Grant: Discovery Grants Program - Individual

<p><strong>Vision:</strong> Appropriate analysis of data is a key part of genetic research. Large datasets with complex correlation structures, confounding factors and diverse data types are available. But integrating the data is complicated. My overall hypothesis is that data integration will improve our ability to identify the genes that influence complex traits (traits that are influenced by both genes and the environment). I propose to develop innovative methods to allow researchers to interrogate diverse data sources efficiently and appropriately. <br /> <strong>Background:</strong> Much data is available from genome-wide association studies (GWAS). These are studies of large numbers of individuals both with and without a disorder that are used to identify genetic variants that have difference frequencies between the groups. Such studies were conceptualized as a hypothesis free approach and have borne much fruit. We have identified hundreds of genetic variants that increase to schizophrenia and inflammatory bowel disease. However, an increased understanding of the functional characteristics of the genome and genetic pathways provides motivation to incorporate other data into association studies. Functional information about DNA tells us whether it is involved in protein manufacture. Not just whether it is part of the blueprint for a protein but also whether is controls the amount of the protein produced. In terms of pathway information we know what genes work together, for example, the group of proteins that works together to respond to bacterial exporsure. <br /> <strong>Significance:</strong> Data integration methods are required because despite the advances in complex disease genetics there is still a considerable gap between how much we know genes contribute to a trait and how much of that so called ""heritability"" we have been able to attribute to specific genes. Data integration methods will be one of the tools that can be used to close this gap. The findings will improve our understanding of complex traits leading to successful treatment of these diseases.<br /> <strong>Originality:</strong> Although a burgeoning field of research there are no methods that deal with all three data sources at one time. The goal of this research is to incorporate information about the functional characteristics of the genome, with association data and data about biological pathways.<br /> <strong>Outcomes: </strong>I will provide functional measures on all common genetic variants, new statistcal methods to incorporate data types and user friendly computer programs that are able to execute these methods.</p>