GePhEx

The last ten years have witnessed considerable expansion into various omics data that has resulted in an explosion of publicly available heterogeneous biological datasets. Recent genotyping and profiling technologies enable the scientific community to investigate disease-related genomic alterations in human disorders. At the same time, it becomes increasingly clear that some complex diseases result from the interaction between individual genetic background and environmental factors, as for lung or coronary heart diseases. While promising biological treatments are being explored, health professionals progressively advocate medical educational or preventive interventions, for which the clinical benefits have been positively evaluated by previous studies. 

Such interventions comprise the transmission of medical knowledge on phenotypic traits or symptoms and hence improve the patient survival by for instance triggering earlier testing. These interventions also instruct on measures that can counteract the onset of a complex disease (e.g., diabetes, chronic respiratory diseases or rheumatoid arthritis) by avoiding or modifying key extrinsic risk factors (e.g., tobacco or alcohol consumption, unhealthy diet). It is also crucial to identify the combined causal effects of environmental factors to propose efficient treatments with few or no side-effects. Hence, effective interventions should be based on the most exhaustive and accurate information on the phenotypic traits (phenome) and the environmental exposures (exposome) in the context of a complex disease. 

The GePhEx (Genome-Phenome-Exposome) project proposes to automatically discover the phenome and the exposome associated to genomic alterations in the context of a given human complex disease and to learn the causal relationships between symptoms, environmental factors and impacted genes. This project is dealing with critical public health issues as the discovery of new environmental determinants or phenotypic traits of a disease could help to establish efficient medical recommendations and favor earlier diagnosis. The novel analytic methods proposed by GePhEx will enable (i) automatic discovery of exposures and their associated phenotypic traits from large amount of publicly available biological data and scientific literature, (ii) causally relate phenome, exposome and genome entities in the context of a specific disease and (iii) provide an easy-to-use web application to improve patient self-awareness and practitioners early diagnosis.