2017
DOI: 10.1371/journal.pone.0169918
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Collective Genetic Interaction Effects and the Role of Antigen-Presenting Cells in Autoimmune Diseases

Abstract: Autoimmune diseases occur when immune cells fail to develop or lose their tolerance toward self and destroy body’s own tissues. Both insufficient negative selection of self-reactive T cells and impaired development of regulatory T cells preventing effector cell activation are believed to contribute to autoimmunity. Genetic predispositions center around the major histocompatibility complex (MHC) class II loci involved in antigen presentation, the key determinant of CD4+ T cell activation. Recent studies suggest… Show more

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Cited by 8 publications
(4 citation statements)
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“…Extensions of the independent loci (IL) analysis by pairwise testing may partially capture such effects, but in the largest SZ meta-analysis to date, it was reported that pairwise tests did not yield new associations. 4 In this work, we characterized non-additive interaction effects of common variants on SZ and BP risks using a recently developed algorithm (discrete discriminant analysis, DDA), 9 , 10 which greatly extends the scale of interacting partners simultaneously considered beyond SNP pairs by using the regularized inference of high-dimensional interactions within large SNP groups. Over-fitting is avoided by optimizing the risk prediction quantified by the area under the curve (AUC) of receiver operating characteristics, estimated from cross-validation.…”
Section: Introductionmentioning
confidence: 99%
“…Extensions of the independent loci (IL) analysis by pairwise testing may partially capture such effects, but in the largest SZ meta-analysis to date, it was reported that pairwise tests did not yield new associations. 4 In this work, we characterized non-additive interaction effects of common variants on SZ and BP risks using a recently developed algorithm (discrete discriminant analysis, DDA), 9 , 10 which greatly extends the scale of interacting partners simultaneously considered beyond SNP pairs by using the regularized inference of high-dimensional interactions within large SNP groups. Over-fitting is avoided by optimizing the risk prediction quantified by the area under the curve (AUC) of receiver operating characteristics, estimated from cross-validation.…”
Section: Introductionmentioning
confidence: 99%
“…Epistatic interactions between HLA and non-HLA genetic variants have been previously reported for other autoimmune diseases including SLE, MS, rheumatoid arthritis, ankylosing spondylitis and psoriasis (Matzaraki et al, 2017;Diaz-Gallo et al, 2018;Hanson et al, 2020). In addition, a significant interaction between r9273363 (HLA DQB1) and SNPs rs9272219 (HLA DQA1) was reported in RA and type 1 diabetes, and this HLA region was identified as epigenetically active in B cells (Woo et al, 2017). Since Lamin B1 and LAP2 are expressed in the thymus, the LAP2 690Cys variant may indirectly affect AIRE function in medullary TECs, perhaps altering the presentation of AQP4 or other antigens in the thymus, allowing autoreactive T cells that recognize these antigens to escape negative selection.…”
Section: Discussionmentioning
confidence: 77%
“…Furthermore, String gene ontology enrichment analysis was performed in order to investigate any links between gene ontology and biological processes for the differentially expressed genes (Supplementary Table 1). As antigen presenting cells and their HLA-proteins which present peptides to T cells have pivotal roles in immune reactivity and tolerance, it is to no surprise that String reported an association between FOXP3 and HLA-DQB1 as "being comentioned" in PubMed-abstracts in numerous articles, e.g (42). The gene ontology enrichment strengthened this association by pointing out the relevance of HLA-molecules for proper immune function.…”
Section: Pathway Analysesmentioning
confidence: 97%