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

doi:10.21203/rs.3.rs-2776347/v2

Background: The interferon-gamma (IFN-γ) signaling pathway is activated in systemic lupus erythematosus (SLE). 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

• 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.

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 autoantibodies¹. 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 disease⁷. 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 patients¹¹. 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 SLE¹². 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 exposures¹⁴. Genetic variants significantly related to exposure are selected as instrumental variables (IVs) to infer the causality¹⁵. 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.

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 analysis¹⁶: (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 study¹⁷. 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 markers¹⁸. 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 r² < 0.001, window size = 10,000 kb); (3) exclusion of SNPs that are palindromic with intermediate allele frequencies¹⁹. 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 bias²⁰. 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 analysis²¹. 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 SLE²⁴. The IVW method is most effective when all IVs are valid. However, the presence of horizontal pleiotropy can lead to biased inferences²⁵. 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 scenarios²⁶. 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 SNPs²⁷.

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 pleiotropy²⁶. 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 pleiotropy²⁸. Funnel plots were employed to visualize MR analyses and to search for asymmetry as an indicator of pleiotropy²⁹. 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.

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 ( r²<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 (r²<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)

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 levels³³. 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.³⁵discovered 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 functions³⁶. 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 cells³⁹. 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 cells⁴⁰. Second, B cells act as inflammatory mediators, producing pathogenic antibodies to enhance the inflammatory response and directly damaging tissues and cells⁴¹. 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 cells⁵⁰. CD8⁺ T cells in the peripheral blood of SLE patients typically have reduced production of granzyme B and perforin and exhibit impaired cytolytic function⁵¹. 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.

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.

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.

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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
Exposure	MR method	No. of SNPs	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
Outcome	MR method	No. of SNPs	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 < 1; β coefficients = 0, OR = 1; β coefficients > 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.

The authors declare no competing interests.

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

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