Multiscale networks in multiple sclerosis.

Complex diseases such as Multiple Sclerosis (MS) cover a wide range of biological scales, from genes and proteins to cells and tissues, up to the full organism. In fact, any phenotype for an organism is dictated by the interplay among these scales. We conducted a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data, obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. Multilayer networks were constructed using mutual information for topological analysis, and Boolean simulations were constructed using Pearson correlation to identified paths within and among all layers. The path more commonly found from the Boolean simulations connects protein MK03, with total T cells, the thickness of the retinal nerve fiber layer (RNFL), and the walking speed. This path contains nodes involved in protein phosphorylation, glial cell differentiation, and regulation of stress-activated MAPK cascade, among others. Specific paths identified were subsequently analyzed by flow cytometry at the single-cell level. Combinations of several proteins (GSK3AB, HSBP1 or RS6) and immune cells (Th17, Th1 non-classic, CD8, CD8 Treg, CD56 neg, and B memory) were part of the paths explaining the clinical phenotype. The advantage of the path identified from the Boolean simulations is that it connects information about these known biological pathways with the layers at higher scales (retina damage and disability). Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype.

Prognostic value of single-subject grey matter networks in early multiple sclerosis.

The identification of prognostic markers in early multiple sclerosis (MS) is challenging and requires reliable measures that robustly predict future disease trajectories. Ideally, such measures should make inferences at the individual level to inform clinical decisions. This study investigated the prognostic value of longitudinal structural networks to predict 5-year Expanded Disability Status Scale (EDSS) progression in patients with relapsing-remitting MS (RRMS). We hypothesized that network measures, derived from MRI, outperform conventional MRI measurements at identifying patients at risk of developing disability progression. This longitudinal, multicentre study within the Magnetic Resonance Imaging in MS (MAGNIMS) network included 406 patients with RRMS (mean age = 35.7 ± 9.1 years) followed up for 5 years (mean follow-up = 5.0 ± 0.6 years). EDSS was determined to track disability accumulation. A group of 153 healthy subjects (mean age = 35.0 ± 10.1 years) with longitudinal MRI served as controls. All subjects underwent MRI at baseline and again 1 year after baseline. Grey matter atrophy over 1 year and white matter lesion load were determined. A single-subject brain network was reconstructed from T1-weighted scans based on grey matter atrophy measures derived from a statistical parameter mapping-based segmentation pipeline. Key topological measures, including network degree, global efficiency and transitivity, were calculated at single-subject level to quantify network properties related to EDSS progression. Areas under receiver operator characteristic (ROC) curves were constructed for grey matter atrophy and white matter lesion load, and the network measures and comparisons between ROC curves were conducted. The applied network analyses differentiated patients with RRMS who experience EDSS progression over 5 years through lower values for network degree [H(2) = 30.0, P < 0.001] and global efficiency [H(2) = 31.3, P < 0.001] from healthy controls but also from patients without progression. For transitivity, the comparisons showed no difference between the groups [H(2) = 1.5, P = 0.474]. Most notably, changes in network degree and global efficiency were detected independent of disease activity in the first year. The described network reorganization in patients experiencing EDSS progression was evident in the absence of grey matter atrophy. Network degree and global efficiency measurements demonstrated superiority of network measures in the ROC analyses over grey matter atrophy and white matter lesion load in predicting EDSS worsening (all P-values < 0.05). Our findings provide evidence that grey matter network reorganization over 1 year discloses relevant information about subsequent clinical worsening in RRMS. Early grey matter restructuring towards lower network efficiency predicts disability accumulation and outperforms conventional MRI predictors.

Using The Virtual Brain to study the relationship between structural and functional connectivity in patients with multiple sclerosis: a multicenter study.

The relationship between structural connectivity (SC) and functional connectivity (FC) captured from magnetic resonance imaging, as well as its interaction with disability and cognitive impairment, is not well understood in people with multiple sclerosis (pwMS). The Virtual Brain (TVB) is an open-source brain simulator for creating personalized brain models using SC and FC. The aim of this study was to explore SC-FC relationship in MS using TVB. Two different model regimes have been studied: stable and oscillatory, with the latter including conduction delays in the brain. The models were applied to 513 pwMS and 208 healthy controls (HC) from 7 different centers. Models were analyzed using structural damage, global diffusion properties, clinical disability, cognitive scores, and graph-derived metrics from both simulated and empirical FC. For the stable model, higher SC-FC coupling was associated with pwMS with low Single Digit Modalities Test (SDMT) score (F=3.48, P$\lt$0.05), suggesting that cognitive impairment in pwMS is associated with a higher SC-FC coupling. Differences in entropy of the simulated FC between HC, high and low SDMT groups (F=31.57, P$\lt$1e-5), show that the model captures subtle differences not detected in the empirical FC, suggesting the existence of compensatory and maladaptive mechanisms between SC and FC in MS.

Deep neural networks learn general and clinically relevant representations of the ageing brain.

The discrepancy between chronological age and the apparent age of the brain based on neuroimaging data - the brain age delta - has emerged as a reliable marker of brain health. With an increasing wealth of data, approaches to tackle heterogeneity in data acquisition are vital. To this end, we compiled raw structural magnetic resonance images into one of the largest and most diverse datasets assembled (n=53542), and trained convolutional neural networks (CNNs) to predict age. We achieved state-of-the-art performance on unseen data from unknown scanners (n=2553), and showed that higher brain age delta is associated with diabetes, alcohol intake and smoking. Using transfer learning, the intermediate representations learned by our model complemented and partly outperformed brain age delta in predicting common brain disorders. Our work shows we can achieve generalizable and biologically plausible brain age predictions using CNNs trained on heterogeneous datasets, and transfer them to clinical use cases.

Quantitative proteomics reveals protein dysregulation during T cell activation in multiple sclerosis patients compared to healthy controls.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune, neurodegenerative disorder with a strong genetic component that acts in a complex interaction with environmental factors for disease development. CD4+ T cells are pivotal players in MS pathogenesis, where peripherally activated T cells migrate to the central nervous system leading to demyelination and axonal degeneration. Through a proteomic approach, we aim at identifying dysregulated pathways in activated T cells from MS patients as compared to healthy controls. METHODS: CD4+ T cells were purified from peripheral blood from MS patients and healthy controls by magnetic separation. Cells were left unstimulated or stimulated in vitro through the TCR and costimulatory CD28 receptor for 24 h prior to sampling. Electrospray liquid chromatography-tandem mass spectrometry was used to measure protein abundances. RESULTS: Upon T cell activation the abundance of 1801 proteins was changed. Among these proteins, we observed an enrichment of proteins expressed by MS-susceptibility genes. When comparing protein abundances in T cell samples from healthy controls and MS patients, 18 and 33 proteins were differentially expressed in unstimulated and stimulated CD4+ T cells, respectively. Moreover, 353 and 304 proteins were identified as proteins exclusively induced upon T cell activation in healthy controls and MS patients, respectively and dysregulation of the Nur77 pathway was observed only in samples from MS patients. CONCLUSIONS: Our study highlights the importance of CD4+ T cell activation for MS, as proteins that change in abundance upon T cell activation are enriched for proteins encoded by MS susceptibility genes. The results provide evidence for proteomic disturbances in T cell activation in MS, and pinpoint to dysregulation of the Nur77 pathway, a biological pathway known to limit aberrant effector T cell responses.

Serum neurofilament as a predictor of 10-year grey matter atrophy and clinical disability in multiple sclerosis: a longitudinal study.

BACKGROUND: The predictive value of serum neurofilament light chain (sNfL) on long-term prognosis in multiple sclerosis (MS) is still unclear. OBJECTIVE: Investigate the relation between sNfL levels over a 2-year period in patients with relapsing-remitting MS, and clinical disability and grey matter (GM) atrophy after 10 years. METHODS: 85 patients, originally enrolled in a multicentre, randomised trial of ω-3 fatty acids, participated in a 10-year follow-up visit. sNfL levels were measured by Simoa quarterly until month 12, and then at month 24. The appearance of new gadolinium-enhancing (Gd+) lesions was assessed monthly between baseline and month 9, and then at months 12 and 24. At the 10-year follow-up visit, brain atrophy measures were obtained using FreeSurfer. RESULTS: Higher mean sNfL levels during early periods of active inflammation (Gd+ lesions present or recently present) predicted lower total (ß=-0.399, p=0.040) and deep (ß=-0.556, p=0.010) GM volume, lower mean cortical thickness (ß=-0.581, p=0.010) and higher T2 lesion count (ß=0.498, p=0.018). Of the clinical outcomes, higher inflammatory sNfL levels were associated with higher disability measured by the dominant hand Nine-Hole Peg Test (ß=0.593, p=0.004). Mean sNfL levels during periods of remission (no Gd+ lesions present or recently present) did not predict GM atrophy or disability progression. CONCLUSION: Higher sNfL levels during periods of active inflammation predicted more GM atrophy and specific aspects of clinical disability 10 years later. The findings suggest that subsequent long-term GM atrophy is mainly due to neuroaxonal degradation within new lesions.

Serum neurofilament light chain concentration predicts disease worsening in multiple sclerosis.

BACKGROUND: Serum neurofilament light (sNfL) chain is a promising biomarker reflecting neuro-axonal injury in multiple sclerosis (MS). However, the ability of sNfL to predict outcomes in real-world MS cohorts requires further validation. OBJECTIVE: The aim of the study is to investigate the associations of sNfL concentration, magnetic resonance imaging (MRI) and retinal optical coherence tomography (OCT) markers with disease worsening in a longitudinal European multicentre MS cohort. METHODS: MS patients (n = 309) were prospectively enrolled at four centres and re-examined after 2 years (n = 226). NfL concentration was measured by single molecule array assay in serum. The patients' phenotypes were thoroughly characterized with clinical examination, retinal OCT and MRI brain scans. The primary outcome was disease worsening at median 2-year follow-up. RESULTS: Patients with high sNfL concentrations (⩾8 pg/mL) at baseline had increased risk of disease worsening at median 2-year follow-up (odds ratio (95% confidence interval) = 2.8 (1.5-5.3), p = 0.001). We found no significant associations of MRI or OCT measures at baseline with risk of disease worsening. CONCLUSION: Serum NfL concentration was the only factor associated with disease worsening, indicating that sNfL is a useful biomarker in MS that might be relevant in a clinical setting.

Enhancement of cranial nerves in Lyme neuroborreliosis: incidence and correlation with clinical symptoms and prognosis.

PURPOSE: Symptoms of cranial neuritis are a common presentation of Lyme neuroborreliosis (LNB). Imaging studies are scarce and report contradictory low prevalence of enhancement compared to clinical studies of cranial neuropathy. We hypothesized that MRI enhancement of cranial nerves in LNB is underreported, and aimed to assess the prevalence and clinical impact of cranial nerve enhancement in early LNB. METHODS: In this prospective, longitudinal cohort study, 69 patients with acute LNB were examined with MRI of the brain. Enhancement of cranial nerves III-XII was rated. MRI enhancement was correlated to clinical findings of neuropathy in the acute phase and after 6 months. RESULTS: Thirty-nine of 69 patients (57%) had pathological cranial nerve enhancement. Facial and oculomotor nerves were most frequently affected. There was a strong correlation between enhancement in the distal internal auditory canal and parotid segments of the facial nerve and degree of facial palsy (gamma = 0.95, p < .01, and gamma = 0.93, p < .01), despite that 19/37 nerves with mild-moderate enhancement in the distal internal auditory canal segment showed no clinically evident palsy. Oculomotor and abducens nerve enhancement did not correlate with eye movement palsy (gamma = 1.00 and 0.97, p = .31 for both). Sixteen of 17 patients with oculomotor and/or abducens nerve enhancement had no evident eye movement palsy. CONCLUSIONS: MRI cranial nerve enhancement is common in LNB patients, but it can be clinically occult. Facial and oculomotor nerves are most often affected. Enhancement of the facial nerve distal internal auditory canal and parotid segments correlate with degree of facial palsy.

Exploring the role of the multiple sclerosis susceptibility gene CLEC16A in T cells.

C-type lectin-like domain family 16 member A (CLEC16A) is associated with autoimmune disorders, including multiple sclerosis (MS), but its functional relevance is not completely understood. CLEC16A is expressed in several immune cells, where it affects autophagic processes and receptor expression. Recently, we reported that the risk genotype of an MS-associated single nucleotide polymorphism in CLEC16A intron 19 is associated with higher expression of CLEC16A in CD4+ T cells. Here, we show that CLEC16A expression is induced in CD4+ T cells upon T cell activation. By the use of imaging flow cytometry and confocal microscopy, we demonstrate that CLEC16A is located in Rab4a-positive recycling endosomes in Jurkat TAg T cells. CLEC16A knock-down in Jurkat cells resulted in lower cell surface expression of the T cell receptor, however, this did not have a major impact on T cell activation response in vitro in Jurkat nor in human, primary CD4+ T cells.

Quantitative proteomic analyses of CD4+ and CD8+ T cells reveal differentially expressed proteins in multiple sclerosis patients and healthy controls.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune, neuroinflammatory disease, with an unclear etiology. However, T cells play a central role in the pathogenesis by crossing the blood-brain-barrier, leading to inflammation of the central nervous system and demyelination of the protective sheath surrounding the nerve fibers. MS has a complex inheritance pattern, and several studies indicate that gene interactions with environmental factors contribute to disease onset. METHODS: In the current study, we evaluated T cell dysregulation at the protein level using electrospray liquid chromatography-tandem mass spectrometry to get novel insights into immune-cell processes in MS. We have analyzed the proteomic profiles of CD4+ and CD8+ T cells purified from whole blood from 13 newly diagnosed, treatment-naive female patients with relapsing-remitting MS and 14 age- and sex-matched healthy controls. RESULTS: An overall higher protein abundance was observed in both CD4+ and CD8+ T cells from MS patients when compared to healthy controls. The differentially expressed proteins were enriched for T-cell specific activation pathways, especially CTLA4 and CD28 signaling in CD4+ T cells. When selectively analyzing proteins expressed from the genes most proximal to > 200 non-HLA MS susceptibility polymorphisms, we observed differential expression of eight proteins in T cells between MS patients and healthy controls, and there was a correlation between the genotype at three MS genetic risk loci and protein expressed from proximal genes. CONCLUSION: Our study provides evidence for proteomic differences in T cells from relapsing-remitting MS patients compared to healthy controls and also identifies dysregulation of proteins encoded from MS susceptibility genes.

Restriction spectrum imaging of white matter and its relation to neurological disability in multiple sclerosis.

BACKGROUND: Restriction spectrum imaging (RSI) is a recently introduced magnetic resonance imaging diffusion technique. The utility of RSI in multiple sclerosis (MS) is unknown. OBJECTIVE: To investigate the association between RSI-derived parameters and neurological disability in MS. METHODS: Seventy-seven relapsing-remitting MS patients were scanned with RSI on a 3-T scanner. RSI-derived parameters: fast and slow apparent diffusion coefficient (sADC), fractional anisotropy, restricted fractional anisotropy, neurite density (ND), cellularity, extracellular water fraction, and free water fraction, were obtained in white matter lesions (WML) and normal appearing white matter (NAWM). Patients were divided into three groups according to their expanded disability status scale (EDSS): with minimal, low, and substantial disability (<2.5, 2.5-3, and >3, respectively). Group comparisons and correlation analyses were performed. RESULTS: All tested RSI-derived parameters differed between WML and NAWM ( p < 0.001 for all pairwise comparisons). The sADC in WML showed largest difference across disability subgroups (analysis of variance (ANOVA): F = 5.1, η2 = 0.12, p = 0.008). ND in NAWM showed strongest correlation with disability (ϱ = -0.39, p < 0.001). CONCLUSION: The strongest correlation with EDSS of ND obtained in NAWM indicates that processes outside lesions are important for disability in MS. Our study suggests that RSI-derived parameters may help understand the "clinico-radiological paradox" and improve disease monitoring in MS.

Assessing genome-wide significance for the detection of differentially methylated regions.

DNA methylation plays an important role in human health and disease, and methods for the identification of differently methylated regions are of increasing interest. There is currently a lack of statistical methods which properly address multiple testing, i.e. control genome-wide significance for differentially methylated regions. We introduce a scan statistic (DMRScan), which overcomes these limitations. We benchmark DMRScan against two well established methods (bumphunter, DMRcate), using a simulation study based on real methylation data. An implementation of DMRScan is available from Bioconductor. Our method has higher power than alternative methods across different simulation scenarios, particularly for small effect sizes. DMRScan exhibits greater flexibility in statistical modeling and can be used with more complex designs than current methods. DMRScan is the first dynamic approach which properly addresses the multiple-testing challenges for the identification of differently methylated regions. DMRScan outperformed alternative methods in terms of power, while keeping the false discovery rate controlled.

The interaction between smoking and HLA genes in multiple sclerosis: replication and refinement.

Interactions between environment and genetics may contribute to multiple sclerosis (MS) development. We investigated whether the previously observed interaction between smoking and HLA genotype in the Swedish population could be replicated, refined and extended to include other populations. We used six independent case-control studies from five different countries (Sweden, Denmark, Norway, Serbia, United States). A pooled analysis was performed for replication of previous observations (7190 cases, 8876 controls). Refined detailed analyses were carried out by combining the genetically similar populations from the Nordic studies (6265 cases, 8401 controls). In both the pooled analyses and in the combined Nordic material, interactions were observed between HLA-DRB*15 and absence of HLA-A*02 and between smoking and each of the genetic risk factors. Two way interactions were observed between each combination of the three variables, invariant over categories of the third. Further, there was also a three way interaction between the risk factors. The difference in MS risk between the extremes was considerable; smokers carrying HLA-DRB1*15 and lacking HLA-A*02 had a 13-fold increased risk compared with never smokers without these genetic risk factors (OR 12.7, 95% CI 10.8-14.9). The risk of MS associated with HLA genotypes is strongly influenced by smoking status and vice versa. Since the function of HLA molecules is to present peptide antigens to T cells, the demonstrated interactions strongly suggest that smoking alters MS risk through actions on adaptive immunity.

Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis.

Multiple sclerosis is a common disease of the central nervous system in which the interplay between inflammatory and neurodegenerative processes typically results in intermittent neurological disturbance followed by progressive accumulation of disability. Epidemiological studies have shown that genetic factors are primarily responsible for the substantially increased frequency of the disease seen in the relatives of affected individuals, and systematic attempts to identify linkage in multiplex families have confirmed that variation within the major histocompatibility complex (MHC) exerts the greatest individual effect on risk. Modestly powered genome-wide association studies (GWAS) have enabled more than 20 additional risk loci to be identified and have shown that multiple variants exerting modest individual effects have a key role in disease susceptibility. Most of the genetic architecture underlying susceptibility to the disease remains to be defined and is anticipated to require the analysis of sample sizes that are beyond the numbers currently available to individual research groups. In a collaborative GWAS involving 9,772 cases of European descent collected by 23 research groups working in 15 different countries, we have replicated almost all of the previously suggested associations and identified at least a further 29 novel susceptibility loci. Within the MHC we have refined the identity of the HLA-DRB1 risk alleles and confirmed that variation in the HLA-A gene underlies the independent protective effect attributable to the class I region. Immunologically relevant genes are significantly overrepresented among those mapping close to the identified loci and particularly implicate T-helper-cell differentiation in the pathogenesis of multiple sclerosis.

Power estimation for non-standardized multisite studies.

A concern for researchers planning multisite studies is that scanner and T1-weighted sequence-related biases on regional volumes could overshadow true effects, especially for studies with a heterogeneous set of scanners and sequences. Current approaches attempt to harmonize data by standardizing hardware, pulse sequences, and protocols, or by calibrating across sites using phantom-based corrections to ensure the same raw image intensities. We propose to avoid harmonization and phantom-based correction entirely. We hypothesized that the bias of estimated regional volumes is scaled between sites due to the contrast and gradient distortion differences between scanners and sequences. Given this assumption, we provide a new statistical framework and derive a power equation to define inclusion criteria for a set of sites based on the variability of their scaling factors. We estimated the scaling factors of 20 scanners with heterogeneous hardware and sequence parameters by scanning a single set of 12 subjects at sites across the United States and Europe. Regional volumes and their scaling factors were estimated for each site using Freesurfer's segmentation algorithm and ordinary least squares, respectively. The scaling factors were validated by comparing the theoretical and simulated power curves, performing a leave-one-out calibration of regional volumes, and evaluating the absolute agreement of all regional volumes between sites before and after calibration. Using our derived power equation, we were able to define the conditions under which harmonization is not necessary to achieve 80% power. This approach can inform choice of processing pipelines and outcome metrics for multisite studies based on scaling factor variability across sites, enabling collaboration between clinical and research institutions.

Genome-Wide Association Study of Late-Onset Myasthenia Gravis: Confirmation of TNFRSF11A and Identification of ZBTB10 and Three Distinct HLA Associations.

To investigate the genetics of late-onset myasthenia gravis (LOMG), we conducted a genome-wide association study imputation of>6 million single nucleotide polymorphisms (SNPs) in 532 LOMG cases (anti-acetylcholine receptor [AChR] antibody positive; onset age≥50 years) and 2,128 controls matched for sex and population substructure. The data confirm reported TNFRSF11A associations (rs4574025, P = 3.9 × 10-7, odds ratio [OR] 1.42) and identify a novel candidate gene, ZBTB10, achieving genome-wide significance (rs6998967, P = 8.9 × 10-10, OR 0.53). Several other SNPs showed suggestive significance including rs2476601 (P = 6.5 × 10-6, OR 1.62) encoding the PTPN22 R620W variant noted in early-onset myasthenia gravis (EOMG) and other autoimmune diseases. In contrast, EOMG-associated SNPs in TNIP1 showed no association in LOMG, nor did other loci suggested for EOMG. Many SNPs within the major histocompatibility complex (MHC) region showed strong associations in LOMG, but with smaller effect sizes than in EOMG (highest OR ~2 versus ~6 in EOMG). Moreover, the strongest associations were in opposite directions from EOMG, including an OR of 0.54 for DQA1*05:01 in LOMG (P = 5.9 × 10-12) versus 2.82 in EOMG (P = 3.86 × 10-45). Association and conditioning studies for the MHC region showed three distinct and largely independent association peaks for LOMG corresponding to (a) MHC class II (highest attenuation when conditioning on DQA1), (b) HLA-A and (c) MHC class III SNPs. Conditioning studies of human leukocyte antigen (HLA) amino acid residues also suggest potential functional correlates. Together, these findings emphasize the value of subgrouping myasthenia gravis patients for clinical and basic investigations and imply distinct predisposing mechanisms in LOMG.

Allelic imbalance of multiple sclerosis susceptibility genes IKZF3 and IQGAP1 in human peripheral blood.

BACKGROUND: Multiple sclerosis is a chronic inflammatory, demyelinating disease of the central nervous system. Recent genome-wide studies have revealed more than 110 single nucleotide polymorphisms as associated with susceptibility to multiple sclerosis, but their functional contribution to disease development is mostly unknown. RESULTS: Consistent allelic imbalance was observed for rs907091 in IKZF3 and rs11609 in IQGAP1, which are in strong linkage disequilibrium with the multiple sclerosis associated single nucleotide polymorphisms rs12946510 and rs8042861, respectively. Using multiple sclerosis patients and healthy controls heterozygous for rs907091 and rs11609, we showed that the multiple sclerosis risk alleles at IKZF3 and IQGAP1 are expressed at higher levels as compared to the protective allele. Furthermore, individuals homozygous for the multiple sclerosis risk allele at IQGAP1 had a significantly higher total expression of IQGAP1 compared to individuals homozygous for the protective allele. CONCLUSIONS: Our data indicate a possible regulatory role for the multiple sclerosis-associated IKZF3 and IQGAP1 variants. We suggest that such cis-acting mechanisms may contribute to the multiple sclerosis association of single nucleotide polymorphisms at IKZF3 and IQGAP1.

Genetic overlap between multiple sclerosis and several cardiovascular disease risk factors.

BACKGROUND: Epidemiological findings suggest a relationship between multiple sclerosis (MS) and cardiovascular disease (CVD) risk factors, although the nature of this relationship is not well understood. OBJECTIVE: We used genome-wide association study (GWAS) data to identify shared genetic factors (pleiotropy) between MS and CVD risk factors. METHODS: Using summary statistics from a large, recent GWAS (total n > 250,000 individuals), we investigated overlap in single nucleotide polymorphisms (SNPs) associated with MS and a number of CVD risk factors including triglycerides (TG), low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, body mass index, waist-to-hip ratio, type 2 diabetes, systolic blood pressure, and C-reactive protein level. RESULTS AND CONCLUSION: Using conditional enrichment plots, we found 30-fold enrichment of MS SNPs for different levels of association with LDL and TG SNPs, with a corresponding reduction in conditional false discovery rate (FDR). We identified 133 pleiotropic loci outside the extended major histocompatibility complex with conditional FDR < 0.01, of which 65 are novel. These pleiotropic loci were located on 21 different chromosomes. Our findings point to overlapping pathobiology between clinically diagnosed MS and cardiovascular risk factors and identify novel common variants associated with increased MS risk.

Genetic variants are major determinants of CSF antibody levels in multiple sclerosis.

Immunological hallmarks of multiple sclerosis include the production of antibodies in the central nervous system, expressed as presence of oligoclonal bands and/or an increased immunoglobulin G index-the level of immunoglobulin G in the cerebrospinal fluid compared to serum. However, the underlying differences between oligoclonal band-positive and -negative patients with multiple sclerosis and reasons for variability in immunoglobulin G index are not known. To identify genetic factors influencing the variation in the antibody levels in the cerebrospinal fluid in multiple sclerosis, we have performed a genome-wide association screen in patients collected from nine countries for two traits, presence or absence of oligoclonal bands (n = 3026) and immunoglobulin G index levels (n = 938), followed by a replication in 3891 additional patients. We replicate previously suggested association signals for oligoclonal band status in the major histocompatibility complex region for the rs9271640*A-rs6457617*G haplotype, correlated with HLA-DRB1*1501, and rs34083746*G, correlated with HLA-DQA1*0301 (P comparing two haplotypes = 8.88 × 10(-16)). Furthermore, we identify a novel association signal of rs9807334, near the ELAC1/SMAD4 genes, for oligoclonal band status (P = 8.45 × 10(-7)). The previously reported association of the immunoglobulin heavy chain locus with immunoglobulin G index reaches strong evidence for association in this data set (P = 3.79 × 10(-37)). We identify two novel associations in the major histocompatibility complex region with immunoglobulin G index: the rs9271640*A-rs6457617*G haplotype (P = 1.59 × 10(-22)), shared with oligoclonal band status, and an additional independent effect of rs6457617*G (P = 3.68 × 10(-6)). Variants identified in this study account for up to 2-fold differences in the odds of being oligoclonal band positive and 7.75% of the variation in immunoglobulin G index. Both traits are associated with clinical features of disease such as female gender, age at onset and severity. This is the largest study population so far investigated for the genetic influence on antibody levels in the cerebrospinal fluid in multiple sclerosis, including 6950 patients. We confirm that genetic factors underlie these antibody levels and identify both the major histocompatibility complex and immunoglobulin heavy chain region as major determinants.

Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis.

Multiple sclerosis is a chronic, often disabling, disease of the central nervous system affecting more than 1 in 1,000 people in most western countries. The inflammatory lesions typical of multiple sclerosis show autoimmune features and depend partly on genetic factors. Of these genetic factors, only the HLA gene complex has been repeatedly confirmed to be associated with multiple sclerosis, despite considerable efforts. Polymorphisms in a number of non-HLA genes have been reported to be associated with multiple sclerosis, but so far confirmation has been difficult. Here, we report compelling evidence that polymorphisms in IL7R, which encodes the interleukin 7 receptor alpha chain (IL7Ralpha), indeed contribute to the non-HLA genetic risk in multiple sclerosis, demonstrating a role for this pathway in the pathophysiology of this disease. In addition, we report altered expression of the genes encoding IL7Ralpha and its ligand, IL7, in the cerebrospinal fluid compartment of individuals with multiple sclerosis.