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      Identification of TNF-α as Major Susceptible Risk Locus for Vitiligo: A Systematic Review and Meta-Analysis Study in the Asian Population

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          Abstract

          Introduction: Vitiligo is a common depigmentation disorder characterized by defined white patches on the skin and affecting around 0.5% to 2% of the general population. Genetic association studies have identified several pre-disposing genes and single nucleotide polymorphisms (SNPs) for vitiligo pathogenesis; nonetheless, the reports are often conflicting and rarely conclusive. This comprehensive meta-analysis study was designed to evaluate the effect of the risk variants on vitiligo aetiology and covariate stratified vitiligo risk in the Asian population, considering all the studies published so far. Methods: We followed a systematic and comprehensive search to identify the relevant vitiligo-related candidate gene association studies in PubMed using specific keywords. After data extraction, we calculated, for the variants involved, the study-level unadjusted odds ratio, standard errors, and 95% confidence intervals by using logistic regression with additive, dominant effect, and recessive models using R software package (R, 3.4.2) “metafor.” Subgroup analysis was performed using logistic regression (generalized linear model; “glm”) of disease status on subgroup-specific genotype counts. For a better understanding of the likely biological function of vitiligo-associated variant obtained through the meta-analysis, in silico functional analyses, through standard publicly available web tools, were also conducted. Results: Thirty-one vitiligo-associated case-control studies on eleven SNPs were analysed in our study. In the fixed-effect meta-analysis, one variant upstream of TNF-α gene: rs1800629 was found to be associated with vitiligo risk in the additive ( p = 4.26E−06), dominant ( p = 1.65E−7), and recessive ( p = 0.000453) models. After Benjamini-Hochberg false discovery rate (FDR) correction, rs1800629/TNF-α was found to be significant at 5% FDR in the dominant ( p adj = 1.82E−6) and recessive models ( p adj = 0.0049). In silico characterization revealed the prioritized variant to be regulatory in nature and thus having potential to contribute towards vitiligo pathogenesis. Conclusion: Our study constitutes the first comprehensive meta-analysis of candidate gene-based association studies reported in the whole of the Asian population, followed by an in silico analysis of the vitiligo-associated variant. According to the findings of our study, TNF-α single nucleotide variant rs1800629G>A has a risk association, potentially contributing to vitiligo pathogenesis in the Asian population.

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          Most cited references67

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          Conducting Meta-Analyses inRwith themetaforPackage

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            A basic introduction to fixed-effect and random-effects models for meta-analysis.

            There are two popular statistical models for meta-analysis, the fixed-effect model and the random-effects model. The fact that these two models employ similar sets of formulas to compute statistics, and sometimes yield similar estimates for the various parameters, may lead people to believe that the models are interchangeable. In fact, though, the models represent fundamentally different assumptions about the data. The selection of the appropriate model is important to ensure that the various statistics are estimated correctly. Additionally, and more fundamentally, the model serves to place the analysis in context. It provides a framework for the goals of the analysis as well as for the interpretation of the statistics. In this paper we explain the key assumptions of each model, and then outline the differences between the models. We conclude with a discussion of factors to consider when choosing between the two models. Copyright © 2010 John Wiley & Sons, Ltd. Copyright © 2010 John Wiley & Sons, Ltd.
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              Assessing heterogeneity in meta-analysis: Q statistic or I2 index?

              In meta-analysis, the usual way of assessing whether a set of single studies is homogeneous is by means of the Q test. However, the Q test only informs meta-analysts about the presence versus the absence of heterogeneity, but it does not report on the extent of such heterogeneity. Recently, the I(2) index has been proposed to quantify the degree of heterogeneity in a meta-analysis. In this article, the performances of the Q test and the confidence interval around the I(2) index are compared by means of a Monte Carlo simulation. The results show the utility of the I(2) index as a complement to the Q test, although it has the same problems of power with a small number of studies.

                Author and article information

                Journal
                DRM
                Dermatology
                10.1159/issn.1018-8665
                Dermatology
                Dermatology
                S. Karger AG
                1018-8665
                1421-9832
                2024
                June 2024
                20 February 2024
                : 240
                : 3
                : 376-386
                Affiliations
                [a ]Department of Genetics, University of Calcutta, Kolkata, India
                [b ]Department of Biochemistry, University of Calcutta, Kolkata, India
                [c ]Department of Microbiology, University of Calcutta, Kolkata, India
                [d ]Department of Biotechnology, KIIT University, Bhubaneswar, India
                [e ]National Institute of Biomedical Genomics, Kalyani, India
                Author notes
                *Samsiddhi Bhattacharjee, sb1@nibmg.ac.in, Mainak Sengupta, sengupta.mainak@gmail.com
                Article
                536480 Dermatology 2024;240:376–386
                10.1159/000536480
                38377977
                4d529ddd-6c35-4930-9e1f-5c323433c942
                © 2024 S. Karger AG, Basel
                History
                : 02 June 2023
                : 21 January 2024
                Page count
                Figures: 2, Tables: 3, Pages: 11
                Funding
                We would like to thank the Department of Science and Technology-Promotion of University Research and Scientific Excellence (DST-PURSE), Government of India, for providing funds to the University of Calcutta for academic infrastructural facilities. Tithi Dutta and Arpan Saha are supported by Senior Research Fellowship from University Grant Commission [UGC], Government of India. We have not received any extramural funding for the preparation of the data or the manuscript.
                Categories
                Research Article

                Medicine
                Vitiligo,Single nucleotide polymorphisms,Meta-analysis,Asian population,Pathogenesis,TNF-α
                Medicine
                Vitiligo, Single nucleotide polymorphisms, Meta-analysis, Asian population, Pathogenesis, TNF-α

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