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Objective@#To evaluate the bidirectional association between periodontitis and Sjögren's syndrome using the Mendelian randomization (MR) method.@*Methods@#Genome-wide association study (GWAS) data of periodontitis (N = 45 563) and Sjögren's syndrome (N = 214 435) were selected to meet the requirements of the same ethnicity and different regions. Inverse variance-weighted (IVW), MR-Egger, and weighted median (WM) tests were used to evaluate the causal effect. Cochran's Q statistics, MR-Egger intercept, MR-PRESSO and leave-one-out analysis were used as sensitivity analyses to assess the stability and reliability of the results.@*Results@#After screening, the GWAS data of Sjögren's syndrome were based on the Finnish region, and the periodontitis GWAS data were based on the UK region, both of which originated from European ancestry. Using IVW (OR = 1.017, 95% CI = 0.956-1.082), MR-Egger (OR = 0.985, 95% CI= 0.956-1.082), and WM (OR =1.021, 95% CI = 0.948-1.099), no causal effect of Sjögren's syndrome on periodontitis was found using any of the three methods. Conversely, no causal effect of periodontitis on Sjögren's syndrome was found (IVW, OR = 1.024, 95% CI = 0.852-1.230; MR-Egger, OR = 0.978, 95% CI = 0.789-1.212; WM, OR = 1.024, 95% CI = 0.846-1.260). The sensitivity analyses indicated that the results were stable and reliable. Cochran's Q test and MR-PRESSO revealed that there was no significant heterogeneity among the instrumental variables, which included single nucleotide polymorphisms (SNPs). The intercept of MR-Egger regression indicated no pleiotropy in the included SNPs. No individual SNP was found that significantly affected the results using the leave-one-out method.@*Conclusion@#This study does not support a bidirectional causal effect between periodontitis and Sjögren's syndrome.
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AIM: To analyze the causality between type 2 diabetes mellitus(T2DM)and age-related macular degeneration(ARMD)based on two-sample Mendelian randomization(MR).METHODS: T2DM and ARMD samples were extracted from the FinnGen database. Inverse variance weighted(IVW)was used as the main analysis method, MR-Egger and weighted median(WM)as supplementary methods to analyze the potential relationship between them. In addition, Cochran Q test and MR-Egger intercept were also used to analyze the sensitivity, and the P-value was used as the index of research results.RESULTS: IVW showed that T2DM was associated with the incidence of exudative ARMD(OR=1.14, 95%CI 1.01～1.28, P=0.021), but it was not significantly associated with the incidence of atrophic ARMD(OR=0.96, 95%CI 0.86～1.07, P=0.554). The results of sensitivity analysis confirmed that there was no heterogeneity and pleiotropy in this study, and the results were reliable.CONCLUSION: There is a causal relationship between T2DM and exudative ARMD. Considering the high rate of blindness caused by ARMD, it is of great significance to recognize and control the risk factors of ARMD to reduce its prevalence rate and early diagnosis and treatment.
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The meta-analysis has become a widely used tool for many applications in bioinformatics, including genome-wide association studies. A commonly used approach for meta-analysis is the fixed effects model approach, for which there are two popular methods: the inverse variance-weighted average method and weighted sum of z-scores method. Although previous studies have shown that the two methods perform similarly, their characteristics and their relationship have not been thoroughly investigated. In this paper, we investigate the optimal characteristics of the two methods and show the connection between the two methods. We demonstrate that the each method is optimized for a unique goal, which gives us insight into the optimal weights for the weighted sum of z-scores method. We examine the connection between the two methods both analytically and empirically and show that their resulting statistics become equivalent under certain assumptions. Finally, we apply both methods to the Wellcome Trust Case Control Consortium data and demonstrate that the two methods can give distinct results in certain study designs.