A ‘‘Candidate-Interactome’’ Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a ‘‘candidate interactome’’ (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms. Citation: Mechelli R, Umeton R, Policano C, Annibali V, Coarelli G, et al. (2013) A ‘‘Candidate-Interactome’’ Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis. PLoS ONE 8(5): e63300. doi:10.1371/journal.pone.0063300 Editor: Luwen Zhang, University of Nebraska Lincoln, United States of America Received December 12, 2012; Accepted March 29, 2013; Published May 16, 2013 Copyright: 2013 Mechelli et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was funded by Italian Multiple Sclerosis Foundation grants (2007/R/17 and 2011/R/31) to MS. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: marco.salvetti@uniroma1.it . These authors contributed equally to this work. " Membership of the International Multiple Sclerosis Genetics Consortium (IMSGC) and the Wellcome Trust Case Control Consortium,2 (WTCCC2) is provided in the Acknowledgments.