We exploited genetic information to assess the role of non-genetic factors in multifactorial diseases. To this aim we isolated candidate "interactomes" (i.e. groups of genes whose products are known to physically interact with environmental exposures and biological processes, plausibly relevant for disease pathogenesis) and analyzed nominal statistical evidence of association with genetic predisposition to multiple sclerosis (MS) and other inflammatory and non-inflammatory complex disorders. The interaction between genotype and Herpesviruses emerged as specific for MS, with Epstein Barr virus (EBV) showing higher levels of significance compared to Human Herpesvirus 8 (HHV8) and, more evidently, to cytomegalovirus (CMV). In accord with this result, when we classified the MS-associated genes contained in the interactomes into canonical pathways, the analysis converged towards biological functions of B cells, in particular the CD40 pathway. When we analyzed peripheral blood transcriptomes in persons with MS, we found a significant dysregulation of MS-associated genes belonging to the EBV interactome in primary progressive MS.This study indicates that the interaction between herpesviruses and predisposing genetic background is of causal significance in MS, and provides a mechanistic explanation for the long-recognized association between EBV and this condition.Many studies on environmental exposures in multifactorial diseases are correlative and associative in nature, hence the need to focus on causality and mechanism (Fischbach, 2018). Genome-wide association studies (GWAS) have the capability to inform about the causal relevance of associations between environmental exposures and disease. However, the difficulty in extracting value from the study of gene-environment interactions limits the interpretation of GWAS, therefore hindering what could be a virtuous 'genes-to-environment-to genes-again' iterative learning process (Visscher et al., 2017).In principle, Mendelian randomization allows to test for a causal effect of an epidemiological association. With this method, an environmental exposure is deemed to be plausibly causal if a genetic variant influencing the exposure is also directly associated with the disease (Davey Smith and Hemani, 2014). However, the influence of genetic variants on environmental exposures that associate with multifactorial diseases is far from being fully understood, particularly at the genome-wide level. More actionable data are available about the interaction, at the protein level, between human gene products and exposures. Furthermore, it has been shown that interacting disease-associated proteins have the propensity to influence each other in biologically relevant modules (Menche et al., 2015). Hence, in an approach akin to Mendelian randomization, we tested the possible causal significance of environmental exposures by measuring, at the genome-wide level, which genetic variants interacting with the exposure (i.e. influencing the exposure) were significantly enriched in MS GWAS data. ...