Genomics has dramatically improved our understanding of the molecular origins of certain human diseases. But, our health is also influenced by the cumulative impact of exposures experienced across the life course (termed “exposome”). The study of the high-dimensional exposome offers a new paradigm for investigating environmental contributions to disease etiology. However, there is a lack of advanced bioinformatics tools for managing these data, characterizing the exposome and associating the exposome to health outcomes and different omic layers. We provide a generic framework called rexposome project, developed in the R/Bioconductor architecture that includes object-oriented classes and methods to leverage high-dimensional exposome data in disease association studies including its integration with a variety of high-throughput data types.