SummaryThe immune system is highly diverse, but characterization of its genetic architecture has lagged behind the vast progress made by genome-wide association studies (GWASs) of emergent diseases. Our GWAS for 54 functionally relevant phenotypes of the adaptive immune system in 489 healthy individuals identifies eight genome-wide significant associations explaining 6%–20% of variance. Coding and splicing variants in PTPRC and COMMD10 are involved in memory T cell differentiation. Genetic variation controlling disease-relevant T helper cell subsets includes RICTOR and STON2 associated with Th2 and Th17, respectively, and the interferon-lambda locus controlling regulatory T cell proliferation. Early and memory B cell differentiation stages are associated with variation in LARP1B and SP4. Finally, the latrophilin family member ADGRL2 correlates with baseline pro-inflammatory interleukin-6 levels. Suggestive associations reveal mechanisms of autoimmune disease associations, in particular related to pro-inflammatory cytokine production. Pinpointing these key human immune regulators offers attractive therapeutic perspectives.

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

Objective Evidence for a role of microglia in the pathogenesis of multiple sclerosis (MS) is growing. We investigated association of microglial markers at time of diagnostic lumbar puncture (LP) with different aspects of disease activity (relapses, disability, magnetic resonance imaging parameters) up to 6 years later in a cohort of 143 patients. Methods In cerebrospinal fluid (CSF), we measured 3 macrophage and microglia‐related proteins, chitotriosidase (CHIT1), chitinase‐3–like protein 1 (CHI3L1 or YKL‐40), and soluble triggering receptor expressed on myeloid cells 2 (sTREM2), as well as a marker of neuronal damage, neurofilament light chain (NfL), using enzyme‐linked immunosorbent assay and electrochemiluminescence. We investigated the same microglia‐related markers in publicly available RNA expression data from postmortem brain tissue. Results CHIT1 levels at diagnostic LP correlated with 2 aspects of long‐term disease activity after correction for multiple testing. First, CHIT1 increased with reduced tissue integrity in lesions at a median 3 years later (p = 9.6E‐04). Second, CHIT1 reflected disease severity at a median 5 years later (p = 1.2E‐04). Together with known clinical covariates, CHIT1 levels explained 12% and 27% of variance in these 2 measures, respectively, and were able to distinguish slow and fast disability progression (area under the curve = 85%). CHIT1 was the best discriminator of chronic active versus chronic inactive lesions and the only marker correlated with NfL (r = 0.3, p = 0.0019). Associations with disease activity were, however, independent of NfL. Interpretation CHIT1 CSF levels measured during the diagnostic LP reflect microglial activation early on in MS and can be considered a valuable prognostic biomarker for future disease activity. ANN NEUROL 2020;87:633–645

Background Striking changes in the demographic pattern of multiple sclerosis (MS) strongly indicate an influence of modifiable exposures, which lend themselves well to intervention. It is important to pinpoint which of the many environmental, lifestyle, and sociodemographic changes that have occurred over the past decades, such as higher smoking and obesity rates, are responsible. Mendelian randomization (MR) is an elegant tool to overcome limitations inherent to observational studies and leverage human genetics to inform prevention strategies in MS. Methods We use genetic variants from the largest genome-wide association study for smoking phenotypes (initiation: N = 378, heaviness: N = 55, lifetime smoking: N = 126) and body mass index (BMI, N = 656) and apply these as instrumental variables in a two-sample MR analysis to the most recent meta-analysis for MS. We adjust for the genetic correlation between smoking and BMI in a multivariable MR. Results In univariable and multivariable MR, smoking does not have an effect on MS risk nor explains part of the association between BMI and MS risk. In contrast, in both analyses each standard deviation increase in BMI, corresponding to roughly 5 kg/m2 units, confers a 30% increase in MS risk. Conclusion Despite observational studies repeatedly reporting an association between smoking and increased risk for MS, MR analyses on smoking phenotypes and MS risk could not confirm a causal relationship. This is in contrast with BMI, where observational studies and MR agree on a causal contribution. The reasons for the discrepancy between observational studies and our MR study concerning smoking and MS require further investigation.

Objective: Many multiple sclerosis (MS) genetic susceptibility variants have been identified, but understanding disease heterogeneity remains a key challenge. Relapses are a core feature of MS and a common primary outcome of clinical trials, with prevention of relapses benefiting patients immediately and potentially limiting long-term disability accrual. We aim to identify genetic variation associated with relapse hazard in MS by analyzing the largest study population to date. Methods: We performed a genomewide association study (GWAS) in a discovery cohort and investigated the genomewide significant variants in a replication cohort. Combining both cohorts, we captured a total of 2,231 relapses occurring before the start of any immunomodulatory treatment in 991 patients. For assessing time to relapse, we applied a survival analysis utilizing Cox proportional hazards models. We also investigated the association between MS genetic risk scores and relapse hazard and performed a gene ontology pathway analysis. Results: The low-frequency genetic variant rs11871306 within WNT9B reached genomewide significance in predicting relapse hazard and replicated (meta-analysis hazard ratio (HR) = 2.15, 95% confidence interval (CI) = 1.70-2.78, p = 2.07 × 10 −10 ). A pathway analysis identified an association of the pathway "response to vitamin D" with relapse hazard (p = 4.33 × 10 −6 ). The MS genetic risk scores, however, were not associated with relapse hazard. Interpretation: Genetic factors underlying disease heterogeneity differ from variants associated with MS susceptibility. Our findings imply that genetic variation within the Wnt signaling and vitamin D pathways contributes to differences in relapse occurrence. The present study highlights these cross-talking pathways as potential modulators of MS disease activity.

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Variant rs12988804 in LRP2, the first example of a genome-wide significant association with relapse rate in MS, is replicated in an independent study.

Background and ObjectivesDecreased vitamin D levels and obesity are associated with an increased risk for multiple sclerosis (MS). However, whether they also affect the disease course after onset remains unclear. With larger data sets now available, we used Mendelian randomization (MR) to determine whether serum 25-hydroxyvitamin D (25OHD) and body mass index (BMI) are causally associated with MS risk and, moving beyond susceptibility toward heterogeneity, with relapse hazard.MethodsWe used genetic variants from 4 distinct genome-wide association studies (GWASs) for serum 25OHD in up to 416,247 individuals and for BMI from a GWAS in 681,275 individuals. Applying 2-sample MR, we examined associations of 25OHD and BMI with the risk of MS, with summary statistics from the International Multiple Sclerosis Genetics Consortium GWAS in 14,802 MS cases and 26,703 controls. In addition, we examined associations with relapse hazard, with data from our GWAS in 506 MS cases.ResultsA 1-SD increase in genetically predicted natural-log transformed 25OHD levels decreased odds of MS up to 28% (95% CI: 12%–40%, p = 0.001) and decreased hazard for a relapse occurring up to 43% (95% CI: 15%–61%, p = 0.006). A 1-SD increase in genetically predicted BMI, corresponding to roughly 5 kg/m2, increased risk for MS with 30% (95% CI: 15%–47%, p = 3.76 × 10−5). On the contrary, we did not find evidence for a causal role of higher BMI with an increased hazard for occurrence of a relapse.DiscussionThis study supports causal effects of genetically predicted serum 25OHD concentrations and BMI on risk of MS. In contrast, serum 25OHD but not BMI is significantly associated with relapse hazard after onset. These findings might offer clinical implications for both prevention and treatment.