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Statistical Genomics (MAST30033)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

Year of offer2017
Subject levelUndergraduate Level 3
Subject codeMAST30033
Campus
Parkville
Availability
Semester 2
FeesSubject EFTSL, Level, Discipline & Census Date

This subject introduces the biology and technology underlying modern genomics data, features of the resulting data types including the frequency and patterns of error and missingness, and the statistical methods used to analyse them. It will include hands-on data analysis using R software. The material covered will evolve as genomics technology and practice change, and will span the following four areas: introduction to genomics technology and the resulting data (approx 25% of course), population genetics (approx 20% of course) including stochastic models and statistical inference, association analysis (approx 40% of course) including tests of association and major sources of confounding, and heritability and prediction (approx 15% of course) both in human genetics and for animal and plant breeding.

Intended learning outcomes

On completion of this subject, students should have:

  • Ability to explain the key genomics assays, their purpose and the strengths and limitations of the data generated.
  • An understanding of the role of population genetics theory in interpreting genomics data.
  • Ability to perform a range of association analyses using SNP and sequence data.
  • Awareness of the major problems in association analyses that can lead to false inferences.
  • An understanding of the strengths and weaknesses of SNP-based heritability relative to traditional measures of heritability.
  • Ability to explain the use of statistical models in predicting phenotype from genomic data, and the uses and limitations of genomic prediction.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. In particular

  • computer-based data handling and statistical analysis of large data sets using the R software (students are expected to have some skills at entry but the subject will take them to a higher level)
  • ability to read, understand, modify and use short computer programs
  • time-management: completing assignments according to deadlines while making judgments about time required for different pars of the assignment.

Last updated: 21 June 2017