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Applied Computational Genomics (CHME0034)

Key information

Faculty
Faculty of Population Health Sciences
Teaching department
Institute of Health Informatics
Credit value
15
Restrictions
This an optional module for students on the MSc Health Data Science and MRes in Artificial Intelligence in Enabled Healthcare.
Timetable

Alternative credit options

There are no alternative credit options available for this module.

Description

In this module, students with limited or no prior knowledge of genetics will learn how to curate and quality control genetic data from various platforms (such as SNP-microarray and next generation sequencing), perform a range of applied analysis methods, critically interpret and evaluate results and relate findings to current knowledge from literature and public databases. The focus of the module is on current applied methodologies within the field of human genetics (discovery and genetic epidemiology), with a strong emphasis on practical application of methods and genetics software, alongside lectures introducing the needed background knowledge. This is an intensive practical course for anyone wanting to have hands-on experience of application of the latest methodology within human genetics.

By the end of the module you should be able to:

  • Understand the major sources of genetic variation in the genome
  • Perform and understand importance of quality control of genetic genotyping output
  • Infer the genetic architecture of a trait using results from association studies and whole genome approaches.
  • Design and analyse a basic genome-wide association study using appropriate software.
  • Consider population structure and its application to genetic association studies.
  • Work with a range of genetic data types: e.g. genotyped, imputed, sequenced, polygenic risk scores.
  • Utilise online bioinformatic resources to explore the functional properties of genetic variation.
  • Apply results from genetic association studies to investigate genetic correlation and causality in epidemiological associations.

You will learn though a combination of lectures, invited speakers, computer-based practicals and group work.

Module deliveries for 2024/25 academic year

Intended teaching term: Term 2 ÌýÌýÌý Postgraduate (FHEQ Level 7)

Teaching and assessment

Mode of study
In person
Methods of assessment
100% Coursework
Mark scheme
Numeric Marks

Other information

Number of students on module in previous year
52
Module leader
Dr Johan Thygesen
Who to contact for more information
ihi.education@ucl.ac.uk

Last updated

This module description was last updated on 8th April 2024.

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