Background <p>Systemic lupus erythematosus (SLE) is influenced by a complex array of factors, encompassing genetic, hormonal, and microbial components. This study seeks to investigate the causal relationships between specific skin microbiotas and SLE using a Mendelian randomization (MR) analysis and a Bayesian weighted Mendelian randomization (BWMR) analysis.</p> Methods <p>We utilized genome-wide association study (GWAS) data to investigate the role of skin microbiota in SLE. Single nucleotide polymorphisms (SNPs) were employed as instrumental variables (IVs) in 9 Mendelian randomization methods, including the inverse variance weighted (IVW) method, MR-Egger method, weighted median method, weighted mode, simple mode method, contamination mixture (ConMix) method, robust adjusted profile score (RAPS) method, constrained maximum likelihood and model averaging (CML-MA), and debiased inverse-variance weighted (dIVW) method. Additionally, sensitivity analyses such as leave-one-out analysis, Cochran’s <i>Q</i> test, and Egger intercept test were conducted to ensure the robustness of the Mendelian randomization results. Finally, we applied Bayesian weighted Mendelian randomization (BWMR) approach to validate our MR findings.</p> Results <p>In the MR analysis of the skin microbiota (KORA FF4) and SLE, the IVW method revealed that ASV042 (<i>Acinetobacter</i> (unc.)) _Antecubitalfossa_Moist (OR = 0.964, 95% CI = 0.941–0.988, <i>p</i> = 0.003) showed protective effects against SLE, while three taxa exhibited positive associations with SLE risk: ASV005 (<i>Propionibacterium granulosum</i>) _Retroauricularfold_Sebaceous (OR = 1.051, 95% CI = 1.013–1.091, <i>p</i> = 0.008), the phylum <i>Proteobacteria</i> _Retroauricularfold_Sebaceous (OR = 1.048, 95% CI = 1.004–1.094, <i>p</i> = 0.033), and the class <i>Betaproteobacteria</i> _Retroauricularfold_Sebaceous (OR = 1.049, 95% CI = 1.00&#xa0;l–1.098, <i>p</i> = 0.044). In the MR analysis of the skin microbiota (PopGen) and SLE, five taxa demonstrated protective effects: ASV004 (<i>Corynebacterium</i> (unc.)) _Forehead_Sebaceous (OR = 0.960, 95% CI = 0.927–0.994, <i>p</i> = 0.023), ASV005 (<i>Propionibacterium granulosum</i>) _Antecubitalfossa_Moist (OR = 0.964, 95% CI = 0.930–0.999, <i>p</i> = 0.042), ASV007 (<i>Anaerococcus</i> (unc.)) _Forehead_Sebaceous (OR = 0.969, 95% CI = 0.942–0.996, <i>p</i> = 0.027), the genus <i>Kocuria</i> _Volarforearm_Dry (OR = 0.964, 95% CI = 0.935–0.995, <i>p</i> = 0.023), and the class <i>Betaproteobacteria</i> _Antecubitalfossa_Moist (OR = 0.952, 95% CI = 0.918–0.988, <i>p</i> = 0.010), while ASV039 (<i>Acinetobacter</i> (unc.)) _Antecubitalfossa_Moist (OR = 1.024, 95% CI = 1.000–1.048, <i>p</i> = 0.049) was found to potentially increase risk in SLE. The findings were further supported by BWMR analysis, adding credibility to this research. Sensitivity analyses confirmed robustness of these findings.</p> Conclusion <p>Through genetic approaches, our study has illustrated specific skin microbiotas that may influence SLE, potentially facilitating earlier detection and enhancing the efficacy of treatment alternatives for patients with SLE.</p> <Table Float="No" ID="Taba"> <tgroup cols="2"> <colspec align="left" colname="c1" colnum="1" /> <colspec align="left" colname="c2" colnum="2" /> <tbody> <row> <entry align="left" nameend="c2" namest="c1"> <p><b>Key points</b></p> <p>• <i>This is the first study to uncover the potential causal relationship&#xa0;between skin microbiota and SLE using MR analysis.</i></p> <p>• <i>The study has identified a potential causal relationship between 10&#xa0;skin microbiotas and SLE</i>.&#xa0;</p> <p>• <i>A series of sensitivity analyses and BWMR were conducted to&#xa0;validate the robustness of our findings.</i></p> </entry> </row> </tbody> </tgroup> </Table>