The presence of IgM bands reflects a process that may diminish the risk of PML by counteracting the excess of immunosuppression that may occur during natalizumab therapy.

Several variants in strong linkage disequilibrium (LD) at the SP140 locus have been associated with multiple sclerosis (MS), Crohn's disease (CD) and chronic lymphocytic leukemia (CLL). To determine the causal polymorphism, we have integrated high-density data sets of expression quantitative trait loci (eQTL), using GEUVADIS RNA sequences and 1000 Genomes genotypes, with MS-risk variants of the high-density Immunochip array performed by the International Multiple Sclerosis Genetic Consortium (IMSGC). The variants most associated with MS were also correlated with a decreased expression of the full-length RNA isoform of SP140 and an increase of an isoform lacking exon 7. By exon splicing assay, we have demonstrated that the rs28445040 variant was the causal factor for skipping of exon 7. Western blots of peripheral blood mononuclear cells from MS patients showed a significant allele-dependent reduction of the SP140 protein expression. To confirm the association of this functional variant with MS and to compare it with the best-associated variant previously reported by GWAS (rs10201872), a case-control study including 4384 MS patients and 3197 controls was performed. Both variants, in strong LD (r(2) = 0.93), were found similarly associated with MS [P-values, odds ratios: 1.9E-9, OR = 1.35 (1.22-1.49) and 4.9E-10, OR = 1.37 (1.24-1.51), respectively]. In conclusion, our data uncover the causal variant for the SP140 locus and the molecular mechanism associated with MS risk. In addition, this study and others previously reported strongly suggest that this functional variant may be shared with other immune-mediated diseases as CD and CLL.

The presence of CSF IgM OCB may be a biomarker for a subset of PPMS patients with more active inflammatory disease, who may benefit from immune-directed treatments.

Multiple sclerosis is a leading cause of neurological disability in adults. Heterogeneity in multiple sclerosis clinical presentation has posed a major challenge for identifying genetic variants associated with disease outcomes. To overcome this challenge, we used prospectively ascertained clinical outcomes data from the largest international multiple sclerosis Registry, MSBase. We assembled a cohort of deeply phenotyped individuals of European ancestry with relapse-onset multiple sclerosis. We used unbiased genome-wide association study and machine learning approaches to assess the genetic contribution to longitudinally defined multiple sclerosis severity phenotypes in 1,813 individuals. Our primary analyses did not identify any genetic variants of moderate to large effect sizes that met genome-wide significance thresholds. The strongest signal was associated with rs7289446 (β=-0.4882, P = 2.73 × 10−7), intronic to SEZ6L on chromosome 22. However, we demonstrate that clinical outcomes in relapse-onset multiple sclerosis are associated with multiple genetic loci of small effect sizes. Using a machine learning approach incorporating over 62,000 variants together with clinical and demographic variables available at multiple sclerosis disease onset, we could predict severity with an area under the receiver operator curve of 0.84 (95% CI 0.79–0.88). Our machine learning algorithm achieved positive predictive value for outcome assignation of 80% and negative predictive value of 88%. This outperformed our machine learning algorithm that contained clinical and demographic variables alone (area under the receiver operator curve 0.54, 95% CI 0.48–0.60). Secondary, sex-stratified analyses identified two genetic loci that met genome-wide significance thresholds. One in females (rs10967273; βfemale =0.8289, P = 3.52 × 10−08), the other in males (rs698805; βmale = -1.5395, P = 4.35 × 10−08), providing some evidence for sex dimorphism in multiple sclerosis severity. Tissue enrichment and pathway analyses identified an overrepresentation of genes expressed in central nervous system compartments generally, and specifically in the cerebellum (P = 0.023). These involved mitochondrial function, synaptic plasticity, oligodendroglial biology, cellular senescence, calcium and g-protein receptor signalling pathways. We further identified six variants with strong evidence for regulating clinical outcomes, the strongest signal again intronic to SEZ6L (adjusted hazard ratio 0.72, P = 4.85 × 10−4). Here we report a milestone in our progress towards understanding the clinical heterogeneity of multiple sclerosis outcomes, implicating functionally distinct mechanisms to multiple sclerosis risk. Importantly, we demonstrate that machine learning using common single nucleotide variant clusters, together with clinical variables readily available at diagnosis can improve prognostic capabilities at diagnosis, and with further validation has the potential to translate to meaningful clinical practice change.