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Despite recent progress in the characterization of genetic loci associated with multiple sclerosis (MS) risk, the ubiquitous linkage disequilibrium operating across the genome has stalled efforts to distinguish causative variants from proxy single-nucleotide polymorphisms (SNPs). Here, we have identified through fine mapping and meta-analysis EVI5 as the most plausible disease risk gene within the 1p22.1 locus. We further show that an exonic SNP associated with risk induces changes in superficial hydrophobicity patterns of the coiled-coil domain of EVI5, which, in turns, affects the EVI5 interactome. Immunoprecipitation of wild-type and mutated EVI5 followed by mass spectrometry generated a roster of disease-specific interactors functionally linked to lipid metabolism. Among the exclusive binding partners of the risk variant, we describe the novel interaction with sphingosine 1-phosphate lyase (SGPL1)-a key enzyme for the creation of the sphingosine-1 phosphate gradient, which is relevant to the pathogenic process and therapeutic management of MS.

Multiple sclerosis (MS) is the most common autoimmune disease of the central nervous system (CNS). It is characterized by the infiltration of autoreactive immune cells into the CNS, which target the myelin sheath, leading to the loss of neuronal function. Although it is accepted that MS is a multifactorial disorder with both genetic and environmental factors influencing its development and course, the molecular pathogenesis of MS has not yet been fully elucidated. Here, we studied the longitudinal gene expression profiles of whole-blood RNA from a cohort of 195 MS patients and 66 healthy controls. We analyzed these transcriptomes at both the individual transcript and the biological pathway level. We found 62 transcripts to be significantly up-regulated in MS patients; the expression of 11 of these genes was counter-regulated by interferon treatment, suggesting partial restoration of a 'healthy' gene expression profile. Global pathway analyses linked the proteasome and Wnt signaling to MS disease processes. Since genotypes from a subset of individuals were available, we were able to identify expression quantitative trait loci (eQTL), a number of which involved two genes of the MS gene signature. However, all these eQTL were also present in healthy controls. This study highlights the challenge posed by analyzing transcripts from whole blood and how these can be mitigated by using large, well-characterized cohorts of patients with longitudinal follow-up and multi-modality measurements.

Genome-wide association studies (GWAS) testing several hundred thousand SNPs have been performed in multiple sclerosis (MS) and other complex diseases. Typically, the number of markers in which the evidence for association exceeds the genome-wide significance threshold is very small, and markers that do not exceed this threshold are generally neglected. Classical statistical analysis of these datasets in MS revealed genes with known immunological functions. However, many of the markers showing modest association may represent false negatives. We hypothesize that certain combinations of genes flagged by these markers can be identified if they belong to a common biological pathway. Here we conduct a pathway-oriented analysis of two GWAS in MS that takes into account all SNPs with nominal evidence of association (P < 0.05). Gene-wise P-values were superimposed on a human protein interaction network and searches were conducted to identify sub-networks containing a higher proportion of genes associated with MS than expected by chance. These sub-networks, and others generated at random as a control, were categorized for membership of biological pathways. GWAS from eight other diseases were analyzed to assess the specificity of the pathways identified. In the MS datasets, we identified sub-networks of genes from several immunological pathways including cell adhesion, communication and signaling. Remarkably, neural pathways, namely axon-guidance and synaptic potentiation, were also over-represented in MS. In addition to the immunological pathways previously identified, we report here for the first time the potential involvement of neural pathways in MS susceptibility.

Multiple sclerosis (MS) is a demyelinating autoimmune disease of the central nervous system. While its etiology is not well understood, genetic factors are clearly involved. Until recently, most genetic studies in MS have been association studies using the case-control design testing specific candidate genes and studying only sporadic cases. The only consistently replicated finding has been an association with the HLA-DR2 allele within the major histocompatibility complex (MHC) on chromosome 6. Using the genetic linkage design, however, evidence for and against linkage of the MHC to MS has been found, fostering suggestions that sporadic and familial MS have different etiologies. Most recently, two of four genomic screens demonstrated linkage to the MHC, although specific allelic associations were not tested. Here, a dataset of 98 multiplex families was studied to test for an association to the HLA-DR2 allele in familial MS and to determine if genetic linkage to the MHC was due solely to such an association. Three highly polymorphic markers (HLA-DR, D6S273 and TNFβ) in the MHC demonstrated strong genetic linkage (parametric Iod scores of 4.60, 2.20 and 1.24, respectively) and a specific association with the HLA-DR2 allele was confirmed (TDT; P < 0.001). Stratifying the results by HLA-DR2 status showed that the linkage results were limited to families segregating HLA-DR2 alleles. These results demonstrate that genetic linkage to the MHC can be explained by the HLA-DR2 allelic association. They also indicate that sporadic and familial MS share a common genetic susceptibility. In addition, preliminary calculations suggest that the MHC explains between 17 and 62% of the genetic etiology of MS. This heterogeneity is also supported by the minority of families showing no linkage or association with loci within the MHC. [ABSTRACT FROM AUTHOR]

The primary genetic risk factor in multiple sclerosis (MS) is the HLA-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has yet to be elucidated. Several lines of evidence support a role for neuroendocrine system involvement in autoimmunity which may, in part, be genetically determined. Here, we comprehensively investigated variation within eight candidate hypothalamic-pituitary-adrenal (HPA) axis genes and susceptibility to MS. A total of 326 SNPs were investigated in a discovery dataset of 1343 MS cases and 1379 healthy controls of European ancestry using a multi-analytical strategy. Random Forests, a supervised machine-learning algorithm, identified eight intronic SNPs within the corticotrophin-releasing hormone receptor 1 or CRHR1 locus on 17q21.31 as important predictors of MS. On the basis of univariate analyses, six CRHR1 variants were associated with decreased risk for disease following a conservative correction for multiple tests. Independent replication was observed for CRHR1 in a large meta-analysis comprising 2624 MS cases and 7220 healthy controls of European ancestry. Results from a combined meta-analysis of all 3967 MS cases and 8599 controls provide strong evidence for the involvement of CRHR1 in MS. The strongest association was observed for rs242936 (OR = 0.82, 95% CI = 0.74-0.90, P = 9.7 × 10(-5)). Replicated CRHR1 variants appear to exist on a single associated haplotype. Further investigation of mechanisms involved in HPA axis regulation and response to stress in MS pathogenesis is warranted.

To explore the potential value of recently developed high-density linkage mapping methods in the analysis of complex disease we have regenotyped five nuclear families first studied in the 1996 UK multiple sclerosis linkage genome screen, using Applied Biosystems high-density microsatellite linkage mapping set, the Illumina BeadArray linkage mapping panel (version 3) and the Affymetrix GeneChip Human Mapping 10K array. We found that genotyping success, information extraction and genotyping accuracy were improved with all systems. These improvements were particularly marked with the SNP-based methods (Illumina and Affymetrix), with little difference between these. The extent of additional information extracted is considerable, indicating that reanalysis of existing multiplex families using these newer systems would substantially increase power., (Copyright 2004 Oxford University Press)