De Jager PL, Jia X, Wang J et al.Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet 41:776-782

Division of Molecular Immunology, Center for Neurologic Diseases, Department of Neurology, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA.
Nature Genetics (Impact Factor: 29.35). 08/2009; 41(7):776-82. DOI: 10.1038/ng.401
Source: PubMed

ABSTRACT We report the results of a meta-analysis of genome-wide association scans for multiple sclerosis (MS) susceptibility that includes 2,624 subjects with MS and 7,220 control subjects. Replication in an independent set of 2,215 subjects with MS and 2,116 control subjects validates new MS susceptibility loci at TNFRSF1A (combined P = 1.59 x 10(-11)), IRF8 (P = 3.73 x 10(-9)) and CD6 (P = 3.79 x 10(-9)). TNFRSF1A harbors two independent susceptibility alleles: rs1800693 is a common variant with modest effect (odds ratio = 1.2), whereas rs4149584 is a nonsynonymous coding polymorphism of low frequency but with stronger effect (allele frequency = 0.02; odds ratio = 1.6). We also report that the susceptibility allele near IRF8, which encodes a transcription factor known to function in type I interferon signaling, is associated with higher mRNA expression of interferon-response pathway genes in subjects with MS.

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Available from: Jacob Mccauley, Sep 26, 2015
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    • "In conclusion, while combining datasets by imputation can lead to a more powerful GWAS (Becker et al., 2009; Hao et al., 2009) by allowing successful identification of SNPs associated with various phenotypes (Sanna et al., 2008; Willer et al., 2008; Zeggini et al., 2008; De Jager et al., 2009), the described decrease in signal inherent to imputation can partially offset any gain in power resulting from the combination of studies. Important implications of this finding include the fact that some truly associated variants may not be detected, and that some genomewide significant findings may have larger true effect sizes than estimated. "
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    ABSTRACT: Imputation is a commonly used technique that exploits linkage disequilibrium to infer missing genotypes in genetic datasets, using a well-characterized reference population. While there is agreement that the reference population has to match the ethnicity of the query dataset, it is common practice to use the same reference to impute genotypes for a wide variety of phenotypes. We hypothesized that using a reference composed of samples with a different phenotype than the query dataset would introduce imputation bias. To test this hypothesis we used GWAS datasets from Amyotrophic Lateral Sclerosis (ALS), Parkinson Disease (PD), and Crohn's Disease (CD). First, we masked and then performed imputation of 100 disease-associated markers and 100 non-associated markers from each study. Two references for imputation were used in parallel: one consisting of healthy controls and another consisting of patients with the same disease. We assessed the discordance (imprecision) and bias (inaccuracy) of imputation by comparing predicted genotypes to those assayed by SNP-chip. We also assessed the bias on the observed effect size when the predicted genotypes were used in a GWAS study. When healthy controls were used as reference for imputation, a significant bias was observed, particularly in the disease-associated markers. Using cases as reference significantly attenuated this bias. For nearly all markers, the direction of the bias favored the non-risk allele. In GWAS studies of the three diseases (with healthy reference controls from the 1000 genomes as reference), the mean OR for disease-associated markers obtained by imputation was lower than that obtained using original assayed genotypes. We found that the bias is inherent to imputation as using different methods did not alter the results. In conclusion, imputation is a powerful method to predict genotypes and estimate genetic risk for GWAS. However, a careful choice of reference population is needed to minimize biases inherent to this approach.
    Frontiers in Genetics 02/2015; 6. DOI:10.3389/fgene.2015.00030
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    • "Furthermore, a single nucleotide polymorphism (SNP) identified in the largest MS genome-wide association study [41], has recently been reported as occurring in the TNFRSF1A gene, that encodes TNFR1, and has been shown to be associated with an increased susceptibility to MS development. It is now understood that this SNP results in the expression of a soluble form of TNFR1, which can block pan TNF signalling [42]. "
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    ABSTRACT: Tumour necrosis factor (TNF) is a proinflammatory cytokine that is known to regulate inflammation in a number of autoimmune diseases, including multiple sclerosis (MS). Although targeting of TNF in models of MS has been successful, the pathological role of TNF in MS remains unclear due to clinical trials where the non-selective inhibition of TNF resulted in exacerbated disease. Subsequent experiments have indicated that this may have resulted from the divergent effects of the two TNF receptors, TNFR1 and TNFR2. Here we show that the selective targeting of TNFR1 with an antagonistic antibody ameliorates symptoms of the most common animal model of MS, experimental autoimmune encephalomyelitis (EAE), when given following both a prophylactic and therapeutic treatment regime. Our results demonstrate that antagonistic TNFR1-specific antibodies may represent a therapeutic approach for the treatment of MS in the future.
    PLoS ONE 02/2014; 9(2):e90117. DOI:10.1371/journal.pone.0090117 · 3.23 Impact Factor
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    • "In this context, genome-wide SNP analyses shed new light on understanding the onset and progression of MS as they identify susceptibility factors likely influencing the disease (De Jager et al., 2009; Disanto et al., 2012; Beecham et al., 2013). Besides classically known HLA genes, a number of additional genes have been designated as MS susceptibility factors, including IRF8 (De Jager et al., 2009; Disanto et al., 2012). IRF8 is a transcription factor of the IRF family known to direct development of macrophages and DCs (Tamura et al., 2005). "
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    ABSTRACT: Recent epidemiological studies have identified interferon regulatory factor 8 (IRF8) as a susceptibility factor for multiple sclerosis (MS). However, how IRF8 influences the neuroinflammatory disease has remained unknown. By studying the role of IRF8 in experimental autoimmune encephalomyelitis (EAE), a mouse model of MS, we found that Irf8(-/-) mice are resistant to EAE. Furthermore, expression of IRF8 in antigen-presenting cells (APCs, such as macrophages, dendritic cells, and microglia), but not in T cells, facilitated disease onset and progression through multiple pathways. IRF8 enhanced αvβ8 integrin expression in APCs and activated TGF-β signaling leading to T helper 17 (Th17) cell differentiation. IRF8 induced a cytokine milieu that favored growth and maintenance of Th1 and Th17 cells, by stimulating interleukin-12 (IL-12) and IL-23 production, but inhibiting IL-27 during EAE. Finally, IRF8 activated microglia and exacerbated neuroinflammation. Together, this work provides mechanistic bases by which IRF8 contributes to the pathogenesis of MS.
    Immunity 01/2014; 40(2):187-98. DOI:10.1016/j.immuni.2013.11.022 · 21.56 Impact Factor
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