Ability of epistatic interactions of cytokine single‐nucleotide polymorphisms to predict susceptibility to disease subsets in systemic sclerosis patients

Abstract

Objective. Gene-gene interaction, or epistasis, is considered a ubiquitous component of complex human diseases such as systemic sclerosis (SSc). Epistasis is difficult to model by traditional parametric approaches; therefore, nonparametric computational algorithms, such as multifactor dimensionality reduction (MDR), have been developed. Methods. A total of 242 consecutive unrelated Italian SSc patients and an equal number of well-matched healthy controls were genotyped for 22 cytokine single-nucleotide polymorphisms (SNPs; 13 cytokine genes). The distribution of the SNPs between controls and SSc patients, controls and limited cutaneous SSc (lcSSc) patients, and controls and diffuse cutaneous SSc (dcSSc) patients was tested by the MDR constructive induction algorithm and by focused interaction testing framework (FITF), a logistic regression-based approach. Results. None of the studied SNPs had main independent effects on SSc or disease subset susceptibility, therefore no epistatic interaction was detectable by FITF. The MDR analysis showed a significant epistatic interaction among the interleukin-2 (IL-2) G-330T, IL-6 C-174G, and interferon-␥ AUTR5644T SNPs and the IL-1 receptor Cpst1970T, IL-6 Ant565G, and IL-10 C-819T SNPs in lcSSc and dcSSc susceptibility, respectively. The relevance of the single multilocus attributes constructed by the MDR inductive algorithm was then confirmed by the parametric approach (P < 0.001 for both controls versus lcSSc patients and controls versus dcSSc patients). Conclusion. We provide evidence for gene-gene interaction among cytokine SNPs in the context of SSc. The interaction among cytokine SNPs with a profibrotic or a regulatory function on profibrotic interleukins is relevant to the susceptibility to SSc subsets and it appears to be more important than the contribution of any single cytokine SNP.

Aggressive behavior, related conduct problems, and variation in genes affecting dopamine turnover

“…Specifically, haplotypes appear to be more informative in differentiating groups of interest, seem to result in a higher power of differentiation, and are less vulnerable to the adjustments of P-values because they require fewer comparisons. These findings underscore the importance of considering variation across a whole gene rather than at a particular polymorphism closely aligned with findings from medical genetics [Beretta et al, 2008;Francis et al, 2008].…”

Graves’ disease and gene polymorphism of TNF-α, IL-2, IL-6, IL-12, and IFN-γ

“…A significant epistatic interaction between IL-2 (330T/G) and systemic sclerosis has recently been reported [20]. Our data revealed significantly increased frequencies of the G allele and the GG genotype.…”

Combined effect of pro- and anti-inflammatory cytokine gene polymorphisms on susceptibility to liver cirrhosis in Tunisian HCV-infected patients

“…In reality, in multifactorial diseases like HCV infection, where the host, the pathogen and the environmental factors all contribute to disease outcome, genetic association studies focused on a single polymorphism are often weak and non-informative. Therefore, studying the joint contribution of multiple loci that interact in the same biological pathway, such as inflammation, fibrogenesis, angiogenesis, etc., seems to be more significant in predicting disease risk by enhancing the major effects of a single gene [39,40,54,55]. In this regard, Ahluwalia et al [56] reported a tenfold increased risk of nephropathy conferred by the co-occurrence of risk-associated genotypes of three inflammatory genes, CCL2, CCR5, and MMP9 in type 2 diabetes patients.…”

“…These contradictory results could be partly explained by the extreme complexity in the cytokine network related to the intricate interactions of cytokines by which they induce or suppress their own synthesis or that of other cytokines, and antagonize or synergize with each other in many different and often redundant ways. On the other hand, it is now obvious that interactions between host genetic polymorphisms should be taken into account in defining complex and multifactorial disease risk, such as hepatitis C. Although the risk attributed to an individual polymorphism is often very small, it has been repeatedly demonstrated that a combination of specific genotypes may be a more significant and powerful approach in predicting disease risk [38][39][40][41].…”