Background:Circulating autoantibodies (AB) against brain-antigens, often deemed pathological, receive increasing attention. We assessed predispositions and seroprevalence/characteristics of 49 AB in >7000 individuals.Methods:Exploratory cross-sectional cohort study, investigating deeply phenotyped neuropsychiatric patients and healthy individuals of GRAS Data Collection for presence/characteristics of 49 brain-directed serum-AB. Predispositions were evaluated through GWAS of NMDAR1-AB carriers, analyses of immune check-point genotypes, APOE4 status, neurotrauma. Chi-square, Fisher’s exact tests and logistic regression analyses were used.Results:Study of N=7025 subjects (55.8% male; 41±16 years) revealed N=1133 (16.13%) carriers of any AB against 49 defined brain-antigens. Overall, age dependence of seroprevalence (OR=1.018/year; 95% CI [1.015-1.022]) emerged, but no disease association, neither general nor with neuropsychiatric subgroups. Males had higher AB seroprevalence (OR=1.303; 95% CI [1.144-1.486]). Immunoglobulin class (N for IgM:462; IgA:487; IgG:477) and titers were similar. Abundant were NMDAR1-AB (7.7%). Low seroprevalence (1.25%-0.02%) was seen for most AB (e.g. amphiphysin, KCNA2, ARHGAP26, GFAP, CASPR2, MOG, Homer-3, KCNA1, GLRA1b, GAD65). Non-detectable were others. GWAS of NMDAR1-AB carriers revealed three genome-wide significant SNPs, two intergenic, one in TENM3, previously autoimmune disease-associated. Targeted analysis of immune check-point genotypes (CTLA4, PD1, PD-L1) uncovered effects on humoral anti-brain autoimmunity (OR=1.55; 95% CI [1.058-2.271]) and disease likelihood (OR=1.43; 95% CI [1.032-1.985]). APOE4 carriers (∼19%) had lower seropositivity (OR=0.766; 95% CI [0.625-0.933]). Neurotrauma predisposed to NMDAR1-AB seroprevalence (IgM: OR=1.599; 95% CI [1.022-2.468]).Conclusions:Humoral autoimmunity against brain-antigens, frequent across health and disease, is predicted by age, gender, genetic predisposition, and brain injury. Seroprevalence, immunoglobulin class, or titers do not predict disease
