Genome-wide study in Germans identifies four novel multiple sclerosis risk genes and confirms already known gene loci.

Pain is frequent in multiple sclerosis (MS) and includes different types, with neuropathic pain (NP) being most closely related to MS pathology. However, prevalence estimates vary largely, and causal relationships between pain and biopsychosocial factors in MS are largely unknown. Longitudinal studies might help to clarify the prevalence and determinants of pain in MS. To this end, we analyzed data from 410 patients with newly diagnosed clinically isolated syndrome or relapsing-remitting MS participating in the prospective multicenter German National MS Cohort Study (NationMS) at baseline and after 4 years. Pain was assessed by self-report using the PainDETECT Questionnaire. Neuropsychiatric assessment included tests for fatigue, depression, and cognition. In addition, sociodemographic and clinical data were obtained. Prevalence of pain of any type was 40% and 36% at baseline and after 4 years, respectively, whereas prevalence of NP was 2% and 5%. Pain of any type and NP were both strongly linked to fatigue, depression, and disability. This link was even stronger after 4 years than at baseline. Moreover, changes in pain, depression, and fatigue were highly correlated without any of these symptoms preceding the others. Taken together, pain of any type seems to be much more frequent than NP in early nonprogressive MS. Moreover, the close relationship between pain, fatigue, and depression in MS should be considered for treatment decisions and future research on a possible common pathophysiology.

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a PLOS Computational Biology | https://doi. Data Availability Statement:DeepWAS is an open source collaborative initiative available in the GitHub repository https://github.com/cellmapslab/ DeepWAS. The informed consents given by KORA study participants do not cover data posting in public databases. However, data are available upon request from KORA-gen (https://epi.helmholtzmuenchen.de/). Data requests can be submitted online and are subject to approval by the KORA Board. KKNMS data are available upon approved multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/ cellmapslab/DeepWAS. Author summaryIn the era of steadily increasing amounts of available genetic data, we still lack novel and innovative ideas on how to improve fine-mapping of regulatory variants identified by genome-wide association studies (GWAS), especially in non-coding regions. Current approaches for the identification of functional variants conduct functional annotation after the GWAS analysis either using position-based overlaps of each variant with regulatory elements or deep-learning-based methods predicting regulatory effects per variant on cell-type-specific chromatin features. We here present DeepWAS, which integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Our results provide evidence that DeepWAS results directly identify disease/trait-associated SNPs with a common effect on a specific chromatin feature in a relevant tissue. We can show for multiple sclerosis, major depressive disorder, and body height, that the SNPs identified by DeepWAS are at least nominally significant in classical univariate GWAS analysis of the same cohorts or larger published GWAS. By integ...