Mendelian Randomization Analyses of the Impact of Autoimmune Disease on Lymphoma

Background: Autoimmune diseases (AIDs) are a heterogeneous group of disorders caused by abnormal immune responses. Results from previous investigations have shown associations between the risk of lymphoma and a history of autoimmune diseases. However, it is unknown if this association is causal or confounded. Mendelian randomization (MR) is a genetic epidemiology method using genetic variants as predictors to estimate causal effects of a modifiable exposure on an outcome. Herein, we estimated the causal effect of AID on lymphoma in a general European population using MR.

Methods: A two-sample MR framework was conducted to estimate the causal effect of genetic liability for AID on lymphoma. A random-effects (IVW) method was used for the MR estimation, which allowed the instruments to be invalid if the horizontal pleiotropy was balanced on the whole. Lymphoma risk estimates were presented as odds ratios (OR) with 95% confidence (95% CI) intervals per unit increase in log odds of AID. Statistical analysis was conducted using Two-sample MR R package.

Results:The Genome-wide association studies (GWAS) data for lymphoma (1,752 cases with lymphoma and 359,442 control samples) were analyzed. The GWAS summary statistics of AID and diseases including RA, SLE, psoriasis (PsO), sjögren syndrome (SS), ankylosing spondylitis (AS), and systemic sclerosis (SSc) were obtained from a recent published GWAS, with 42,202 European AID cases and 176,590 control cases. Using 36 AID-related SNPs, strong evidence of a potential causal effect of AID on the risk of lymphoma was found (OR = 1.001, 95% CI = 1.000-1.002, p = 0.040). Meanwhile, similar risk estimates were gained using the MR-Egger regression (OR = 1.002, 95% CI = 1.000-1.003, p = 0.018) and weighted median approaches (OR = 1.001, 95% CI = 1.000-1.002, p=0.152). Heterogeneity was observed with a Cochran Q-test derived p value as 0.002 of MR-Egger and p value as 0.001 of IVW, which could be ignored for the consistence of the MR estimates. Slopes of MR regressions and causal estimates associated with each SNP are illustrated. In addition, according to the leave-one-out sensitivity analysis, no single SNP strongly disrupted the overall effect of AID on lymphoma. No unbalanced horizontal pleiotropy or evident heterogeneity was found.

We further explored the association between single autoimmune disease and lymphoma. In the MR analysis, the only potential causal effect of SS on risks of lymphoma was detected (OR = 1.002, 95%CI = 1.001-1.002,p < 0.01). The same direction of association was obtained using other MR estimators. A Cochran Q-test derived p value was 0.056 of IVW and 0.063 of MR-Egger, indicating no heterogeneity. Additional sensitivity analyses suggested that these effects were robust to various MR assumptions.

Conclusions:Our study is the first to report the potential causality of AID on lymphoma through MR analysis. In addition, a strong relationship between SS and risk of lymphoma was found. Therefore, for patients with autoimmune diseases, long-term and close follow-up is required to be alert to the occurrence of lymphoma.

Disclosures: No relevant conflicts of interest to declare.

No relevant conflicts of interest to declare.