Module 6: Systems Genomics and Pharmacogenomics
This module highlights some of the downstream analyses that can be used to understand the biology underlying associations observed in a GWAS datasets, and map disease-associated genetic variants to genes and drugs. The module
will introduce statistical methods for molecular QTL (quantitative trait loci) studies and integrative omics analysis that combine GWAS summary statistics with omic datasets. Methods and resources that will be covered in this module include transcriptome-wide QTL analysis, Mendelian Randomisation, SMR, and Connectivity Map.
Prerequisites: This module assumes a basic understanding of GWAS. If you are not familiar with GWAS, we highly recommend attending Module 1 Genetic Mapping in advance. A basic knowledge of statistics and programming in R and Linux is required for this module.
Goal: By the end of the module, you will acquire the skills to follow up GWAS results to identify relevant biology and candidate drugs that could be investigated further. You will learn how to combine different omic datasets with GWAS data, as well as learn some of the caveats of these datasets that need to be considered when interpreting the data.
Primary instructor:
A/Prof Sonia Shah
A/Prof Sonia Shah leads the Genomics in Health Group at the Institute for Molecular Bioscience. She is a National Heart Foundation Future Leader Fellow with a research focus on pharmacogenomics and using genomic data to improve understanding, prevention and treatment of cardiovascular disease.