The International Multiple Sclerosis Genetics Consortium reports the discovery of 48 new susceptibility variants for multiple sclerosis through targeted follow-up of immune-related loci. They also report fine mapping of association signals at established susceptibility loci and provide insights into the immune system processes underlying this disease. Using the ImmunoChip custom genotyping array, we analyzed 14,498 subjects with multiple sclerosis and 24,091 healthy controls for 161,311 autosomal variants and identified 135 potentially associated regions (P < 1.0 × 10−4). In a replication phase, we combined these data with previous genome-wide association study (GWAS) data from an independent 14,802 subjects with multiple sclerosis and 26,703 healthy controls. In these 80,094 individuals of European ancestry, we identified 48 new susceptibility variants (P < 5.0 × 10−8), 3 of which we found after conditioning on previously identified variants. Thus, there are now 110 established multiple sclerosis risk variants at 103 discrete loci outside of the major histocompatibility complex. With high-resolution Bayesian fine mapping, we identified five regions where one variant accounted for more than 50% of the posterior probability of association. This study enhances the catalog of multiple sclerosis risk variants and illustrates the value of fine mapping in the resolution of GWAS signals.

Background: There is no consensus method for determining progression of disability in patients with multiple sclerosis (MS) when each patient has had only a single assessment in the course of the disease. Methods: Using data from two large longitudinal databases, the authors tested whether cross-sectional disability assessments are representative of disease severity as a whole. An algorithm, the Multiple Sclerosis Severity Score (MSSS), which relates scores on the Expanded Disability Status Scale (EDSS) to the distribution of disability in patients with comparable disease durations, was devised and then applied to a collection of 9,892 patients from 11 countries to create the Global MSSS. In order to compare different methods of detecting such effects the authors simulated the effects of a genetic factor on disability. Results: Cross-sectional EDSS measurements made after the first year were representative of overall disease severity. The MSSS was more powerful than the other methods the authors tested for detecting different rates of disease progression. Conclusion: The Multiple Sclerosis Severity Score (MSSS) is a powerful method for comparing disease progression using single assessment data. The Global MSSS can be used as a reference table for future disability comparisons. While useful for comparing groups of patients, disease fluctuation precludes its use as a predictor of future disability in an individual.

Background: Multiple sclerosis (MS) is a complex disease with new drugs becoming available in the past years. There is a need for a reference tool compiling current data to aid professionals in treatment decisions. Objectives: To develop an evidence-based clinical practice guideline for the pharmacological treatment of people with MS. Methods: This guideline has been developed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology and following the updated EAN recommendations. Clinical questions were formulated in Patients–Intervention–Comparator–Outcome (PICO) format and outcomes were prioritized. The quality of evidence was rated into four categories according to the risk of bias. The recommendations with assigned strength (strong and weak) were formulated based on the quality of evidence and the risk-benefit balance. Consensus between the panelists was reached by use of the modified nominal group technique. Results: A total of 10 questions were agreed, encompassing treatment efficacy, response criteria, strategies to address suboptimal response and safety concerns and treatment strategies in MS and pregnancy. The guideline takes into account all disease-modifying drugs approved by the European Medicine Agency (EMA) at the time of publication. A total of 21 recommendations were agreed by the guideline working group after three rounds of consensus. Conclusion: The present guideline will enable homogeneity of treatment decisions across Europe.

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinson's disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10⁻⁸) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10⁻⁸). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies. The genetic basis of Parkinson's disease is complex, i.e. it is determined by a number of different disease-causing and disease-predisposing genes. Especially the latter have proven difficult to find, evidenced by more than 800 published genetic association studies, typically showing discrepant results. To facilitate the interpretation of this large and continuously increasing body of data, we have created a freely available online database (“PDGene”: http://www.pdgene.org) which provides an exhaustive account of all published genetic association studies in PD. One particularly useful feature is the calculation and display of up-to-date summary statistics of published data for overlapping DNA sequence variants (polymorphisms). These meta-analyses revealed eleven gene loci that showed a statistically very significant (P<5×10⁻⁸; a.k.a. genome-wide significance) association with risk for PD: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, SYT11/RAB25. In addition and purely by data-mining, we identified one novel PD susceptibility locus in a gene called ITGA8 (rs7077361, P = 1.3×10⁻⁸). We note that our continuously updated database represents the most comprehensive research synopsis of genetic association studies in PD to date. In addition to vastly facilitating the work of other PD geneticists, our approach may serve as a valuable example for other complex diseases.