Statistical Genomics (SGX)

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.


Annual Availability

SGX is delivered in alternate (odd) years.

Coordinator

Prof David Balding, Melbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne


COORDINATORS:
Prof David Balding Melbourne Integrative Genomics, School of BioSciences and School of Mathematics & Statistics, University of Melbourne Semester 2
General outline

Prerequisites

Mathematical Background for Biostatistics, Data Management and Statistical Computing, Probability and Distribution Theory, Principles of Statistical Inference, Linear Models

Time commitment

8-12 hours total study time per week

Semester availability

Semester 2 in year of delivery.

Assessment

Assignments 60%: three written assignments, each worth 20% and a final assignment (at-home) written examination 40%.

Prescribed Texts

Handbook of Statistical Genomics (Eds: Balding, Marioni and Moltke, 4th ed, Wiley 2019). This is an expensive reference that few will be able to buy, but online access should be available through your university library; if not, arrangements will be made. For details, including ISBN, see the BCA Textbook and Software Guide

Special Computer Requirements

“R” (freeware – coordinator will give instructions on how to download)

Content

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.  We will use the statistical package R to perform regression-based analyses of genetic data.

Special Computer Requirements

Course notes, assignment material and interaction facilities available online We will also use some of the 18 online lectures on Statistical Genetics offered by Henry Stewart Talks, available at https://hstalks.com/playlist/963/statistical-genetics/. Access details will be provided.