Stratified Medicine
| Module title | Stratified Medicine |
|---|---|
| Module code | HPDM098 |
| Academic year | 2025/6 |
| Credits | 30 |
| Module staff | Professor Andrew Wood (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 0 | 10 | 0 |
| Number students taking module (anticipated) | 20 |
|---|
Module description
Genetic and phenotypic health data are becoming available in millions of people from around the world, through health care systems (including the NHS) and large-scale biobanks (e.g. UK Biobank). These data are being used to predict disease risk and health outcomes, and to separate (stratify) groups of individuals based on these features. In this module you will learn how these data are used in disease prediction and classification. You will apply and develop statistical models and computational algorithms for the analysis of these data for patient stratification. In this module you will use Python, statistical programming languages (e.g. R), database technologies (e.g. SQLite), in silico command line tools, and Linux.
Module aims - intentions of the module
This module will cover computational and statistical methodologies applied to phenotypic and genetic data to stratify individuals into meaningful groups linked to disease. You will learn about the sources of large-scale phenotype and genomic data (and their limitations), , data storage methods (including binary data handling, and database platforms and SQL), and the computational and statistical methodologies used to stratify individuals into groups at higher risk of disease.
You will also be taught fundamental concepts in human genetics that underpin common analyses of genetic data and learn how to interpret findings from these analyses. You will gain insight into how these findings can be used in drug development. Theoretical sessions will be followed by practical workshops and assessments.
On this module we will also update the importance of data security and management, including how the FAIR principles apply in genetic data – Findable, Accessible, Interoperable and reusable. https://www.go-fair.org/fair-principles/ , that will include, for example, use of GitHub and other data repositories.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Demonstrate knowledge of sources, applications, and limitations of phenotypic and genetic health data
- 2. Analyse cross-sectional and temporal individual-level data related to human health to identify groups of individuals at higher risk of disease
- 3. Demonstrate knowledge of techniques used for the data storage of large-scale human health dataset, including binary data processing and database querying
- 4. Apply fundamental concepts in human genetics that underpin analyses of genetic data
- 5. Demonstrate knowledge of methods used to capture genetic data and associated algorithms
- 6. Apply statistical and machine-learning methods to infer sex and genetic ancestry from genetic data
- 7. Apply computational and statistical methods to identify genetic variation associated with susceptibility to common multifactorial diseases
- 8. Interpret findings from genetic studies and apply statistical modelling to build genetic predictors of disease
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 9. Interrogation of phenotypic datasets from a variety of sources and formats
- 10. Interrogate major sources of health data to identify groups of individuals at higher risk of disease
- 11. Demonstrate the ability to infer characteristics of biological samples through the incorporation of reference data
- 12. Interrogate genetic data to identify genetic variation associated with common disease
- 13. Demonstrate the ability to use genetics as a predictor of common disease risk
- 14. Understand how analytical code for health data is managed and tools made available, through resources such as GitHub
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 15. Understand and critically appraise academic research papers in research field
- 16. Communicate findings from computational and statistical analyses effectively with peers, tutors and the wider public
Syllabus plan
Whilst the module's precise content may vary from year to year, an example of an overall structure is as follows:
• Overview of stratified medicine.
• Sources, applications, and limitations of phenotypic and genetic health data
• Analysis of health data sources (e.g. Hospital Episode Statistics) for defining disease status
• Methods for identifying patterns in longitudinal data from primary care data and hospital records
• Analysis of wearable devices (accelerometers) to define patterns of physical activity
• Fundamentals of binary data and use for health data compression
• Fundamentals of database platforms for data storage and SQL language for data extraction
• Fundamentals of human genetics, including the “central-dogma”, classes of genetic variation, linkage disequilibrium, Hardy-Weinberg equilibrium, and heritability.
• Fundamentals of monogenic syndrome genetics and common disease genetics
• Methods for capturing genetic information from DNA microarrays and quality control.
• Methods for inferring genetic ancestry and sex, and the implications for genetic analyses (e.g. population stratification and quality control)
• Methods for identifying genetic variants associated with common diseases and risk factors (regression-based genome-wide association analyses and meta-analysis).
• Utilising genetic associations and statistical feature selection to build and evaluate genetic risk scores for disease prediction.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 70 | 230 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning & Teaching activities | 20 | Lectures |
| Scheduled Learning & Teaching activities | 50 | Computer lab workshops |
| Guided independent study | 150 | Coursework and associated preparation |
| Guided independent study | 80 | Background reading |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Computer lab exercises | 30 minutes | All | Oral staff and peer feedback |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Coursework 1: Using phenotypic data to stratify individuals into high-risk groups for disease | 40 | Code + 1000-word report | 1-3,9,10,14-16 | Written |
| Coursework 2: Using genetics to stratify individuals into sub-diabetes groups. | 60 | Code + 1000-word report | 3-16 | Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Coursework 1: Using phenotypic data to stratify individuals into high-risk groups for disease 40% | Coursework 1: Using phenotypic data to stratify individuals into high-risk groups for disease (Code + 1000-word report) | 1-3,9,10,14-16 | Typically within six weeks of the result |
| Coursework 2: Using genetics to stratify individuals into sub-diabetes groups. 60% | Using genetics to stratify individuals into sub-diabetes groups. (Code + 1000-word report) | 3-16 | Typically within six weeks of the result |
Re-assessment notes
Please refer to the TQA section on Referral/Deferral: http://as.exeter.ac.uk/academic-policy-standards/tqa-manual/aph/consequenceoffailure/
Indicative learning resources - Basic reading
- Essential Medical Statistics. Kirkwood and Stern, Blackwell Science. (Available online: http://encore.exeter.ac.uk/iii/encore/record/C__Rb3519976 )
- Genetics and Genomics in Medicine. Strachan, Goodship and Chinnery. Garland Science. (Available online: http://encore.exeter.ac.uk/iii/encore/record/C__Rb4104793 )
- Python for Data Analysis: Data Wrangling with Pandas, NumPy and iPython. McKinney, D. O’Reilly. (Available online: http://encore.exeter.ac.uk/iii/encore/record/C__Rb4069046 )
Indicative learning resources - Web based and electronic resources
Key words search
Disease, hospital data, primary care data, accelerometer, genetic data, genetics, stratification, sequencing, risk score, prediction, databases
| Credit value | 30 |
|---|---|
| Module ECTS | 15 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 13/12/2019 |
| Last revision date | 24/04/2023 |
