and the use of molecular data in quantitative genetic models. These models and concepts, and the use of the genomic data, are ubiquitous in the analyses of genetic data.

This is a compulsory course for the Quantitative Genetics MSc programmes, forming part of the foundation for the second semester.
Course description Overview of main topics:

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.

There are four components to the assessment. Quizzes based on each week of lectures (10% of the course mark), two in-course assignments involving data analysis (16% and 24%) and a class test (50%). The class test will take place during the exam period and has questions based on course work and interpretation of analyses.

Background knowledge:
Students must have taken Population Genetics (PGBI11124) and Quantitative Genetics (PGBI11125), or their equivalent, to provide a background in genetics and familiarity with the concepts of kinship and heritability, allele and genotype frequencies, and additive and dominance effects of alleles. We assume that students are familiar with basic statistics including linear regression, the ideas of factors and covariates in analysis of variance, and likelihood. Computer practicals are performed in R and models are fitted using the software package ASReml-R. No familiarity with ASReml is assumed but a basic knowledge of R would be beneficial. A familiarity with very basic matrix concepts is helpful and support for this is available.
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
Course Delivery Information
Academic year 2023/24, 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 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) There are four components to the assessment. Quizzes based on each week of lectures (10% of the course mark), two in-course assignments involving data analysis (16% and 24%) and the exam (50%). The exam will take place during the exam period and has questions based on course work and interpretation of analyses.
Feedback Feedback is provided on all quiz questions, all questions contained within the computer practicals and both assessments. Additional feedback is available from the course lecturer(s) who are also available during the computer practicals.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)3:00
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
Additional Information
Graduate Attributes and Skills Not entered
Additional Class Delivery Information The course is delivered over 5 weeks in Block 3 of Semester 2, with two sessions each week. Each session includes a lecture and a computer based practical aligned to the lectures. Lectures are a mix of pre-recorded videos and in-person presentations. Past class tests are available and a revision lecture is provided.
KeywordsQGM,statistics,animal model,mixed model,genetic evaluation,genomic evaluation
Course organiserDr Sara Knott
Tel: (0131 6)50 5444
Course secretaryMiss Zofia Bekas
Tel: (0131 6)50 5513
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