Genetics University — Research, Education, Medical Genetics

Courses & programmes

Advanced education in genomic science

Explore evidence-based curricula for clinicians, researchers, laboratory professionals and students. Every offering connects disciplinary foundations with applied case studies, laboratory work or reproducible data analysis.

Modern lecture hall for scientific education

4

Advanced programmes

6

Academic courses

Flexible

Modular professional study

Specialised programmes

From methodology to responsible application

These programmes are designed as coherent learning pathways. They progress from disciplinary and methodological foundations through quality control to interpretation, documentation and critical appraisal of evidence. The published content allows prospective participants and institutional partners to review scope, requirements and learning outcomes transparently before making an enquiry.

GenomicsGU-G500

Medical & Clinical Genomics

Sequencing-based diagnostics from raw reads to a defensible clinical report, taught through worked cases.

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Format
Advanced Programme
Duration
12 weeks
Workload
120 hours
Pacing
Modular, 10 hours per week

Target audience

Clinicians, laboratory scientists and graduate students working with diagnostic sequencing data.

Prerequisites

  • Foundational human genetics equivalent to GU-101.
  • Basic command-line familiarity.

Learning objectives

  • Design and quality-control a diagnostic sequencing workflow.
  • Classify sequence variants against established evidence frameworks.
  • Write a clinical report that states evidence strength and residual uncertainty.
  • Apply consent and data-protection requirements to genomic results.

Curriculum & applied work

  1. 01

    Genome architecture and variant classes

    Reference genomes, coordinate systems, SNVs, indels, structural and repeat variation.

    Computational exercise: annotating a variant set against GRCh38.

  2. 02

    NGS pipelines and quality control

    Library preparation, alignment, coverage, duplication, batch effects, joint calling.

    Laboratory-linked assignment: reading QC reports and setting acceptance thresholds.

  3. 03

    Variant interpretation

    Evidence criteria, population frequency filtering, segregation, functional and computational evidence.

    Case studies: five variants classified with written justification.

  4. 04

    Clinical cases and reporting

    Phenotype-driven filtering, secondary findings, reanalysis and reclassification.

    Case study: full diagnostic report for an undiagnosed paediatric case.

  5. 05

    Ethics, consent and governance

    Broad and dynamic consent, the right not to know, cross-border data transfer, equity in reference data.

    Written position paper on a contested reporting scenario.

EpigeneticsGU-E430

Translational Epigenomics & Chromatin Biology

How chromatin state is measured, interpreted and targeted therapeutically, with emphasis on assay validity.

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Format
Executive Masterclass
Duration
10 weeks
Workload
90 hours
Pacing
Modular, 9 hours per week

Target audience

Researchers and clinician-scientists working on gene regulation, oncology or developmental disorders.

Prerequisites

  • Molecular biology at graduate level.
  • Familiarity with sequencing data concepts.

Learning objectives

  • Select the appropriate epigenomic assay for a given biological question.
  • Interpret methylation and chromatin data with attention to confounding.
  • Evaluate the evidence base for epigenetic therapeutic targets.

Curriculum & applied work

  1. 01

    DNA methylation

    CpG landscapes, imprinting, array versus bisulfite sequencing, epigenetic clocks and their limits.

    Computational assignment: differential methylation analysis with cell-composition correction.

  2. 02

    Histone modifications and chromatin state

    Writers, readers and erasers, ChIP-seq and CUT&RUN, chromatin state segmentation.

    Laboratory assignment: designing controls for a chromatin profiling experiment.

  3. 03

    Non-coding RNA regulation

    miRNA, lncRNA and enhancer RNAs, mechanisms of action and the evidence required to claim one.

    Critical appraisal of two contradicting ncRNA mechanism papers.

  4. 04

    Three-dimensional genome organisation

    Topologically associating domains, enhancer-promoter contacts, Hi-C and derived methods.

    Computational assignment: interpreting a structural variant that disrupts a domain boundary.

  5. 05

    Therapeutic targeting

    Hypomethylating agents, HDAC and BET inhibitors, biomarkers of response, trial evidence.

    Case study: appraising an epigenetic therapy in haematological malignancy.

Multi-OMICSGU-M620

Integrated Systems Biology & Multi-Omic Analytics

Statistical integration of genomic, transcriptomic, proteomic and metabolomic layers into interpretable models.

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Format
Advanced Programme
Duration
14 weeks
Workload
140 hours
Pacing
Modular, 10 hours per week

Target audience

Computational researchers and data-oriented clinician-scientists building multi-layer analyses.

Prerequisites

  • Working knowledge of R or Python.
  • Applied statistics including regression and multiple testing.

Learning objectives

  • Harmonise heterogeneous omic datasets with documented provenance.
  • Apply and compare integration methods without overstating inferred causality.
  • Build reproducible, containerised multi-omic workflows.

Curriculum & applied work

  1. 01

    Data layers and harmonisation

    Normalisation across platforms, batch correction, missingness and identifier mapping.

    Computational assignment: harmonising three public cohort datasets.

  2. 02

    Genomics-transcriptomics integration

    eQTL mapping, allele-specific expression, colocalisation and transcriptome-wide association.

    Computational assignment: colocalisation analysis for a disease locus.

  3. 03

    Proteomic and metabolomic layers

    Protein and metabolite quantification, correlation with transcript levels, pQTL and mQTL analysis.

    Computational assignment: cross-layer correlation with confounder adjustment.

  4. 04

    Single-cell and spatial analysis

    Clustering, annotation, trajectory inference, multimodal single-cell integration, spatial context.

    Computational assignment: annotating a multimodal single-cell dataset.

  5. 05

    Pathway and network modelling

    Enrichment methods, network inference, latent factor models, causal inference and its assumptions.

    Case study: building and critiquing a multi-omic model of a disease pathway.

  6. 06

    Reproducibility and reporting

    Workflow managers, containers, versioned data, sharing and FAIR practice.

    Final project: a containerised, peer-reviewed integration workflow.

ProteomicsGU-P450

Clinical Proteomics & Structural Genomics

Mass spectrometry, protein structure and biomarker evidence, from instrument output to clinical validation.

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Format
Professional Certificate
Duration
10 weeks
Workload
100 hours
Pacing
Modular, 10 hours per week

Target audience

Laboratory scientists, translational researchers and clinicians evaluating protein-based diagnostics.

Prerequisites

  • Biochemistry at graduate level.
  • Introductory statistics.

Learning objectives

  • Choose and justify a proteomic acquisition strategy for a clinical question.
  • Interpret structural evidence for the effect of a missense variant.
  • Assess a candidate biomarker against validation and reporting standards.

Curriculum & applied work

  1. 01

    Mass spectrometry in medicine

    Sample preparation, data-dependent versus data-independent acquisition, quantification strategies.

    Laboratory assignment: designing a plasma proteomics acquisition plan.

  2. 02

    Post-translational modifications

    Phosphorylation, glycosylation and ubiquitination, enrichment methods, site-level false discovery.

    Computational assignment: reanalysing a phosphoproteomic dataset.

  3. 03

    Structure and variant effect

    Crystallography, cryo-EM, predicted structures, confidence metrics and their misuse in variant calls.

    Case study: structural evidence for and against pathogenicity of a missense variant.

  4. 04

    Protein-protein interaction networks

    Affinity purification, proximity labelling, interaction databases and their confidence scores.

    Computational assignment: constructing and filtering an interaction subnetwork.

  5. 05

    Biomarker discovery and validation

    Discovery cohorts, targeted verification, clinical assay validation, regulatory and reporting standards.

    Case study: appraising a published biomarker claim against validation criteria.

Academic courses

Additional structured study

Focused courses develop defined competencies for different audiences. Learning outcomes, modules and assessment are stated transparently for every offering.

GU-101Foundation · 12 weeks

Foundations of Human Genetics

From Mendelian inheritance to genome structure, with an emphasis on reasoning from evidence.

Content

  1. 01Genome structure and variation. Chromosomes, genes, regulatory elements and the classes of human genetic variation.
  2. 02Inheritance and pedigrees. Monogenic patterns, penetrance, expressivity and mosaicism.
  3. 03From genotype to phenotype. Molecular mechanisms linking sequence change to clinical presentation.
  4. 04Reading the literature. Study design, effect size, replication and the limits of published claims.

Learning outcomes

  • Describe the organisation of the human genome at sequence and chromosome scale.
  • Apply inheritance patterns to pedigree interpretation.
  • Distinguish correlation from causation in genetic association claims.

Assessment: Continuous problem sets and a written final examination.

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GU-320Advanced · 8 weeks

Clinical Variant Interpretation

Applying established classification frameworks to sequence variants in diagnostic practice.

Content

  1. 01Evidence frameworks. Criteria, evidence strength and the Bayesian view of classification.
  2. 02Population data. Allele frequency filtering and the pitfalls of reference bias.
  3. 03Functional and computational evidence. Assay validity, predictor calibration and appropriate weighting.
  4. 04Reporting and communication. Report structure, uncertain results and communicating with referring teams.

Learning outcomes

  • Assemble and weigh evidence lines for a candidate variant.
  • Document a defensible classification and its uncertainty.
  • Plan reanalysis and reclassification over time.

Assessment: Portfolio of classified cases with written justification.

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GU-410Graduate · 10 weeks

Computational Genomics

Reproducible analysis of sequencing data, from alignment to statistical inference.

Content

  1. 01Sequencing data and quality control. Read alignment, coverage, duplication and batch effects.
  2. 02Variant discovery. Short variants, structural variants and joint calling.
  3. 03Statistical inference. Association testing, multiple comparison control and confounding.
  4. 04Reproducibility. Workflow managers, containers, provenance and data sharing.

Learning outcomes

  • Build reproducible variant-calling workflows.
  • Evaluate quality metrics and sources of technical artefact.
  • Interpret association results with appropriate correction.

Assessment: Reproducible analysis project with peer review.

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GU-260Intermediate · 6 weeks

Communicating Genetic Risk

How probabilistic genetic findings are understood, misunderstood and best conveyed.

Content

  1. 01Risk literacy. Natural frequencies, framing effects and numeracy.
  2. 02Uncertain results. Variants of uncertain significance and secondary findings.
  3. 03Family implications. Cascade testing, consent and the duty to inform relatives.

Learning outcomes

  • Translate absolute and relative risk into accessible language.
  • Recognise common misinterpretations of genetic results.
  • Structure a disclosure conversation.

Assessment: Recorded simulated consultation with structured feedback.

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GU-010Open access · 4 weeks

Understanding Your Genome

A short, non-technical course on what genetic tests measure and what they do not.

Content

  1. 01What a genome is. Cells, chromosomes, genes and variation between people.
  2. 02What tests can tell you. Test types, accuracy and the meaning of a negative result.
  3. 03Privacy and consent. Who holds genetic data and what rights you have.

Learning outcomes

  • Explain the difference between screening and diagnosis.
  • Evaluate claims made by consumer genetic products.

Assessment: Optional self-assessment quizzes.

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GU-350Advanced · 6 weeks

Ethics of Genomic Medicine

Consent, data governance and equity in the application of genomic technologies.

Content

  1. 01Consent and autonomy. Broad consent, dynamic consent and the right not to know.
  2. 02Data governance. Access committees, federated analysis and cross-border transfer.
  3. 03Equity. Representation in reference data and the transferability of findings.

Learning outcomes

  • Analyse consent models for broad genomic data use.
  • Assess equity implications of ancestry-biased resources.

Assessment: Written position paper.

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