Postgraduate Course: Population Genomic Analysis (PGBI11126)
Course Outline
| School | School of Biological Sciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | Population genomic analyses are used to tackle a wide range of fundamental questions in evolutionary biology. Genome scale comparisons of genetic diversity have not only revolutionized our understanding of our own history but are increasingly used in various applied contexts ranging from medical and crop genetics to the modelling of disease outbreaks. This course covers the core concepts of modern population genomic analysis which focuses on modelling the ancestry of samples of genomes. It covers both the mathematical models and computational algorithms that describe the ancestry of genomes in evolving populations and shows how these are applied in practice to make inferences about the interplay of evolutionary forces (genetic drift, recombination, selection and demographic history) from genome sequence data using hands-on data examples. |
| Course description |
The course assumes a basic understanding of population genetics and is taught as a series of computer practicals which are delivered as interactive jupyter notebooks. The focus is on sample based population genomics which models the ancestral relationships of genomes and the genetic variation these contain. The course includes a detailed exposition of the coalescent, the canonical model of sample ancestry and the relevant data structures (genealogies, tree-sequences and graphs) for describing genetic ancestry. A major aim of the course is to understand how this basic stochastic model can i) be extended to include all fundamental evolutionary forces (recombination, population structure, admixture and natural selection) and ii) is used to make inferences from modern day samples using mathematical analysis, phylogenetics and simulation approaches.
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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 2022/23, Not available to visiting students (SS1)
|
Quota: 30 |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 30,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Weekly ICAs in the form of a short quiz (numeric and MCQ) on Learn (25% of the mark in«br /»
total). 75% of the mark will be based on a timed computer practical (class exam) in week 5. |
| Feedback |
Students will receive feedback on their weekly ICA. This will be held at the start of each practical and also gives students the opportunity to have any questions they might have about the lecture content (a Q&A). The formative assessment will be the weekly computer practicals (feedback will be given during the practical and model answers/analyses will be released at the end of each practical).
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe and visualize population genetic variation in whole genome sequence data using population genetic summary statistics/estimators, model based inference based on genealogies and non-parametric methods (e.g. PCA)
- Have a firm grasp of the stochastic models and data structures that describe the ancestral relationships of genomes
- Interpret genetic variation in terms of the fundamental evolutionary forces. In particular be able to make statistical inferences about past demography and selection
- Evaluate conclusions drawn from population genomic analysis in relation to both fundamental and applied questions in biology
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Additional Information
| Graduate Attributes and Skills |
Cognitive skills (evaluation & critical analysis of BIG data)
Numeracy and IT skills (mathematical/statistical analysis, bioinformatic workflows and simulation algorithms needed to solve complex data analysis)
Autonomy, accountability and working with others |
| Keywords | Population Genomic Analysis,Biology,Quantitative Genetics and Genomic Analsyis |
Contacts
| Course organiser | Dr Konrad Lohse
Tel: (0131 6)50 7335
Email: |
Course secretary | Miss Zofia Bekas
Tel: (0131 6)50 5513
Email: |
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