Journal article
A "candidate-interactome" aggregate analysis of genome-wide association data in multiple sclerosis
- Abstract:
- 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, HHV8-Kaposi 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 and 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 831.2KB, Terms of use)
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- Publisher copy:
- 10.1371/journal.pone.0063300
Authors
Contributors
+ Donnelly, P
- Institution:
- University of Oxford
- Division:
- MPLS Division
- Department:
- Statistics
- Role:
- Contributor
- Publisher:
- Public Library of Science
- Journal:
- PLoS ONE More from this journal
- Volume:
- 8
- Issue:
- 5
- Pages:
- ARTN e63300
- Publication date:
- 2013-05-16
- Acceptance date:
- 2013-03-29
- DOI:
- EISSN:
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1932-6203
- Language:
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English
- Keywords:
- Pubs id:
-
403035
- UUID:
-
uuid:2065fdc3-cbb9-4666-b869-2849b87a32d1
- Local pid:
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pubs:403035
- Source identifiers:
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403035
- Deposit date:
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2013-11-16
- ARK identifier:
Terms of use
- Copyright holder:
- Mechelli et al
- Copyright date:
- 2013
- Notes:
- 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.
- Licence:
- CC Attribution (CC BY)
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