MSc Genomic Medicine

This module will provide an introduction to the basis of genotyping and detection of genetic variation. There will be an introduction to deep resequencing, including library preparation methods, sequencing chemistries and platforms. This module will provide a brief overview of methodologies for, and application of, detecting genetic changes including base substitutions (SNV) and copy number variants (CNV). This module will provide an overview of bioinformatics approaches and pipelines for the analysis of genomic data. In addition there will be an introduction to different ’omics technologies and approaches, and strategies to prioritise the pathogenicity of variants.

By the end of this module the student will be able to:

• Have a fundamental knowledge and understanding of the basis of genotyping and detection of genetic variation.
• Have a basic understanding of, and be able to apply, bioinformatics approaches to the analysis and contextualization of genomic data.
• Understand which methodology to utilise to detect different types of genetic variants, and be able to interpret sequencing data and contextualise genetic variants with regard to likely pathogenicity.
• Be able to use literature and online resources to access information on disease and genetic variation.
• Describe techniques that can be applied to transcriptomics, metabolomics and proteomic analysis.

The course contains ~30 hours lectures, including pre-recorded content, tutorials and small group workshops.

• Describe and critically evaluate a range of up-to-date genomic technologies and platforms used to sequence targeted parts of the genome or whole genomes.
• Annotate and interpret variants with regard to likely pathogenicity.
• Discuss the application of other techniques commonly used to interrogate genomic variation in the clinical setting.
• Appraise technology platforms for applications in medical genomics either for research or medical diagnostic purposes.
• Use publicly available bioinformatics approaches and tools to determine the likely pathogenicity of a sequence variant.
• Describe techniques that can be applied to transcriptomics, metabolomics and proteomic analysis.

 

Critically evaluate the different ‘omics’ technologies and platforms and their application to genomic medicine and the impact of personalised medicine.

• Discuss and critically appraise approaches to the bioinformatics analysis and interpretation of ‘omics’ data
• Evaluate the pathogenicity of variants identified in whole genome sequencing and other genomic technologies

• Develop their problem solving skills through collaboration in group working and debate.
• Enhance their oral and written presentation skills.

Formative feedback will be provided through computer workshops, small group discussions and feedback on group oral presentations

Includes but not limited to:

•    A global reference for human genetic variation. Nature. 2015 (526):68-74.
•    National Human Genome Research Institute Fact Sheets:www.genome.gov/about-genomics/fact-sheets/
•    100,000 Genomes Project Pilot Investigators. 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report. N Engl J Med. 2021 Nov 11;385(20):1868-1880.
•    ENCODE Project Consortium, Nature. 2012 (489):52-75.
•    Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Rizvi, AH, et al. Nat Biotechnol. 2017. 35(6): 551-560.
•    Emerging applications of metabolomics in drug discovery and precision medicine. Wishart DS. Nature Rev Drug Discov. 2016 Jul;15(7):473-84
•    Diagnostic gene sequencing panels – from design to report – technical standard of ACMG. Bean LJH. Genetics in Medicine. 2020. 22(3). 453-461.
•    Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Richards S et al. Genet Med. 2015 May;17(5):405-24.
•    ACGS Best Practice Guidelines for Variant Classification in Rare Disease 2020 v4.01. Ellard S. et al. https://www.acgs.uk.com/quality/best-practice-guidelines/