Statistical Methods for the Analysis of Genome-Wide Association StudiesPractical advice and guidance

Published June 2008 9 lectures
Dr. Jonathan Marchini
University of Oxford, UK

Over the last year first generation Genome-Wide Association Studies (GWAS) have discovered more than 100 regions of the genome containing genetic variants that are associated with risk of common human diseases such as diabetes, coronary artery disease, prostate and breast cancer, rheumatoid arthritis, inflammatory bowel disease and age related macular... read moredegeneration. These discoveries have rapidly increased the knowledge of the biological mechanisms of human diseases and work is now underway to turn these discoveries into treatments and preventative measures.

It seems clear that this experimental design will continue to play a key role in human disease genetics for the foreseeable future. The datasets produced by these experiments are very large and complex and the analysis is a challenging and intricate task requiring expertise in several specialist statistical methods.

This series has been designed to cover all aspects of the statistical analysis of genome-wide association studies. In each lecture we aim to provide an introduction to statistical issues, provide a survey of the literature and recommend computational tools relevant to the task. To illustrate the issues and methods we have used the analysis of the Wellcome Trust Case-Control Consortium as a case study.