Evidence for Polygenic Susceptibility to Multiple Sclerosis—The Shape of Things to Come
2010; Elsevier BV; Volume: 86; Issue: 4 Linguagem: Inglês
10.1016/j.ajhg.2010.02.027
ISSN1537-6605
Tópico(s)Evolution and Genetic Dynamics
ResumoIt 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. It 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. Multiple sclerosis (MS [MIM 126200]) is a complex demyelinating disorder of the central nervous system. The etiology of the disease is not well understood but epidemiological studies provide undeniable evidence that genetic factors are involved.1Kenealy S.J. Pericak-Vance M.A. Haines J.L. The genetic epidemiology of multiple sclerosis.J. Neuroimmunol. 2003; 143: 7-12Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar, 2Mumford C.J. Wood N.W. Kellar-Wood H. Thorpe J.W. Miller D.H. Compston D.A. The British Isles survey of multiple sclerosis in twins.Neurology. 1994; 44: 11-15Crossref PubMed Google Scholar, 3Robertson N.P. Fraser M. Deans J. Clayton D. Walker N. Compston D.A. Age-adjusted recurrence risks for relatives of patients with multiple sclerosis.Brain. 1996; 119: 449-455Crossref PubMed Scopus (180) Google Scholar, 4Sadovnick A.D. Ebers G.C. Genetics of multiple sclerosis.Neurol. Clin. 1995; 13: 99-118PubMed Google Scholar Despite these extensive data, there was little progress identifying relevant genes prior to the emergence of genome-wide association studies (GWAS). For more than 30 years the only known MS genetic risk locus was the major histocompatibility complex (MHC) on chromosome 6p21.5Barcellos L.F. Sawcer S. Ramsay P.P. Baranzini S.E. Thomson G. Briggs F. Cree B.C. Begovich A.B. Villoslada P. Montalban X. et al.International Multiple Sclerosis Genetics ConsortiumHeterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis.Hum. Mol. Genet. 2006; 15: 2813-2824Crossref PubMed Scopus (248) Google Scholar, 6Yeo T.W. De Jager P.L. Gregory S.G. Barcellos L.F. Walton A. Goris A. Fenoglio C. Ban M. Taylor C.J. Goodman R.S. et al.International Multiple Sclerosis Genetics ConsortiumA second major histocompatibility complex susceptibility locus for multiple sclerosis.Ann. Neurol. 2007; 61: 228-236Crossref PubMed Scopus (139) Google Scholar All this changed in 2007 when the International Multiple Sclerosis Genetics Consortium (IMSGC) completed the first MS GWAS7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar and identified one new susceptibility locus, IL2RA (MIM 147730), and simultaneously with two other reports confirmed another susceptibility locus, IL7R (MIM 146661).8Gregory S.G. Schmidt S. Seth P. Oksenberg J.R. Hart J. Prokop A. Caillier S.J. Ban M. Goris A. Barcellos L.F. et al.Multiple Sclerosis Genetics GroupInterleukin 7 receptor alpha chain (IL7R) shows allelic and functional association with multiple sclerosis.Nat. Genet. 2007; 39: 1083-1091Crossref PubMed Scopus (506) Google Scholar, 9Lundmark F. Duvefelt K. Iacobaeus E. Kockum I. Wallström E. Khademi M. Oturai A. Ryder L.P. Saarela J. Harbo H.F. et al.Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis.Nat. Genet. 2007; 39: 1108-1113Crossref PubMed Scopus (393) Google Scholar Since then progress has been rapid and the list of established MS risk loci has grown considerably (see Table 1). To date all of the newly identified MS risk alleles are common, exert only modest individual effects on risk (odds ratios 1.1–1.3), and ostensibly act independently. Even in combination with the much more substantial effects attributable to the MHC, these known MS risk loci account for less than half of the familial clustering observed epidemiologically. It is inescapable that other genetic risk factors exist and that much remains to be discovered about the genetics of MS. To aid in this discovery process, it would be helpful to estimate the form of this as-yet-undiscovered genetically determined risk.Table 1Established Non-MHC MS Risk AllelesSusceptibility GeneRS NumberOR EstimateAlleleReferenceIL7RArs68979321.18CGregory et al., 2007;8Gregory S.G. Schmidt S. Seth P. Oksenberg J.R. Hart J. Prokop A. Caillier S.J. Ban M. Goris A. Barcellos L.F. et al.Multiple Sclerosis Genetics GroupInterleukin 7 receptor alpha chain (IL7R) shows allelic and functional association with multiple sclerosis.Nat. Genet. 2007; 39: 1083-1091Crossref PubMed Scopus (506) Google Scholar IMSGC, 2007;7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar Lundmark et al., 20079Lundmark F. Duvefelt K. Iacobaeus E. Kockum I. Wallström E. Khademi M. Oturai A. Ryder L.P. Saarela J. Harbo H.F. et al.Variation in interleukin 7 receptor alpha chain (IL7R) influences risk of multiple sclerosis.Nat. Genet. 2007; 39: 1108-1113Crossref PubMed Scopus (393) Google ScholarIL2RArs127224891.25CIMSGC, 20077Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. 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A genome-wide scan in forty large pedigrees with multiple sclerosis.J. Hum. Genet. 2007; 52: 955-962Crossref PubMed Scopus (25) Google Scholar coupled with the total absence of any consistent evidence for linkage outside the MHC region11Sawcer S. Ban M. Maranian M. Yeo T.W. Compston A. Kirby A. Daly M.J. De Jager P.L. Walsh E. Lander E.S. et al.International Multiple Sclerosis Genetics ConsortiumA high-density screen for linkage in multiple sclerosis.Am. J. Hum. Genet. 2005; 77: 454-467Abstract Full Text Full Text PDF PubMed Scopus (233) Google Scholar indicates that rare alleles exerting very large effects are unlikely to play a prominent role in the genetics of MS. The relationship between familial recurrence risk and the degree of relatedness provides some guidance to the underlying genetic architecture of MS12Risch N. Linkage strategies for genetically complex traits. I. Multilocus models.Am. J. Hum. Genet. 1990; 46: 222-228PubMed Google Scholar, 13Wang W.Y. Barratt B.J. Clayton D.G. Todd J.A. Genome-wide association studies: Theoretical and practical concerns.Nat. Rev. Genet. 2005; 6: 109-118Crossref PubMed Scopus (884) Google Scholar, 14Yang Q. Khoury M.J. Friedman J. Little J. Flanders W.D. How many genes underlie the occurrence of common complex diseases in the population?.Int. J. Epidemiol. 2005; 34: 1129-1137Crossref PubMed Scopus (145) Google Scholar and several studies have demonstrated that this relationship is distinctly nonlinear, suggesting that susceptibility is probably determined by multiple risk alleles, each exerting modest individual effects.1Kenealy S.J. Pericak-Vance M.A. Haines J.L. The genetic epidemiology of multiple sclerosis.J. Neuroimmunol. 2003; 143: 7-12Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar, 2Mumford C.J. Wood N.W. Kellar-Wood H. Thorpe J.W. Miller D.H. Compston D.A. The British Isles survey of multiple sclerosis in twins.Neurology. 1994; 44: 11-15Crossref PubMed Google Scholar, 3Robertson N.P. Fraser M. Deans J. Clayton D. Walker N. Compston D.A. Age-adjusted recurrence risks for relatives of patients with multiple sclerosis.Brain. 1996; 119: 449-455Crossref PubMed Scopus (180) Google Scholar, 4Sadovnick A.D. Ebers G.C. Genetics of multiple sclerosis.Neurol. Clin. 1995; 13: 99-118PubMed Google Scholar, 15Lindsey J.W. Familial recurrence rates and genetic models of multiple sclerosis.Am. J. Med. Genet. A. 2005; 135: 53-58Crossref PubMed Scopus (19) Google Scholar, 16Sadovnick A.D. Dyment D. Ebers G.C. Genetic epidemiology of multiple sclerosis.Epidemiol. Rev. 1997; 19: 99-106Crossref PubMed Scopus (53) Google Scholar Unfortunately, this type of segregation analysis is insensitive to the number of involved loci and is largely unable to distinguish between dozens of modest effects, hundreds of weak effects, or thousands of very weak effects.15Lindsey J.W. Familial recurrence rates and genetic models of multiple sclerosis.Am. J. Med. Genet. A. 2005; 135: 53-58Crossref PubMed Scopus (19) Google Scholar In line with the polygenic model proposed by Fisher,17Fisher R.A. The correlations between relatives on the supposition of Mendelian inheritance.Trans. R. Soc. Edinb. 2009; 52: 399-433Crossref Scopus (2291) Google Scholar it is logical to anticipate that the genetic architecture underlying susceptibility to MS will involve a wide spectrum of risk allele frequencies and effect sizes.13Wang W.Y. Barratt B.J. Clayton D.G. Todd J.A. Genome-wide association studies: Theoretical and practical concerns.Nat. Rev. Genet. 2005; 6: 109-118Crossref PubMed Scopus (884) Google Scholar Under such a polygenic model, we would expect that many of the SNPs tested in a GWAS will be genuinely associated with disease, although most to only a minor degree. In this context we should see a systematic inflation of association scores across the genome, the pattern of which would be a reflection of the linkage disequilibrium (LD) between tested variants and the underlying risk alleles. The genome-wide inflation of association scores, beyond that which would be expected through sampling variance alone, is a well-recognized aspect of GWAS; however, most of this phenomenon is known to stem from subtle experimental biases, such as hidden population stratification and differential missingness.13Wang W.Y. Barratt B.J. Clayton D.G. Todd J.A. Genome-wide association studies: Theoretical and practical concerns.Nat. Rev. Genet. 2005; 6: 109-118Crossref PubMed Scopus (884) Google Scholar In a recently published ground-breaking study, the International Schizophrenia Consortium (ISC)18Purcell S.M. Wray N.R. Stone J.L. Visscher P.M. O'Donovan M.C. Sullivan P.F. Sklar P. International Schizophrenia ConsortiumCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder.Nature. 2009; 460: 748-752Crossref PubMed Scopus (3135) Google Scholar confirmed the existence of polygenic influences in schizophrenia (MIM 181500) by showing that a small fraction of the variance in disease status (∼3%) was significantly associated with a score based on multiple SNPs, none of which were significantly associated with the disease at a genome-wide level in their own right. In short, they showed that despite sampling variance and the confounding influences of population stratification and differential missingness, the systematic influences of polygenic effects can be demonstrated. The ISC further demonstrated that the extent of variance explained increased as they incorporated additional SNPs with less significant p values into their scoring. With simulations they showed that this pattern most probably indicated that polygenic risk in schizophrenia is determined by modest numbers of larger effects and increasing numbers of smaller effects.18Purcell S.M. Wray N.R. Stone J.L. Visscher P.M. O'Donovan M.C. Sullivan P.F. Sklar P. International Schizophrenia ConsortiumCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder.Nature. 2009; 460: 748-752Crossref PubMed Scopus (3135) Google Scholar Two previously published MS GWAS data sets were employed in this analysis. Data from the International Multiple Sclerosis Genetics Consortium (IMSGC) GWAS7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar was used in the discovery phase, while data from the GWAS performed by the Partners MS Center at the Brigham and Women's Hospital (BWH) and published as part of a recent meta-analysis19De Jager P.L. Jia X. Wang J. de Bakker P.I. Ottoboni L. Aggarwal N.T. Piccio L. Raychaudhuri S. Tran D. Aubin C. et al.International MS Genetics ConsortiumMeta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci.Nat. Genet. 2009; 41: 776-782Crossref PubMed Scopus (609) Google Scholar was used in the target phase. In both of these GWAS, MS was diagnosed according to standardized clinical criteria20McDonald W.I. Compston A. Edan G. Goodkin D. Hartung H.P. Lublin F.D. McFarland H.F. Paty D.W. Polman C.H. Reingold S.C. et al.Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis.Ann. Neurol. 2001; 50: 121-127Crossref PubMed Scopus (5665) Google Scholar and stringent quality control (QC) measures were applied to the data prior to analysis (see original publications for details7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar, 19De Jager P.L. Jia X. Wang J. de Bakker P.I. Ottoboni L. Aggarwal N.T. Piccio L. Raychaudhuri S. Tran D. Aubin C. et al.International MS Genetics ConsortiumMeta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci.Nat. Genet. 2009; 41: 776-782Crossref PubMed Scopus (609) Google Scholar). The IMSGC GWAS7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar was based on the Affymetrix GeneChip Human Mapping 500K Array Set and consisted of reliable data from 334,923 SNPs in 931 affected individuals and 2,431 controls: 1,475 from the Wellcome Trust Case Control Consortium (randomly selected from the 1958 British Birth Cohort and the UK National Blood Transfusion Service) and 956 from the National Institute of Mental Health. Of the 931 cases, 453 were ascertained from sites across the UK, and 478 were ascertained from the Partners Healthcare MS Center in Boston and the UCSF MS Center. The BWH GWAS19De Jager P.L. Jia X. Wang J. de Bakker P.I. Ottoboni L. Aggarwal N.T. Piccio L. Raychaudhuri S. Tran D. Aubin C. et al.International MS Genetics ConsortiumMeta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci.Nat. Genet. 2009; 41: 776-782Crossref PubMed Scopus (609) Google Scholar was based on the Affymetrix Genome-wide Human SNP Array 6.0 (GeneChip 6.0) which, after excluding 54 individuals included in both GWAS, provided reliable data from 713,683 SNPs in 806 affected individuals ascertained from the Partners Healthcare MS Center in Boston and 1,720 controls ascertained by the MIGen consortium21Kathiresan S. Voight B.F. Purcell S. Musunuru K. Ardissino D. Mannucci P.M. Anand S. Engert J.C. Samani N.J. Schunkert H. et al.Myocardial Infarction Genetics Consortium,Wellcome Trust Case Control ConsortiumGenome-wide association of early-onset myocardial infarction with single nucleotide polymorphisms and copy number variants.Nat. Genet. 2009; 41: 334-341Crossref PubMed Scopus (858) Google Scholar (394 from Spain, 63 from Sweden, 12 from Finland, 746 from Seattle) and an additional 357 controls ascertained in Boston. To avoid the issue of scoring hemizygous individuals, we based our analysis solely on autosomal SNPs and excluded X and Y linked variants. SNPs with minor allele frequency <0.05 were also excluded because in the sample sizes available to us these SNPs have little or no power. To avoid confounding influences from established MS susceptibility variants (Table 1), we excluded these along with all SNPs within 1 Mb of each established risk variant. Because of the more extensive LD in the MHC region and greater influence of this region, exclusion was extended to the entire MHC (chromosome 6, 29700KB–33300KB). After applying these exclusions, we were left with 311,315 SNPs with high-quality genotypes common to both studies. By using the genotypes from both discovery and target sets, we further pruned this consensus SNP panel with the PLINK software suite to remove SNPs in strong linkage disequilibrium with each other and thereby generate a set of 59,470 essentially independent SNPs. We used an r2 threshold of 0.2 within a 500 SNP window, sliding 5 SNPs at a time. We applied established methods for computing individual risk score profiles22Wray N.R. Goddard M.E. Visscher P.M. Prediction of individual genetic risk to disease from genome-wide association studies.Genome Res. 2007; 17: 1520-1528Crossref PubMed Scopus (386) Google Scholar, 23Evans D.M. Visscher P.M. Wray N.R. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk.Hum. Mol. Genet. 2009; 18: 3525-3531Crossref PubMed Scopus (216) Google Scholar to assess the polygenic effect of multiple sclerosis. With the PLINK software suite, Cochran-Mantel-Haenszel test statistics were computed for all SNPs in the discovery set, stratifying by geographic origin (UK versus US samples). Based on these CMH association tests, overlapping sets of scoring SNPs were generated based on test significance threshold (pT): pT < 0.01, pT < 0.05, and pT deciles between 0.1 and 1 (Table 2).Table 2Target GWAS Statistics for Each Scoring SNP Setp Value Threshold RangeNumber of SNPsp ValueNagelkerke's Pseudo-R20-0.017463.14E-090.01260-0.053,2721.39E-160.02430-0.16,4262.45E-180.02710-0.212,6272.62E-190.02870-0.318,5647.53E-190.02790-0.424,5651.29E-190.02920-0.530,3658.93E-200.02940-0.636,1603.35E-200.03010-0.742,0751.22E-200.03080-0.848,0121.73E-200.03050-0.953,7481.05E-200.03090-159,4706.12E-210.0313The number of scoring SNPs falling into each p value threshold defined set is shown (note these numbers are of course overlapping). Both the p value and the pseudo-R2 are reported as a difference between a full model containing score and covariates and a reduced model containing covariates only, evaluated by likelihood ratio test. Open table in a new tab The number of scoring SNPs falling into each p value threshold defined set is shown (note these numbers are of course overlapping). Both the p value and the pseudo-R2 are reported as a difference between a full model containing score and covariates and a reduced model containing covariates only, evaluated by likelihood ratio test. By using the odds ratio point estimates from the discovery set, we calculated an aggregate score for each individual in the target set equal to the number of score alleles weighted by the log of the odds ratio. Missing genotypes in target individuals were given the mean score for that locus as seen across the individuals not missing a genotype at that locus. All score profiles were calculated via PLINK's score function. To allow correction for any residual signal attributable to established risk alleles that had not been removed by our regional exclusion of SNPs flanking established risk alleles, we generated a score profile based on the known susceptibility variants listed in Table 1 (with rs3135388 as the surrogate for the MHC effect). Where susceptibility SNPs were not typed in the target set, we selected proxies with the highest r2 to the susceptibility SNP, with ties broken by lowest physical distance (Table S1 available online). With the aggregate scores generated in the target set, we conducted logistic regression analysis to test the relationship between the computed scores and disease status. The score based on known-susceptibility variants and the total number of nonmissing alleles over all SNPs were included as covariates in the analysis. Logistic regression analysis was conducted with STATA 10.1. The variance in case/control status explained by the score statistic was estimated as the difference in variance (with the Nagelkerke pseudo-R2) between a model including score and the covariates (full model) versus the covariates alone (reduced model). p values were computed as 1-df likelihood ratio tests between the full and reduced models. To test for polygenic influences in multiple sclerosis, we considered data from two MS GWAS7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar, 19De Jager P.L. Jia X. Wang J. de Bakker P.I. Ottoboni L. Aggarwal N.T. Piccio L. Raychaudhuri S. Tran D. Aubin C. et al.International MS Genetics ConsortiumMeta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci.Nat. Genet. 2009; 41: 776-782Crossref PubMed Scopus (609) Google Scholar and explored how the risk of MS was related to the effects of multiple loci considered collectively (“en masse”).18Purcell S.M. Wray N.R. Stone J.L. Visscher P.M. O'Donovan M.C. Sullivan P.F. Sklar P. International Schizophrenia ConsortiumCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder.Nature. 2009; 460: 748-752Crossref PubMed Scopus (3135) Google Scholar After excluding SNPs in LD with known MS susceptibility variants (see Table 1) and filtering SNPs on the basis of quality control measures, allele frequency, and mutual LD, we established a set of 59,470 essentially independent autosomal SNPs common to both MS GWAS. These scoring SNPs were then considered in seven sets defined on the basis of nominal significance thresholds observed in the first GWAS7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar (Table 2 indicates the number of scoring SNPs in each significance threshold defined set). The first GWAS7Hafler D.A. Compston A. Sawcer S. Lander E.S. Daly M.J. De Jager P.L. de Bakker P.I. Gabriel S.B. Mirel D.B. Ivinson A.J. et al.International Multiple Sclerosis Genetics ConsortiumRisk alleles for multiple sclerosis identified by a genomewide study.N. Engl. J. Med. 2007; 357: 851-862Crossref PubMed Scopus (1329) Google Scholar (the discovery data set) was used to establish a score for each allele at each of the 59,470 SNPs. Although these odds ratio point estimates have wide confidence intervals, they are inevitably correlated with the true risk for the assessed alleles. In the target data set (the second MS GWAS19De Jager P.L. Jia X. Wang J. de Bakker P.I. Ottoboni L. Aggarwal N.T. Piccio L. Raychaudhuri S. Tran D. Aubin C. et al.International MS Genetics ConsortiumMeta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci.Nat. Genet. 2009; 41: 776-782Crossref PubMed Scopus (609) Google Scholar), we then calculated the aggregate score for each sample by summing the number of risk alleles present in the individual weighted by the score for that allele as calculated in the discovery set. These aggregate scores were then subject to logistic regression analysis to determine whether they account for significant variance in disease status within the target data set. This process was repeated for each of the sets of scoring SNPs. Each of the scoring SNP sets showed highly significant evidence for association to disease status (p = 3.14E-9–p = 6.12E-21), confirming that polygenic inheritance determined by as-yet-unidentified genetic variation is relevant in multiple sclerosis (see Figure 1 and Table 2). Our analysis provides highly significant evidence for a polygenic component to the genetics of MS. As with other complex diseases, it is becoming increasingly clear that single genes explaining a large proportion of heritability do not exist.24Manolio T.A. Collins F.S. Cox N.J. Goldstein D.B. Hindorff L.A. Hunter D.J. McCarthy M.I. Ramos E.M. Cardon L.R. Chakravarti A. et al.Finding the missing heritability of complex diseases.Nature. 2009; 461: 747-753Crossref PubMed Scopus (5379) Google Scholar The results of this study indicate that MS risk is governed in part by the cumulative effect of multiple genetic variants scattered across the genome, each contributing only a modest individual effect. In contrast to the results of the schizophrenia study, the variance explained in MS (as measured by the Nagelkerke's pseudo-R2 value) tended to stabilize once the SNP inclusion cut-off reached an initial p value of 0.2. On the surface, this might suggest that SNPs with somewhat larger effects (e.g., odds ratios of 1.1–1.3) are more prevalent in MS than in schizophrenia. However, the fact that the level of pseudo-R2 does not fall in scoring sets based on less significant thresholds suggests that this flattening may be due to limited power more than an absence of genuinely associated variants among those SNPs with p values of 0.2–0.5. Some portion of the identified polygenic effect could be due to cryptic population stratification remaining in the samples of European descent. However, common methods for correcting for such stratification rely on the assumption that there is no widely dispersed genomic effect on the trait. This is exactly what we are testing, so applying such correction will probably eliminate a true polygenic effect. As pointed out in the original paper from the ISC,18Purcell S.M. Wray N.R. Stone J.L. Visscher P.M. O'Donovan M.C. Sullivan P.F. Sklar P. International Schizophrenia ConsortiumCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder.Nature. 2009; 460: 748-752Crossref PubMed Scopus (3135) Google Scholar en masse effects like those we have demonstrated are unlikely to result from stratification because the same structure would need to be present in both data sets to produce correlation between studies, a highly unlikely scenario. Although this analysis provides convincing evidence for the existence of a polygenic influence on susceptibility, it provides little if any guidance as to the location of the relevant variants. Mapping these additional effects can be achieved only through larger GWAS. Most of the common variants with odds ratio > 1.2 should emerge from a GWAS involving ∼10,000 cases and ∼10,000 controls, but even larger studies will be required to map weaker effects. This work was supported by the National Institutes of Health (NS049477, NS032830, MH065215), the Wellcome Trust (084702/Z/08/Z), and the Cambridge NIHR Biomedical Research Centre. This study makes use of data generated by the WTCCC and NIMH. Study participants were recruited in agreement with protocols of the institutional review board at each institution. For a full list of members of the IMSGC, see http://www.imsgc.org. Contributing authors to this work are William S. Bush (Vanderbilt University), Stephen J. Sawcer (University of Cambridge), Philip L. de Jager (Brigham & Women's Hospital and Harvard Medical School), Jorge R. Oksenberg (UCSF), Jacob L. McCauley (University of Miami, Miller School of Medicine), Margaret A. Pericak-Vance (University of Miami, Miller School of Medicine), and Jonathan L. Haines (Vanderbilt University). W.S.B. and S.J.S. contributed equally to this work. Correspondence should be directed to W.S.B. ( [email protected] ). Download .pdf (.08 MB) Help with pdf files Document S1. One Table The URL for data presented herein is as follows:Online Mendelian Inheritance in Man (OMIM), http://www.ncbi.nlm.nih.gov/Omim/
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