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Statistical Genomics (POPH90124)
Graduate courseworkPoints: 12.5Online
Please refer to the return to campus page for more information on these delivery modes and students who can enrol in each mode based on their location.
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 2
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Overview
Availability | Semester 2 - Online |
---|---|
Fees | Look up fees |
Statistical genomics is the application of statistical methods to understand genomes, their structure, function and evolutionary history, in many different scientific contexts, including: understanding biological mechanisms in health and disease, predicting outcomes and identifying individuals and their relatedness. Bioinformatics is an overlapping term that suggests more emphasis on data management and software pipelines. The course will also cover aspects of Genomic epidemiology, an overlapping field in which statistical genomics methods are used with family or population data to study causes of disease.
Intended learning outcomes
Knowledge
Upon completion of this subject, students should be able to:
- Describe core mechanisms of genetics, including mutation, recombination and selection;
- Explain the concept of heritability and its estimation; and
- Explain key features of data and statistical models used in the fields of transcriptomics, epigenetics, microbiome analysis and phylogenetics.
Skills
Upon completion of this subject, students should be able to:
- Use the Wright-Fisher and coalescent models of population genetics for simulation and inference;
- Perform sequence analysis using hidden Markov models;
- Perform a genetic association analysis, including the assessment of possible confounding; and
- Access and interpret genomic data from public online resources.
Apply Knowledge & Skills
Upon completion of this subject, students should be able to:
- Use genome-wide SNP data to develop prediction models.
Generic skills
On completion students should have developed independent problem solving, facility with abstract reasoning, clarity of written expression, sound communication of technical concepts.
Last updated: 3 November 2022
Eligibility and requirements
Prerequisites
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90101 | Introduction to Statistical Computing | Semester 1 (Dual-Delivery - Parkville) |
12.5 |
POPH90018 | Data Management & Statistical Computing | No longer available |
AND
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90100 | Inference Methods in Biostatistics | March (Dual-Delivery - Parkville) |
12.5 |
POPH90017 | Principles of Statistical Inference | No longer available |
AND
One of
Code | Name | Teaching period | Credit Points |
---|---|---|---|
MAST90102 | Linear Regression | Semester 2 (Dual-Delivery - Parkville) |
12.5 |
POPH90120 | Linear Models | No longer available |
Corequisites
None
Non-allowed subjects
None
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 3 November 2022
Assessment
Description | Timing | Percentage |
---|---|---|
Assignment 1. Mixture of short answer and simulation using R, with interpretation.
| Week 3 | 20% |
Assignment 2. Mixture of short answer, data analysis, simulation using R, with interpretation.
| Week 7 | 20% |
Assignment 3. Mixture of short answer, data analysis and/or simulation using R, with interpretation.
| Week 11 | 20% |
Take-home exam. Mixture of short essay, data analysis and/or simulation using R, with interpretation. Retrieval and interpretation of online genomics resources.
| During the examination period | 40% |
Last updated: 3 November 2022
Dates & times
- Semester 2 - Online
Coordinator David Balding Mode of delivery Online Contact hours None: Subject is taught wholly on line. Total time commitment 170 hours Teaching period 26 July 2021 to 24 October 2021 Last self-enrol date 6 August 2021 Census date 31 August 2021 Last date to withdraw without fail 24 September 2021 Assessment period ends 19 November 2021 Semester 2 contact information
Melbourne School of Population and Global Health
OR
Currently enrolled students:
- General information: https://ask.unimelb.edu.au
- Email: Contact Stop 1
Future Students:
- Further Information: MSPGH Website
- Email: Enquiry Form
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
Recommended texts and other resources
The Handbook of Statistical Genomics, D Balding, J Marioni, I Moltke Eds, 4th edition, Wiley 2019. Available online or in print.
Special Computer Requirements: "R" (freeware - coordinator will give instructions on how to download)
Resources Provided to Students: Printed course notes and assignment material will be provided to students via post.
- Subject notes
This subject is delivered online via our partners in the Biostatistics Collaboration of Australia (www.bca.edu.au). It is not generally available in the Master of Public Health nor in any program outside the Melbourne School of Population and Global Health (MSPGH).
- Related Handbook entries
This subject contributes to the following:
Type Name Course Graduate Diploma in Biostatistics Course Master of Biostatistics - Links to additional information
Last updated: 3 November 2022