A combination of variations in multiple genes and environmental factors contribute to the susceptibility and progression of different chronic diseases in humans. Comprehensive understanding of the interactions between multiple genetic and environmental factors will more accurately predict a risk of contracting a disease or a particular chronic disease and treatment response to explain the etiology than any single genetic or environmental factor. Although advances in the knowledge of measuring genetic variants and the amount of data available has steadily been increasing, a major barrier to further the success of molecular epidemiology studies, especially those with a environment-gene interactions, is to determine an appropriate biostatistical method and modeling strategy for analysis and interpretation of results. In this session, gene-environment interactions will be discussed through each specific chronic disease-based approach to address the question of how genetic variations and environment can influence susceptibility to the individual type of chronic disease. It will also highlight and summarize epigenetic changes that increase the risk for susceptibility to a particular type of disease, particularly in the presence of specific environmental factors. This session invites papers on the biostatistical methodology for study designs and on different modeling approaches to uncover the complexity of disease pathology that lead to distinct molecular phenotypes; bioinformatics approaches for using high dimensional genetic and environmental data for discovery and analysis of gene-environment interactions; and biostatistical methods to analyze continuous traits related to a human disease or disorder with multiple genetic and environmental components.