Grant: Operating Grant: Data Analysis Using Existing CDN Databases and Cohorts

DNA methylation (DNAm) is an important determinant of how our genes are turned on and off, both as part of normal human development, and in response to different environmental exposures. DNAm is thought to contribute to the risk of disease, but large numbers of people are required to detect these associations. On the other hand, we can use statistical methods to link multiple existing data types and databases to answer questions on the role of DNAm in disease that no single dataset can answer alone. We will use newly generated data on genetic factors and DNAm, as well as existing data on environmental exposures, from >800 children followed from birth in the Canadian Healthy Infant Longitudinal Development (CHILD) Study. First, we will identify genetic factors that influence DNAm in early childhood in CHILD, including in response to environmental exposures such as smoking during pregnancy. Second, we will use statistical methods to link these data with separate studies of genetic factors and disease risk, for example, asthma, that include tens of thousands of individuals. In this way, we will identify DNAm patterns in early childhood that affect disease risk. The CHILD study is the ideal dataset for this project as few other studies have the same richness of genetic, epigenetic, and environmental data as we have for CHILD participants. Our project team is well equipped to analyze these multiple data types, and our project will lead to a better understanding of the biology behind common childhood diseases like asthma.