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Unit of study_
BSTA5013: Statistical Genomics (SGX)
2024 unit information
Statistical genomics is the application of statistical methods to understand genomes, their structure, function and history, in many different scientific contexts, including understanding biological mechanisms in health and disease. Statistical genomics is characterised by large datasets, high-dimensional regression models, stochastic processes, and computationally-intensive statistical methods. The aim of this unit is to learn about relevant biology and terminology, to understand the most important mathematical models and inference methods in statistical genetics, to be able to test for association between genetic variants and outcomes of interest, and to use genome-wide statistical models to help understand the genetic mechanisms underlying a trait and to predict outcomes. The statistical package R will be used to perform regression-based analyses of genetic data.
Unit details and rules
Managing faculty or University school:
Medicine and Health
| Study level | Postgraduate |
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| Academic unit | Public Health |
| Credit points | 6 |
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Prerequisites:
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BSTA5004 and (BSTA5210 or BSTA5211 or BSTA5007) |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Describe core mechanisms and central dogma of genetics
- LO2. Perform sequence analysis using hidden Markov models
- LO3. Access genomic data from public databases
- LO4. Perform a genetic association analysis, including the assessment of possible confounding
- LO5. Explain the concept of heritability and its estimation
- LO6. Use genome-wide SNP data to develop prediction models
- LO7. Explain key features of data and statistical models used in the fields of transcriptomics, epigenetics, and single-cell omics
- LO8. Effectively communicate results of statistical analyses in genomics and related areas
Unit availability
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Modes of attendance (MoA)
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