BACKGROUND: Statistical epistasis, or \"gene-gene interaction\" in genetic association studies, means the nonadditive effects between the polymorphic sites on two different genes affecting the same phenotype. In the genetic association analysis of complex traits, nevertheless, the researchers haven\'t found enough clues of statistical epistasis so far.
METHODS: We developed a statistical model where the statistical epistasis was presented as an extra linkage disequilibrium between the polymorphic sites of different risk genes. The power of statistical test for identifying the gene-gene interaction was calculated and then compared in different hypothesis scenarios.
RESULTS: Our results show the statistical power increases with the increasing of interaction coefficient, relative risk, and linkage disequilibrium with genetic markers. However, the power of interaction discovery is much lower than that of regular single-site association test. When rigorous criteria were employed in statistical tests, the identification of gene-gene interaction became a very difficult task. Since the criterion of significance was given to be p-value ≤ 5.0 × 10-8, the same as that of many genome-wide association studies, there is little chance to identify the gene-gene interaction in all kind of circumstances.
CONCLUSIONS: The lack of epistasis tends to be an inevitable result caused by the statistical principles of methods in the genetic association studies and therefore is the inherent characteristic of the research itself.

Rheumatoid arthritis (RA) is an autoimmune disease which can lead to progressive and functional disability. Literature data suggest that some inflammatory proteins are dysregulated in RA patients and its genetic polymorphisms may contribute to the aetiology and pathogenesis of disease in different ethnic groups. Polymorphisms in IL1β, IL18, NFKB1 and IFNG genes were studied in different populations with RA, but the analysis indicated contradictory results. Thereby, we hypothesised that polymorphisms in these genes could have a combined effect on susceptibility to and severity of disease. We evaluated the +3953 C/T IL1β (rs1143634), -137 G/C IL18 (rs187238), -94 ins/del ATTG NFKB1 (rs28362491) and +874 T/A IFNG (rs2430561) polymorphisms in the northeastern Brazilian population. Peripheral blood samples were collected and DNA extraction was conducted. The polymorphisms were evaluated by RFLP and ARMS-PCR. An association was observed in rs1143634 which showed a protective effect against development of RA in carriers of the T allele (OR = 0.58; 95% CI 0.36-0.92; p = .020). In addition, we found an association among genotypes of the rs1143634 with the HAQ index (p = .021) and rs2430561 with DAS28 (p = .029) and CDAI (p = .029). In relation to combined effects of these SNPs (C/C to rs1143634, G/G to rs187238, I/I to rs28362491 and AA to rs2430561) we found a significant association with decreased functional disability (HAQ index p < .001) and ESR (p = .034), indicating a lower disease activity in carriers of these genotypes. GLM analysis confirmed these associations (HAQ (F = 5.497; p < .001) and ESR (F = 2.727; p = .032)). Our analysis indicated that in the studied population +3953 C/T IL-1β (rs1143634), -137 G/C IL-18 (rs187238), -94 ins/del ATTG NFKB1 (rs28362491) and +874 T/A IFNG (rs2430561) polymorphisms can together contribute to RA severity although they do not individually influence the disease.

Autoimmune thyroid diseases (AITD), which include Hashimoto thyroiditis (HT), Graves\' disease (GD) and primary idiopathic myxoedema (PIM), are recognized by their clinical and genetic heterogeneity. In this study, we have carried on a global approach gathering 20 year genetic and clinical data on a Tunisian multigenerational family (Akr). Our purpose was search for a combined genotype involved in AITD susceptibility using 33 gene polymorphisms. The Akr pedigree is composed of more than 400 members distributed on 10 generations. Clinical follow-up was performed by appreciation of the thyroid gland and measurement of both thyroid hormone and auto antibody levels. We used FBAT software to test for association between gene polymorphisms and AITDs. Clinical follow-up has showed that the number of AITD patients has increased from 25 to 78 subjects subdivided on 51 cases of GD, 22 PIM and 5 HT. Concerning genetic analysis, our study has revealed new gene association when compared with our previous analysis (considering single genes). Thus, PTPN22, TG and VDR gene polymorphisms have became associated with p-values ranging from 4.6  10(- 2) to 4  10(- 3) when considered with other genes on the same chromosome; giving evidence for gene interaction. The most significant association was found with the MHC region (p = 7.15 10(- 4)). Moreover, and among gene polymorphisms explored, our analysis has identified some of them as AITD biomarkers. Indeed, PDS gene polymorphisms were associated with either exophthalmia or goiter (p-values from 10(- 2) to 10(- 3)). In conclusion, our study gives evidence for gene interaction in AITD development confirming genetic complexity of these diseases.

OBJECTIVE: Cytokines and related molecules in immune-response pathways seem important in deciding the outcome of the host-pathogen interactions towards different polar forms in leprosy. We studied the role of significant and functionally important single-nucleotide polymorphisms (SNPs) in these genes, published independently from our research group, through combined interaction with an additional analysis of the in silico network outcome, to understand how these impact the susceptibility towards the disease, leprosy.
METHODS: The study was designed to assess an overall combined contribution of significantly associated individual SNPs to reflect on epistatic interactions and their outcome in the form of the disease, leprosy. Furthermore, in silico approach was adopted to carry out protein-protein interaction study between PARK2 and proinflammatory/anti-inflammatory cytokines.
METHODS: Population-based case-control study involved the data of North India. Protein-protein interaction networks were constructed using cytoscape.
METHODS: Study included the data available from 2305 Northern Indians samples (829 patients with leprosy; 1476 healthy controls), generated by our research group.
METHODS: For genotype interaction analysis, all possible genotype combinations between selected SNPs were used as an independent variable, using binary logistic regression with the forward likelihood ratio method, keeping the gender as a covariate.
RESULTS: Interaction analysis between PARK2 and significant SNPs of anti-inflammatory/proinflammatory cytokine genes, including BAT1 to BTNL2-DR spanning the HLA (6p21.3) region in a case-control comparison, showed that the combined analysis of: (1) PARK2, tumour necrosis factor (TNF), BTNL2-DR, interleukin (IL)-10, IL-6 and TGFBR2 increased the risk towards leprosy (OR=2.54); (2) PARK2, BAT1, NFKBIL1, LTA, TNF-LTB, IL12B and IL10RB provided increased protection (OR=0.26) in comparison with their individual contribution.
CONCLUSIONS: Epistatic SNP-SNP interactions involving PARK2 and cytokine genes provide an additive risk towards leprosy susceptibility. Furthermore, in silico protein-protein interaction of PARK2 and important proinflammatory/anti-inflammatory molecules indicate that PARK2 is central to immune regulation, regulating the production of different cytokines on infection.