Dataset Information

Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs.

ABSTRACT: Multiple sclerosis (MS) is a complex disease with underlying genetic and environmental factors. Although the contribution of alleles within the major histocompatibility complex (MHC) are known to exert strong effects on MS risk, much remains to be learned about the contributions of loci with more modest effects identified by genome-wide association studies (GWASs), as well as loci that remain undiscovered. We use a recently developed method to estimate the proportion of variance in disease liability explained by 475,806 single nucleotide polymorphisms (SNPs) genotyped in 1,854 MS cases and 5,164 controls. We reveal that ~30% of MS genetic liability is explained by SNPs in this dataset, the majority of which is accounted for by common variants. These results suggest that the unaccounted for proportion could be explained by variants that are in imperfect linkage disequilibrium with common GWAS SNPs, highlighting the potential importance of rare variants in the susceptibility to MS.

SUBMITTER: Watson CT

PROVIDER: S-EPMC3480808 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs.

Watson Corey T CT Disanto Giulio G Breden Felix F Giovannoni Gavin G Ramagopalan Sreeram V SV

Scientific reports 20121025

Multiple sclerosis (MS) is a complex disease with underlying genetic and environmental factors. Although the contribution of alleles within the major histocompatibility complex (MHC) are known to exert strong effects on MS risk, much remains to be learned about the contributions of loci with more modest effects identified by genome-wide association studies (GWASs), as well as loci that remain undiscovered. We use a recently developed method to estimate the proportion of variance in disease liabi ...[more]

PMID: 23105968

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Project description:Multiple sclerosis (MS) is characterized as an autoimmune demyelinating disease. Numerous family studies have confirmed a strong genetic component underlying its etiology. After several decades of frustrating research, the advent and application of affordable genotyping of dense SNP maps in large data sets has ushered in a new era in which rapid progress is being made in our understanding of the genetics underlying many complex traits. For MS, one of the first discoveries to emerge in this new era was the association with rs6897932[T244I] in the interleukin-7 receptor alpha chain (IL7RA) gene (Gregory et al. in Nat Genet 39(9):1083-1091, 2007; International Multiple Sclerosis Genetics Consortium in N Engl J Med 357(9):851-862, 2007; Lundmark in Nat Genet 39(9):1108-1113, 2007), a discovery that was accompanied by functional data that suggest this variant is likely to be causative rather than a surrogate proxy (Gregory et al. in Nat Genet 39(9):1083-1091, 2007). We hypothesized that variations in other genes functionally related to IL7RA might also influence MS. We investigated this hypothesis by examining genes in the extended biological pathway related to IL7RA to identify novel associations. We identified 73 genes with putative functional relationships to IL7RA and subsequently genotyped 7,865 SNPs in and around these genes using an Illumina Infinium BeadChip assay. Using 2,961 case-control data sets, two of the gene regions examined, IL7 and SOCS1, had significantly associated single-nucleotide polymorphisms (SNPs) that further replicated in an independent case-control data set (4,831 samples) with joint p values as high as 8.29 x 10(-6) and 3.48 x 10(-7), respectively, exceeding the threshold for experiment-wise significance. Our results also implicate two additional novel gene regions that are likely to be associated with MS: PRKCE with p values reaching 3.47 x 10(-4), and BCL2 with p values reaching 4.32 x 10(-4). The TYK2 gene, which also emerged in our analysis, has recently been associated with MS (Ban et al. 2009). These results help to further delineate the genetic architecture of MS and validate our pathway approach as an effective method to identify novel associations in a complex disease.

Dataset Information

Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs.

Publications

Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs.

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