• Arch Med Sci · Mar 2019

    Lack of association of -863C/A (rs1800630) polymorphism of tumor necrosis factor-a gene with rheumatoid arthritis.

    • Tayyaba Sadaf, Peter John, Attya Bhatti, and Javaid M Malik.
    • Department of Health Care Biotechnology, Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, Pakistan.
    • Arch Med Sci. 2019 Mar 1; 15 (2): 531-536.

    IntroductionMultifunctional pro-inflammatory cytokine tumor necrosis factor-α (TNF-α) has been implicated in a variety of inflammatory diseases including rheumatoid arthritis (RA). TNF-α polymorphisms are mostly located in its promoter region and play a significant role in disease susceptibility and severity. We therefore sought to investigate TNFA -863C/A (rs1800630) polymorphism association with RA activity in our Pakistani study group.Material And MethodsA total of 268 human subjects were enrolled. Among them, 134 were RA patients and 134 were controls. In this study the physical parameters of RA patients were collected, and the disease activity was measured by DAS28. The genotypes were determined following the allele-specific PCR along with the pre-requisite internal amplification controls. Subsequently, data were analyzed statistically for any significant association including χ2/Fisher's exact test using GraphPad prism 6 software.ResultsWe found that the TNF-α -863 C/A (rs1800630) variant was not differentially segregated between cases and controls in either genotype frequency, with χ2 of 2.771 and a p-value of 0.2502, or allele frequency, with χ2 of 2.741 and a p-value of 0.0978, with an odds ratio (95% CI) of 0.7490 (0.5317-1.055).ConclusionsThe lack of positive association of TNF-α -863(rs1800630) polymorphism in our study group implies that TNF-α -863 polymorphism is not a susceptible marker to RA and cannot serve as a genetic factor for screening RA patients in Pakistan. There might be other factors that may influence disease susceptibility. However, further investigations on additional larger and multi-regional population samples are required to determine the consequences of genetic variations for disease prognosis.

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