2024/25 Taught Postgraduate Module Catalogue

OGDS5203M Big Data: Rare and Common Disorders

15 creditsClass Size: 150

Module manager: Dr Carmel Toomes
Email: c.toomes@leeds.ac.uk

Taught: 1 Mar to 30 Apr, 1 Mar to 30 Apr (2mth)(adv yr) View Timetable

Year running 2024/25

Pre-requisite qualifications

Students are required to meet the programme entry requirements prior to studying the module.

Module replaces

None

This module is not approved as an Elective

Module summary

This module will provide insight into the way big data are impacting our understanding of human disease and the development of therapies. It will focus on a variety of disorders ranging from rare Mendelian disease to common disorders of complex aetiology. The content will be delivered by focusing on exemplar cases and will take the students on the journey from defining the medical problem and understanding the disease mechanisms, all the way to the development of treatments and therapies. Topics reviewed will include gene replacement therapy, pharmacogenetics, phenotype-genotype correlations, patient datasets, whole genome sequencing, antisense oligonucleotide (AON)-based therapies and CRISPR-mediated gene editing. At the end of this course, students should have a thorough understanding of the exciting way big data is influencing medical research and practice today, and what opportunities it offers for the future, but they will also have an appreciation of the problems and limitations that it brings.

Objectives

The objectives of this module are to demonstrate how the latest technologies and datasets are impacting on biomedical and clinical research, and leading to precision therapies, through the use of real-life examples ranging from rare Mendelian diseases to common complex conditions.

Learning outcomes
On successful completion of the module students will be able to:

1. Describe the molecular basis of a range of Mendelian and complex human disorders;
2. Demonstrate advanced knowledge of how different types of large data are utilised to research rare and complex disease including whole exome and genome sequencing, genome-wide association studies, whole genome linkage/autozygosity mapping, transcriptomics and patient datasets;
3. Evaluate the approaches taken to analysing a range of big data outputs;
4. Describe the theoretical basis and application of different forms of molecular therapies including gene replacement therapy, antisense oligonucleotide (AON)-based therapies, CRISPR-mediated gene editing and genotype-guided drugs;
5. Critically appraise recent scientific literature in the field of human genetics, functional genomics and precision medicine.

Skills outcomes
Knowledge and understanding of:

- Mendelian and complex human disorders
- Molecular genetic techniques and datasets
- Molecular therapeutics
- Awareness of ethical issues in inherited disease

Syllabus

Indicative content for this module includes:

1. Big data in biology
2. Next Generation Sequencing Data Analysis (Part I)
3. Next Generation Sequencing Data Analysis (Part II)
4. Complex disorders
5. Complex inheritance
6. Precision Therapies

Teaching methods

Delivery type	Number	Length hours	Student hours
Discussion forum	6	2.00	12.00
WEBINAR	1	1.50	1.50
WEBINAR	5	1.00	5.00
Independent online learning hours			42.00
Private study hours			89.50
Total Contact hours			18.50
Total hours (100hr per 10 credits)			150.00

Private study

Across each week of learning students will actively engage with pre-prepared teaching and learning resources which scaffold learners to achieve learning outcomes (independent online learning). Each week follows a set pattern of acquiring knowledge which is then applied to a substantive activity which will usually be authentic to real-world application. Weekly asynchronous discussions (such as discussion boards) allow for peer-to-peer and peer-to-tutor discussion which supports completion of the substantive activity. At the end of each week of learning students consolidate their learning through reflective activities and a weekly live webinar session with the module tutor. Each unit also provides students with the opportunity for exploration and self-directed learning as is expected at masters level (private study).

Opportunities for Formative Feedback

The module’s digital learning materials provide regular opportunities for participants to check their understanding and gain feedback (e.g., case studies with short answer questions and automated feedback, MCQs with detailed feedback on correct/incorrect answers).

The individual unit webinars and discussion forums provide opportunities for formative feedback from peers and tutors.

The questions and exercises in the summative assessments (practical and open book exam) will be in the same format as those provided in select units so the students will be familiar with the question styles and will have had the opportunity to ask the teaching staff questions before the final assessment.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Online Test	30.00
Practical	At the end of the module, students will be required to analyse a dataset and submit a formal report. This will include their results and short questions/tasks to test their understanding on an individual basis.	70.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

There is no reading list for this module

Last updated: 18/11/2024

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