Imputation-based statistical methods for genetic studies of human complex disease

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It has been known for some time that the risk of developing diseases such as Coronary Heart Disease, Type II Diabetes and Lung Cancer are influenced by an individual?s genetic code. The last year has a seen an explosion in the number of disease genes that have been discovered for diseases such as these, and have been made possible by a new type of study design known as a genome-wide association study. These studies collect data at up to 1 million positions across the human genome from thousands of individuals, with and without a specific disease. I recently developed a method that takes this data and uses it to predict the genetic data at millions of other positions across the genome that have not been sampled. This new data makes it more likely that a disease gene will be uncovered, and facilitates the combination of datasets from different research groups around the world. I now propose to extend and develop the method to allow the analysis of new types of genetic data that are becoming available. This analysis will allow the discovery of new disease genes and subsequently lead to better treatments and predictive tests for many common complex diseases.