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


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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    • "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.
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    • "To identify the functionally important polymorphisms, new SNPs are additionally sought in the loci revealed in GWAS, the approach being known as a fineemapping study. A situation is also possible where different polyy morphic regions of one gene are not closely linked to each other, but are independently associated with MS (in particular, independent effects were assumed for two SNPs of TNFRSF1A[35]). Another regularity to note is that the MSSassociated SNPs occur mostly in introns or intergene regions, suggesting a regulatory role for the SNPs. "
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    ABSTRACT: Genome-wide association study (GWAS) provides a powerful tool for investigating the genetic architecture of human polygenic diseases and is generally used to identify the genetic factors of disease susceptibility, clinical phenotypes, and treatment response. The differences in allele frequencies of single nucleotide polymorphisms (SNPs) distributed throughout the genome are analyzed with a microarray technique or other technologies that allow simultaneous genotyping at several tens of thousands to several millions of SNPs per sample. Owing to its power to find out highly reliable differences between patients and controls, GWAS became a common approach to identification of the genetic susceptibility factors in complex diseases of a polygenic nature. Using multiple sclerosis (MS) as a prototype complex disease, the review considers the main achievements and challenges of using GWAS to identify the genes involved in the disease and, therefore, to better understand the pathogenetic molecular mechanisms and genetic risk factors.
    No preview · Article · Jul 2014 · Molecular Biology
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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.
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