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Abstract
BACKGROUND Autoimmune hepatitis has an unknown cause and genetic associations that are not disease-specific or always present. Clarification of its missing causality and heritability could improve prevention and management strategies. AIMS Describe the key epigenetic and genetic mechanisms that could account for missing causality and heritability in autoimmune hepatitis; indicate the prospects of these mechanisms as pivotal factors; and encourage investigations of their pathogenic role and therapeutic potential. METHODS English abstracts were identified in PubMed using multiple key search phases. Several hundred abstracts and 210 full-length articles were reviewed. RESULTS Environmental induction of epigenetic changes is the prime candidate for explaining the missing causality of autoimmune hepatitis. Environmental factors (diet, toxic exposures) can alter chromatin structure and the production of micro-ribonucleic acids that affect gene expression. Epistatic interaction between unsuspected genes is the prime candidate for explaining the missing heritability. The non-additive, interactive effects of multiple genes could enhance their impact on the propensity and phenotype of autoimmune hepatitis. Transgenerational inheritance of acquired epigenetic marks constitutes another mechanism of transmitting parental adaptations that could affect susceptibility. Management strategies could range from lifestyle adjustments and nutritional supplements to precision editing of the epigenetic landscape. CONCLUSIONS Autoimmune hepatitis has a missing causality that might be explained by epigenetic changes induced by environmental factors and a missing heritability that might reflect epistatic gene interactions or transgenerational transmission of acquired epigenetic marks. These unassessed or under-evaluated areas warrant investigation.
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Goudey B, Abraham G, Kikianty E, Wang Q, Rawlinson D, Shi F, Haviv I, Stern L, Kowalczyk A, Inouye M. Interactions within the MHC contribute to the genetic architecture of celiac disease. PLoS One 2017; 12:e0172826. [PMID: 28282431 PMCID: PMC5345796 DOI: 10.1371/journal.pone.0172826] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2016] [Accepted: 02/10/2017] [Indexed: 01/04/2023] Open
Abstract
Interaction analysis of GWAS can detect signal that would be ignored by single variant analysis, yet few robust interactions in humans have been detected. Recent work has highlighted interactions in the MHC region between known HLA risk haplotypes for various autoimmune diseases. To better understand the genetic interactions underlying celiac disease (CD), we have conducted exhaustive genome-wide scans for pairwise interactions in five independent CD case-control studies, using a rapid model-free approach to examine over 500 billion SNP pairs in total. We found 14 independent interaction signals within the MHC region that achieved stringent replication criteria across multiple studies and were independent of known CD risk HLA haplotypes. The strongest independent CD interaction signal corresponded to genes in the HLA class III region, in particular PRRC2A and GPANK1/C6orf47, which are known to contain variants for non-Hodgkin's lymphoma and early menopause, co-morbidities of celiac disease. Replicable evidence for statistical interaction outside the MHC was not observed. Both within and between European populations, we observed striking consistency of two-locus models and model distribution. Within the UK population, models of CD based on both interactions and additive single-SNP effects increased explained CD variance by approximately 1% over those of single SNPs. The interactions signal detected across the five cohorts indicates the presence of novel associations in the MHC region that cannot be detected using additive models. Our findings have implications for the determination of genetic architecture and, by extension, the use of human genetics for validation of therapeutic targets.
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Affiliation(s)
- Benjamin Goudey
- NICTA Victoria Research Lab, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
- Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
- IBM Research, Australia, Level 5, Carlton, Victoria, Australia
| | - Gad Abraham
- Centre for Systems Genomics, The University of Melbourne, Parkville, Victoria, Australia
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
| | - Eder Kikianty
- Department of Mathematics, University of Johannesburg, Auckland Park, South Africa
| | - Qiao Wang
- NICTA Victoria Research Lab, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Dave Rawlinson
- NICTA Victoria Research Lab, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Fan Shi
- NICTA Victoria Research Lab, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Izhak Haviv
- Faculty of Medicine, Bar Ilan University, Safed, Israel
| | - Linda Stern
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
| | - Adam Kowalczyk
- NICTA Victoria Research Lab, The University of Melbourne, Parkville, Victoria, Australia
- Centre for Epidemiology and Biostatistics, The University of Melbourne, Parkville, Victoria, Australia
- Center for Neural Engineering, The University of Melbourne, Parkville, Victoria, Australia
| | - Michael Inouye
- Centre for Systems Genomics, The University of Melbourne, Parkville, Victoria, Australia
- School of BioSciences, The University of Melbourne, Parkville, Victoria, Australia
- Department of Pathology, The University of Melbourne, Parkville, Victoria, Australia
- * E-mail:
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Rose AM, Bell LCK. Epistasis and immunity: the role of genetic interactions in autoimmune diseases. Immunology 2012; 137:131-8. [PMID: 22804709 DOI: 10.1111/j.1365-2567.2012.03623.x] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Autoimmune disorders are a complex and varied group of diseases that are caused by breakdown of self-tolerance. The aetiology of autoimmunity is multi-factorial, with both environmental triggers and genetically determined risk factors. In recent years, it has been increasingly recognized that genetic risk factors do not act in isolation, but rather the combination of individual additive effects, gene-gene interactions and gene-environment interactions determine overall risk of autoimmunity. The importance of gene-gene interactions, or epistasis, has been recently brought into focus, with research demonstrating that many autoimmune diseases, including rheumatic arthritis, autoimmune glomerulonephritis, systemic lupus erythematosus and multiple sclerosis, are influenced by epistatic interactions. This review sets out to examine the basic mechanisms of epistasis, how epistasis influences the immune system and the role of epistasis in two major autoimmune conditions, systemic lupus erythematosus and multiple sclerosis.
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Affiliation(s)
- Anna M Rose
- Department of Genetics, UCL Institute of Ophthalmology, London, UK.
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