Module Aims: This module aims to provide an introduction to genetic epidemiology, to increase students’ awareness of the field and to give them enough knowledge to be able to e.g. read papers, listen to talks or take an informed part in conversations about genetic epidemiology. For those students whose work will include genetic epidemiology, this module should provide a firm foundation for their future learning and research, as well as for attending the Genomics in Action module.
Module Learning Outcomes:
By the end of the module students should be able to:
- Understand genetic variation and the key concepts and vocabulary currently used in genetic epidemiology.
- Critically read and analyse simple reports of genetic epidemiological studies.
- Appreciate the special features of genetic data (e.g. linkage disequilibrium, confounding by population stratification) that require attention in the design, analysis and interpretation of different types of genetic epidemiology studies.
- Understand the role of genetic factors as determinants of disease, and how different types of genetic epidemiological studies can be used to study diseases.
- Appreciate some of the complexities around how genomic data is handled and interpreted.
- Conduct a simple genome-wide association study, including quality control and interpretation of results.
- Understand the fundamentals of post-GWAS follow-up methods, such as genetic fine-mapping, phenome-wide association studies, colocalisation and functional genomics
- Understand the main principles and uses of Mendelian randomisation.
- Understand the main principles involved in using genetic data for risk modelling, including the use of genetic risk-modelling software.
- Appreciate the relevance of ancestry and diversity in genetic studies, as well as the biases that may be caused and efforts to mitigate them.
Pre-requisites: Principles of Epidemiology, Principles of Biostatistics, and Applied Data Analysis. A reasonable understanding of fundamental epidemiological and biostatistical concepts, such as case-control studies, bias, reverse causation, confounding, correlation, hypothesis-testing and statistical power.
Teaching Strategy: A combination of lectures (mostly including short interactive aspects) and computer practicals.
Assessment: Individual written assignment, to be handed in 1 week after the module concludes. The assignment will involve conducting some simple statistical and bioinformatic analysis on a provided dataset, based on concepts and methods introduced during the taught and practical sessions. The assignment will be summarised in a ‘technical report’ that describes the methods used, a concise description of the results (using display items as appropriate) and interpretation of the findings.
Module Length: 4 days