Postgraduate Course: Linkage and Association in Genome Analysis (PGBI11086)
|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
|Summary||This course introduces statistical approaches to understand the control of trait variation using information from known DNA variants, such as single nucleotide polymorphisms. These approaches include heritability estimation, QTL mapping using linkage and genome wide association studies and methods that use results from these analyses to predict genetic merit or investigate function.
This is a compulsory course for the Quantitative Genetics MSc programmes, forming part of the foundation for the second semester and builds on the courses delivered in the first semester: Population Genetics (PGBI11124), Quantitative Genetics (PGBI11125) and Statistics and Data Analysis (PGBI11003).
Overview of main topics:
Each week we focus on a different topic starting with an introduction to genomic data, then how it can be used to dissect genetic variation and identify causative loci and finally considering subsequent analyses that can be performed to further our knowledge on the control of complex traits. The methods include heritability estimation, genetic linkage for trait locus mapping and genome wide association studies, we consider study design and the use of imputation and meta-analysis. Finally we introduce post-GWAS approaches such as Mendelian randomisation, polygenic risk scores, LD score regression and the integration of functional information. The course aims to provide the theoretical background to the approaches used and practical experience of performing the analyses.
Students must have taken Population Genetics (PGBI11124) and Quantitative Genetics (PGBI11125), or their equivalent, in order to have a background in genetics and be familiar with the concepts of linkage and linkage disequilibrium, kinship and heritability. We assume that students are familiar with basic statistics including probability, hypothesis testing and estimation using regression, analysis of variance and likelihood. Knowledge of mixed models for genetic data using the relationship matrix is beneficial. Hence we also recommend the courses Quantitative Genetic Models (PGBI11085), Statistics and Data Analysis (PGBI11003). If you haven't taken these courses but are familiar with the background material you should be fine. If you lack some background, it is still possible to succeed in the course but you may have to do additional work/reading to catch up. The data analyses use publicly available software packages, including R. So some familiarity with R would be beneficial.
Assessment is through in-course assessment (60% of the course mark) and formal examination (40%). The coursework involves taking a set of data through a sequence of analyses (introductory analyses, GWAS and post-GWAS) and the analyses and results written as components of a short paper. Altogether there are four assessments with the final assessment being the abstract for the complete study.
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 32,
Supervised Practical/Workshop/Studio Hours 24,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Assessment is through in-course assessment (60% of the course mark) and formal examination (40%).
Coursework ¿ 60% of the course mark: A set of data will be taken through a sequence of analyses (introductory analyses, GWAS and post-GWAS) and the analyses and results written as components of a short paper. Altogether there are four assessments with the final assessment being the abstract for the complete study.
||Written feedback is given for the assessed components of the course. Additional feedback can be sought from the teaching staff, especially during the interactive sessions.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
On completion of this course, the student will be able to:
- critically understand the analytical approaches for the dissection of genetic variation of complex traits and the identification of causative loci
- identify the most appropriate analyses for a problem, being aware of the potential strengths and shortfalls of these techniques, and perform these analyses to understand the control of genetic variation
- present and discuss analyses performed, identifying appropriate information to justify the methods used and explain the results
|Graduate Attributes and Skills
|Additional Class Delivery Information
||We have two sessions a week focussing on the same topic. The topic is introduced through pre-recorded videos. In the first live session we recap the pre-recorded material and tackle a problem related to the content. These problems are usually similar to exam questions. The second session takes place in a computer lab and the aim is to analyse data using the approach being considered that week. This reinforces the theory and gives experience of developing scripts and interpreting results. We encourage interaction, both between students and with the teaching staff, in all sessions.
|Course organiser||Dr Sara Knott
Tel: (0131 6)50 5444
|Course secretary||Miss Zofia Bekas
Tel: (0131 6)50 5513