Practical GWAS Using Linux and R


23-27 October 2017


Workshop overview

The past decade has witnessed an astonishing development and the universal use of genome wide association studies (GWAS) in identification and characterisation of genetic variants underlying disorders and other variations in human and other species, which has an immense impact in biomedical research. This is owing to the ability to efficiently generate and process large quantity of genetic polymorphisms as well as to integrate with other sources such as gene expression and methylation. To tackle challenges in GWAS, a lot of methods and techniques have been established but many others are still evolving. The workshop therefore intends to give a grand picture as well as practical aspects of GWAS.

Targeted audience and assumed background

The purpose of this workshop is to render both a broad picture and computational details of GWAS to biomedical researchers and related fields. It sets to explore the biological, statistical, and computational concepts, methodologies and practices involving a variety of software based on Linux and R. Examples of consortium contributions will also be given. These will be particularly beneficial to those who come with their own problems and wish to implement the analysis.


Workshop structure

The workshop contains both lecture and computer sessions, designed to help participants to understand the background, methodology and implementation. The computer session is designed to facilitate data analysis and interpretation.






The workshop consists of the following modules.



Module 1. Overview


Module 2. Genetic association


Module 3. GWAS


Module 4. Advanced topics


Module 5. Additional topics



Each takes approximately a day.



Dr Jing Hua Zhao


Trained in medicine, medical statistics and statistical genetics, he had worked on statistical and computational methods for epidemiological and public health studies at several institutions until 2005, when he joined the MRC Epidemiology Unit, University of Cambridge, to work on design and analysis of GWAS such as the EPIC-Norfolk, the Fenland and the InterAct. He has also participated in numerous genetic analysis workshops which involve both simulated and real data such as those from the Framingham heart study. Besides methodological development, data analysis, and other academic activities, he has also had tutorials on genetic dissection of complex traits with focus on GWAS at UseR! 2008, 2009, and 2010 Conferences and contributed a Henry-Stewart talk on genetic association with R.



Assistant Instructor

Dr Qi Guo


Dr Guo has research interest in statistical genomics and is a research fellow at Cardiovascular Epidemiology Unit at the University of Cambridge. Prior to coming to Cambridge, she has capitalised from research in metabolomics and via statistical analysis and integration of multi-omics datasets at Imperial College London. After she joined the University of Cambridge, she led a large-scale GWAS and meta-analysis of multiple studies of breast cancer survival for Breast Cancer Association Consortium (BCAC). Currently, she is working on discovery of rare genomic determinants of protein expression levels for ~4000 distinct proteins using whole exome sequencing technology for the INTERVAL project. Her work involves extensive use of statistical, computational and epidemiological methodologies.
Dr Guo has given lectures on genetic epidemiology and genetic association studies for the MPhil course in Public Health and Epidemiology, and supervises epidemiology and biostatistics course for under graduate students at Clare College, both within the University of Cambridge.



Cancellation Policy:


> 30  days before the start date = 30% cancellation fee

< 30 days before the start date= No Refund.


Physalia-courses cannot be held responsible for any travel fees, accommodation or other expenses incurred to you as a result of the cancellation.