1,721,071 research outputs found
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Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls.
No full textMultiple sclerosis (MS) is an inflammatory CNS disease with a substantial genetic component, originally mapped to only the human leukocyte antigen (HLA) region. In the last 5 years, a total of seven genome-wide association studies and one meta-analysis successfully identified 57 non-HLA susceptibility loci. Here, we merged nominal statistical evidence of association and physical evidence of interaction to conduct a protein-interaction-network-based pathway analysis (PINBPA) on two large genetic MS studies comprising a total of 15,317 cases and 29,529 controls. The distribution of nominally significant loci at the gene level matched the patterns of extended linkage disequilibrium in regions of interest. We found that products of genome-wide significantly associated genes are more likely to interact physically and belong to the same or related pathways. We next searched for subnetworks (modules) of genes (and their encoded proteins) enriched with nominally associated loci within each study and identified those modules in common between the two studies. We demonstrate that these modules are more likely to contain genes with bona fide susceptibility variants and, in addition, identify several high-confidence candidates (including BCL10, CD48, REL, TRAF3, and TEC). PINBPA is a powerful approach to gaining further insights into the biology of associated genes and to prioritizing candidates for subsequent genetic studies of complex traits
Network-based multiple sclerosis pathway analysis with GWAS data from 15,000 cases and 30,000 controls.
No full textThe Genetic Association of Variants in <i>CD6, TNFRSF1A</i> and <i>IRF8</i> to Multiple Sclerosis: A Multicenter Case-Control Study
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Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk.
Full text linkMultiple sclerosis is a complex neurological disease, with ∼20% of risk heritability attributable to common genetic variants, including >230 identified by genome-wide association studies. Multiple strands of evidence suggest that much of the remaining heritability is also due to additive effects of common variants rather than epistasis between these variants or mutations exclusive to individual families. Here, we show in 68,379 cases and controls that up to 5% of this heritability is explained by low-frequency variation in gene coding sequence. We identify four novel genes driving MS risk independently of common-variant signals, highlighting key pathogenic roles for regulatory T cell homeostasis and regulation, IFNγ biology, and NFκB signaling. As low-frequency variants do not show substantial linkage disequilibrium with other variants, and as coding variants are more interpretable and experimentally tractable than non-coding variation, our discoveries constitute a rich resource for dissecting the pathobiology of MS
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NR1H3 p.Arg415Gln Is Not Associated to Multiple Sclerosis Risk.
Full text linkA recent study by Wang et al. (2016a) claims that the low-frequency variant NR1H3 p.Arg415Gln is sufficient to cause multiple sclerosis in certain individuals and determines a patient's likelihood of primary progressive disease. We sought to replicate this finding in the International MS Genetics Consortium (IMSGC) patient collection, which is 13-fold larger than the collection of Wang et al. (2016a), but we find no evidence that this variant is associated with either MS or disease subtype. Wang et al. (2016a) also report a common variant association in the region, which we show captures the association the IMSGC reported in 2013. Therefore, we conclude that the reported low-frequency association is a false positive, likely generated by insufficient sample size. The claim of NR1H3 mutations describing a Mendelian form of MS-of which no examples exist-can therefore not be substantiated by data. This Matters Arising paper is in response to Wang et al. (2016a), published in Neuron. See also the related Matters Arising paper by Minikel and MacArthur (2016) and the response by Wang et al. (2016b), published in this issue
Evidence for Polygenic Susceptibility to Multiple Sclerosis—The Shape of Things to Come
No full textIt is well established that the risk of developing multiple sclerosis is substantially increased in the relatives of affected individuals and that most of this increase is genetically determined. The observed pattern of familial recurrence risk has long suggested that multiple variants are involved, but it has proven difficult to identify individual risk variants and little has been established about the genetic architecture underlying susceptibility. By using data from two independent genome-wide association studies (GWAS), we demonstrate that a substantial proportion of the thousands of variants that individually fail to show statistically significant evidence of association have allele frequencies in cases that are skewed away from the null distribution through the effects of multiple as-yet-unidentified risk loci. The collective effect of 12,627 SNPs with Cochran-Mantel-Haenszel test (p < 0.2) in our discovery GWAS set optimally explains ∼3% of the variance in MS risk in our independent target GWAS set, estimated by Nagelkerke's pseudo-R2. This model has a highly significant fit (p = 9.90E-19). These results statistically demonstrate a polygenic component to MS susceptibility and suggest that the risk alleles identified to date represent just the tip of an iceberg of risk variants likely to include hundreds of modest effects and possibly thousands of very small effects
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