Assessing the causality of interferon-γ and its receptor 1/2 with systemic lupus erythematosus risk using genetic data

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This study aimed to assess the causal association between IFN-γ, IFN-γ receptor 1 (IFN-γR1), and IFN-γR2 and SLE using a bidirectional Mendelian-randomization design. Methods: Genetic instruments for exposure to IFN-γ, IFN-γR1, and IFN-γR2 were derived from a large genome-wide association study (GWAS) that included a sample size of 3301 participants. Instrumental variables for SLE were selected from another independent GWAS analysis comprising 5201 cases and 6099 controls with European ancestry. Bidirectional two-sample Mendelian randomization (MR) was performed using inverse variance weighting, MR-Egger regression, and weighted median methods. A series of sensitivity analyses were conducted to assess the robustness of the results. Results: The inverse variance weighting showed that IFN-γ had a positive causal association with the risk of SLE (odd ratio [OR]=1.24, 95% confidence interval [CI]: 1.03–1.47, P =0.018). IFN-γR2 levels were not associated with SLE risk after adjustment for multiple comparisons (OR=0.85, 95% CI: 0.73–0.99), P =0.034). No genetic association was also detected between IFN-γR1 and SLE (OR=0.97, 95% CI: 0.79–1.19), P =0.768). Evidence from bidirectional MR did not support reverse causality. The weighted median regression also showed directionally similar estimates. Conclusion: Higher levels of IFN-γ are significantly associated with an increased risk of SLE, providing insights into the pathogenesis of SLE. mendelian randomization interferon-γ interferon-γ receptor risk factor single nucleotide polymorphism systemic lupus erythematosus Figures Figure 1 Figure 2 Figure 3 Key Points • Higher levels of interferon (IFN)-γ are associated with an increased risk of SLE. In contrast, IFN-γ receptor 1 (IFN-γR1) and IFN-γ receptor 2 (IFN-γR2) levels were not causally related to SLE risk. • It shows that IFN-γ plays an important role in SLE, and inhibiting IFN-γ levels has a potential role in the treatment of SLE. IFN-γR, mainly IFNγR2, plays a role in SLE and IFN-γR2 is a potential therapeutic target for the treatment of SLE. Introduction Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by an increase in autoantibodies and immune complexes, due to an abnormal immune system that produces autoantibodies 1 . The pathogenesis of SLE is related to genetic, environmental factors, and immune abnormalities 2-6 . Interferon-γ (IFN-γ) has been identified as a mediator in the development of SLE and is considered a risk factor for the disease 7 . IFN-γ, a critical cytokine associated with the development of autoimmune diseases, is produced by T lymphocytes, macrophages, mucosal epithelial cells, and natural killer cells 8,9 . The functional IFN-γ sensor consists of two subunits: IFN-γ receptor 1 (IFN-γR1) and IFN-γR2. Upon interacting with the IFN-γ receptor (IFN-γR1/2), IFN-γ activates the Janus kinase (JAK) signaling pathway and the transcription protein (STAT), leading to changes in the immune system that regulate various immune cells and mediate the development of SLE 10,11 . It has been reported that the IFN-γ signaling pathway is activated in SLE patients 11 . Moreover, studies have shown cross-interference between IFN-γ and major histocompatibility complex molecules, where IFN-γ activates the transcription of class I and II major histocompatibility complex molecules, contributing to the development and severity of SLE 12 . In the mouse lupus model, treating New Zealand Black (NZB) /New Zealand White(NZW) mice with soluble mouse IFN-γRs inhibited chronic lupus lesions, highlighting the significance of these receptors in SLE development. Although elevated IFN-γ levels are associated with SLE 1,13 , this association may be influenced by reverse causation, confounding, and selection bias (i.e., selective survival before recruitment). Understanding the role of IFN-γ and IFN-γR in SLE could aid in preventing the incidence of SLE and in the development of new therapeutic targets. Mendelian randomization (MR) is a genetic epidemiology approach that assesses the casual association between outcomes and exposures 14 . Genetic variants significantly related to exposure are selected as instrumental variables (IVs) to infer the causality 15 . The IVs that affect the exposure affect the results proportionally if the exposure is causal. Compared with traditional observational studies, MR analysis can overcome confounding factors, loss of follow-up, the time-consuming nature, and other difficulties in conventional studies. Therefore, in this study, we used a two-sample MR analysis to investigate the causal relationship between three exposures (IFN-γ, IFN-γR1, and IFN-γR2) and SLE. Methods 2.1 Study Design The overall design used for this work is illustrated in Figure 1. We first conducted forward MR analyses to investigate the effects of IFN-γ, IFN-γR1, and IFN-γR2 on SLE risk using data. Briefly, IFN-γ, IFN-γR1, and IFN-γR2 served as the exposures, while SLE served as the outcome. Single-nucleotide polymorphisms (SNPs) significantly associated with IFN-γ, IFN-γR1, and IFN-γR2 were selected as IVs based on strict inclusion and exclusion criteria. A series of sensitivity analyses were performed for significant associations. Subsequently, we performed reverse MR analyses to examine whether the genetic liability to SLE influences levels of IFN-γ, IFN-γR1, and IFN-γR2. There are three core assumptions for selecting IVs in MR analysis 16 : (1) the genetic variants used as proxies for the exposure are robustly associated with the exposure; (2) there is no confounding of the selected IVs with the outcome; and (3) the IVs affect the outcome risk only through the exposure, not through other pathways. 2.2 Data Sources Regarding the exposure, genetic instruments for IFN-γ, IFN-γR1, and IFN-γR2 were derived from a large genome-wide association study (GWAS), which included a total sample size of 3301 participants. This involved creating and interrogating a genetic atlas of the human plasma proteome, using an expanded version of an aptamer-based multiplex protein assay (SOMAscan) to quantify 3622 plasma proteins in 3301 healthy participants from the INTERVAL study 17 . As for the outcome, we utilized publicly available summary statistic datasets from GWASs for SLE, involving 5201 SLE cases and 9066 healthy controls of European ancestry, covering a total of 7,071,163 markers 18 . These SNPs were used to perform bidirectional MR analysis in each dataset. To eliminate population stratification bias, all SNPs and their accompanying summary data were retrieved from studies that exclusively included populations of European ancestry. All the data used in this study are publicly available in the GWAS summary datasets. 2.3 Selection of SNPs All SNPs considered in this study met the following criteria: (1) strongly associated with exposure based on genome-wide significance; (2) avoidance of linkage disequilibrium (LD) complications (pairwise r 2 < 0.001, window size = 10,000 kb); (3) exclusion of SNPs that are palindromic with intermediate allele frequencies 19 . MR analysis requires that genetic variation be associated with exposure but not with potential confounders. Thus, we used PhenoScannerV2 (http://www.phenoscanner.medschl.cam.ac.uk/) to check if the SNPs were associated with confounders. All F -statistics were greater than 10, suggesting that our selection of IVs is unlikely to be affected by weak instrument bias 20 . Finally, we identified the independent SNPs at the study-specific genome-wide significance ( P <5×10 -6 ) associated with IFN-γ, IFN-γR1, and IFN-γR2 levels as IVs (Supporting information Table S1). Although fewer SNP-IFN-γ associations reached the genome-wide significance threshold ( P <5×10 -8 ), many previous studies have accepted p <5×10 -6 as the threshold for study significance, as in the study of the causal relationship between mammalian Target of Rapamycin-dependent eukaryotic translation initiation factor (EIF)-4E and EIF-4A circulating protein levels and type 2 diabetes in MR analysis 21 . This threshold was used, and the final result was deemed robust and clinically significant. For the selection of outcome-associated SNPs, we performed reverse MR analysis with a threshold of P <5×10 -8 to identify SNPs highly associated with SLE as IVs, with IFN-γ, IFN-γR1, and IFN-γR2 levels as outcomes. We performed the same methods to screen SNPs associated with SLE (Supporting information Table S2) 22,23 . 2.4 Mendelian Randomization analysis The inverse-variance weighted (IVW) method was applied to derive an overall weighted estimate of the potential causal effect by calculating the MR-derived odds ratio (OR) of SLE risk for IFN-γ, IFN-γR1, and IFN-γR2. This involves integrating the Wald ratio estimates of each SNP through meta-analysis (β coefficient of SNPs for SLE divided by β coefficient of SNPs for IFN-γ, IFN-γR1, and IFN-γR2) to obtain the overall effect of IFN-γ, IFN-γR1, and IFN-γR2 on SLE 24 . The IVW method is most effective when all IVs are valid. However, the presence of horizontal pleiotropy can lead to biased inferences 25 . Accordingly, MR-Egger and the weighted median methods enhance the IVW estimates by providing more reliable, albeit less efficient, estimates across a broader range of scenarios 26 . The weighted median method estimates the causal effect from the median of the weighted empirical density function of the SNP-outcome/SNP-exposure ratio estimates, offering valid estimates when ≥50% of the information comes from valid SNPs 27 . 2.5 Sensitivity Analyses Sensitivity analyses with different assumptions were performed to enhance the reliability of the results, addressing pleiotropy and heterogeneity. The MR-Egger method allows for the assessment of directional pleiotropy by introducing an intercept in the weighted regression model. A significant MR-Egger intercept indicates the presence of directional pleiotropy 26 . MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analyses were conducted to identify and correct potential outliers, thereby helping to mitigate the effects of potential horizontal pleiotropy 28 . Funnel plots were employed to visualize MR analyses and to search for asymmetry as an indicator of pleiotropy 29 . We utilized the IVW method and MR-Egger regression to detect heterogeneity, quantified by Cochran’s Q statistic; a P value of <0.05 was considered indicative of significant heterogeneity. A “leave-one-out” sensitivity analysis was also conducted to pinpoint potentially influential SNPs. The causal relationship would be deemed credible and stable if the results of the leave-one-out analysis were consistent with those of the overall IVW analysis. Forest plots were utilized to visualize the outcomes of leave-one-out analyses, evaluating the stability of effect sizes by recalculating the causal estimates from IVW after excluding one SNP at a time to ascertain if the estimates were biased or influenced by an outlier. All statistical analyses were conducted using R (version 4.0.3) (R Foundation for Statistical Computing, Vienna, Austria). The IVW, weighted median, and MR-Egger regression methods were performed using the “TwoSampleMR” package. The MR-PRESSO test was performed using the “MRPRESSO” package. Due to multiple testing, associations with P -values less than the Bonferroni corrected threshold a =0.05/3=0.017 were regarded as statistically significant, and associations with a P -value≥ 0.017 and <0.05 as suggestively significant. Results 3.1 Selection of SNPs For forward MR analyses, a total of 26 SNPs, including 9 SNPs associated with IFN-γ (rs115861866, rs138094598, rs141059739, rs1440480, rs2363910, rs5747241, rs7459901, rs75268621, rs7567468), 8 SNPs associated with IFN-γR1 (rs7080536, rs111770018, rs117480750, rs73044907, rs117748849, rs62245853, rs12034435, rs79708242) and 9 SNPs associated with IFN-γR2 (rs111566682, rs117210563, rs140878030, rs17709867, rs4540249, rs72639485, rs9586564), were selected by filtering the threshold of significance ( P <5×10 -6 ), false discovery rate (FDR)<5%, and LD ( r 2 <0.001). None of the genetic variants was palindromic with intermediate allele frequencies (Supporting information Table S3). Correspondingly, 40 SNPs associated with SLE were screened for reverse MR analyses (Supporting information Table S4). The above 40 SNPs passed the filtered significance threshold ( P <5×10 -8 ) with a false discovery rate (FDR)<5% and LD ( r 2 <0.001) (Supporting information Table S3,4). 3.2 Mendelian Randomization analysis The results of IVW estimates indicated that higher levels of IFN-γ were associated with an increased risk of SLE (OR=1.24, 95% confidence interval [CI]: 1.03–1.47, P =0.018). However, the results from the weighted median analysis (OR= 1.07, 95% CI: 0.86–1.33, P =0.548) and MR-Egger (OR=1.39, 95% CI: 0.85–2.26, P =0.228) did not support the findings from the IVW analysis. In the IVW analysis, IFN-γR2 levels were not associated with an increased risk of SLE after adjustment for multiple comparisons (OR=0.85, 95% CI: 0.73–0.99, P =0.034). The weighted median (OR=0.89, 95% CI: 0.73–1.08, P =0.245) and MR-Egger (OR=0.89, 95% CI: 0.59–1.35, P =0.612) analyses also produced consistent directions for effect estimation. Regarding the effect of IFN-γ on SLE, although the results from the weighted median analysis in our study do not support those from the IVW analysis, the IVW method is our primary method and is considered more robust, with the remaining methods serving as sensitivity analyses. Therefore, based on the IVW results, there is a significant relationship between elevated IFN-γ levels and an increased risk of SLE, and since the P -value is close to 0.017, we consider there to be a potential causal relationship between them. Since the MR results for IFN-γR2 levels and increased risk of SLE did not pass the Bonferroni correction, we do not consider them to indicate a potentially causal relationship. There is a lack of evidence to suggest an association between genetic predisposition to SLE and IFN-γR1. Using the multiplicative random effect IVW model as the primary estimator, genetic predisposition to IFN-γR1 was not found to be associated with the risk of SLE (OR=0.97, 95% CI: 0.79–1.19, P =0.768). A null association was also observed using MR-Egger (OR=0.80, 95% CI: 0.55–1.14, P =0.264). Consistently, the weighted median analysis provided little evidence of causal effects of IFN-γR1 levels on SLE (OR=0.84, 95% CI: 0.70–1.02, P =0.085). (Figure 2A–C, Table 1). 3.3 Sensitivity Analyses Horizontal pleiotropy between IVs and the outcome was evaluated by MR-Egger regression, and the results indicated no evidence for a significant intercept. The "MR-PRESSO outlier test" was used to eliminate abnormal SNPs (outliers) and estimate the corrected results. MR-PRESSO analysis identified no outlying SNP in associations between IFN-γ, IFN-γR1, IFN-γR2, and SLE, suggesting that the observed associations might not be affected by pleiotropy. Regarding heterogeneity, the Q statistics of the IVW test and the MR-Egger regression demonstrated no significant heterogeneity, except for the association between IFN-γR1 and SLE (MR-Egger, P =0.078; IVW, P =0.045). In addition, the leave-one-out sensitivity analysis showed that the results of the MR analysis were stable. (Figure 2D–I, Table 1) 3.4 Reverse Analysis MR To investigate the causal association of SLE on IFN-γ, IFN-γR1, and IFN-γR2 levels, we conducted reverse MR analysis using SLE as the exposure and IFN-γ, IFN-γR1, and IFN-γR2 levels as the outcomes. We identified SNPs highly correlated with SLE as IVs. The results of the IVW analysis showed no significant causal association of SLE on IFN-γ levels (OR=0.99, 95% CI: 0.96–1.03, P =0.730), IFN-γR1 levels (OR=1.00, 95% CI: 0.97–1.04, P =0.801), and IFN-γR2 levels (OR=0.99, 95% CI: 0.96–1.02, P =0.656). No significant pleiotropy among the SNPs (P > 0.05) was observed when conducting MR-Egger. MR-PRESSO analysis found no abnormal SNPs in the association of SLE with IFN-γ, IFN-γR1, and IFN-γR2, suggesting that the observed associations may not be affected by horizontal pleiotropy. Mild heterogeneity was detected in IFN-γ (MR-Egger, P =0.031; IVW, P =0.037), indicating slight bias caused by a few SNPs. The results of the funnel plot and leave-one-out sensitivity analysis demonstrated that the association of SLE with IFN-γ, IFN-γR1, and IFN-γR2 levels was not significantly affected by any individual SNP. (Figure 3, Table 2) Discussion We elucidate the causal relationship between IFN-γ or IFN-γR and SLE through two-sample MR analysis in both directions. After eliminating complex confounders, genetic evidence suggests that higher levels of IFN-γ are significantly associated with an increased risk of SLE. IFN-γR1 and IFN-γR2 do not have an obvious causal relationship with SLE. To further clarify the causal relationship, we performed reverse MR analysis, and the results showed no significant relationship between SLE risk and levels of IFN-γ or IFN-γR. Patients with SLE are known to have significantly higher levels of IFN-γ mRNA and protein than do healthy individuals, along with elevated levels of mRNA produced by IFN-induced gene type II (Interferon regulatory factor 1, Guanylate-binding Protein 1, C-X-C motif chemokine ligand 9 , C-X-C motif chemokine ligand 10, and serpin family G member 1) 30 , 31 . Thomason et al. 32 demonstrated that IFN-γ activation could indicate disease activity in SLE patients. Additionally, the response to ustekinumab treatment in SLE patients was associated with the suppression of serum IFN-γ levels 33 . Furthermore, Jacob et al. 34 found that in NZB/NZW F1 mice, IFN-γ monoclonal antibodies showed good efficacy in vivo for lupus nephritis. Hron et al. 35 discovered that IFN-RII deficiency protected MRL /LPR mice from severe autoimmune-related lymphadenopathy, autoantibodies, and kidney disease. These studies confirm that elevated IFN-γ levels are associated with an increased risk of SLE, supporting our analysis. In conducting the analysis, it is crucial to clarify the mechanism from exposure to results. IFN-γ operates through a signaling pathway that binds to IFN-γR, expressed on most cells, and activates JAK1 and JAK2. This activation leads to the phosphorylation of STAT1, which then binds to the IFN-γ activation site for gene transcription, completing related functions 36 . Understanding the IFN-γ signaling pathway helps explain the causal role of IFN-γ and IFN-γR in SLE, clarifying the disease's development path and providing theoretical support for future clinical treatments. The pathogenesis of SLE is complex, mediated by immune disorders that include the loss of immune self-tolerance and enhanced T and B cell responses. IFN-γ, a major pro-inflammatory cytokine, plays a significant role in regulating the function of key immune cells, including B cells and T cells, contributing to the development of SLE 37,38 . First, it leads to disordered regulation of T cells. In the pathogenesis of SLE, imbalances between T helper (Th)1 and Th2 cells are common. IFN-γ signaling significantly inhibits the differentiation of CD4 + T cells into Th2 cells, leading to an imbalance between Th1 and Th2 cells 39 . Th1 cells can secrete IFN-γ to promote SLE-related pathologies, and IFN-γ, in turn, enhances the pathogenic role of Th1 cells. IFN-γ plays a crucial role in the differentiation and maturation of Th1 cells 40 . Second, B cells act as inflammatory mediators, producing pathogenic antibodies to enhance the inflammatory response and directly damaging tissues and cells 41 . IFN-γ signaling stimulates T cells and antigen-presenting cells to produce B-lymphocyte-stimulating factor, which aids in the differentiation and survival of B cells 42-45 . Third, defects in Treg cell function or quantity, crucial for maintaining peripheral immune tolerance, are thought to contribute to the pathogenesis of SLE. 46 Recent studies have demonstrated IFN-γ's ability to directly inhibit the function of Treg cells, leading to a loss of autoimmune tolerance and mediating the development of SLE. 47-49 . Fourth, IFN-γ signaling directly influences several aspects of CD8 + T cell biology. Most importantly, IFN-γ is essential for the cytolytic capacity of CD8 + T cells 50 . CD8 + T cells in the peripheral blood of SLE patients typically have reduced production of granzyme B and perforin and exhibit impaired cytolytic function 51 . This impairment hampers the removal of autoreactive B cells and increases autoantibodies, accelerating the onset of lupus. The IFN-γ signaling pathway is crucial for both innate and adaptive immunity, requiring the presence of IFN-γR for IFN-γ's biological activities and signaling. Nakashima et al. 52 discovered amino acid polymorphisms (Val14Met) within IFN-γR1, with the frequency of the Met14 allele in SLE patients being significantly higher than in healthy controls. Similarly, amino acid polymorphisms (Gln64Arg) were identified in IFN-γR2. The highest risk of developing SLE was observed in individuals with the IFNGR1 Met14/Val14 genotype and the IFNGR2 Gln64/Gln64 genotype, highlighting their critical role in SLE susceptibility. Xu et al. 53 found that the IFN-γR2 Arg64/Arg64 genotype reduced the risk of SLE ( P = 0.047). However, our findings indicate that IFN-γR2 and IFN-γR1 are not associated with SLE, suggesting that further research into the pathogenesis of SLE is necessary to identify more effective therapeutic targets for clinical treatment. The strength of our study lies in the MR study design, which mitigates unobserved confounding and reverse causation by using genetic variation as a proxy for IFN-γ and IFN-γR. Beyond the method's advantages, our study expands on previous MR studies concerning IFN-γ and IFN-γR versus SLE. First, we employed bidirectional MR and found evidence from only one direction, with IFN-γ showing a potential causal relationship with SLE, a relationship not observed with IFN-γR. Second, while most research focuses on IFN-α, IFN-γ also plays a significant role in SLE, with both being utilized together in some pathways. Our study not only investigates IFN-γ but also its receptors, providing a more comprehensive understanding of the risk factors for SLE. However, this study has some limitations. First, although we utilized the largest available GWAS, some identified only a few genome-wide significant SNPs, which could result in relatively weak genetic instruments. To address this, we applied statistical criteria to include additional SNPs as instruments. This approach has been utilized in other MR studies where the number of known genome-wide significant SNPs is limited. Additionally, due to the unknown biological action of these SNPs, it is impossible to fully rule out pleiotropic mechanisms without detailed functional follow-up of these loci, although we conducted the most up-to-date array of sensitivity analyses to mitigate the impact of horizontal pleiotropy. While horizontal pleiotropy is a concern for MR inference, vertical pleiotropy—where an exposure affects an outcome via other variables along the same causal pathway—is considered acceptable. Third, the few SNPs with heterogeneity in our reverse MR experiment caused minimal bias. However, the association of SLE with IFN-γ, IFN-γR1, and IFN-γR2 levels was not significantly affected by any single SNP. Fourth, due to the lack of GWAS for IFN-γR to date, we were unable to use strict statistical thresholds to study the effects of IFN-γ and IFN-γR on SLE. Nevertheless, the more relaxed IV thresholds used have been employed in many previous studies. Although we did not find a causal effect of IFN-γR on SLE, previous studies have indicated that IFN-γR2 levels are associated with SLE risk, suggesting the need for more IFN-γR GWAS to further investigate this relationship. Conclusion Our findings have corroborated a causal association between IFN-γ and SLE, which may influence clinical decisions about patient management with SLE. Higher levels of IFN-γ are associated with an increased risk of SLE. Although our results do not support a causal association between IFN-γR1 and IFN-γR2 and SLE, further research is needed to clarify the effect of IFN-γR on SLE. Declarations Acknowledgments Data about the IFN-γ-related GWAS data was obtained from the INTERVAL study Proteomics- GWAS data. We thank all involved investigators for sharing their data. We want to acknowledge the participants and investigators of the Integrative Epidemiology Unit (IEU) GWAS database. Conflict of Interests The authors declare that there is no conflict of interest regarding the publication of this paper. Ethical Statement All the data used in this study were from public databases and did not require ethical approval. This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors. Author Contribution Study design and manuscript writing: MJC. KXY. And JWH. Data extraction, quality assessment, analysis and interpretation of data: MJC. KXY. And JWH. All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. He had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published. Data availability The datasets generated and analysed during the current study are available in the IEU open gwas project [https://gwas.mrcieu.ac.uk/], and the GWAS ID are prot-a-1428, prot-a-1430, prot-a-1432, ebi-a-GCST003156, respectively. References 1 Fanouriakis, A., Tziolos, N., Bertsias, G. & Boumpas, D. T. Update οn the diagnosis and management of systemic lupus erythematosus. Ann Rheum Dis 80, 14-25, doi:10.1136/annrheumdis-2020-218272 (2021). 2 Murphy, G. & Isenberg, D. A. New therapies for systemic lupus erythematosus - past imperfect, future tense. Nat Rev Rheumatol 15, 403-412, doi:10.1038/s41584-019-0235-5 (2019). 3 Catalina, M. D., Owen, K. A., Labonte, A. C., Grammer, A. C. & Lipsky, P. E. The pathogenesis of systemic lupus erythematosus: Harnessing big data to understand the molecular basis of lupus. J Autoimmun 110, 102359, doi:10.1016/j.jaut.2019.102359 (2020). 4 Fava, A. & Petri, M. Systemic lupus erythematosus: Diagnosis and clinical management. J Autoimmun 96, 1-13, doi:10.1016/j.jaut.2018.11.001 (2019). 5 Kaul, A. et al. Systemic lupus erythematosus. Nat Rev Dis Primers 2, 16039, doi:10.1038/nrdp.2016.39 (2016). 6 Dörner, T. & Furie, R. Novel paradigms in systemic lupus erythematosus. Lancet 393, 2344-2358, doi:10.1016/s0140-6736(19)30546-x (2019). 7 Liu, W., Li, M., Wang, Z. & Wang, J. IFN-γ Mediates the Development of Systemic Lupus Erythematosus. Biomed Res Int 2020, 7176515, doi:10.1155/2020/7176515 (2020). 8 Psarras, A., Emery, P. & Vital, E. M. Type I interferon-mediated autoimmune diseases: pathogenesis, diagnosis and targeted therapy. Rheumatology (Oxford) 56, 1662-1675, doi:10.1093/rheumatology/kew431 (2017). 9 Ding, H., Wang, G., Yu, Z., Sun, H. & Wang, L. Role of interferon-gamma (IFN-γ) and IFN-γ receptor 1/2 (IFNγR1/2) in regulation of immunity, infection, and cancer development: IFN-γ-dependent or independent pathway. Biomed Pharmacother 155, 113683, doi:10.1016/j.biopha.2022.113683 (2022). 10 Encalada-Díaz, M. I. [3D Printing and Orthopedics of Mexico]. Acta Ortop Mex 36, 1 (2022). 11 Liu, M. et al. Higher activation of the interferon-gamma signaling pathway in systemic lupus erythematosus patients with a high type I IFN score: relation to disease activity. Clin Rheumatol 37, 2675-2684, doi:10.1007/s10067-018-4138-7 (2018). 12 Groettrup, M., Khan, S., Schwarz, K. & Schmidtke, G. Interferon-gamma inducible exchanges of 20S proteasome active site subunits: why? Biochimie 83, 367-372, doi:10.1016/s0300-9084(01)01251-2 (2001). 13 Ozmen, L. et al. Experimental therapy of systemic lupus erythematosus: the treatment of NZB/W mice with mouse soluble interferon-gamma receptor inhibits the onset of glomerulonephritis. Eur J Immunol 25, 6-12, doi:10.1002/eji.1830250103 (1995). 14 Li, C., Yang, W., Wei, Q. & Shang, H. Causal Association of Leukocytes Count and Amyotrophic Lateral Sclerosis: a Mendelian Randomization Study. Mol Neurobiol 57, 4622-4627, doi:10.1007/s12035-020-02053-7 (2020). 15 Dan, Y. L. et al. Circulating adiponectin levels and systemic lupus erythematosus: a two-sample Mendelian randomization study. Rheumatology (Oxford) 60, 940-946, doi:10.1093/rheumatology/keaa506 (2021). 16 Gagliano Taliun, S. A. & Evans, D. M. Ten simple rules for conducting a mendelian randomization study. PLoS Comput Biol 17, e1009238, doi:10.1371/journal.pcbi.1009238 (2021). 17 Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73-79, doi:10.1038/s41586-018-0175-2 (2018). 18 Bentham, J. et al. Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. Nat Genet 47, 1457-1464, doi:10.1038/ng.3434 (2015). 19 Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. & Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27, 1133-1163, doi:10.1002/sim.3034 (2008). 20 Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40, 755-764, doi:10.1093/ije/dyr036 (2011). 21 Soliman, G. A. & Schooling, C. M. Causal association between mTOR-dependent EIF-4E and EIF-4A circulating protein levels and type 2 diabetes: a Mendelian randomization study. Sci Rep 10, 15737, doi:10.1038/s41598-020-71987-8 (2020). 22 Pierce, B. L., Ahsan, H. & Vanderweele, T. J. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40, 740-752, doi:10.1093/ije/dyq151 (2011). 23 Palmer, T. M. et al. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat Methods Med Res 21, 223-242, doi:10.1177/0962280210394459 (2012). 24 Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G. & Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. Eur J Epidemiol 30, 543-552, doi:10.1007/s10654-015-0011-z (2015). 25 Lin, Z., Deng, Y. & Pan, W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. PLoS Genet 17, e1009922, doi:10.1371/journal.pgen.1009922 (2021). 26 Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44, 512-525, doi:10.1093/ije/dyv080 (2015). 27 Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol 40, 304-314, doi:10.1002/gepi.21965 (2016). 28 Verbanck, M., Chen, C. Y., Neale, B. & Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50, 693-698, doi:10.1038/s41588-018-0099-7 (2018). 29 Bowden, J. et al. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. Int J Epidemiol 47, 1264-1278, doi:10.1093/ije/dyy101 (2018). 30 Fava, A. et al. Integrated urine proteomics and renal single-cell genomics identify an IFN-γ response gradient in lupus nephritis. JCI Insight 5, doi:10.1172/jci.insight.138345 (2020). 31 Csiszár, A., Nagy, G., Gergely, P., Pozsonyi, T. & Pócsik, E. Increased interferon-gamma (IFN-gamma), IL-10 and decreased IL-4 mRNA expression in peripheral blood mononuclear cells (PBMC) from patients with systemic lupus erythematosus (SLE). Clin Exp Immunol 122, 464-470, doi:10.1046/j.1365-2249.2000.01369.x (2000). 32 Thomason, J. L., Obih, U. M., Koelle, D. M., Lood, C. & Hughes, A. G. An interferon-gamma release assay as a novel biomarker in systemic lupus erythematosus. Rheumatology (Oxford) 59, 3479-3487, doi:10.1093/rheumatology/keaa161 (2020). 33 Cesaroni, M. et al. Suppression of Serum Interferon-γ Levels as a Potential Measure of Response to Ustekinumab Treatment in Patients With Systemic Lupus Erythematosus. Arthritis Rheumatol 73, 472-477, doi:10.1002/art.41547 (2021). 34 Jacob, C. O., van der Meide, P. H. & McDevitt, H. O. In vivo treatment of (NZB X NZW)F1 lupus-like nephritis with monoclonal antibody to gamma interferon. J Exp Med 166, 798-803, doi:10.1084/jem.166.3.798 (1987). 35 Hron, J. D. & Peng, S. L. Type I IFN protects against murine lupus. J Immunol 173, 2134-2142, doi:10.4049/jimmunol.173.3.2134 (2004). 36 Darnell, J. E., Jr., Kerr, I. M. & Stark, G. R. Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. Science 264, 1415-1421, doi:10.1126/science.8197455 (1994). 37 Schroder, K., Hertzog, P. J., Ravasi, T. & Hume, D. A. Interferon-gamma: an overview of signals, mechanisms and functions. J Leukoc Biol 75, 163-189, doi:10.1189/jlb.0603252 (2004). 38 Tau, G. & Rothman, P. Biologic functions of the IFN-gamma receptors. Allergy 54, 1233-1251, doi:10.1034/j.1398-9995.1999.00099.x (1999). 39 Oriss, T. B., McCarthy, S. A., Morel, B. F., Campana, M. A. & Morel, P. A. Crossregulation between T helper cell (Th)1 and Th2: inhibition of Th2 proliferation by IFN-gamma involves interference with IL-1. J Immunol 158, 3666-3672 (1997). 40 Cope, A., Le Friec, G., Cardone, J. & Kemper, C. The Th1 life cycle: molecular control of IFN-γ to IL-10 switching. Trends Immunol 32, 278-286, doi:10.1016/j.it.2011.03.010 (2011). 41 Szelinski, F., Lino, A. C. & Dörner, T. B cells in systemic lupus erythematosus. Curr Opin Rheumatol 34, 125-132, doi:10.1097/bor.0000000000000865 (2022). 42 Harigai, M. et al. Excessive production of IFN-gamma in patients with systemic lupus erythematosus and its contribution to induction of B lymphocyte stimulator/B cell-activating factor/TNF ligand superfamily-13B. J Immunol 181, 2211-2219, doi:10.4049/jimmunol.181.3.2211 (2008). 43 Scapini, P. et al. Proinflammatory mediators elicit secretion of the intracellular B-lymphocyte stimulator pool (BLyS) that is stored in activated neutrophils: implications for inflammatory diseases. Blood 105, 830-837, doi:10.1182/blood-2004-02-0564 (2005). 44 Yan, M. et al. Identification of a receptor for BLyS demonstrates a crucial role in humoral immunity. Nat Immunol 1, 37-41, doi:10.1038/76889 (2000). 45 Do, R. K. et al. Attenuation of apoptosis underlies B lymphocyte stimulator enhancement of humoral immune response. J Exp Med 192, 953-964, doi:10.1084/jem.192.7.953 (2000). 46 Bonelli, M., Smolen, J. S. & Scheinecker, C. Treg and lupus. Ann Rheum Dis 69 Suppl 1, i65-66, doi:10.1136/ard.2009.117135 (2010). 47 Olalekan, S. A., Cao, Y., Hamel, K. M. & Finnegan, A. B cells expressing IFN-γ suppress Treg-cell differentiation and promote autoimmune experimental arthritis. Eur J Immunol 45, 988-998, doi:10.1002/eji.201445036 (2015). 48 Chang, J. H., Kim, Y. J., Han, S. H. & Kang, C. Y. IFN-gamma-STAT1 signal regulates the differentiation of inducible Treg: potential role for ROS-mediated apoptosis. Eur J Immunol 39, 1241-1251, doi:10.1002/eji.200838913 (2009). 49 Kelchtermans, H. et al. Defective CD4+CD25+ regulatory T cell functioning in collagen-induced arthritis: an important factor in pathogenesis, counter-regulated by endogenous IFN-gamma. Arthritis Res Ther 7, R402-415, doi:10.1186/ar1500 (2005). 50 Siegel, J. P. Effects of interferon-gamma on the activation of human T lymphocytes. Cell Immunol 111, 461-472, doi:10.1016/0008-8749(88)90109-8 (1988). 51 Comte, D. et al. Signaling Lymphocytic Activation Molecule Family Member 7 Engagement Restores Defective Effector CD8+ T Cell Function in Systemic Lupus Erythematosus. Arthritis Rheumatol 69, 1035-1044, doi:10.1002/art.40038 (2017). 52 Nakashima, H. et al. Polymorphisms within the interleukin-10 receptor cDNA gene (IL10R) in Japanese patients with systemic lupus erythematosus. Rheumatology (Oxford) 38, 1142-1144, doi:10.1093/rheumatology/38.11.1142 (1999). 53 Xu, Y. et al. Correlation between some Th1 and Th2 cytokine receptor gene polymorphisms and systemic lupus erythematosus in Chinese patients. Int J Dermatol 46, 1129-1135, doi:10.1111/j.1365-4632.2007.03258.x (2007). Tables Table1 Mendelian randomization of IFN-γ, IFN-γR1, and IFN-γR2 levels on the risk of SLE. Exposure MR method No. of SNPs Association Heterogeneity Egger regression β coefficients OR p -value Cochran Q p -value Intercept p -value IFN-γ levels MR Egger 9 0.32887 1.40 0.228 10.69006 0.153 -0.02271 0.626 IVW 9 0.21126 1.24 0.018 11.08669 0.197 Weighted median 9 0.06725 1.07 0.548 NA NA IFN-γR1 levels MR Egger 8 -0.22757 0.80 0.264 11.34511 0.078 0.05509 0.252 IVW 8 -0.03064 0.97 0.768 14.37791 0.045 Weighted median 8 -0.16921 0.84 0.085 NA NA IFN-γR2 levels MR Egger 9 -0.11252 0.89 0.612 5.99048 0.541 -0.00859 0.813 IVW 9 -0.16118 0.85 0.034 6.05086 0.642 Weighted median 9 -0. 11684 0.89 0.245 NA NA ORs estimate the relationship between systemic lupus erythematosus risk and IFN-γ, IFN-γR1, and IFN-γR2. OR < 1: genetic variants with higher/lower physiological serum IFN-γ, IFN-γR1, and IFN-γR2 levels are associated with decreased risk of SLE and vice versa. Abbreviations: IFN-γ, Interferon-gamma; IFN-γR1, Interferon gamma receptor 1; IFN-γR2, Interferon gamma receptor 2;IVW, inverse variance weighted analysis; MR, mendelian randomization; NA, not available; No. of SNPs, number of single nucleotide polymorphisms; OR, odds ratios; SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism. Table 2 The output of Mendelian randomization of the risk of SLE on IFN-γ, IFN-γR1, and IFN-γR2 levels. Outcome MR method No. of SNPs Association Heterogeneity Egger regression β coefficients OR p -value Cochran Q p -value Intercept p -value IFN-γ Levels MR Egger 40 -0.01973 0.98 0.596 55.88461 0.031 0.00553 0.675 IVW 40 -0.00615 0.99 0.730 56.14684 0.037 Weighted median 40 -0.01483 0.99 0.524 NA NA IFN-γR1 levels MR Egger 40 -0.03478 0.97 0.312 47.48358 0.139 0.01587 0.196 IVW 40 0.00421 1.00 0.801 49.64406 0.118 Weighted median 40 -0.00818 0.99 0.722 NA NA IFN-γR2 levels MR Egger 40 0.01420 1.01 0.643 16.95165 0.999 -0.00847 0.438 IVW 40 -0.00660 0.99 0.656 17.56615 0.999 Weighted median 40 0.00266 1.00 0.897 NA NA The β-coefficients represent the log OR of SLE risk for each additional effect allele (β coefficients < 0, OR 0, OR > 1). β coefficients < 0: genetic variants with physiological serum IFN-γ, IFN-γR1, and IFN-γR2 levels are associated with reduced risk of SLE and vice versa. Abbreviations: IFN-γ, Interferon-gamma; IFN-γR1, Interferon gamma receptor 1; IFN-γR2, Interferon gamma receptor 2; IVW, inverse variance weighted analysis; MR, mendelian randomization; NA, not available; No. of SNPs, number of single nucleotide polymorphisms; OR, odds ratios; SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterial.docx Supplymentary material Cite Share Download PDF Status: Published Journal Publication published 12 May, 2024 Read the published version in Rheumatology & Autoimmunity → Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2776347","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":191225502,"identity":"a8bcbdce-d805-440d-9f24-2a15b42c4f0e","order_by":0,"name":"Minjing Chang","email":"","orcid":"","institution":"Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, Shanxi, China.","correspondingAuthor":false,"prefix":"","firstName":"Minjing","middleName":"","lastName":"Chang","suffix":""},{"id":191225503,"identity":"21f72255-253d-4278-b4a4-81b87c26766f","order_by":1,"name":"Kai-Xin 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He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYBACAxCRYAMk2BsbDnyokJDjJ05LGpDgOXzw4YwzFsaSDcRoYQBpkUhLNuZsq0jcQEiLOXvvwQcPEmzy5B1yzKQZ50kwbmBgfvjoBh4tlj3nkg0SEtKKDQ+cMZMu3CbBbM7AZmycg89hN3LMJBJ/HE7c2NhjJj1zmwSbZQMPmzReLfffmP9ISPifuLGZx0yad44Ej8EBQlpu8JgxJCQcSJzPxpZszNsgIUFYy5m8ZImEhOTEDTzMwEA+JmEg2UzIL8fPHvz4I8Eucf78h8CorKmr72dvfvgYnxZgFEL1HoAJMONVjqRFvoGgylEwCkbBKBipAADGvVHlTe8puQAAAABJRU5ErkJggg==","orcid":"","institution":"Shanxi Key Laboratory of Big Data for Clinical Decision, Shanxi Medical University, Taiyuan, Shanxi, China","correspondingAuthor":true,"prefix":"","firstName":"Peifeng","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2023-04-04 11:44:31","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-2776347/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-2776347/v2","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1002/rai2.12125","type":"published","date":"2024-05-13T00:49:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54792239,"identity":"f04498b6-51fa-4a37-83b6-9c51c08cde7e","added_by":"auto","created_at":"2024-04-16 21:53:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101856,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of bidirectional two-sample MR study on the association between IFN-γ, IFN-γR1, IFN-γR2, and SLE.\u003c/p\u003e\n\u003cp\u003e① Variants must be significantly associated with the exposure. ② Genetic instruments are independent of confounders; ③Genetic variants affect the outcome only via exposure. Dashed lines point to irrelevant links, while the cross showed an impassable association between variants and confounders or the outcome. IFN-γ, Interferon-gamma; IFN-γR1, Interferon gamma receptor 1; IFN-γR2, Interferon gamma receptor 2; MR, Mendelian randomization; SLE, systemic lupus erythematosus; SNP, single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2776347/v2/e3ca7447283285f54a66157e.png"},{"id":54792242,"identity":"ad381993-afe8-408e-aad0-c482ee1a0111","added_by":"auto","created_at":"2024-04-16 21:53:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":594698,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots, funnel plots and leave-one-out analysis for the forward MR analysis.\u003c/p\u003e\n\u003cp\u003e(A–C): Scatter plots for IVW, MR-Egger, and weighted median analysis methods of genetic associations with IFN-γ, IFN-γR1, and IFN-γR2 against the genetic associations with SLE. Plots of effect sizes for SNP IFN-γ, IFN-γR1, and IFN-γR2 associations (x-axis, SD units) and SNP-SLE associations (y-axis, log OR) against standard error lines. The slope of each bar indicates the causal association of each method. The blue line is the inverse-variance variance weighted estimates, the green line indicates weighted median estimates, and the dark blue line indicates MR-Egger estimates. (D–F): Heterogeneity of genetic associations with IFN-γ, IFN-γR1, and IFN-γR2 with SLE assessed by funnel plot. The blue line represents the IVW estimate, and the dark blue line represents the MR‐Egger estimate. (G–I): Leave-one-out sensitivity analysis of single SNP of IFN-γ, IFN-γR1, and IFN-γR2 for SLE. Leave-one-out sensitivity analysis to determine if individual SNPs disproportionately affect the association of SLE. Each black point in the forest plot represents the MR analysis (using IVW), excluding that particular SNP. The overall analysis, including all SNPs, is also shown for comparison. (A, D, G: IFN-γ; B, E, H: IFN-γR1; C, F, I: IFN-γR2). IFN-γ, Interferon-gamma; IFN-γR1, Interferon gamma receptor 1; IFN-γR2, Interferon gamma receptor 2; IVW, inverse-variance weighted; MR, Mendelian randomization; OR, odds ratios; SLE, systemic lupus erythematosus; SNP, single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2776347/v2/26d2089ff4f200856128a377.png"},{"id":54792240,"identity":"6d611c1e-294f-4066-83ee-95a2d6aa2185","added_by":"auto","created_at":"2024-04-16 21:53:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":926759,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots, funnel plots and leave-one-out analysis for the reverse MR analysis.\u003c/p\u003e\n\u003cp\u003e(A–C): Scatter plots for IVW, MR-Egger, and weighted median analysis methods of SLE against the genetic associations with IFN-γ, IFN-γR1, and IFN-γR2. Plots of effect sizes for SNP-SLE association (x-axis, SD units) and the SNP IFN-γ, IFN-γR1, and IFN-γR2 associations (y-axis, log OR) against standard error bars. The slopes of each line indicate the causal association for each method. The blue line is the inverse-variance variance weighted estimates, the green line indicates weighted median estimates, and the dark blue line indicates MR-Egger estimates. (D–F): Heterogeneity of genetic associations with SLE with IFN-γ, IFN-γR1, and IFN-γR2 assessed by funnel plot. The blue line represents the IVW, and the dark blue line represents the MR‐Egger estimate. (G–I): Leave-one-out sensitivity analysis of single SNP of SLE for IFN-γ, IFN-γR1, and IFN-γR2. Leave-one-out sensitivity analysis to determine if individual SNPs disproportionately affect the association of IFN-γ, IFN-γR1, and IFN-γR2. Each black point in the forest plot represents the MR analysis (using IVW), excluding that particular SNP. The overall analysis, including all SNPs, is also shown for comparison. (A, D, G: IFN-γ; B, E, H: IFN-γR1; C, F, I: IFN-γR2). IFN-γ, Interferon-gamma; IFN-γR1, Interferon gamma receptor 1; IFN-γR2, Interferon gamma receptor 2; IVW, inverse-variance weighted; MR, Mendelian randomization; OR, odds ratios; SLE, systemic lupus erythematosus; SNP, single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2776347/v2/e98044b1b87a3869b24295e1.png"},{"id":56416549,"identity":"af7be312-59d1-492b-ab3b-ca58a85076d9","added_by":"auto","created_at":"2024-05-14 00:49:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1963944,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2776347/v2/a9fd7182-d1ff-435d-aae0-cd205a650f0f.pdf"},{"id":54792516,"identity":"e0d1a097-a5af-4fe4-924a-da5bc751b6a0","added_by":"auto","created_at":"2024-04-16 22:01:57","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":118100,"visible":true,"origin":"","legend":"\u003cp\u003eSupplymentary material\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2776347/v2/3bfcfea9e64e9491187cc794.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eAssessing the causality of interferon-γ and its receptor 1/2 with systemic lupus erythematosus risk using genetic data\u003c/p\u003e","fulltext":[{"header":"Key Points","content":"\u003cp\u003e\u0026bull; Higher levels of interferon (IFN)-\u0026gamma; are associated with an increased risk of SLE. In contrast, IFN-\u0026gamma;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ereceptor 1 (IFN-\u0026gamma;R1) and IFN-\u0026gamma;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ereceptor 2 (IFN-\u0026gamma;R2) levels were not causally related to SLE risk.\u003c/p\u003e\n\u003cp\u003e\u0026bull; It shows that IFN-\u0026gamma; plays an important role in SLE, and inhibiting IFN-\u0026gamma; levels has a potential role in the treatment of SLE. IFN-\u0026gamma;R, mainly IFN\u0026gamma;R2, plays a role in SLE and IFN-\u0026gamma;R2 is a potential therapeutic target for the treatment of SLE.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eSystemic lupus erythematosus (SLE) is a chronic autoimmune disorder characterized by an increase in autoantibodies and immune complexes, due to an abnormal immune system that produces autoantibodies\u003csup\u003e1\u003c/sup\u003e. The pathogenesis of SLE is related to genetic, environmental factors, and immune abnormalities\u003csup\u003e2-6\u003c/sup\u003e. Interferon-\u0026gamma; (IFN-\u0026gamma;) has been identified as a mediator in the development of SLE and is considered a risk factor for the disease\u003csup\u003e7\u003c/sup\u003e. IFN-\u0026gamma;, a critical cytokine associated with the development of autoimmune diseases, is produced by T lymphocytes, macrophages, mucosal epithelial cells, and natural killer cells\u003csup\u003e8,9\u003c/sup\u003e. The functional IFN-\u0026gamma; sensor consists of two subunits: IFN-\u0026gamma; receptor 1 (IFN-\u0026gamma;R1) and IFN-\u0026gamma;R2. Upon interacting with the IFN-\u0026gamma; receptor (IFN-\u0026gamma;R1/2), IFN-\u0026gamma; activates the Janus kinase (JAK) signaling pathway and the transcription protein (STAT), leading to changes in the immune system that regulate various immune cells and mediate the development of SLE\u003csup\u003e10,11\u003c/sup\u003e. It has been reported that the IFN-\u0026gamma; signaling pathway is activated in SLE patients\u003csup\u003e11\u003c/sup\u003e. Moreover, studies have shown cross-interference between IFN-\u0026gamma; and major histocompatibility complex molecules, where IFN-\u0026gamma; activates the transcription of class I and II major histocompatibility complex molecules, contributing to the development and severity of SLE\u003csup\u003e12\u003c/sup\u003e. In the mouse lupus model, treating New Zealand Black (NZB) /New Zealand White(NZW) mice with soluble mouse IFN-\u0026gamma;Rs inhibited chronic lupus lesions, highlighting the significance of these receptors in SLE development. Although elevated IFN-\u0026gamma; levels are associated with SLE\u003csup\u003e1,13\u003c/sup\u003e, this association may be influenced by reverse causation, confounding, and selection bias (i.e., selective survival before recruitment). Understanding the role of IFN-\u0026gamma; and IFN-\u0026gamma;R in SLE could aid in preventing the incidence of SLE and in the development of new therapeutic targets.\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR) is a genetic epidemiology approach that assesses the casual association between outcomes and exposures\u003csup\u003e14\u003c/sup\u003e. Genetic variants significantly related to exposure are selected as instrumental variables (IVs) to infer the causality\u003csup\u003e15\u003c/sup\u003e. The IVs that affect the exposure affect the results proportionally if the exposure is causal. Compared with traditional observational studies, MR analysis can overcome confounding factors, loss of follow-up, the time-consuming nature, and other difficulties in conventional studies. Therefore, in this study, we used a two-sample MR analysis to investigate the causal relationship between three exposures (IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2) and SLE.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e2.1 Study Design\u003c/p\u003e\n\u003cp\u003eThe overall design used for this work is illustrated in Figure 1. We first conducted forward MR analyses to investigate the effects of IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 on SLE risk using data. Briefly, IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 served as the exposures, while SLE served as the outcome. Single-nucleotide polymorphisms (SNPs) significantly associated with IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 were selected as IVs based on strict inclusion and exclusion criteria. A series of sensitivity analyses were performed for significant associations. Subsequently, we performed reverse MR analyses to examine whether the genetic liability to SLE influences levels of IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2. There are three core assumptions for selecting IVs in MR analysis\u003csup\u003e16\u003c/sup\u003e: (1) the genetic variants used as proxies for the exposure are robustly associated with the exposure; (2) there is no confounding of the selected IVs with the outcome; and (3) the IVs affect the outcome risk only through the exposure, not through other pathways.\u003c/p\u003e\n\u003cp\u003e2.2 Data Sources\u003c/p\u003e\n\u003cp\u003eRegarding the exposure, genetic instruments for IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 were derived from a large genome-wide association study (GWAS), which included a total sample size of 3301 participants. This involved creating and interrogating a genetic atlas of the human plasma proteome, using an expanded version of an aptamer-based multiplex protein assay (SOMAscan) to quantify 3622 plasma proteins in 3301 healthy participants from the INTERVAL study\u003csup\u003e17\u003c/sup\u003e. As for the outcome, we utilized publicly available summary statistic datasets from GWASs for SLE, involving 5201 SLE cases and 9066 healthy controls of European ancestry, covering a total of 7,071,163 markers\u003csup\u003e18\u003c/sup\u003e. These SNPs were used to perform bidirectional MR analysis in each dataset.\u003c/p\u003e\n\u003cp\u003eTo eliminate population stratification bias, all SNPs and their accompanying summary data were retrieved from studies that exclusively included populations of European ancestry. All the data used in this study are publicly available in the GWAS summary datasets.\u003c/p\u003e\n\u003cp\u003e2.3 Selection of SNPs\u003c/p\u003e\n\u003cp\u003eAll SNPs considered in this study met the following criteria: (1) strongly associated with exposure based on genome-wide significance; (2) avoidance of linkage disequilibrium (LD) complications (pairwise \u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e \u0026lt; 0.001, window size = 10,000 kb); (3) exclusion of SNPs that are palindromic with intermediate allele frequencies\u003csup\u003e19\u003c/sup\u003e. MR analysis requires that genetic variation be associated with exposure but not with potential confounders. Thus, we used PhenoScannerV2 (http://www.phenoscanner.medschl.cam.ac.uk/) to check if the SNPs were associated with confounders. All \u003cem\u003eF\u003c/em\u003e-statistics were greater than 10, suggesting that our selection of IVs is unlikely to be affected by weak instrument bias\u003csup\u003e20\u003c/sup\u003e. Finally, we identified the independent SNPs at the study-specific genome-wide significance (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e-6\u003c/sup\u003e) associated with IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels as IVs (Supporting information Table S1). Although fewer SNP-IFN-\u0026gamma; associations reached the genome-wide significance threshold (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e), many previous studies have accepted \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e-6\u003c/sup\u003e as the threshold for study significance, as in the study of the causal relationship between mammalian Target of Rapamycin-dependent eukaryotic translation initiation factor (EIF)-4E and EIF-4A circulating protein levels and type 2 diabetes in MR analysis\u003csup\u003e21\u003c/sup\u003e. This threshold was used, and the final result was deemed robust and clinically significant.\u003c/p\u003e\n\u003cp\u003eFor the selection of outcome-associated SNPs, we performed reverse MR analysis with a threshold of \u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e to identify SNPs highly associated with SLE as IVs, with IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels as outcomes. We performed the same methods to screen SNPs associated with SLE (Supporting information Table S2)\u003csup\u003e22,23\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.4 Mendelian Randomization analysis\u003c/p\u003e\n\u003cp\u003eThe inverse-variance weighted (IVW) method was applied to derive an overall weighted estimate of the potential causal effect by calculating the MR-derived odds ratio (OR) of SLE risk for IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2. This involves integrating the Wald ratio estimates of each SNP through meta-analysis (\u0026beta; coefficient of SNPs for SLE divided by \u0026beta; coefficient of SNPs for IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2) to obtain the overall effect of IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 on SLE\u003csup\u003e24\u003c/sup\u003e. The IVW method is most effective when all IVs are valid. However, the presence of horizontal pleiotropy can lead to biased inferences\u003csup\u003e25\u003c/sup\u003e. Accordingly, MR-Egger and the weighted median methods enhance the IVW estimates by providing more reliable, albeit less efficient, estimates across a broader range of scenarios\u003csup\u003e26\u003c/sup\u003e. The weighted median method estimates the causal effect from the median of the weighted empirical density function of the SNP-outcome/SNP-exposure ratio estimates, offering valid estimates when \u0026ge;50% of the information comes from valid SNPs\u003csup\u003e27\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.5 Sensitivity Analyses\u003c/p\u003e\n\u003cp\u003eSensitivity analyses with different assumptions were performed to enhance the reliability of the results, addressing pleiotropy and heterogeneity. The MR-Egger method allows for the assessment of directional pleiotropy by introducing an intercept in the weighted regression model. A significant MR-Egger intercept indicates the presence of directional pleiotropy\u003csup\u003e26\u003c/sup\u003e. MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) analyses were conducted to identify and correct potential outliers, thereby helping to mitigate the effects of potential horizontal pleiotropy\u003csup\u003e28\u003c/sup\u003e. Funnel plots were employed to visualize MR analyses and to search for asymmetry as an indicator of pleiotropy\u003csup\u003e29\u003c/sup\u003e. We utilized the IVW method and MR-Egger regression to detect heterogeneity, quantified by Cochran\u0026rsquo;s Q statistic; a \u003cem\u003eP\u003c/em\u003e value of \u0026lt;0.05 was considered indicative of significant heterogeneity. A \u0026ldquo;leave-one-out\u0026rdquo; sensitivity analysis was also conducted to pinpoint potentially influential SNPs. The causal relationship would be deemed credible and stable if the results of the leave-one-out analysis were consistent with those of the overall IVW analysis. Forest plots were utilized to visualize the outcomes of leave-one-out analyses, evaluating the stability of effect sizes by recalculating the causal estimates from IVW after excluding one SNP at a time to ascertain if the estimates were biased or influenced by an outlier. All statistical analyses were conducted using R (version 4.0.3) (R Foundation for Statistical Computing, Vienna, Austria). The IVW, weighted median, and MR-Egger regression methods were performed using the \u0026ldquo;TwoSampleMR\u0026rdquo; package. The MR-PRESSO test was performed using the \u0026ldquo;MRPRESSO\u0026rdquo; package. Due to multiple testing, associations with \u003cem\u003eP\u003c/em\u003e-values less than the Bonferroni corrected threshold \u003cem\u003ea\u003c/em\u003e=0.05/3=0.017 were regarded as statistically significant, and associations with a \u003cem\u003eP\u003c/em\u003e-value\u0026ge; 0.017 and \u0026lt;0.05 as suggestively significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e3.1 Selection of SNPs\u003c/p\u003e\n\u003cp\u003eFor forward MR analyses, a total of 26 SNPs, including 9 SNPs associated with IFN-\u0026gamma; (rs115861866, rs138094598, rs141059739, rs1440480, rs2363910, rs5747241, rs7459901, rs75268621, rs7567468), 8 SNPs associated with IFN-\u0026gamma;R1 (rs7080536, rs111770018, rs117480750, rs73044907, rs117748849, rs62245853, rs12034435, rs79708242) and 9 SNPs associated with IFN-\u0026gamma;R2 (rs111566682, rs117210563, rs140878030, rs17709867, rs4540249, rs72639485, rs9586564), were selected by filtering the threshold of significance (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e-6\u003c/sup\u003e), false discovery rate (FDR)\u0026lt;5%, and LD ( \u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u0026lt;0.001). None of the genetic variants was palindromic with intermediate allele frequencies (Supporting information Table S3). Correspondingly, 40 SNPs associated with SLE were screened for reverse MR analyses (Supporting information Table S4). The above 40 SNPs passed the filtered significance threshold (\u003cem\u003eP\u0026nbsp;\u003c/em\u003e\u0026lt;5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e) with a false discovery rate (FDR)\u0026lt;5% and LD (\u003cem\u003er\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u0026lt;0.001) (Supporting information Table S3,4).\u003c/p\u003e\n\u003cp\u003e3.2 Mendelian Randomization analysis\u003c/p\u003e\n\u003cp\u003eThe results of IVW estimates indicated that higher levels of IFN-\u0026gamma; were associated with an increased risk of SLE (OR=1.24, 95% confidence interval [CI]: 1.03\u0026ndash;1.47, \u003cem\u003eP\u003c/em\u003e=0.018). However, the results from the weighted median analysis (OR= 1.07, 95% CI: 0.86\u0026ndash;1.33, \u003cem\u003eP\u003c/em\u003e=0.548) and MR-Egger (OR=1.39, 95% CI: 0.85\u0026ndash;2.26, \u003cem\u003eP\u003c/em\u003e=0.228) did not support the findings from the IVW analysis. In the IVW analysis, IFN-\u0026gamma;R2 levels were not associated with an increased risk of SLE after adjustment for multiple comparisons (OR=0.85, 95% CI: 0.73\u0026ndash;0.99, \u003cem\u003eP\u003c/em\u003e=0.034). The weighted median (OR=0.89, 95% CI: 0.73\u0026ndash;1.08, \u003cem\u003eP\u003c/em\u003e=0.245) and MR-Egger (OR=0.89, 95% CI: 0.59\u0026ndash;1.35, \u003cem\u003eP\u003c/em\u003e=0.612) analyses also produced consistent directions for effect estimation. Regarding the effect of IFN-\u0026gamma; on SLE, although the results from the weighted median analysis in our study do not support those from the IVW analysis, the IVW method is our primary method and is considered more robust, with the remaining methods serving as sensitivity analyses. Therefore, based on the IVW results, there is a significant relationship between elevated IFN-\u0026gamma; levels and an increased risk of SLE, and since the \u003cem\u003eP\u003c/em\u003e-value is close to 0.017, we consider there to be a potential causal relationship between them. Since the MR results for IFN-\u0026gamma;R2 levels and increased risk of SLE did not pass the Bonferroni correction, we do not consider them to indicate a potentially causal relationship.\u003c/p\u003e\n\u003cp\u003eThere is a lack of evidence to suggest an association between genetic predisposition to SLE and IFN-\u0026gamma;R1. Using the multiplicative random effect IVW model as the primary estimator, genetic predisposition to IFN-\u0026gamma;R1 was not found to be associated with the risk of SLE (OR=0.97, 95% CI: 0.79\u0026ndash;1.19, \u003cem\u003eP\u003c/em\u003e=0.768). A null association was also observed using MR-Egger (OR=0.80, 95% CI: 0.55\u0026ndash;1.14, \u003cem\u003eP\u003c/em\u003e=0.264). Consistently, the weighted median analysis provided little evidence of causal effects of IFN-\u0026gamma;R1 levels on SLE (OR=0.84, 95% CI: 0.70\u0026ndash;1.02, \u003cem\u003eP\u003c/em\u003e=0.085). (Figure 2A\u0026ndash;C, Table 1).\u003c/p\u003e\n\u003cp\u003e3.3 Sensitivity Analyses\u003c/p\u003e\n\u003cp\u003eHorizontal pleiotropy between IVs and the outcome was evaluated by MR-Egger regression, and the results indicated no evidence for a significant intercept. The \u0026quot;MR-PRESSO outlier test\u0026quot; was used to eliminate abnormal SNPs (outliers) and estimate the corrected results. MR-PRESSO analysis identified no outlying SNP in associations between IFN-\u0026gamma;, IFN-\u0026gamma;R1, IFN-\u0026gamma;R2, and SLE, suggesting that the observed associations might not be affected by pleiotropy. Regarding heterogeneity, the \u003cem\u003eQ\u0026nbsp;\u003c/em\u003estatistics of the IVW test and the MR-Egger regression demonstrated no significant heterogeneity, except for the association between IFN-\u0026gamma;R1 and SLE (MR-Egger, \u003cem\u003eP\u003c/em\u003e=0.078; IVW, \u003cem\u003eP\u003c/em\u003e=0.045). In addition, the leave-one-out sensitivity analysis showed that the results of the MR analysis were stable. (Figure 2D\u0026ndash;I, Table 1)\u003c/p\u003e\n\u003cp\u003e3.4 Reverse Analysis MR\u003c/p\u003e\n\u003cp\u003eTo investigate the causal association of SLE on IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels, we conducted reverse MR analysis using SLE as the exposure and IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels as the outcomes. We identified SNPs highly correlated with SLE as IVs. The results of the IVW analysis showed no significant causal association of SLE on IFN-\u0026gamma; levels (OR=0.99, 95% CI: 0.96\u0026ndash;1.03, \u003cem\u003eP\u003c/em\u003e=0.730), IFN-\u0026gamma;R1 levels (OR=1.00, 95% CI: 0.97\u0026ndash;1.04, \u003cem\u003eP\u003c/em\u003e=0.801), and IFN-\u0026gamma;R2 levels (OR=0.99, 95% CI: 0.96\u0026ndash;1.02, \u003cem\u003eP\u003c/em\u003e=0.656). No significant pleiotropy among the SNPs (P \u0026gt; 0.05) was observed when conducting MR-Egger. MR-PRESSO analysis found no abnormal SNPs in the association of SLE with IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2, suggesting that the observed associations may not be affected by horizontal pleiotropy. Mild heterogeneity was detected in IFN-\u0026gamma; (MR-Egger, \u003cem\u003eP\u003c/em\u003e=0.031; IVW, \u003cem\u003eP\u003c/em\u003e=0.037), indicating slight bias caused by a few SNPs. The results of the funnel plot and leave-one-out sensitivity analysis demonstrated that the association of SLE with IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels was not significantly affected by any individual SNP. (Figure 3, Table 2)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe elucidate the causal relationship between IFN-\u0026gamma; or IFN-\u0026gamma;R and SLE through two-sample MR analysis in both directions. After eliminating complex confounders, genetic evidence suggests that higher levels of IFN-\u0026gamma; are significantly associated with an increased risk of SLE. IFN-\u0026gamma;R1 and IFN-\u0026gamma;R2 do not have an obvious causal relationship with SLE. To further clarify the causal relationship, we performed reverse MR analysis, and the results showed no significant relationship between SLE risk and levels of IFN-\u0026gamma; or IFN-\u0026gamma;R.\u003c/p\u003e\n\u003cp\u003ePatients with SLE are known to have significantly higher levels of IFN-\u0026gamma; mRNA and protein than do healthy individuals, along with elevated levels of mRNA produced by IFN-induced gene type II (Interferon regulatory factor 1, Guanylate-binding Protein 1, C-X-C motif chemokine ligand 9 , C-X-C motif chemokine ligand 10, and serpin family G member 1)\u003csup\u003e30\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e31\u003c/sup\u003e. Thomason et al. \u003csup\u003e32\u003c/sup\u003e demonstrated that IFN-\u0026gamma; activation could indicate disease activity in SLE patients. \u0026nbsp;Additionally, the response to ustekinumab treatment in SLE patients was associated with the suppression of serum IFN-\u0026gamma; levels\u003csup\u003e33\u003c/sup\u003e. Furthermore, Jacob et al. \u003csup\u003e34\u003c/sup\u003e found that in NZB/NZW F1 mice, IFN-\u0026gamma; monoclonal antibodies showed good efficacy \u003cem\u003ein vivo\u003c/em\u003e for lupus nephritis. Hron et al.\u003csup\u003e35\u003c/sup\u003ediscovered that IFN-RII deficiency protected \u003cem\u003eMRL\u003c/em\u003e/LPR mice from severe autoimmune-related lymphadenopathy, autoantibodies, and kidney disease. These studies confirm that elevated IFN-\u0026gamma; levels are associated with an increased risk of SLE, supporting our analysis. In conducting the analysis, it is crucial to clarify the mechanism from exposure to results. IFN-\u0026gamma; operates through a signaling pathway that binds to IFN-\u0026gamma;R, expressed on most cells, and activates JAK1 and JAK2. This activation leads to the phosphorylation of STAT1, which then binds to the IFN-\u0026gamma; activation site for gene transcription, completing related functions\u003csup\u003e36\u003c/sup\u003e. Understanding the IFN-\u0026gamma; signaling pathway helps explain the causal role of IFN-\u0026gamma; and IFN-\u0026gamma;R in SLE, clarifying the disease\u0026apos;s development path and providing theoretical support for future clinical treatments. The pathogenesis of SLE is complex, mediated by immune disorders that include the loss of immune self-tolerance and enhanced T and B cell responses. IFN-\u0026gamma;, a major pro-inflammatory cytokine, plays a significant role in regulating the function of key immune cells, including B cells and T cells, contributing to the development of SLE\u003csup\u003e37,38\u003c/sup\u003e. First, it leads to disordered regulation of T cells. In the pathogenesis of SLE, imbalances between T helper (Th)1 and Th2 cells are common. IFN-\u0026gamma; signaling significantly inhibits the differentiation of CD4\u003csup\u003e+\u003c/sup\u003e T cells into Th2 cells, leading to an imbalance between Th1 and Th2 cells\u003csup\u003e39\u003c/sup\u003e. Th1 cells can secrete IFN-\u0026gamma; to promote SLE-related pathologies, and IFN-\u0026gamma;, in turn, enhances the pathogenic role of Th1 cells. IFN-\u0026gamma; plays a crucial role in the differentiation and maturation of Th1 cells\u003csup\u003e40\u003c/sup\u003e. Second, B cells act as inflammatory mediators, producing pathogenic antibodies to enhance the inflammatory response and directly damaging tissues and cells\u003csup\u003e41\u003c/sup\u003e. IFN-\u0026gamma; signaling stimulates T cells and antigen-presenting cells to produce B-lymphocyte-stimulating factor, which aids in the differentiation and survival of B cells\u003csup\u003e42-45\u003c/sup\u003e. Third, defects in Treg cell function or quantity, crucial for maintaining peripheral immune tolerance, are thought to contribute to the pathogenesis of SLE.\u003csup\u003e46\u003c/sup\u003e Recent studies have demonstrated IFN-\u0026gamma;\u0026apos;s ability to directly inhibit the function of Treg cells, leading to a loss of autoimmune tolerance and mediating the development of SLE.\u003csup\u003e47-49\u003c/sup\u003e. Fourth, IFN-\u0026gamma; signaling directly influences several aspects of CD8\u003csup\u003e+\u003c/sup\u003e T cell biology. Most importantly, IFN-\u0026gamma; is essential for the cytolytic capacity of CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e50\u003c/sup\u003e. CD8\u003csup\u003e+\u003c/sup\u003e T cells in the peripheral blood of SLE patients typically have reduced production of granzyme B and perforin and exhibit impaired cytolytic function\u003csup\u003e51\u003c/sup\u003e. This impairment hampers the removal of autoreactive B cells and increases autoantibodies, accelerating the onset of lupus.\u003c/p\u003e\n\u003cp\u003eThe IFN-\u0026gamma; signaling pathway is crucial for both innate and adaptive immunity, requiring the presence of IFN-\u0026gamma;R for IFN-\u0026gamma;\u0026apos;s biological activities and signaling. Nakashima et al.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e52\u003c/sup\u003e discovered amino acid polymorphisms (Val14Met) within IFN-\u0026gamma;R1, with the frequency of the Met14 allele in SLE patients being significantly higher than in healthy controls. Similarly, amino acid polymorphisms (Gln64Arg) were identified in IFN-\u0026gamma;R2. The highest risk of developing SLE was observed in individuals with the IFNGR1 Met14/Val14 genotype and the IFNGR2 Gln64/Gln64 genotype, highlighting their critical role in SLE susceptibility. Xu et al. \u003csup\u003e53\u003c/sup\u003e found that the IFN-\u0026gamma;R2 Arg64/Arg64 genotype reduced the risk of SLE (\u003cem\u003eP\u003c/em\u003e = 0.047). However, our findings indicate that IFN-\u0026gamma;R2 and IFN-\u0026gamma;R1 are not associated with SLE, suggesting that further research into the pathogenesis of SLE is necessary to identify more effective therapeutic targets for clinical treatment.\u003c/p\u003e\n\u003cp\u003eThe strength of our study lies in the MR study design, which mitigates unobserved confounding and reverse causation by using genetic variation as a proxy for IFN-\u0026gamma; and IFN-\u0026gamma;R. Beyond the method\u0026apos;s advantages, our study expands on previous MR studies concerning IFN-\u0026gamma; and IFN-\u0026gamma;R versus SLE. First, we employed bidirectional MR and found evidence from only one direction, with IFN-\u0026gamma; showing a potential causal relationship with SLE, a relationship not observed with IFN-\u0026gamma;R. Second, while most research focuses on IFN-\u0026alpha;, IFN-\u0026gamma; also plays a significant role in SLE, with both being utilized together in some pathways. Our study not only investigates IFN-\u0026gamma; but also its receptors, providing a more comprehensive understanding of the risk factors for SLE.\u003c/p\u003e\n\u003cp\u003eHowever, this study has some limitations. First, although we utilized the largest available GWAS, some identified only a few genome-wide significant SNPs, which could result in relatively weak genetic instruments. To address this, we applied statistical criteria to include additional SNPs as instruments. This approach has been utilized in other MR studies where the number of known genome-wide significant SNPs is limited. Additionally, due to the unknown biological action of these SNPs, it is impossible to fully rule out pleiotropic mechanisms without detailed functional follow-up of these loci, although we conducted the most up-to-date array of sensitivity analyses to mitigate the impact of horizontal pleiotropy. While horizontal pleiotropy is a concern for MR inference, vertical pleiotropy\u0026mdash;where an exposure affects an outcome via other variables along the same causal pathway\u0026mdash;is considered acceptable. Third, the few SNPs with heterogeneity in our reverse MR experiment caused minimal bias. However, the association of SLE with IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels was not significantly affected by any single SNP. Fourth, due to the lack of GWAS for IFN-\u0026gamma;R to date, we were unable to use strict statistical thresholds to study the effects of IFN-\u0026gamma; and IFN-\u0026gamma;R on SLE. Nevertheless, the more relaxed IV thresholds used have been employed in many previous studies. Although we did not find a causal effect of IFN-\u0026gamma;R on SLE, previous studies have indicated that IFN-\u0026gamma;R2 levels are associated with SLE risk, suggesting the need for more IFN-\u0026gamma;R GWAS to further investigate this relationship.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings have corroborated a causal association between IFN-\u0026gamma; and SLE, which may influence clinical decisions about patient management with SLE. Higher levels of IFN-\u0026gamma; are associated with an increased risk of SLE. Although our results do not support a causal association between IFN-\u0026gamma;R1 and IFN-\u0026gamma;R2 and SLE, further research is needed to clarify the effect of IFN-\u0026gamma;R on SLE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eData about the IFN-\u0026gamma;-related GWAS data was obtained from the INTERVAL study Proteomics- GWAS data. We thank all involved investigators for sharing their data. We want to acknowledge the participants and investigators of the Integrative Epidemiology Unit (IEU) GWAS database.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest regarding the publication of this paper.\u003c/p\u003e\n\u003cp\u003eEthical Statement\u003c/p\u003e\n\u003cp\u003eAll the data used in this study were from public databases and did not require ethical approval. This article is based on previously conducted studies and does not contain any new studies with human participants or animals performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eStudy design and manuscript writing: MJC. KXY. And JWH. Data extraction, quality assessment, analysis and interpretation of data: MJC. KXY. And JWH. All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be published. Dr. He had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole, and have given their approval for this version to be published.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the IEU open\u003c/p\u003e\n\u003cp\u003egwas project [https://gwas.mrcieu.ac.uk/], and the GWAS ID are prot-a-1428, prot-a-1430, prot-a-1432, ebi-a-GCST003156, respectively.\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e1\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fanouriakis, A., Tziolos, N., Bertsias, G. \u0026amp; Boumpas, D. T. Update \u0026omicron;n the diagnosis and management of systemic lupus erythematosus. \u003cem\u003eAnn Rheum Dis\u003c/em\u003e 80, 14-25, doi:10.1136/annrheumdis-2020-218272 (2021).\u003c/p\u003e\n\u003cp\u003e2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Murphy, G. \u0026amp; Isenberg, D. A. New therapies for systemic lupus erythematosus - past imperfect, future tense. \u003cem\u003eNat Rev Rheumatol\u003c/em\u003e 15, 403-412, doi:10.1038/s41584-019-0235-5 (2019).\u003c/p\u003e\n\u003cp\u003e3\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Catalina, M. D., Owen, K. A., Labonte, A. C., Grammer, A. C. \u0026amp; Lipsky, P. E. The pathogenesis of systemic lupus erythematosus: Harnessing big data to understand the molecular basis of lupus. \u003cem\u003eJ Autoimmun\u003c/em\u003e 110, 102359, doi:10.1016/j.jaut.2019.102359 (2020).\u003c/p\u003e\n\u003cp\u003e4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fava, A. \u0026amp; Petri, M. Systemic lupus erythematosus: Diagnosis and clinical management. \u003cem\u003eJ Autoimmun\u003c/em\u003e 96, 1-13, doi:10.1016/j.jaut.2018.11.001 (2019).\u003c/p\u003e\n\u003cp\u003e5\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kaul, A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Systemic lupus erythematosus. \u003cem\u003eNat Rev Dis Primers\u003c/em\u003e 2, 16039, doi:10.1038/nrdp.2016.39 (2016).\u003c/p\u003e\n\u003cp\u003e6\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;D\u0026ouml;rner, T. \u0026amp; Furie, R. Novel paradigms in systemic lupus erythematosus. \u003cem\u003eLancet\u003c/em\u003e 393, 2344-2358, doi:10.1016/s0140-6736(19)30546-x (2019).\u003c/p\u003e\n\u003cp\u003e7\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Liu, W., Li, M., Wang, Z. \u0026amp; Wang, J. IFN-\u0026gamma; Mediates the Development of Systemic Lupus Erythematosus. \u003cem\u003eBiomed Res Int\u003c/em\u003e 2020, 7176515, doi:10.1155/2020/7176515 (2020).\u003c/p\u003e\n\u003cp\u003e8\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Psarras, A., Emery, P. \u0026amp; Vital, E. M. Type I interferon-mediated autoimmune diseases: pathogenesis, diagnosis and targeted therapy. \u003cem\u003eRheumatology (Oxford)\u003c/em\u003e 56, 1662-1675, doi:10.1093/rheumatology/kew431 (2017).\u003c/p\u003e\n\u003cp\u003e9\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ding, H., Wang, G., Yu, Z., Sun, H. \u0026amp; Wang, L. Role of interferon-gamma (IFN-\u0026gamma;) and IFN-\u0026gamma; receptor 1/2 (IFN\u0026gamma;R1/2) in regulation of immunity, infection, and cancer development: IFN-\u0026gamma;-dependent or independent pathway. \u003cem\u003eBiomed Pharmacother\u003c/em\u003e 155, 113683, doi:10.1016/j.biopha.2022.113683 (2022).\u003c/p\u003e\n\u003cp\u003e10\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Encalada-D\u0026iacute;az, M. I. [3D Printing and Orthopedics of Mexico]. \u003cem\u003eActa Ortop Mex\u003c/em\u003e 36, 1 (2022).\u003c/p\u003e\n\u003cp\u003e11\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Liu, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Higher activation of the interferon-gamma signaling pathway in systemic lupus erythematosus patients with a high type I IFN score: relation to disease activity. \u003cem\u003eClin Rheumatol\u003c/em\u003e 37, 2675-2684, doi:10.1007/s10067-018-4138-7 (2018).\u003c/p\u003e\n\u003cp\u003e12\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Groettrup, M., Khan, S., Schwarz, K. \u0026amp; Schmidtke, G. Interferon-gamma inducible exchanges of 20S proteasome active site subunits: why? \u003cem\u003eBiochimie\u003c/em\u003e 83, 367-372, doi:10.1016/s0300-9084(01)01251-2 (2001).\u003c/p\u003e\n\u003cp\u003e13\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Ozmen, L.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Experimental therapy of systemic lupus erythematosus: the treatment of NZB/W mice with mouse soluble interferon-gamma receptor inhibits the onset of glomerulonephritis. \u003cem\u003eEur J Immunol\u003c/em\u003e 25, 6-12, doi:10.1002/eji.1830250103 (1995).\u003c/p\u003e\n\u003cp\u003e14\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Li, C., Yang, W., Wei, Q. \u0026amp; Shang, H. Causal Association of Leukocytes Count and Amyotrophic Lateral Sclerosis: a Mendelian Randomization Study. \u003cem\u003eMol Neurobiol\u003c/em\u003e 57, 4622-4627, doi:10.1007/s12035-020-02053-7 (2020).\u003c/p\u003e\n\u003cp\u003e15\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Dan, Y. L.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Circulating adiponectin levels and systemic lupus erythematosus: a two-sample Mendelian randomization study. \u003cem\u003eRheumatology (Oxford)\u003c/em\u003e 60, 940-946, doi:10.1093/rheumatology/keaa506 (2021).\u003c/p\u003e\n\u003cp\u003e16\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gagliano Taliun, S. A. \u0026amp; Evans, D. M. Ten simple rules for conducting a mendelian randomization study. \u003cem\u003ePLoS Comput Biol\u003c/em\u003e 17, e1009238, doi:10.1371/journal.pcbi.1009238 (2021).\u003c/p\u003e\n\u003cp\u003e17\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Sun, B. B.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Genomic atlas of the human plasma proteome. \u003cem\u003eNature\u003c/em\u003e 558, 73-79, doi:10.1038/s41586-018-0175-2 (2018).\u003c/p\u003e\n\u003cp\u003e18\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bentham, J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Genetic association analyses implicate aberrant regulation of innate and adaptive immunity genes in the pathogenesis of systemic lupus erythematosus. \u003cem\u003eNat Genet\u003c/em\u003e 47, 1457-1464, doi:10.1038/ng.3434 (2015).\u003c/p\u003e\n\u003cp\u003e19\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lawlor, D. A., Harbord, R. M., Sterne, J. A., Timpson, N. \u0026amp; Davey Smith, G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. \u003cem\u003eStat Med\u003c/em\u003e 27, 1133-1163, doi:10.1002/sim.3034 (2008).\u003c/p\u003e\n\u003cp\u003e20\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Burgess, S. \u0026amp; Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 40, 755-764, doi:10.1093/ije/dyr036 (2011).\u003c/p\u003e\n\u003cp\u003e21\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Soliman, G. A. \u0026amp; Schooling, C. M. Causal association between mTOR-dependent EIF-4E and EIF-4A circulating protein levels and type 2 diabetes: a Mendelian randomization study. \u003cem\u003eSci Rep\u003c/em\u003e 10, 15737, doi:10.1038/s41598-020-71987-8 (2020).\u003c/p\u003e\n\u003cp\u003e22\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Pierce, B. L., Ahsan, H. \u0026amp; Vanderweele, T. J. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 40, 740-752, doi:10.1093/ije/dyq151 (2011).\u003c/p\u003e\n\u003cp\u003e23\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Palmer, T. M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Using multiple genetic variants as instrumental variables for modifiable risk factors. \u003cem\u003eStat Methods Med Res\u003c/em\u003e 21, 223-242, doi:10.1177/0962280210394459 (2012).\u003c/p\u003e\n\u003cp\u003e24\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G. \u0026amp; Thompson, S. G. Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors. \u003cem\u003eEur J Epidemiol\u003c/em\u003e 30, 543-552, doi:10.1007/s10654-015-0011-z (2015).\u003c/p\u003e\n\u003cp\u003e25\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lin, Z., Deng, Y. \u0026amp; Pan, W. Combining the strengths of inverse-variance weighting and Egger regression in Mendelian randomization using a mixture of regressions model. \u003cem\u003ePLoS Genet\u003c/em\u003e 17, e1009922, doi:10.1371/journal.pgen.1009922 (2021).\u003c/p\u003e\n\u003cp\u003e26\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bowden, J., Davey Smith, G. \u0026amp; Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 44, 512-525, doi:10.1093/ije/dyv080 (2015).\u003c/p\u003e\n\u003cp\u003e27\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bowden, J., Davey Smith, G., Haycock, P. C. \u0026amp; Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. \u003cem\u003eGenet Epidemiol\u003c/em\u003e 40, 304-314, doi:10.1002/gepi.21965 (2016).\u003c/p\u003e\n\u003cp\u003e28\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Verbanck, M., Chen, C. Y., Neale, B. \u0026amp; Do, R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. \u003cem\u003eNat Genet\u003c/em\u003e 50, 693-698, doi:10.1038/s41588-018-0099-7 (2018).\u003c/p\u003e\n\u003cp\u003e29\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bowden, J.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the Radial plot and Radial regression. \u003cem\u003eInt J Epidemiol\u003c/em\u003e 47, 1264-1278, doi:10.1093/ije/dyy101 (2018).\u003c/p\u003e\n\u003cp\u003e30\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Fava, A.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Integrated urine proteomics and renal single-cell genomics identify an IFN-\u0026gamma; response gradient in lupus nephritis. \u003cem\u003eJCI Insight\u003c/em\u003e 5, doi:10.1172/jci.insight.138345 (2020).\u003c/p\u003e\n\u003cp\u003e31\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Csisz\u0026aacute;r, A., Nagy, G., Gergely, P., Pozsonyi, T. \u0026amp; P\u0026oacute;csik, E. Increased interferon-gamma (IFN-gamma), IL-10 and decreased IL-4 mRNA expression in peripheral blood mononuclear cells (PBMC) from patients with systemic lupus erythematosus (SLE). \u003cem\u003eClin Exp Immunol\u003c/em\u003e 122, 464-470, doi:10.1046/j.1365-2249.2000.01369.x (2000).\u003c/p\u003e\n\u003cp\u003e32\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Thomason, J. L., Obih, U. M., Koelle, D. M., Lood, C. \u0026amp; Hughes, A. G. An interferon-gamma release assay as a novel biomarker in systemic lupus erythematosus. \u003cem\u003eRheumatology (Oxford)\u003c/em\u003e 59, 3479-3487, doi:10.1093/rheumatology/keaa161 (2020).\u003c/p\u003e\n\u003cp\u003e33\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cesaroni, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Suppression of Serum Interferon-\u0026gamma; Levels as a Potential Measure of Response to Ustekinumab Treatment in Patients With Systemic Lupus Erythematosus. \u003cem\u003eArthritis Rheumatol\u003c/em\u003e 73, 472-477, doi:10.1002/art.41547 (2021).\u003c/p\u003e\n\u003cp\u003e34\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Jacob, C. O., van der Meide, P. H. \u0026amp; McDevitt, H. O. In vivo treatment of (NZB X NZW)F1 lupus-like nephritis with monoclonal antibody to gamma interferon. \u003cem\u003eJ Exp Med\u003c/em\u003e 166, 798-803, doi:10.1084/jem.166.3.798 (1987).\u003c/p\u003e\n\u003cp\u003e35\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hron, J. D. \u0026amp; Peng, S. L. Type I IFN protects against murine lupus. \u003cem\u003eJ Immunol\u003c/em\u003e 173, 2134-2142, doi:10.4049/jimmunol.173.3.2134 (2004).\u003c/p\u003e\n\u003cp\u003e36\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Darnell, J. E., Jr., Kerr, I. M. \u0026amp; Stark, G. R. Jak-STAT pathways and transcriptional activation in response to IFNs and other extracellular signaling proteins. \u003cem\u003eScience\u003c/em\u003e 264, 1415-1421, doi:10.1126/science.8197455 (1994).\u003c/p\u003e\n\u003cp\u003e37\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Schroder, K., Hertzog, P. J., Ravasi, T. \u0026amp; Hume, D. A. Interferon-gamma: an overview of signals, mechanisms and functions. \u003cem\u003eJ Leukoc Biol\u003c/em\u003e 75, 163-189, doi:10.1189/jlb.0603252 (2004).\u003c/p\u003e\n\u003cp\u003e38\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tau, G. \u0026amp; Rothman, P. Biologic functions of the IFN-gamma receptors. \u003cem\u003eAllergy\u003c/em\u003e 54, 1233-1251, doi:10.1034/j.1398-9995.1999.00099.x (1999).\u003c/p\u003e\n\u003cp\u003e39\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Oriss, T. B., McCarthy, S. A., Morel, B. F., Campana, M. A. \u0026amp; Morel, P. A. Crossregulation between T helper cell (Th)1 and Th2: inhibition of Th2 proliferation by IFN-gamma involves interference with IL-1. \u003cem\u003eJ Immunol\u003c/em\u003e 158, 3666-3672 (1997).\u003c/p\u003e\n\u003cp\u003e40\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cope, A., Le Friec, G., Cardone, J. \u0026amp; Kemper, C. The Th1 life cycle: molecular control of IFN-\u0026gamma; to IL-10 switching. \u003cem\u003eTrends Immunol\u003c/em\u003e 32, 278-286, doi:10.1016/j.it.2011.03.010 (2011).\u003c/p\u003e\n\u003cp\u003e41\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Szelinski, F., Lino, A. C. \u0026amp; D\u0026ouml;rner, T. B cells in systemic lupus erythematosus. \u003cem\u003eCurr Opin Rheumatol\u003c/em\u003e 34, 125-132, doi:10.1097/bor.0000000000000865 (2022).\u003c/p\u003e\n\u003cp\u003e42\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Harigai, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Excessive production of IFN-gamma in patients with systemic lupus erythematosus and its contribution to induction of B lymphocyte stimulator/B cell-activating factor/TNF ligand superfamily-13B. \u003cem\u003eJ Immunol\u003c/em\u003e 181, 2211-2219, doi:10.4049/jimmunol.181.3.2211 (2008).\u003c/p\u003e\n\u003cp\u003e43\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Scapini, P.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Proinflammatory mediators elicit secretion of the intracellular B-lymphocyte stimulator pool (BLyS) that is stored in activated neutrophils: implications for inflammatory diseases. \u003cem\u003eBlood\u003c/em\u003e 105, 830-837, doi:10.1182/blood-2004-02-0564 (2005).\u003c/p\u003e\n\u003cp\u003e44\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Yan, M.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Identification of a receptor for BLyS demonstrates a crucial role in humoral immunity. \u003cem\u003eNat Immunol\u003c/em\u003e 1, 37-41, doi:10.1038/76889 (2000).\u003c/p\u003e\n\u003cp\u003e45\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Do, R. K.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Attenuation of apoptosis underlies B lymphocyte stimulator enhancement of humoral immune response. \u003cem\u003eJ Exp Med\u003c/em\u003e 192, 953-964, doi:10.1084/jem.192.7.953 (2000).\u003c/p\u003e\n\u003cp\u003e46\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Bonelli, M., Smolen, J. S. \u0026amp; Scheinecker, C. Treg and lupus. \u003cem\u003eAnn Rheum Dis\u003c/em\u003e 69 Suppl 1, i65-66, doi:10.1136/ard.2009.117135 (2010).\u003c/p\u003e\n\u003cp\u003e47\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Olalekan, S. A., Cao, Y., Hamel, K. M. \u0026amp; Finnegan, A. B cells expressing IFN-\u0026gamma; suppress Treg-cell differentiation and promote autoimmune experimental arthritis. \u003cem\u003eEur J Immunol\u003c/em\u003e 45, 988-998, doi:10.1002/eji.201445036 (2015).\u003c/p\u003e\n\u003cp\u003e48\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Chang, J. H., Kim, Y. J., Han, S. H. \u0026amp; Kang, C. Y. IFN-gamma-STAT1 signal regulates the differentiation of inducible Treg: potential role for ROS-mediated apoptosis. \u003cem\u003eEur J Immunol\u003c/em\u003e 39, 1241-1251, doi:10.1002/eji.200838913 (2009).\u003c/p\u003e\n\u003cp\u003e49\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Kelchtermans, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Defective CD4+CD25+ regulatory T cell functioning in collagen-induced arthritis: an important factor in pathogenesis, counter-regulated by endogenous IFN-gamma. \u003cem\u003eArthritis Res Ther\u003c/em\u003e 7, R402-415, doi:10.1186/ar1500 (2005).\u003c/p\u003e\n\u003cp\u003e50\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Siegel, J. P. Effects of interferon-gamma on the activation of human T lymphocytes. \u003cem\u003eCell Immunol\u003c/em\u003e 111, 461-472, doi:10.1016/0008-8749(88)90109-8 (1988).\u003c/p\u003e\n\u003cp\u003e51\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Comte, D.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Signaling Lymphocytic Activation Molecule Family Member 7 Engagement Restores Defective Effector CD8+ T Cell Function in Systemic Lupus Erythematosus. \u003cem\u003eArthritis Rheumatol\u003c/em\u003e 69, 1035-1044, doi:10.1002/art.40038 (2017).\u003c/p\u003e\n\u003cp\u003e52\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Nakashima, H.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Polymorphisms within the interleukin-10 receptor cDNA gene (IL10R) in Japanese patients with systemic lupus erythematosus. \u003cem\u003eRheumatology (Oxford)\u003c/em\u003e 38, 1142-1144, doi:10.1093/rheumatology/38.11.1142 (1999).\u003c/p\u003e\n\u003cp\u003e53\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Xu, Y.\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e Correlation between some Th1 and Th2 cytokine receptor gene polymorphisms and systemic lupus erythematosus in Chinese patients. \u003cem\u003eInt J Dermatol\u003c/em\u003e 46, 1129-1135, doi:10.1111/j.1365-4632.2007.03258.x (2007).\u003c/p\u003e"},{"header":"Tables","content":"\u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eTable1 Mendelian randomization of IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels on the risk of SLE.\u003c/p\u003e\n\u003cdiv align=\"center\" style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;font-weight:bold;'\u003e\n \u003ctable style=\"width: 4.8e+2pt;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:51.05pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eExposure\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:51.65pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR method\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:34.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNo. of SNPs\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:147.65pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eAssociation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:90.6pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eHeterogeneity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:106.25pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eEgger regression\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:68.1pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026beta; coefficients\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eOR\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003ep\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"color:black;\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eCochran \u003cem\u003eQ\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003ep\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"color:black;\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:55.8pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:50.45pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003ep\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"color:black;\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:51.05pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIFN-\u0026gamma;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003elevels\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR Egger\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.32887\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.228\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e10.69006\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.153\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:55.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.02271\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:50.45pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.626\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIVW\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.21126\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.24\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.018\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e11.08669\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.197\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eWeighted median\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.06725\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.07\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.548\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:51.05pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIFN-\u0026gamma;R1\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003elevels\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR Egger\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.22757\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.80\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.264\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e11.34511\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.078\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:55.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.05509\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:50.45pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.252\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIVW\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.03064\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.768\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e14.37791\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.045\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eWeighted median\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e8\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.16921\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.84\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.085\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:51.05pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIFN-\u0026gamma;R2\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003elevels\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR Egger\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.11252\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.612\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e5.99048\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.541\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:55.8pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.00859\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:50.45pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.813\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:51.65pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIVW\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.16118\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.85\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.034\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e6.05086\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.642\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:51.65pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eWeighted median\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:34.75pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e9\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:68.1pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0. 11684\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:35.75pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.89\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:43.8pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.245\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:54.4pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:36.2pt;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;height:29.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eORs estimate the relationship between systemic lupus erythematosus risk and IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2.\u0026thinsp;OR \u0026lt;\u0026thinsp;1: genetic variants with higher/lower physiological serum IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels are associated with decreased risk of SLE and vice versa.\u003c/p\u003e\n\u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eAbbreviations: IFN-\u0026gamma;, Interferon-gamma; IFN-\u0026gamma;R1, Interferon gamma receptor 1; IFN-\u0026gamma;R2, Interferon gamma receptor 2;IVW, inverse variance weighted analysis; MR, mendelian randomization; NA, not available; No. of SNPs, number of single nucleotide polymorphisms; OR, odds ratios; SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism.\u003c/p\u003e\n\u003cp\u003e\u003cspan style='font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cbr\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eTable 2 The output of Mendelian randomization of the risk of SLE on IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels.\u003c/p\u003e\n\u003cdiv align=\"center\" style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;font-weight:bold;'\u003e\n \u003ctable style=\"width: 106%;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width:11.04%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eOutcome\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:11.98%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR method\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width:8.22%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNo. of SNPs\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width:30.5%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eAssociation\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:18.7%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eHeterogeneity\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width:19.56%;border:solid windowtext 1.0pt;border-left:none;background: white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eEgger regression\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:14.34%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e\u0026beta; coefficients\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.44%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eOR\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.72%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003ep\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"color:black;\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eCochran \u003cem\u003eQ\u003c/em\u003e\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003ep\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"color:black;\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.54%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.02%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cem\u003e\u003cspan style=\"color:black;\"\u003ep\u003c/span\u003e\u003c/em\u003e\u003cspan style=\"color:black;\"\u003e-value\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:11.04%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIFN-\u0026gamma;\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eLevels\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.98%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR Egger\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:14.34%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.01973\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.44%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.98\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.72%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.596\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e55.88461\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.031\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:11.54%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.00553\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:8.02%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:1.15pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.675\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:11.98%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIVW\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:14.34%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.00615\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.44%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.99\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.72%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.730\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e56.14684\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.037\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:11.98%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eWeighted median\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:14.34%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.01483\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.44%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.99\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.72%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.524\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:11.04%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIFN-\u0026gamma;R1\u003c/span\u003e\u003c/p\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003elevels\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.98%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eMR Egger\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:14.34%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.03478\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.44%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.97\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.72%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.312\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e47.48358\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.139\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:11.54%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.01587\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:8.02%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;height:14.0pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.196\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:11.98%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eIVW\u003c/span\u003e\u003c/p\u003e\n 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style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.801\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e49.64406\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.118\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:11.98%;border:none;background:white;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan 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style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e16.95165\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.999\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:11.54%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e-0.00847\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width:8.02%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp 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style=\"width:7.62%;border:none;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.999\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:11.98%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eWeighted median\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.22%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e40\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:14.34%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.00266\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:8.44%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e1.00\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.72%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003e0.897\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:11.08%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:7.62%;border:none;border-bottom:solid windowtext 1.0pt;background:white;padding:0in 5.4pt 0in 5.4pt;\"\u003e\n \u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003e\u003cspan style=\"color:black;\"\u003eNA\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eThe \u0026beta;-coefficients represent the log OR of SLE risk for each additional effect allele (\u0026beta; coefficients\u0026thinsp;\u0026lt;\u0026thinsp;0, OR\u0026thinsp;\u0026lt;\u0026thinsp;1; \u0026beta; coefficients\u0026thinsp;=\u0026thinsp;0, OR\u0026thinsp;=\u0026thinsp;1; \u0026beta; coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0, OR\u0026thinsp;\u0026gt;\u0026thinsp;1). \u0026beta; coefficients\u0026thinsp;\u0026lt;\u0026thinsp;0: genetic variants with physiological serum IFN-\u0026gamma;, IFN-\u0026gamma;R1, and IFN-\u0026gamma;R2 levels are associated with reduced risk of SLE and vice versa.\u003c/p\u003e\n\u003cp style='margin:0in;font-size:14px;font-family:\"Times New Roman\",serif;'\u003eAbbreviations: IFN-\u0026gamma;, Interferon-gamma; IFN-\u0026gamma;R1, Interferon gamma receptor 1; IFN-\u0026gamma;R2, Interferon gamma receptor 2; IVW, inverse variance weighted analysis; MR, mendelian randomization; NA, not available; No. of SNPs, number of single nucleotide polymorphisms; OR, odds ratios; SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"mendelian randomization, interferon-γ, interferon-γ receptor, risk factor, single nucleotide polymorphism, systemic lupus erythematosus","lastPublishedDoi":"10.21203/rs.3.rs-2776347/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2776347/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u0026nbsp;The interferon-gamma (IFN-γ) signaling pathway is activated in systemic lupus erythematosus (SLE). This study aimed\u0026nbsp;to assess the causal association between IFN-γ, IFN-γ\u003cstrong\u003e \u003c/strong\u003ereceptor 1 (IFN-γR1), and IFN-γR2 and SLE using a bidirectional Mendelian-randomization design.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u0026nbsp;Genetic instruments for exposure to IFN-γ, IFN-γR1, and IFN-γR2 were derived from a large genome-wide association study (GWAS) that included a sample size of 3301 participants. Instrumental variables for SLE were selected from another independent GWAS analysis comprising 5201 cases and 6099\u0026nbsp;controls with European ancestry. Bidirectional two-sample Mendelian randomization (MR) was performed using inverse variance weighting, MR-Egger regression, and weighted median methods. A series of sensitivity analyses were conducted to assess the robustness of the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe inverse variance weighting showed that IFN-γ had a positive causal association with the risk of SLE (odd ratio [OR]=1.24,\u0026nbsp;95% confidence interval [CI]:\u0026nbsp;1.03–1.47, \u003cem\u003eP\u003c/em\u003e=0.018). IFN-γR2 levels were not associated with SLE risk after adjustment for multiple comparisons (OR=0.85,\u0026nbsp;95% CI:\u0026nbsp;0.73–0.99), \u003cem\u003eP\u003c/em\u003e=0.034). No genetic association was also\u0026nbsp;detected between IFN-γR1 and SLE (OR=0.97, 95% CI:\u0026nbsp;0.79–1.19), \u003cem\u003eP\u003c/em\u003e=0.768). Evidence from bidirectional MR did not support reverse causality. The weighted median regression also showed directionally similar estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u0026nbsp;\u003c/strong\u003eHigher levels of IFN-γ are significantly associated with an increased risk of SLE, providing insights into the pathogenesis of SLE.\u003c/p\u003e","manuscriptTitle":"Assessing the causality of interferon-γ and its receptor 1/2 with systemic lupus erythematosus risk using genetic data","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-04-16 21:53:52","doi":"10.21203/rs.3.rs-2776347/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2023-04-12 14:54:35","doi":"10.21203/rs.3.rs-2776347/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3a8ef710-d525-499b-8653-fac832e95810","owner":[],"postedDate":"April 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T00:49:46+00:00","versionOfRecord":{"articleIdentity":"rs-2776347","link":"https://doi.org/10.1002/rai2.12125","journal":{"identity":"rheumatology-and-autoimmunity","isVorOnly":true,"title":"Rheumatology \u0026 Autoimmunity"},"publishedOn":"2024-05-13 00:49:46","publishedOnDateReadable":"May 13th, 2024"},"versionCreatedAt":"2024-04-16 21:53:52","video":"","vorDoi":"10.1002/rai2.12125","vorDoiUrl":"https://doi.org/10.1002/rai2.12125","workflowStages":[]},"version":"v2","identity":"rs-2776347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2776347","identity":"rs-2776347","version":["v2"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}