Investigating pleiotropy between depression and autoimmune diseases using the UK Biobank

Background Epidemiological studies have shown increased comorbidity between depression and autoimmune diseases. The mechanisms driving the comorbidity are poorly understood, and a highly powered investigation is needed to understand the relative importance of shared genetic influences. We investigated the evidence for pleiotropy from shared genetic risk alleles between these traits in the UK Biobank (UKB). Methods We defined autoimmune and depression cases using information from hospital episode statistics, self-reported conditions and medications, and mental health questionnaires. Pairwise comparisons of depression prevalence between autoimmune cases and controls, and vice-versa, were performed. Cross-trait polygenic risk score (PRS) analyses were performed to test for pleiotropy, i.e. testing whether PRS for depression could predict autoimmune disease status, and vice-versa. Results We identified 28k cases of autoimmune diseases (pooling across 14 traits) and 324k autoimmune controls, and 65k cases of depression and 232k depression controls. The prevalence of depression was significantly higher in autoimmune cases compared to controls, and vice-versa. PRS for myasthenia gravis and psoriasis were significantly higher in depression cases compared to controls (p < 5.2×10 −5 , R 2 <= 0.04%). PRS for depression were significantly higher in inflammatory bowel disease, psoriasis, psoriatic arthritis, rheumatoid arthritis and type 1 diabetes cases compared to controls (p < 5.8×10 −5 , R 2 range 0.06% to 0.27%), and lower in coeliac disease cases compared to controls (p < 5.4×10 −7 , R 2 range 0.11% to 0.15%). Conclusions Consistent with the literature, depression was more common in individuals with autoimmune diseases compared to controls, and vice-versa, in the UKB. PRS showed some evidence for involvement of shared genetic factors, but the modest R 2 values suggest that shared genetic architecture accounts for only a small proportion of the increased risk across traits.