Aims/hypothesis: The purpose of this study is to manually and semi-automatically curate a database and develop an R package that will act as a comprehensive resource to understand how biological processes are dysregulated due to interactions with environmental factors. Methods: We followed a two-step process to achieve the objectives of this study. First, we conducted a systematic review of the existing gene expression datasets to identify the integrated genomic and environmental factors used in available studies. This enabled us to curate a comprehensive genomic-environmental database for four key environmental factors (smoking, diet, infections and toxic chemicals) associated with various autoimmune and chronic conditions. Second, we developed a statistical analysis package that allows users to understand the relationships between differentially expressed genes and environmental factors under different disease conditions. Results: The initial database search run on the Gene Expression Omnibus (GEO) and the Molecular Signature Database (MSigDB) retrieved a total of 90,018 articles. After title and abstract screening against pre-set criteria, a total of 186 studies were selected. From those, 243 individual sets of genes, or gene modules, were obtained. We then curated a database containing four environmental factors, namely cigarette smoking, diet, infections and toxic chemicals, along with a total of 25789 genes that had an association with one or more of these factors. In 6 case studies, the database and statistical analysis package were then tested with lists of differentially expressed genes obtained from the published literature related to type 1 diabetes, rheumatoid arthritis, small cell lung cancer, cobalt exposure, COVID-19 and smoking. On testing, we uncovered statistically enriched biological processes, which could help us understand the pathways associated with environmental factors and gene modules. Conclusions: A novel curated database and software tool is provided as an R Package. Users can enter a list of genes to discover associated environmental factors under various disease conditions.