9 research outputs found

    Burden of genetic risk variants in multiple sclerosis families in the Netherlands

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    Background: Approximately 20% of multiple sclerosis patients have a family history of multiple sclerosis. Studies of multiple sclerosis aggregation in families are inconclusive. Objective: To investigate the genetic burden based on currently discovered genetic variants for multiple sclerosis risk in patients from Dutch multiple sclerosis multiplex families versus sporadic multiple sclerosis cases, and to study its influence on clinical phenotype and disease prediction. Methods: Our study population consisted of 283 sporadic multiple sclerosis cases, 169 probands from multiplex families and 2028 controls. A weighted genetic risk score based on 102 non-human leukocyte antigen loci and HLA-DRB1*1501 was calculated. Results: The weighted genetic risk score based on all loci was significantly higher in familial than in sporadic cases. The HLA-DRB1*1501 contributed significantly to the difference in genetic burden between the groups. A high weighted genetic risk score was significantly associated with a low age of disease onset in all multiple sclerosis patients, but not in the familial cases separately. The genetic risk score was significantly but modestly better in discriminating familial versus sporadic multiple sclerosis from controls. Conclusion: Familial multiple sclerosis patients are more loaded with the common genetic variants than sporadic cases. The difference is mainly driven by HLA-DRB1*1501. The predictive capacity of genetic loci is poor and unlikely to be useful in clinical settings.</p

    Genome-wide significant association with seven novel multiple sclerosis risk loci

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    Objective: A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods: The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results: Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions: This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases

    Genome-wide significant association with seven novel multiple sclerosis risk loci

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    Objective: A recent large-scale study in multiple sclerosis (MS) using the ImmunoChip platform reported on 11 loci that showed suggestive genetic association with MS. Additional data in sufficiently sized and independent data sets are needed to assess whether these loci represent genuine MS risk factors. Methods: The lead SNPs of all 11 loci were genotyped in 10 796 MS cases and 10 793 controls from Germany, Spain, France, the Netherlands, Austria and Russia, that were independent from the previously reported cohorts. Association analyses were performed using logistic regression based on an additive model. Summary effect size estimates were calculated using fixed-effect meta-analysis. Results: Seven of the 11 tested SNPs showed significant association with MS susceptibility in the 21 589 individuals analysed here. Meta-analysis across our and previously published MS case-control data (total sample size n=101 683) revealed novel genome-wide significant association with MS susceptibility (p<5×10−8) for all seven variants. This included SNPs in or near LOC100506457 (rs1534422, p=4.03×10−12), CD28 (rs6435203, p=1.35×10−9), LPP (rs4686953, p=3.35×10−8), ETS1 (rs3809006, p=7.74×10−9), DLEU1 (rs806349, p=8.14×10−12), LPIN3 (rs6072343, p=7.16×10−12) and IFNGR2 (rs9808753, p=4.40×10−10). Cis expression quantitative locus effects were observed in silico for rs6435203 on CD28 and for rs9808753 on several immunologically relevant genes in the IFNGR2 locus. Conclusions: This study adds seven loci to the list of genuine MS genetic risk factors and further extends the list of established loci shared across autoimmune diseases

    MSJ777202_supplementary_data – Supplemental material for Linkage analysis and whole exome sequencing identify a novel candidate gene in a Dutch multiple sclerosis family

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    <p>Supplemental material, MSJ777202_supplementary_data for Linkage analysis and whole exome sequencing identify a novel candidate gene in a Dutch multiple sclerosis family by Julia Y Mescheriakova, Annemieke JMH Verkerk, Najaf Amin, André G Uitterlinden, Cornelia M van Duijn and Rogier Q Hintzen in Multiple Sclerosis Journal</p

    Genome-wide significant association with seven novel multiple sclerosis risk loci

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    Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility

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    INTRODUCTION: Multiple sclerosis (MS) is an inflammatory and degenerative disease of the central nervous system (CNS) that often presents in young adults. Over the past decade, certain elements of the genetic architecture of susceptibility have gradually emerged, but most of the genetic risk for MS remained unknown. RATIONALE: Earlier versions of the MS genetic map had highlighted the role of the adaptive arm of the immune system, implicating multiple different T cell subsets. We expanded our knowledge of MS susceptibility by performing a genetic association study in MS that leveraged genotype data from 47,429 MS cases and 68,374 control subjects. We enhanced this analysis with an in-depth and comprehensive evaluation of the functional impact of the susceptibility variants that we uncovered. RESULTS: We identified 233 statistically independent associations with MS susceptibility that are genome-wide significant. The major histocompatibility complex (MHC) contains 32 of these associations, and one, the first MS locus on a sex chromosome, is found in chromosome X. The remaining 200 associations are found in the autosomal non-MHC genome. Our genome-wide partitioning approach and large-scale replication effort allowed the evaluation of other variants that did not meet our strict threshold of significance, such as 416 variants that had evidence of statistical replication but did not reach the level of genome-wide statistical significance. Many of these loci are likely to be true susceptibility loci. The genome-wide and suggestive effects jointly explain ~48% of the estimated heritability for MS. Using atlases of gene expression patterns and epigenomic features, we documented that enrichment for MS susceptibility loci was apparent in many different immune cell types and tissues, whereas there was an absence of enrichment in tissue-level brain profiles. We extended the annotation analyses by analyzing new data generated from human induced pluripotent stem cell–derived neurons as well as from purified primary human astrocytes and microglia, observing that enrichment for MS genes is seen in human microglia, the resident immune cells of the brain, but not in astrocytes or neurons. Further, we have characterized the functional consequences of many MS susceptibility variants by identifying those that influence the expression of nearby genes in immune cells or brain. Last, we applied an ensemble of methods to prioritize 551 putative MS susceptibility genes that may be the target of the MS variants that meet a threshold of genome-wide significance. This extensive list of MS susceptibility genes expands our knowledge more than twofold and highlights processes relating to the development, maturation, and terminal differentiation of B, T, natural killer, and myeloid cells that may contribute to the onset of MS. These analyses focus our attention on a number of different cells in which the function of MS variants should be further investigated. Using reference protein-protein interaction maps, these MS genes can also be assembled into 13 communities of genes encoding proteins that interact with one another; this higher-order architecture begins to assemble groups of susceptibility variants whose functional consequences may converge on certain protein complexes that can be prioritized for further evaluation as targets for MS prevention strategies. CONCLUSION: We report a detailed genetic and genomic map of MS susceptibility, one that explains almost half of this disease’s heritability. We highlight the importance of several cells of the peripheral and brain resident immune systems—implicating both the adaptive and innate arms—in the translation of MS genetic risk into an auto-immune inflammatory process that targets the CNS and triggers a neurodegenerative cascade. In particular, the myeloid component highlights a possible role for microglia that requires further investigation, and the B cell component connects to the narrative of effective B cell–directed therapies in MS. These insights set the stage for a new generation of functional studies to uncover the sequence of molecular events that lead to disease onset. This perspective on the trajectory of disease onset will lay the foundation for developing primary prevention strategies that mitigate the risk of developing MS
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