Characterizing the effects of genetic liability to autoimmune conditions on pregnancy outcomes using Mendelian randomization

Abstract Background Autoimmune conditions are common in women of reproductive age. They are associated with an increased risk of adverse pregnancy outcomes; whether this is causal is unclear. Our aim was to explore the causal effects of genetic liability to autoimmune conditions on pregnancy outcomes. Methods We conducted two-sample Mendelian randomization (MR) to estimate effects of genetic liability to ten autoimmune conditions on nine primary and seven secondary pregnancy outcomes. We used data from the MR-PREG collaboration including up to 714,889 pregnancies. Main analyses used the inverse variance weighted method to pool effects across genetic variants. Sensitivity analyses explored bias due to pleiotropic variants and fetal genetics. Results We found evidence for 7 effects of autoimmune condition genetic liability on primary pregnancy outcomes that were robust across all sensitivity analyses. Higher genetic liability to Hashimoto’s thyroiditis was protective against large-for-gestational-age and increased the risk of hypertensive disorders of pregnancy (HDP) and preterm birth. For example, risk of preterm birth increased by 6% (OR = 1.06 (95%CI: 1.02, 1.11)) per doubling in log odds of Hashimoto’s thyroiditis. Genetic liability to type 1 diabetes and systemic lupus erythematosus each increased the risk of preterm birth only. Higher genetic liability to rheumatoid arthritis increased the risk of HDP, while higher ankylosing spondylitis genetic liability reduced the risk of HDP. For multiple sclerosis, systemic sclerosis, coeliac disease, inflammatory bowel disease, and psoriasis, we did not detect any robust effects of increased genetic liability. Conclusions We observed higher genetic liability to Hashimoto’s thyroiditis, type 1 diabetes, and rheumatic conditions causes increased risk of adverse pregnancy outcomes. Further research is warranted to explore the immune mechanisms underlying these relationships, and identify targets for prevention.