Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data
- Joanne H Wang1,
- Derek Pappas1,
- Philip L De Jager2, 3,
- Daniel Pelletier1,
- Paul IW de Bakker3, 4,
- Ludwig Kappos5,
- Chris H Polman6,
- Australian and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene)7,
- Lori B Chibnik2,
- David A Hafler8,
- Paul M Matthews9,
- Stephen L Hauser1, 10,
- Sergio E Baranzini1 and
- Jorge R Oksenberg1, 10Email author
© Wang et al.; licensee BioMed Central Ltd. 2011
Received: 6 August 2010
Accepted: 18 January 2011
Published: 18 January 2011
Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed.
We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool.
In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years).
The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.
Multiple sclerosis (MS) is a common cause of non-traumatic neurological disability in young adults. Extensive epidemiological and laboratory data indicate that genetic susceptibility is an important determinant of MS risk [1, 2]; this risk is modulated by family history, ancestry, gender, age, and geography . The extent of familial clustering is often expressed in terms of the λs parameter derived from the ratio between the risk seen in the siblings of an affected individual and the risk seen in the population . In northern Europeans, the prevalence is 1 per 1,000 in the population and the recurrence risk in a sibling is 2 to 3%; hence, after correcting for age, the λs for MS is approximately 15 to 20. On the other hand, some authors suggest that both of these risks are difficult to assess and the denominator is generally underestimated while the numerator is overestimated [5, 6]; a more accurate value for λs may be less than 10 . In addition, twin studies from several populations consistently show that a monozygotic twin of an MS patient is at higher risk for MS than is a dizygotic twin [8, 9]; however, they vary in their estimation of indices of heritability from 0.25 to 0.76 .
MS behaves as a prototypic complex genetic disorder, and although a single-gene etiology cannot be ruled out for a subset of pedigrees, data from recent genome-wide association studies (GWAS) provide convincing evidence that support a multifactorial and polygenic model of inheritance [11–14]. It is also likely that epistatic and epigenetic events modulate heritability [15–18]. The human leukocyte antigen (HLA) gene cluster in chromosome 6p21.3 represents by far the strongest MS susceptibility locus genome-wide. The primary signal maps to the HLA-DRB1 gene in the class II segment of the locus, but complex hierarchical allelic and/or haplotypic effects and protective signals in the class I region between HLA-A and HLA-C have been reported as well [2, 19–21]. Other susceptibility genes discovered primarily through GWAS include IL2RA, IL7R, EVI5, CD58, CLEC16A, CD226, GPC5, and TYK2 [11, 12, 14, 22–25]. A recent meta-analysis of data from three different GWAS totaling 2,624 MS patients and 7,220 controls identified additional susceptibility SNPs within or next to TNFRSF1A, ICSBP1/IRF8 and CD6 . In addition to gene discovery, these studies are powering a profound paradigm shift in the study of MS by allowing a more accurate description of the genetic contributions to disease susceptibility . Even though the full roster of MS genes remains unknown at this time, we build on the meta-analysis dataset and use logistic regression methodology to estimate the collective genetic risk behind MS susceptibility. In line with other complex diseases , the results remain consistent with the polygenic paradigm and suggest that while much of the genetics of MS remains to be characterized, up to 350 independent variants account for a significant fraction of the genetic component of MS.
Materials and methods
Demographic statistics of study participants
Discovery dataset (N = 8,844)
Validation datasetb (N = 3,606)
(N = 2,124)
(N = 6,720)
(N = 1,618)
(N = 1,988)
IMSGC UK, Affy 500K
IMSGC US, Affy 500K
BWH, Affy 6.0
Gene MSA CH, Illumina 550K
Gene MSA NL, Illumina 550K
Gene MSA US, Illumina 550K
All statistical analyses were performed using SAS v.9.1.3 and JMP Genomics v. 4.0 (SAS Institute, Cary, NC 27513, USA). Principle component analysis was implemented prior to data analysis to assess population substructure. Although no significant population substructure was observed when compared to the HapMap CEU data, a few outliers were removed. We organize the top association analysis results (P < 0.001) of the meta-analysis in the discovery dataset by individual chromosomes and implement a logistic regression analysis using alternation between the type I and type III sums of squares tests to remove markers that are in linkage disequilibrium (LD). The top ranked SNPs (that is, the SNP with the most extreme P-value) are forced into the model first. We then calculate the residual effect of each of the other SNPs after accounting for the effect of the top ranked SNPs. We used gender and sample country of origin (US versus EU, total 6 stratum) as covariates in the model to account for possible population heterogeneity. Furthermore, conditional logistic regression was implemented conditioning on DRB1*15:01 status (Yes versus No) in order to control the effect of genetic heterogeneity. This method is preferred to the conventional logistic regression model in estimating the gene risk effect after 'conditioning out' the baseline risk in DRB1*15:01 carriers and non-carriers, and it is thus efficient in eliminating the redundancy of markers that are in LD with DRB1*15:01. HLA-DRB1*15:01 status was determined using a tagging marker (rs3135388).
Logistic regression stepwise selection was applied to select a set of genes from the identified independent markers and establish a genetic profile to assess the cumulative genetic risk of individuals (P-Hat). Logistic regression is used for prediction of the probability of occurrence of an event by fitting data to a logit function. It is a generalized linear model used for binomial regression. The logit of the unknown binomial probabilities (P-Hat) is modeled as a linear function of the Xi, with a set of explanatory variables, where logit (P-Hat) = ln(P-Hat/1 - P-Hat) = β 0 +β 1 X 1 +β 2 X 2 +···+BiXi; and thus, P-Hat = 1/1+ exp-(β0 + β1X1 + β2X2 + ···+BiXi). The algorithm for calculating the predicted probability is modeled after an event being a MS case, P-Hat = 1/(1+ exp(-Ŷi)), where Ŷi = intercept + βcenter × Xcenter + βgender × Xgender + ∑βj×Xij; βj is the estimated regression coefficient of genetic marker j, and j = 1 to 350; Xij is the fractional genotype of marker j of individual i. The values of intercept, βcenter, βgender, and βj are the maximum likelihood estimates obtained from the logistic regression model. The regression coefficient reflects the differential contribution of each SNP, and the odds ratio is estimated by exponentiating the corresponding regression coefficient. In order to assess how well the genetic profile can differentiate MS cases from the controls, the cumulative genetic risk classification is performed. If Ŷi of an individual is >0, then the individual is classified as a MS case, and if Ŷi is <0, then they are classified as a control. When Ŷi = 0, the estimated probability of being an MS patient is 0.5.
Classification sensitivity and specificity are assessed. Classification sensitivity is defined as the percentage of affected individuals that are classified as an MS case, and specificity as the percentage of controls that are classified as a control. Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk (high, medium, low and misclassified group), with gender as covariate in the model. The Hosmer-Lemeshow goodness-of-fit test was implemented to test if the observed probability is equal to the expected probability based on the fitted model; a P-value <0.05 indicates a lack of fit of the fitted logistic regression model .
Functional gene ontology and annotation
Gene ontology enrichment analysis was done using the DAVID functional annotation tool  and GO Tree Machine, and pathway enrichment was done with the Pathway-Express profiling tool , using default parameters and correcting for multiple comparison by the Benjamini method and the false discovery rate (FDR), respectively.
Estimated cumulative genetic risk using 12 validated multiple sclerosis genesa
Probability of being a MS case
Case (N = 2,062)
Control (N = 6,360)
Stage I analysis
Case-control logistic regression analysis was implemented on the discovery dataset with 8,844 samples (2,124 cases versus 6,720 controls). Two regression models were applied. The first model included center and gender as covariates, whereas the second model included center, gender and DRB1*15:01 status as covariates. A relatively lax threshold of significance was chosen to compensate for the lack of statistical power to detect minor effects. Markers with P-value <0.001 (equivalent to controlling FDR at 25%) from both analyses were selected for further study. Altogether, 11,334 markers (0.44% of the 2.56 million markers) were included in the stage II analysis.
Stage II analysis
Top significant markers (-Log 10(p) > 6)) after adjusting for DRB1*15:0 1 among the 700-independent-gene set
Stage III analysis
Using the identified 713 independent markers, we performed a model fitting analysis to select the optimal set of variants that gave the best estimation of the cumulative genetic risk mediated by common alleles for an individual and that differentiated MS cases from controls. Logistic regression analysis using stepwise-selection with different selection entrance and remaining cutoff values (P = 0.01, P = 0.05, P = 0.1) was implemented. The stepwise-selection process included an alternation between forward selection of a set of significant markers and backward elimination of markers that did not retain significance at the selected threshold after additional markers were placed in the model. The stepwise selection process terminated when additional significant markers could not be fitted into the model. The covariates included in the logistic regression analysis were center and gender. This analysis identified 350 genes using P = 0.05 as the cutoff selecting criteria, including CD58, EVI5, IRF8, RGS1, CD226, TNFRSF1a, CD6, and IL7R. However, IL2Ra, CLEC16a, IRF8, and HLA-C did not survive the stepwise regression analysis.
Classification results using different genetic models
P-Hat (quantiles, case versus control)
Discovery dataset (N = 8,844)
Validation dataset (N = 3,606)
Stage IV analysis
Clinical characteristics of individuals with various degrees of genetic load
Clinical and demographic characteristics of various genetic-load groups
Genetic-load groups by the level of estimated cumulative genetic risk
Clinical and demographic variables
P-Hat = 0.95
P-Hat = 0.75-0.95
P-Hat = 0.5-0.75
P-Hat < 0.5
Sample size, N (%)
MSSS (least-square mean)a
F = 0.41, P = 0.75c
T2-lesion load (mm3) (least-square mean)b
F = 0.98, P = 0.40c
Age of disease onset (years)
F = 2.71, P = 0.03d
DRB1*15:01 +, N (%)
χ2 = 74.13e
DRB1*15:01 -, N (%)
P < 0.0001
Female, N (%)
χ2 = 25.41e
Male, N (%)
P < 0.0001
Multiple Sclerosis Severity Score (MSSS), T2-lesion volumes (mm3), and age of disease onset (years) were analyzed using ANCOVA tests, with gender as covariate in the model. MSSS was transformed using square-root transformation for normality assumption. T2-lesion volumes (mm3) were transformed using cube-root transformation for normality assumption. The global test results did not show statistically significant difference between the four groups on MSSS (F = 0.41, P = 0.75) and T2-lesion volumes (F = 0.98, P = 0.40), whether age of disease onset was placed in the model as a covariate or not (MSSS, F = 0.41, P = 0.74; T2-lesion volumes, F = 0.69, P = 0.56). However, there was a significant difference in age of disease onset between the MS affected misclassified as controls (mean = 36 years) and the other three groups (high group, mean = 33.77 years; medium group, mean = 33.57 years; low group, mean = 33.23 years) (Table 5). Sib concordance in multi-case family studies show that age of onset is the strongest genotype-phenotype association described so far for MS . Therefore, the differences in genetic load driven by the age of onset quantitative trait loci suggest that the two groups (high P-Hat and misclassified) are characterized by overlapping but distinct genetic profiles.
Functional annotation enrichment
Functional annotation of the 350 genes
Cell adhesion (GO:0007155)
G-protein signaling, coupled to cyclic nucleotide second messenger
System development (GO:0048731)
Central nervous system development
Organ development (GO:0048513)
Integral to membrane (GO:0016021)
Integral to plasma membrane (GO:0005887)
Dystrophin-associated glycoprotein complex
Signal transducer activity (GO:0004871)
Transmembrane receptor activity (GO:0004888)
Transmembrane receptor protein phosphatase activity
Amine receptor activity
Hematopoietin/interferon-class (D200-domain) cytokine receptor activity
GPI anchor binding
Calcium-release channel activity
Delayed rectifier potassium channel activity
Enriched KEGG pathways
Cell adhesion molecules (CAMs)
Neuroactive ligand-receptor interaction
Type I diabetes mellitus
Partially powered GWAS and ensuing meta-analysis have identified a number of non-HLA candidate genes associated with MS susceptibility [11–14]. Each significant association has a very modest effect, representing a small share of the genetic variance affecting disease risk. In this follow-up study of the meta-analysis dataset, we applied logistic regression stepwise selection methods and identified 350 variants. We used these markers to build a genetic profile associated with the cumulative genetic risk measured by the probability of an individual being a MS case. In the validation dataset, we tested the model and found that the classification algorithm yielded 62.3% sensitivity and 75.9% specificity, with an AUC of 0.769. These numbers together indicate that the application of the genetic profile built using the meta-analysis discovery dataset does not provide a high discriminatory accuracy in the independent dataset despite a median cumulative genetic risk in the discovery dataset of 0.90 for the case group, and 0.01 for the control group. For the validation dataset, the values are 0.59 for the case group and 0.32 for the control group.
The percentage of variance (R2) explained by predictors in the regression model
The discovery dataset (n = 8,844)
The validation dataset (n = 3,606)
11% (AUCc = 0.68)
27% (AUCc = 0.769)
Several factors could have contributed to the relatively low sensitivity of the selected genes. First, the power of the discovery dataset is more likely inadequate to detect all susceptibility genes. Even though we have used the largest MS genetic dataset available to date, it has been suggested that a dataset with 10,000 cases and 10,000 controls might be able to reach a desirable level of power for GWAS analysis in order to effectively control both type I and type II errors. This is especially valid for less frequent alleles (minor allele frequency ≤10%) and effect size (odds ratio) in the range 1.1 to 1.3 [35, 36]. Second, relevant MS variants may have gone undetected because of the partial genome coverage in the currently available SNP arrays. Third, there are unknown interactions between genes involved in the biochemical pathways that contribute to MS susceptibility. Fourth, the total adjusted R2 of the logistic regression model is 0.75 and the r-square attributable to genetic factors in this model accounted for only 56.5%, suggesting that without fitting environmental triggers into the model, predictive accuracy will remain limited. A large number of environmental exposures have been investigated in MS, but recent epidemiologic and laboratory studies have provided support primarily for vitamin D and Epstein-Barr virus exposure [37, 38]. A recent study suggests that adding environmental risk factors into a predictive algorithm based on genetic variants enhances the case-control status classification . Fifth, due to the suboptimal power in the discovery dataset, it is likely that the selected 350 variants include both true and false signals. The inclusion of false positives in the estimators that fit the discovery dataset does not contribute to the prediction in the validating process, also causing a tractable drop in classification accuracy. Thus, the results shown in Table 4 may contain a portion of overestimation of model fit in the discovery dataset analysis results, indicating that bias could be embedded in predictive modeling when using the association tests approach in marker selection.
All these confounders are reflected in the fact that some individuals in the control group carry a high cumulative genetic risk (P-Hat >0.8). Thus, in this experiment utilizing the most updated MS genetic dataset, a high cumulative genetic risk is not sufficient to predict with high confidence affectation status even in the discovery dataset (Table 4). Additional layers of complexity are represented by the likelihood of unaccountable epistatic interactions, etiological heterogeneity, and epigenetic and random events. These limitations notwithstanding, the genetic risk as assessed here still captures a significant portion of the full cumulative genetic risk (the probability of being a MS case) in the validation dataset between the case (median = 0.59, 75% quartile = 0.74) and control group (median = 0.32, 75% quartile = 0.49). The model with the 350-gene set produced a larger difference of the estimated cumulative genetic risk between case and control groups compared with that produced by the 12-gene set in the models (Figure 3). Thus, the cumulative genetic risk (P-Hat) generated using the 350-gene set can still provide a useful index of the genetic load associated with MS, and provides important mechanistic insights.
Most validated MS susceptibility loci have well-defined roles in immunologic functions, consistent with the hypothesis that MS etiology has its primary roots in early immune system dysregulation, precipitating secondary neuronal degeneration. On the other hand, a network-based pathway analysis of two GWAS in MS, where evidence for genetic association was combined with evidence for protein-protein interaction, demonstrated the role of neural pathway genes (axon guidance and long-term potentiation) in conferring susceptibility . The genetic profile identified in this analysis confirms the significant enrichment of genes involved not only in the immune response but also in nervous system development and neuronal signaling (Table 6). These included genes encoding cell-cell adhesion molecules (CDH2, CADM1, CNTN1, NCAM2, NRXN1, and NRXN3) and several neuronal receptors, such as the G-protein coupled receptors (ADRA1A, ADARA2A, GABRB3, TACR1, CHR3, HTR1B, HTR1E, and HTR2A), as well as the metabotropic glutamate receptor (GRM8) and ionotropic glutamate receptors (GRIK4 and GRIN2B). Interestingly, members of the glutamate receptor pathway have been previously identified by our group in both the network-based study of one of the GWAS datasets included in the meta-analysis utilized here (GRIN2A, GRIK1, GRIK2, GRIK4, GRID2, GRIA1, GRIK4)  and an independent pharmacogenomic study of type I interferon response (GRIA1, GRID2, SLC1A2) . A more recent pharmacogenomic study also identified the ionotropic glutamate receptor (GRIA3) associated with interferon response in MS . These observations further support the proposed mechanism of glutamate excitotoxicity as a precipitating agent of the glial and axonal injury observed in MS [42, 43]. The ramifications of these SNPs on expression or function are unknown; however, their recent and continued identification may help evolve a model of MS pathogenesis with increasing contributions from neuronal genes.
In summary, the cumulative genetic risk estimation using a genetic profile composed of 350 genes provides a useful index of the genetic risk leading to MS. The incomplete classification accuracy reflects most likely the limited power of available genetic datasets and the difficulties in incorporating gene-gene interactions and gene-environment interactions. The imminent publication of larger high-resolution GWAS and transcriptomic studies together with recent progress in identifying true environmental variables will refine this and other modeling approaches for a greater understanding of MS genetics and assessment of translational applications.
analysis of covariance
area under curve
false discovery rate
genome-wide association study
human leukocyte antigen
Multiple Sclerosis Severity Score
receiver operating characteristic
We thank the MS patients and healthy controls who participated in the original genetic studies, and the many dedicated IMSGC, GeneMSA, and ANZgene consortia investigators that participated in the recruitment of study participants, acquisition of relevant clinical data, and analysis of original GWAS data. The contributing authors from The Australian and New Zealand Multiple Sclerosis Genetics Consortium are: Melanie Bahlo, David R Booth, Simon A Broadley, Matthew A Brown, Simon J Foote, Lyn R Griffiths, Trevor J Kilpatrick, Jeanette Lechner-Scott, Pablo Moscato, Victoria M Perreau, Justin P Rubio, Rodney J Scott, Jim Stankovich, Graeme J Stewart, Bruce V Taylor, James Wiley, Patrick Danoy, Helmut Butzkueven, Mark Slee, Judith Greer, Allan Kermode, and William Carroll. This study was supported by NIH grants RO1NS049477 and RO1NS26799, and National Multiple Sclerosis Society grant RG2901. SEB and PLD are Harry Weaver Neuroscience Scholars of the US National MS Society.
- Goodin DS: The causal cascade to multiple sclerosis: a model for MS pathogenesis. PLoS One. 2009, 4: e4565-10.1371/journal.pone.0004565.PubMedPubMed CentralView ArticleGoogle Scholar
- Oksenberg JR, Baranzini SE, Sawcer S, Hauser SL: The genetics of multiple sclerosis: SNPs to pathways to pathogenesis. Nat Rev Genet. 2008, 9: 516-526. 10.1038/nrg2395.PubMedView ArticleGoogle Scholar
- Compston A, Coles A: Multiple sclerosis. Lancet. 2008, 372: 1502-1517. 10.1016/S0140-6736(08)61620-7.PubMedView ArticleGoogle Scholar
- Risch N: Linkage strategies for genetically complex traits. I. Multilocus models. Am J Hum Genet. 1990, 46: 222-228.PubMedPubMed CentralGoogle Scholar
- Guo SW: Inflation of sibling recurrence-risk ratio, due to ascertainment bias and/or overreporting. Am J Hum Genet. 1998, 63: 252-258. 10.1086/301928.PubMedPubMed CentralView ArticleGoogle Scholar
- Sawcer S, Ban M, Wason J, Dudbridge F: What role for genetics in the prediction of multiple sclerosis?. Ann Neurol. 2010, 67: 3-10. 10.1002/ana.21911.PubMedPubMed CentralView ArticleGoogle Scholar
- Hemminki K, Li X, Sundquist J, Hillert J, Sundquist K: Risk for multiple sclerosis in relatives and spouses of patients diagnosed with autoimmune and related conditions. Neurogenetics. 2009, 10: 5-11. 10.1007/s10048-008-0156-y.PubMedView ArticleGoogle Scholar
- Willer CJ, Dyment DA, Risch NJ, Sadovnick AD, Ebers GC: Twin concordance and sibling recurrence rates in multiple sclerosis. Proc Natl Acad Sci USA. 2003, 100: 12877-12882. 10.1073/pnas.1932604100.PubMedPubMed CentralView ArticleGoogle Scholar
- Hansen T, Skytthe A, Stenager E, Petersen HC, Bronnum-Hansen H, Kyvik KO: Concordance for multiple sclerosis in Danish twins: an update of a nationwide study. Mult Scler. 2005, 11: 504-510. 10.1191/1352458505ms1220oa.PubMedView ArticleGoogle Scholar
- Hawkes CH, Macgregor AJ: Twin studies and the heritability of MS: a conclusion. Mult Scler. 2009, 15: 661-667. 10.1177/1352458509104592.PubMedView ArticleGoogle Scholar
- International Multiple Sclerosis Genetics Consortium: Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med. 2007, 357: 851-862. 10.1056/NEJMoa073493.View ArticleGoogle Scholar
- Australian and New Zealand Multiple Sclerosis Genetics Consortium: Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet. 2009, 41: 824-828. 10.1038/ng.396.View ArticleGoogle Scholar
- International Multiple Sclerosis Genetics Consortium: Refining genetic associations in multiple sclerosis. Lancet Neurol. 2008, 7: 567-569. 10.1016/S1474-4422(08)70122-4.View ArticleGoogle Scholar
- Baranzini SE, Wang J, Gibson RA, Galwey N, Naegelin Y, Barkhof F, Radue EW, Lindberg RL, Uitdehaag BM, Johnson MR, Angelakopoulou A, Hall L, Richardson JC, Prinjha RK, Gass A, Geurts JJ, Kragt J, Sombekke M, Vrenken H, Qualley P, Lincoln RR, Gomez R, Caillier SJ, George MF, Mousavi H, Guerrero R, Okuda DT, Cree BA, Green AJ, Waubant E, et al: Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet. 2009, 18: 767-778. 10.1093/hmg/ddn388.PubMedPubMed CentralView ArticleGoogle Scholar
- Casaccia-Bonnefil P, Pandozy G, Mastronardi F: Evaluating epigenetic landmarks in the brain of multiple sclerosis patients: a contribution to the current debate on disease pathogenesis. Prog Neurobiol. 2008, 86: 368-378.PubMedPubMed CentralGoogle Scholar
- Otaegui D, Baranzini SE, Armananzas R, Calvo B, Munoz-Culla M, Khankhanian P, Inza I, Lozano JA, Castillo-Trivino T, Asensio A, Olaskoaga J, de Munain AL: Differential micro RNA expression in PBMC from multiple sclerosis patients. PLoS One. 2009, 4: e6309-10.1371/journal.pone.0006309.PubMedPubMed CentralView ArticleGoogle Scholar
- Brassat D, Motsinger AA, Caillier SJ, Erlich HA, Walker K, Steiner LL, Cree BA, Barcellos LF, Pericak-Vance MA, Schmidt S, Gregory S, Hauser SL, Haines JL, Oksenberg JR, Ritchie MD: Multifactor dimensionality reduction reveals gene-gene interactions associated with multiple sclerosis susceptibility in African Americans. Genes Immun. 2006, 7: 310-315. 10.1038/sj.gene.6364299.PubMedPubMed CentralView ArticleGoogle Scholar
- Gregersen JW, Kranc KR, Ke X, Svendsen P, Madsen LS, Thomsen AR, Cardon LR, Bell JI, Fugger L: Functional epistasis on a common MHC haplotype associated with multiple sclerosis. Nature. 2006, 443: 574-577.PubMedGoogle Scholar
- Yeo TW, De Jager PL, Gregory SG, Barcellos LF, Walton A, Goris A, Fenoglio C, Ban M, Taylor CJ, Goodman RS, Walsh E, Wolfish CS, Horton R, Traherne J, Beck S, Trowsdale J, Caillier SJ, Ivinson AJ, Green T, Pobywajlo S, Lander ES, Pericak-Vance MA, Haines JL, Daly MJ, Oksenberg JR, Hauser SL, Compston A, Hafler DA, Rioux JD, Sawcer S: A second major histocompatibility complex susceptibility locus for multiple sclerosis. Ann Neurol. 2007, 61: 228-236. 10.1002/ana.21063.PubMedPubMed CentralView ArticleGoogle Scholar
- Barcellos LF, Sawcer S, Ramsay PP, Baranzini SE, Thomson G, Briggs F, Cree BC, Begovich AB, Villoslada P, Montalban X, Uccelli A, Savettieri G, Lincoln RR, DeLoa C, Haines JL, Pericak-Vance MA, Compston A, Hauser SL, Oksenberg JR: Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis. Hum Mol Genet. 2006, 15: 2813-2824. 10.1093/hmg/ddl223.PubMedView ArticleGoogle Scholar
- Lincoln MR, Ramagopalan SV, Chao MJ, Herrera BM, Deluca GC, Orton SM, Dyment DA, Sadovnick AD, Ebers GC: Epistasis among HLA-DRB1, HLA-DQA1, and HLA-DQB1 loci determines multiple sclerosis susceptibility. Proc Natl Acad Sci USA. 2009, 106: 7542-7547.PubMedPubMed CentralView ArticleGoogle Scholar
- Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A, Kwiatkowski DP, McCarthy MI, Ouwehand WH, Samani NJ, Todd JA, Donnelly P, Barrett JC, Davison D, Easton D, Evans DM, Leung HT, Marchini JL, Morris AP, Spencer CC, Tobin MD, Attwood AP, Boorman JP, Cant B, Everson U, Hussey JM, Jolley JD, Knight AS, Koch K, Meech E, et al: Association scan of 14,500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat Genet. 2007, 39: 1329-1337. 10.1038/ng.2007.17.PubMedView ArticleGoogle Scholar
- Okuda DT, Srinivasan R, Oksenberg JR, Goodin DS, Baranzini SE, Beheshtian A, Waubant E, Zamvil SS, Leppert D, Qualley P, Lincoln R, Gomez R, Caillier S, George M, Wang J, Nelson SJ, Cree BA, Hauser SL, Pelletier D: Genotype-phenotype correlations in multiple sclerosis: HLA genes influence disease severity inferred by 1HMR spectroscopy and MRI measures. Brain. 2009, 132: 250-259. 10.1093/brain/awn301.PubMedPubMed CentralView ArticleGoogle Scholar
- De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT, Piccio L, Raychaudhuri S, Tran D, Aubin C, Briskin R, Romano S, Baranzini SE, McCauley JL, Pericak-Vance MA, Haines JL, Gibson RA, Naeglin Y, Uitdehaag B, Matthews PM, Kappos L, Polman C, McArdle WL, Strachan DP, Evans D, Cross AH, Daly MJ, Compston A, Sawcer SJ, Weiner HL, et al: Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci. Nat Genet. 2009, 41: 776-782. 10.1038/ng.401.PubMedPubMed CentralView ArticleGoogle Scholar
- De Jager PL, Baecher-Allan C, Maier LM, Arthur AT, Ottoboni L, Barcellos L, McCauley JL, Sawcer S, Goris A, Saarela J, Yelensky R, Price A, Leppa V, Patterson N, de Bakker PI, Tran D, Aubin C, Pobywajlo S, Rossin E, Hu X, Ashley CW, Choy E, Rioux JD, Pericak-Vance MA, Ivinson A, Booth DR, Stewart GJ, Palotie A, Peltonen L, Dubois B, et al: The role of the CD58 locus in multiple sclerosis. Proc Natl Acad Sci USA. 2009, 106: 5264-5269. 10.1073/pnas.0813310106.PubMedPubMed CentralView ArticleGoogle Scholar
- Baranzini SE, Galwey NW, Wang J, Khankhanian P, Lindberg R, Pelletier D, Wu W, Uitdehaag BM, Kappos L, Polman CH, Matthews PM, Hauser SL, Gibson RA, Oksenberg JR, Barnes MR: Pathway and network-based analysis of genome-wide association studies in multiple sclerosis. Hum Mol Genet. 2009, 18: 2078-2090. 10.1093/hmg/ddp120.PubMedPubMed CentralView ArticleGoogle Scholar
- Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P: Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009, 460: 748-752.PubMedGoogle Scholar
- The International HapMap Project. Nature. 2003, 426: 789-796. 10.1038/nature02168.
- MACH Algorithm. [http://www.sph.umich.edu/csg/abecasis/MACH/download/]
- Hosmer DW, Lemeshow S: Applied Logistic Regression. 2000, New York: Wiley, 2View ArticleGoogle Scholar
- Dennis G, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, Lempicki RA: DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 2003, 4: P3-10.1186/gb-2003-4-5-p3.PubMedView ArticleGoogle Scholar
- Zhang B, Schmoyer D, Kirov S, Snoddy J: GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics. 2004, 5: 16-10.1186/1471-2105-5-16.PubMedPubMed CentralView ArticleGoogle Scholar
- Rioux JD, Goyette P, Vyse TJ, Hammarstrom L, Fernando MM, Green T, De Jager PL, Foisy S, Wang J, de Bakker PI, Leslie S, McVean G, Padyukov L, Alfredsson L, Annese V, Hafler DA, Pan-Hammarstrom Q, Matell R, Sawcer SJ, Compston AD, Cree BA, Mirel DB, Daly MJ, Behrens TW, Klareskog L, Gregersen PK, Oksenberg JR, Hauser SL: Mapping of multiple susceptibility variants within the MHC region for 7 immune-mediated diseases. Proc Natl Acad Sci USA. 2009, 106: 18680-18685. 10.1073/pnas.0909307106.PubMedPubMed CentralView ArticleGoogle Scholar
- Hensiek AE, Seaman SR, Barcellos LF, Oturai A, Eraksoi M, Cocco E, Vecsei L, Stewart G, Dubois B, Bellman-Strobl J, Leone M, Andersen O, Bencsik K, Booth D, Celius EG, Harbo HF, Hauser SL, Heard R, Hillert J, Myhr KM, Marrosu MG, Oksenberg JR, Rajda C, Sawcer SJ, Sorensen PS, Zipp F, Compston DA: Familial effects on the clinical course of multiple sclerosis. Neurology. 2007, 68: 376-383. 10.1212/01.wnl.0000252822.53506.46.PubMedView ArticleGoogle Scholar
- Sawcer S: The complex genetics of multiple sclerosis: pitfalls and prospects. Brain. 2008, 131: 3118-3131. 10.1093/brain/awn081.PubMedPubMed CentralView ArticleGoogle Scholar
- Nannya Y, Taura K, Kurokawa M, Chiba S, Ogawa S: Evaluation of genome-wide power of genetic association studies based on empirical data from the HapMap project. Hum Mol Genet. 2007, 16: 2494-2505. 10.1093/hmg/ddm205.PubMedView ArticleGoogle Scholar
- Ascherio A, Munger KL: Environmental risk factors for multiple sclerosis. Part I: the role of infection. Ann Neurol. 2007, 61: 288-299. 10.1002/ana.21117.PubMedView ArticleGoogle Scholar
- Ascherio A, Munger KL: Environmental risk factors for multiple sclerosis. Part II: Noninfectious factors. Ann Neurol. 2007, 61: 504-513. 10.1002/ana.21141.PubMedView ArticleGoogle Scholar
- De Jager PL, Chibnik LB, Cui J, Reischl J, Lehr S, Simon KC, Aubin C, Bauer D, Heubach JF, Sandbrink R, Tyblova M, Lelkova P, Havrdova E, Pohl C, Horakova D, Ascherio A, Hafler DA, Karlson EW: Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score. Lancet Neurol. 2009, 8: 1111-1119. 10.1016/S1474-4422(09)70275-3.PubMedPubMed CentralView ArticleGoogle Scholar
- Byun E, Caillier SJ, Montalban X, Villoslada P, Fernandez O, Brassat D, Comabella M, Wang J, Barcellos LF, Baranzini SE, Oksenberg JR: Genome-wide pharmacogenomic analysis of the response to interferon beta therapy in multiple sclerosis. Arch Neurol. 2008, 65: 337-344. 10.1001/archneurol.2008.47.PubMedView ArticleGoogle Scholar
- Comabella M, Craig DW, Morcillo-Suarez C, Rio J, Navarro A, Fernandez M, Martin R, Montalban X: Genome-wide scan of 500,000 single-nucleotide polymorphisms among responders and nonresponders to interferon beta therapy in multiple sclerosis. Arch Neurol. 2009, 66: 972-978. 10.1001/archneurol.2009.150.PubMedView ArticleGoogle Scholar
- Trapp BD, Stys PK: Virtual hypoxia and chronic necrosis of demyelinated axons in multiple sclerosis. Lancet Neurol. 2009, 8: 280-291. 10.1016/S1474-4422(09)70043-2.PubMedView ArticleGoogle Scholar
- Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, Georgescu C, Romero R: A systems biology approach for pathway level analysis. Genome Res. 2007, 17: 1537-1545. 10.1101/gr.6202607.PubMedPubMed CentralView ArticleGoogle Scholar
- Roxburgh RH, Seaman SR, Masterman T, Hensiek AE, Sawcer SJ, Vukusic S, Achiti I, Confavreux C, Coustans M, le Page E, Edan G, McDonnell GV, Hawkins S, Trojano M, Liguori M, Cocco E, Marrosu MG, Tesser F, Leone MA, Weber A, Zipp F, Miterski B, Epplen JT, Oturai A, Sorensen PS, Celius EG, Lara NT, Montalban X, Villoslada P, Silva AM, et al: Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity. Neurology. 2005, 64: 1144-1151.PubMedView ArticleGoogle Scholar
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