Handbook home
Statistical Genomics (POPH90124)
Graduate courseworkPoints: 12.5Online
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable (login required)(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: https://study.unimelb.edu.au/
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: 31 January 2024