Genemappers 2017April 26 - 28, 2017 Novotel Geelong, VIC


Limited workshop spots available, so please register quickly!



Early bird


Full Registration inc. Workshop  $670.00  $790.00
Student Registration inc. Workshop $430.00 $500.00
Workshop Attendance Only $150.00

Workshop 1: Using R/Bioconductor for RNA-seq analysis

Dr Belinda Phipson, Murdoch Childrens Research Institute, Melbourne
Dr Matthew Ritchie, Walter and Eliza Hall Institute of Medical Research, Melbourne

Overview: Bioconductor ( is a widely used collection of over 1,200 R packages for high-throughput genomic analysis. The ability to easily and efficiently analyse RNA-sequencing (RNA-seq) data is a key strength of the project. In this workshop, participants will learn how to perform a complete analysis using three popular Bioconductor packages. Firstly, edgeR will be used to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the new Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. Combining these three packages highlights the ease with which researchers can turn the raw counts from an RNA-seq experiment into biological insights using R/Bioconductor.

Prerequisites: Basic R programming skills are required.

Participants are requested to bring their own laptop. Please ensure that you have a working version of R (version: 3.4.0 or higher), RStudio and the latest versions of the edgeR, limma, and Glimma packages installed.

Preliminary Program: 





Breakfast + Installation Help



Importing, Preprocessing and Exploratory Data Analysis



Morning Tea Break


Differential Expression Analysis, Gene Set Testing and Visualization


Workshop 2: Understanding the genetic architecture of complex traits through SNP-based heritability analysis

Dr Doug Speed,
University College London
Prof David Balding,
University of Melbourne

Overview: Genome-wide analysis to assess the heritability tagged by SNPs, including how it is distributed across the genome and shared across multiple traits, has been highly productive in helping us to understand the genomic basis of complex traits. The underlying regression model can also be used for phenotype predictions using BLUP and its extensions. However the model involves very large numbers of predictors and strong modeling assumptions are required to tackle the consequent problem of over-fitting. The results can be sensitive to these assumptions, and also to the effects of population structure, genotyping errors and the extent to which rare SNPs are included. We will introduce the GCTA, LDScore and LDAK softwares for performing these analyses, contrast their modeling assumptions and illustrate the powerful inferences that can be made using the basic model and various extensions that have been developed. These will include partitioning heritability by chromosome or SNP function, creating BLUP prediction models and performing bivariate analysis for pairs of traits, using raw genotype data and summary statistics.

Prerequisites: Participants should be proficient in statistics including some familiarity with random-effects regression models. In genetics, knowledge of SNP genotypes and Hardy-Weinberg and linkage equilibria are required. There will not be any hands-on computation in this brief course, but computer scripts and output will be discussed that assume some familiarity with scientific computing using linux. Some familiarity with PLINK would be helpful but is not essential.

Preliminary Program: 

Time Session Room
8.30-9.00 Breakfast Bellarine
9.00-10.20 Introduction to SNP-based heritability analysis and the GCTA, LDScore and LDAK software Bellarine
10.20-10.40 Morning Tea Break
10.40-12,00 Advanced topics: model comparisons, SNP partitioning, extensions of the basic model Bellarine