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Stratified Medicine

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 classification, prediction, and drug design. 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), in silico tools, and Linux.

Module aims - intentions of the module

This module will cover computational and statistical methodologies applied to genetic and phenotypic data to stratify individuals into meaningful groups linked to disease. You will learn about the sources of large-scale phenotype and genomic data (as well as other “-omic” data), their limitations, and apply methodologies for the identification of genetic variation that either cause or predispose people to disease. You will 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. Understand sources, applications, and limitations of phenotypic and “omic” health data
• 2. Apply fundamental concepts in human genetics that underpin analyses of genetic data
• 3. Demonstrate knowledge of methods used to capture genetic data and associated algorithms
• 4. Apply statistical and machine-learning methods to infer sex and ancestry from genetic data
• 5. Apply computational and statistical methods to identify genetic variation associated with susceptibility to common multifactorial diseases through non-sequencing-based studies
• 6. Interpret findings from genetic studies and apply statistical modelling to build genetic predictors of disease
• 7. Apply computational and statistical methods to call genetic variation from sequence data
• 8. Identify mutations likely to be causal for disease through sequencing-based studies
• 9. Manipulate and use data formats designed for storing genetic data - including binary data file

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 10. Interrogation of phenotypic datasets from a variety of sources
• 11. Demonstrate the ability to infer characteristics of biological samples through the incorporation of reference data
• 12. Interrogate major data sources to assess the pathogenic and clinical significance of a sequencing-based result
• 13. Interrogate genetic data to identify genetic variation associated with common disease
• 14. Demonstrate the ability to use genetics as a predictor of common disease risk
• 15. Understand how genetic 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...

• 16. Understand and critically appraise academic research papers in research field
• 17. 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
• Fundamentals of human genetics, including the “central-dogma”, classes of genetic variation, linkage disequilibrium, Hardy-Weinberg equilibrium, and heritability.
• Fundamentals of monogenic syndrome genetics, common disease (non-cancer) genetics, and cancer genetics
• Methods for capturing genetic information from DNA microarrays and quality control.
• Reading/writing of common data formats (including binary) for genetic data on scale.
• Methods for inferring genetic ancestry and sex, and the implications for genetic analyses (e.g. population stratification and quality control)
• Methods for calling genetic variation from human sequence data and quality control.
• Interpreting sequencing-based results for the identification of disease-causing mutations
• 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.
• Fundamentals of pharmacogenetics – using genetics to inform drug development
• Fundamentals of transcriptomics, proteomics, and epigenomics and their application to disease.

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

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.
• Genetics and Genomics in Medicine. Strachan, Goodship and Chinnery. Garland Science.
• Human Genetics and Genomics. Korf and Irons, Wiley-Blackwell.
• Python for Data Analysis: Data Wrangling with Pandas, NumPy and iPython. McKinney, D. O’Reilly.

Indicative learning resources - Web based and electronic resources

https://vle.exeter.ac.uk/course/view.php?id=8443

Key words search

disease, genetic data, genetics, stratification, sequencing, risk score, prediction, omics