Multiple sclerosis susceptibility alleles in African Americans

Abstract

Multiple sclerosis (MS) is an autoimmune demyelinating disease characterized by complex genetics and multifaceted gene–environment interactions. Compared to whites, African Americans have a lower risk for developing MS, but African Americans with MS have a greater risk of disability. These differences between African Americans and whites may represent differences in genetic susceptibility and/or environmental factors. SNPs from 12 candidate genes have recently been identified and validated with MS risk in white populations. We performed a replication study using 918 cases and 656 unrelated controls to test whether these candidate genes are also associated with MS risk in African Americans. CD6, CLEC16a, EVI5, GPC5, and TYK2 contained SNPs that are associated with MS risk in the African American data set. EVI5 showed the strongest association outside the major histocompatibility complex (rs10735781, OR=1.233, 95% CI=1.06–1.43, P-value=0.006). In addition, RGS1 seems to affect age of onset whereas TNFRSF1A seems to be associated with disease progression. None of the tested variants showed results that were statistically inconsistent with the effects established in whites. The results are consistent with shared disease genetic mechanisms among individuals of European and African ancestry.

Introduction

Multiple sclerosis (MS, OMIM 126200) is the most common cause of non-traumatic chronic neurological disability in young adults.1 The incidence of MS seems to have increased considerably over the last century,2 but ancestry remains an important modifier of the global burden of MS; disease prevalence is substantially higher in populations of northern European decent. Other populations, including Asians, Africans, and North and South Amerindians, have a pronounced lower frequency of MS.3 Although African Americans have a higher disease risk compared to black Africans, they have a lower relative risk compared to northern Europeans and white Americans (relative risk of 0.64).4 On the other hand, African Americans with MS seem to have a greater risk of ambulatory disability and more often have symptoms restricted to the spinal cord and optic nerve compared to white patients.5, 6, 7, 8, 9, 10 The differences in the clinical phenotype between African Americans and whites with MS may be due to the influence of genetic and/or environmental factors.

MS is a prototypic multifactorial disease with a complex genetic component. Multiple studies yielded convincing evidence for the presence of a major susceptibility gene or genes in the major histocompatibility complex (MHC) locus on chromosome 6p21.3, but also indicated a polygenic mode of inheritance with each non-MHC gene contributing only a modest effect to the overall risk.11 Spearheaded by the recent remarkable progress in high-throughput genotyping technologies, hypothesis-neutral genome-wide association studies (GWAS) in MS led to the identification of true susceptibility genes and loci of interest, including CD25 antigen (CD25), CD58 antigen (CD58), C-type lectin domain family 16, member A (CLEC16a), interleukin-2 receptor α chain (IL2RA), interleukin-7 receptor (IL7R), glypican 5 (GPC5), regulator of G protein signaling 1 (RGS1), and tyrosine kinase 2 (TYK2).12, 13, 14 A meta-analysis of GWAS data identified additional susceptibility SNPs in or next to tumor necrosis factor receptor superfamily member 1A (TNFRSF1A), interferon regulatory factor 8 (IRF8), and CD6 antigen (CD6).15 The distribution and penetrance of these genetic variations in non-white MS groups is unknown.

In African Americans, earlier studies have shown the association of MHC class II HLA-DRB1*15 alleles with disease susceptibility and progression.16, 17 In addition, the presence of HLA-DRB1*15 alleles in African American patients correlates with the disseminated MS phenotype compared to opticospinal MS.18 However, the HLA effect in disease risk may not be as strong as in whites, suggesting a more prominent role for non-HLA genes. To better describe the MS susceptibility genetic profile in African Americans, the objectives of this study were to confirm in this population the previously identified susceptibility genes in whites affected with MS and to test whether these genes are associated with disease phenotypes. We tested, in 918 well-characterized cases and 656 controls, the allele frequencies of 19 SNPs in 12 MS loci.13, 14, 15, 19, 20 CD6, CLEC16a, ecotropic viral integration site 5 (EVI5), GPC5, and TYK2 contained SNPs that are significantly associated with MS in African Americans. In addition, a preliminary analysis suggests that RGS1 may be associated with age of onset, whereas TNFRSF1A may affect disease severity.

Results

The African American data set used in this study comprises 918 cases and 656 controls. Baseline clinical characteristics of this data set are listed in Table 1. The female to male ratio in cases was 3.7:1 and 2.4:1 in controls. The gender ratios are significantly different (χ2 P=0.0002). Gender, therefore, is fit into the model for all subsequent analyses, thus minimizing the effect of this difference. The average age of onset of MS was 32.4 years and the mean age at time of analysis was 44 years for cases and 43 years for controls (t-test, P=0.081). The majority of the cases (85%) had either relapsing remitting or secondary progressive MS. We genotyped all individuals for the presence of DRB1*1501 or *1503, as these alleles are associated with MS in African Americans.16, 17 DRB1*1501 and *1503 are in HardyWeinberg equilibrium (Pearson's χ2-test, P=0.26).

Table 1 Clinical characteristics of study participants

We used logistic regression analysis to test 19 SNPs in 12 genes, identified earlier in MS data sets of northern European ancestry, for association with MS in African Americans (Table 2). The average genotyping failure rate was 0.43% for all SNPs tested. None of the SNPs deviated from Hardy–Weinberg equilibrium (P>10−2) in controls. Seven of the SNPs in five genes were significantly associated with MS in the African American data set: CD6 rs11230563 (P=0.012, OR=1.203, 95% CI=1.042–1.388), CLEC16a rs12708716 (P=0.029, OR=1.173, 95% CI=1.016–1.357), CLEC16a rs6498169 (P=0.028, OR=1.142, 95% CI=0.923–1.416), EVI5 rs10735781 (P=0.006, OR=1.233, 95% CI=1.063–1.431), EVI5 rs6680578 (P=0.025, OR=1.185, 95% CI=1.021–1.375), GPC5 rs553717 (P=0.007, OR=1.281, 95% CI=1.025–1.602), and TYK2 rs34536443 (P=0.045, OR=2.037). Owing to the low minor allele frequency (0.01) of rs34536443, the results of this association may be inaccurate and warrant further testing. All P-values are uncorrected, but we considered this study as a replication of previously validated findings. Sex was not found to significantly interact with any of the SNPs tested. However, DRB1 status was found to possibly interact with EVI5 (rs10735781, P=0.0493) and CD226 antigen (CD226) (rs763361, P=0.0041) (Table 3). The minor alleles are susceptible for both SNPs in the DRB1 non-carriers group. A recent GWAS study performed using cases from Australia and New Zealand also identified the EVI5DRB1 interaction.21

Table 2 Results from association analysis of multiple sclerosis (MS) risk
Table 3 DRB1*15 stratified odds ratios for interacting loci

Although most of the tested SNPs failed to show significant association in this data set, those that were significant had slightly larger odds ratios when compared with earlier studies in whites (Table 2; Supplementary Figure S1). However, none of the tested SNPs showed significant differences in a Cochrane heterogeneity Q test, signifying no difference in the association between African Americans and whites. Global association between MS and each SNP across the two populations is also shown in Table 2.

As the major MS susceptibility gene HLA-DRB1 was shown earlier to affect some important aspects of the phenotype, we next tested the above 19 SNPs for association with age of onset and Multiple Sclerosis Severity Scale (MSSS) using linear regression analysis with relevant covariates placed in the model (Table 4). As expected, DRB1*1501/*1503 were significantly associated with age of onset (P=7.496 × 10−7).18 Individuals with 0 copies of the DRB1 risk allele had a mean age of onset of 33.3 years. One copy of the DRB1 risk allele reduced the mean age of onset by 2.7 years and two copies of the DRB1 risk allele reduced the mean age of onset by 6.02 years. RGS1 may also influence the age of onset (P=0.006). Individuals with two copies of the rs2760524 minor allele had a reduced mean age of onset of 23.0 years from 32.1 years. TNFRSF1A seems to modify MSSS metrics (P=0.014). One copy of the TNFRSF1A minor allele reduced the mean MSSS by 0.86. With the exception of the HLA, none of these modifier associations survive statistical correction for multiple comparisons. Given the low prior odds that these SNPs are associated with MS phenotypes and the failure of these SNPs to survive correction for multiple comparisons, we consider these data to be preliminary and further testing will be necessary to confirm the association of RGS1 and TNFRSF1A to age of onset and MSSS, respectively.

Table 4 Genotype–phenotype analysis in African Americans

Discussion

Earlier studies have determined an association between HLA and MS in African Americans, albeit not as strong as in whites,16, 17 suggesting common immunological mechanisms underlying the diseases across ethnic backgrounds. In this study, we report the first evidence of genetic association between non-HLA genes and MS risk in this population. CD6, CLEC16a, EVI5, GPC5, and TYK2 convincingly replicated in the African American data set. On the other hand, CD226, CD58, IL7R, IL2Ra, IRF8, RGS1, TNFRSF1A failed to show evidence of association. Two primary explanations exist for this difference: first the study may have been underpowered to detect these associations, and second it is possible that the different linkage disequilibrium patterns across populations may render the selected SNPs less effective in tagging putative causative variants in African Americans. It is also conceivable that some of the genes that influence MS in whites do not do so in African Americans.

With a sample size of 1574 African Americans, one would expect only some of the genetically relevant loci to show statistically significant evidence of association, even if an association exists. In addition, for a majority of the markers, the minor allele frequencies were lower in this African American data set compared with earlier studies in whites. Therefore, this study may have indeed been underpowered to detect associations for some of the SNPs. For example, the minor allele frequencies for SNPs such as rs12044852 (CD58, MAF 0.07), rs2104286 (IL2Ra, MAF 0.07), rs17445836 (IRF8, MAF 0.05), rs2760524 (RGS1, MAF 0.06), rs1800693 (TNFRSF1A, MAF 0.08), and rs34536443 (TYK2, MAF 0.01) are lower than in whites (all MAFs are greater than 0.1 in CEU, www.HapMap.org). To illustrate this effect, power was plotted as a function of minor allele frequency (Supplementary Figure 2). For the SNPs, rs12720222 (TYK2), rs17445836 (IRF8), rs1800693 (TNFRSF1a), rs2816316 (RGS1), rs17424933 (CD6), rs34536443 (TYK2), and rs12044852 (CD58) the power to detect a true association in this data set was low.

However, for SNPs rs2760524 (RGS1), rs2104286 (IL2Ra), rs6897932 (IL7R), rs763361 (CD226), and rs9533762 (GPC5) there seems to have been sufficient statistical power but an association was not identified. In addition, the linkage disequilibrium data in HapMap (www.HapMap.org) suggest that SNPs used to detect association may be less powerful in African Americans, and even if the association of a gene with MS is the same in African Americans and whites, significantly larger data sets may be needed. It should be noted, however, that very large data sets, currently only available for high-risk white populations, may be necessary to categorically exclude or replicate MS susceptibility markers.13 On the basis of US Census figures and the relative risk of MS, the current data set represents an estimated 5% sample of African Americans with MS. It is plausible, nevertheless, that differences in genetic susceptibility SNPs between whites and African Americans may represent true differences in disease heritability and that these SNPs do not influence MS risk in African Americans.

Of the genes associated with MS risk in African Americans, EVI5 displayed the most significant association outside the MHC. The association of EVI5 with MS was first detected in a GWAS performed using 12 360 non-Hispanic whites from the US and the UK (SNPs rs10735781; OR=1.14, P=3.35 × 10−4, and rs6680578; OR=1.11, P=5.00 × 10−4).13 The association was confirmed through the analysis of 240 case–control individuals from a genetically isolated Dutch population (rs10735781; OR=2.01, P=0.01, and rs6680578 OR=1.9, P=0.01) and in a larger data set comprising 2825 individuals from multi-case Canadian families (rs10735781; OR=1.15, P=0.03; and rs6680578; OR=1.15, P=0.04).20 EVI5 is an oncogene implicated in T-cell lymphomas, is a common site of retroviral integration,22 and facilitates cell septation during mitosis.23 EVI5 has been shown to physically bind the small GTPase-binding protein RAB11 in cell culture.24, 25 RAB11 is required for the endocytic recycling of cell surface molecules26 including transferrin,27 IgA,28 and CXCR2 chemokine receptors,29 and has also been implicated in the regulation of cytokinesis,30, 31 neurite extension,32 and the formation of the immune synapse.33 EVI5 competes with RAB effector proteins to bind with RAB11, which suggests a role for EVI5 regulation of downstream RAB11 pathways.25 In CD4+ T cells, uncoordinated 119 activates RAB11 to transport lymphocyte cell-specific protein-tyrosine kinase (LCK) to the plasma membrane.33 On binding of the antigen–MHC complex from the antigen presenting cell, LCK initiates signaling from the T-cell receptor leading to T-cell activation.34, 35 Allelic differences in EVI5 may contribute to altered function of RAB11 and altered formation of the immunological synapse, thus contributing to MS susceptibility. This hypothesis may explain the underlying genetic interaction between EVI5 and DRB1. Further studies will be needed to determine the functional effect of EVI5 allelic variants on RAB11 and the immune synapse.

CD6, CLEC16a, and GPC5 are also associated with MS risk in African Americans. CD6 is a T-cell surface protein involved in the activation of T cells.36 Allelic variants at rs11230563 may result in altered activation of T cells, thereby affecting an individual's susceptibility to MS. CLEC16a has been associated with disease susceptibility in white MS patients13, 15, 21, 37, 38 as well as other autoimmune diseases.38, 39, 40 Although the function of CLEC16a is unknown, CLEC16a is expressed on B cells, dendritic cells, and natural killer cells.41 GPC5, a member of the glypican family, is a cell surface molecule and is a type of heparan sulfate proteoglycan.42 These molecules are implicated in axon guidance and growth, and synapse formation.43, 44 Interestingly, heparan sulfate proteoglycans have been identified in active MS brain lesions in which they may contribute to the sequestering of proinflammatory cytokines.45 GPC5 is expressed in neurons46 and is known to interact with chemokines, extracellular matrix proteins, and growth factors.47 Allelic variants of GPC5 may affect neuronal repair and contribute to differences in MS susceptibility.

The RGS1 SNP, rs2760524, did not show evidence of association with MS risk in African Americans, but our preliminary analysis suggests an effect on age of onset that warrants follow-up in large northern-European data sets. RGS1 is involved in the trafficking of B cells into and out of lymph nodes.48 B-cell-mediated antigen presentations and antibody responses seem to be necessary for the full development of demyelination, both in humans and in experimentally induced disease. B cells from RGS1 knockout mice have increased homing to lymph nodes, increased adhesion to lymph nodes, and faster movement within lymph nodes compared with wild-type B cells.48 In humans with MS, polymorphisms in RGS1 may conceivably lead to changes in B cell mobility, leading to altered recruitment of B cells to the central nervous system, thereby affecting the initiation of MS. Recent studies associated polymorphisms in RGS1 to Celiac disease and type I diabetes, further supporting a key role for RGS1 in the initiation and development of autoimmunity.49, 50, 51

The association of TNFRSF1A-rs1800693 with MS was recently identified through a GWAS meta-analysis.15 The present data suggest that TNFRSF1A, one of the major receptors for TNF-α, which is involved in apoptosis, inflammation, and under certain conditions immunosuppression,52, 53 may also affect disease progression. Polymorphisms in TNFRSF1A may cause altered signaling of TNF-α in the central nervous system, leading to increased activation of T cells, demyelination, and inflammation, thereby affecting the severity of MS.54, 55, 56, 57

In conclusion, in a large African American data set we have tested a selected number of polymorphisms in candidate genes recently shown to be associated with MS in white populations. SNPs in CD6, CLEC16a, EVI5, GPC5, and TYK2 replicated the association with MS risk in African Americans. This is the first observation of an effect by non-HLA genes in a non-white MS group. The data are consistent with a commonality in disease mechanisms among individuals of European and African ancestry, and suggest the prominent role of environmental factors in explaining the paucity of MS in Africa.

Materials and methods

Study participants

The data set studied consisted of 1574 African American individuals, including 918 MS cases, and 656 unrelated control individuals. All study participants are self-reported African Americans, but European ancestry was documented in 985 of the individuals based on the genotyping of 186 SNPs highly informative for African versus European ancestry as described earlier (mean European ancestry=21%).58 All MS subjects met established diagnostic criteria.59 MS phenotypes were characterized by systematic chart review as described.7 Ascertainment protocols and clinical and demographic characteristics were summarized elsewhere.7, 17 Eight individuals with Aquaporin-4 seropositivity were excluded from this study as they have met new diagnostic criteria for neuromyelitis optica.60 Informed consent was obtained from all study participants before participation in the study.

Genotyping

Genotyping of SNPs was performed using validated TaqMan SNP genotyping assays (Applied Biosystems Inc., Foster City, CA, USA) for all SNPs except rs9523762, which was performed using a custom TaqMan SNP genotyping assay (Applied Biosystems Inc.). Each PCR reaction contained 10 ng DNA, 1 × TaqMan Genotyping Master Mix (Applied Biosystems Inc.), and 1 × SNP assay (Applied Biosystems Inc.). Amplification was performed in an ABI 9700 GeneAmp PCR system (Applied Biosystems Inc.). The PCR program consisted of 95 °C for 10 min, followed by 50 cycles of 95 °C for 15 s and 62 °C for 1 min. The plates were then read on an ABI prism 7900HT Sequence Detection System using SDS 2.0 software (Applied Biosystems Inc.). For DRB1, a PCR locus-specific amplification was used as described earlier.16 The average genotyping failure rate was 0.43% for all SNPs tested.

Statistical analysis

Summary statistics in Table 1 were performed using R v2.8.1 (www.r-project.org). All SNP genotypes were tested for deviation from Hardy–Weinberg equilibrium in cases and controls using SNP Stats (http://bioinfo.iconcologia.net/index.php?module=Snpstats).61 To determine SNP associations with MS risk, logistic regression was performed using SAS v.9.1.3 (SAS Institute, Inc., Cary, NC, USA) with sex and DRB1 status as covariates in the regression model. Positive DRB1 status was defined as at least one copy of either the DRB1*1501 or *1503 allele. Two statistical models were tested for each SNP and DRB1*1501/*1503. The genotypic model tested the dominant/recessive/additive allele model and the trend model tested additive allelic effects for each SNP. The model that best fit the data is reported. As this is a replication study, uncorrected P-values are reported. SNP interactions with gender and DRB1 status were tested using SNP Stats (http://bioinfo.iconcologia.net/index.php?module=Snpstats).61 The Cochrane Heterogeneity Q test was performed using the rmeta package in R. The Q test measures the existence of differences between the individual study effects and the pooled effect across studies. The global odds ratio was calculated under a fixed effect model using the inverse variance weighting method.

Genotype–phenotype correlations were tested for age of onset and MSSS. Linear regression was performed using SAS v.9.1.3 (SAS Institute, Inc.). For age of onset, the data followed a normal distribution and were not transformed. For all SNPs tested, gender and DRB1 status were covariates in the linear regression model. For DRB1, only gender was fit into the model. For MSSS, the data were transformed using the ‘normal score transformation’ (yi−1(ri−3/8)/(n+1/4)) to yield a normally distributed-dependent variable for regression analysis.62 Gender, age of onset, and treatment status (Y/N/unknown) were fit into the linear regression model for all SNPs and DRB1. Uncorrected P-values are reported.

References

  1. 1

    Hauser SLaG DS . Multiple sclerosis and other demyelinating diseases. In: Kasper DL, Braunwald E, Fauci AD, Hauser SL, Longo DL, Jameson JL (eds). Harrsion's Principle of Internal Medicine, 16th edn. McGraw-Hill: New York, 2005. pp 2461–2471.

  2. 2

    Orton SM, Herrera BM, Yee IM, Valdar W, Ramagopalan SV, Sadovnick AD et al. Sex ratio of multiple sclerosis in Canada: a longitudinal study. Lancet Neurol 2006; 5: 932–936.

  3. 3

    Rosati G . The prevalence of multiple sclerosis in the world: an update. Neurol Sci 2001; 22: 117–139.

  4. 4

    Wallin MT, Page WF, Kurtzke JF . Multiple sclerosis in US veterans of the Vietnam era and later military service: race, sex, and geography. Ann Neurol 2004; 55: 65–71.

  5. 5

    Buchanan RJ, Martin RA, Wang S, Kim M . Racial analyses of longer-stay nursing home residents with multiple sclerosis. Ethn Dis 2006; 16: 159–165.

  6. 6

    Buchanan RJ, Martin RA, Zuniga M, Wang S, Kim M . Nursing home residents with multiple sclerosis: comparisons of African American residents to white residents at admission. Mult Scler 2004; 10: 660–667.

  7. 7

    Cree BA, Khan O, Bourdette D, Goodin DS, Cohen JA, Marrie RA et al. Clinical characteristics of African Americans vs Caucasian Americans with multiple sclerosis. Neurology 2004; 63: 2039–2045.

  8. 8

    Marrie RA, Cutter G, Tyry T, Vollmer T, Campagnolo D . Does multiple sclerosis-associated disability differ between races? Neurology 2006; 66: 1235–1240.

  9. 9

    Naismith RT, Trinkaus K, Cross AH . Phenotype and prognosis in African-Americans with multiple sclerosis: a retrospective chart review. Mult Scler 2006; 12: 775–781.

  10. 10

    Weinstock-Guttman B, Jacobs LD, Brownscheidle CM, Baier M, Rea DF, Apatoff BR et al. Multiple sclerosis characteristics in African American patients in the New York State Multiple Sclerosis Consortium. Mult Scler 2003; 9: 293–298.

  11. 11

    Oksenberg JR, Baranzini SE, Sawcer S, Hauser SL . The genetics of multiple sclerosis: SNPs to pathways to pathogenesis. Nat Rev 2008; 9: 516–526.

  12. 12

    Wellcome Trust Case Control Consortium, Burton PR, Clayton DG, Cardon LR, Craddock N, Deloukas P, Duncanson A et al. Association scan of 14 500 nonsynonymous SNPs in four diseases identifies autoimmunity variants. Nat Genet 2007; 39: 1329–1337.

  13. 13

    International Multiple Sclerosis Genetics Consortium, Hafler DA, Compston A, Sawcer S, Lander ES, Daly MJ, De Jager PL et al. Risk alleles for multiple sclerosis identified by a genomewide study. N Engl J Med 2007; 357: 851–862.

  14. 14

    Baranzini SE, Wang J, Gibson RA, Galwey N, Naegelin Y, Barkhof F et al. Genome-wide association analysis of susceptibility and clinical phenotype in multiple sclerosis. Hum Mol Genet 2009; 18: 767–778.

  15. 15

    De Jager PL, Jia X, Wang J, de Bakker PI, Ottoboni L, Aggarwal NT 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.

  16. 16

    Caillier SJ, Briggs F, Cree BA, Baranzini SE, Fernandez-Vina M, Ramsay PP et al. Uncoupling the roles of HLA-DRB1 and HLA-DRB5 genes in multiple sclerosis. J Immunol 2008; 181: 5473–5480.

  17. 17

    Oksenberg JR, Barcellos LF, Cree BA, Baranzini SE, Bugawan TL, Khan O et al. Mapping multiple sclerosis susceptibility to the HLA-DR locus in African Americans. Am J Hum Genet 2004; 74: 160–167.

  18. 18

    Cree BA, Reich DE, Khan O, De Jager PL, Nakashima I, Takahashi T et al. Modification of multiple sclerosis phenotypes by African ancestry at HLA. Arch Neurol 2009; 66: 226–233.

  19. 19

    Ban M, Goris A, Lorentzen AR, Baker A, Mihalova T, Ingram G et al. Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor. Eur J Hum Genet 2009; 17: 1309–1313.

  20. 20

    Hoppenbrouwers IA, Aulchenko YS, Ebers GC, Ramagopalan SV, Oostra BA, van Duijn CM et al. EVI5 is a risk gene for multiple sclerosis. Genes Immun 2008; 9: 334–337.

  21. 21

    Australia and New Zealand Multiple Sclerosis Genetics Consortium, Bahlo M, Booth DR, Broadley SA, Brown MA, Foote SJ, Griffiths LR et al. Genome-wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat Genet 2009; 41: 824–828.

  22. 22

    Liao X, Buchberg AM, Jenkins NA, Copeland NG . Evi-5, a common site of retroviral integration in AKXD T-cell lymphomas, maps near Gfi-1 on mouse chromosome 5. J Virol 1995; 69: 7132–7137.

  23. 23

    Faitar SL, Sossey-Alaoui K, Ranalli TA, Cowell JK . EVI5 protein associates with the INCENP-aurora B kinase-survivin chromosomal passenger complex and is involved in the completion of cytokinesis. Exp Cell Res 2006; 312: 2325–2335.

  24. 24

    Dabbeekeh JT, Faitar SL, Dufresne CP, Cowell JK . The EVI5 TBC domain provides the GTPase-activating protein motif for RAB11. Oncogene 2007; 26: 2804–2808.

  25. 25

    Westlake CJ, Junutula JR, Simon GC, Pilli M, Prekeris R, Scheller RH et al. Identification of Rab11 as a small GTPase binding protein for the Evi5 oncogene. Proc Natl Acad Sci USA 2007; 104: 1236–1241.

  26. 26

    Ullrich O, Reinsch S, Urbe S, Zerial M, Parton RG . Rab11 regulates recycling through the pericentriolar recycling endosome. J Cell Biol 1996; 135: 913–924.

  27. 27

    Green EG, Ramm E, Riley NM, Spiro DJ, Goldenring JR, Wessling-Resnick M . Rab11 is associated with transferrin-containing recycling compartments in K562 cells. Biochem Biophys Res Commun 1997; 239: 612–616.

  28. 28

    Wang X, Kumar R, Navarre J, Casanova JE, Goldenring JR . Regulation of vesicle trafficking in madin-darby canine kidney cells by Rab11a and Rab25. J Biol Chem 2000; 275: 29138–29146.

  29. 29

    Fan GH, Lapierre LA, Goldenring JR, Sai J, Richmond A . Rab11-family interacting protein 2 and myosin Vb are required for CXCR2 recycling and receptor-mediated chemotaxis. Mol Biol Cell 2004; 15: 2456–2469.

  30. 30

    Fielding AB, Schonteich E, Matheson J, Wilson G, Yu X, Hickson GR et al. Rab11-FIP3 and FIP4 interact with Arf6 and the exocyst to control membrane traffic in cytokinesis. EMBO J 2005; 24: 3389–3399.

  31. 31

    Wilson GM, Fielding AB, Simon GC, Yu X, Andrews PD, Hames RS et al. The FIP3-Rab11 protein complex regulates recycling endosome targeting to the cleavage furrow during late cytokinesis. Mol Biol Cell 2005; 16: 849–860.

  32. 32

    Saita S, Shirane M, Natume T, Iemura S, Nakayama KI . Promotion of neurite extension by protrudin requires its interaction with vesicle-associated membrane protein-associated protein. J Biol Chem 2009; 284: 13766–13777.

  33. 33

    Gorska MM, Liang Q, Karim Z, Alam R . Uncoordinated 119 protein controls trafficking of Lck via the Rab11 endosome and is critical for immunological synapse formation. J Immunol 2009; 183: 1675–1684.

  34. 34

    Molina TJ, Kishihara K, Siderovski DP, van Ewijk W, Narendran A, Timms E et al. Profound block in thymocyte development in mice lacking p56lck. Nature 1992; 357: 161–164.

  35. 35

    Straus DB, Weiss A . Genetic evidence for the involvement of the lck tyrosine kinase in signal transduction through the T cell antigen receptor. Cell 1992; 70: 585–593.

  36. 36

    Hassan NJ, Simmonds SJ, Clarkson NG, Hanrahan S, Puklavec MJ, Bomb M et al. CD6 regulates T-cell responses through activation-dependent recruitment of the positive regulator SLP-76. Mol Cell Biol 2006; 26: 6727–6738.

  37. 37

    International Multiple Sclerosis Genetics Consortium (IMSGC). The expanding genetic overlap between multiple sclerosis and type I diabetes. Genes Immun 2009; 10: 11–14.

  38. 38

    Zoledziewska M, Costa G, Pitzalis M, Cocco E, Melis C, Moi L et al. Variation within the CLEC16A gene shows consistent disease association with both multiple sclerosis and type 1 diabetes in Sardinia. Genes Immun 2009; 10: 15–17.

  39. 39

    Awata T, Kawasaki E, Tanaka S, Ikegami H, Maruyama T, Shimada A et al. Association of type 1 diabetes with two Loci on 12q13 and 16p13 and the influence coexisting thyroid autoimmunity in Japanese. J Clin Endocrinol Metab 2009; 94: 231–235.

  40. 40

    Marquez A, Varade J, Robledo G, Martinez A, Mendoza JL, Taxonera C et al. Specific association of a CLEC16A/KIAA0350 polymorphism with NOD2/CARD15(-) Crohn's disease patients. Eur J Hum Genet 2009; 17: 1304–1308.

  41. 41

    Robinson MJ, Sancho D, Slack EC, LeibundGut-Landmann S, Reis e Sousa C . Myeloid C-type lectins in innate immunity. Nat Immunol 2006; 7: 1258–1265.

  42. 42

    Veugelers M, Vermeesch J, Reekmans G, Steinfeld R, Marynen P, David G . Characterization of glypican-5 and chromosomal localization of human GPC5, a new member of the glypican gene family. Genomics 1997; 40: 24–30.

  43. 43

    Lee JS, Chien CB . When sugars guide axons: insights from heparan sulphate proteoglycan mutants. Nat Rev 2004; 5: 923–935.

  44. 44

    Van Vactor D, Wall DP, Johnson KG . Heparan sulfate proteoglycans and the emergence of neuronal connectivity. Curr Opin Neurobiol 2006; 16: 40–51.

  45. 45

    van Horssen J, Bo L, Dijkstra CD, de Vries HE . Extensive extracellular matrix depositions in active multiple sclerosis lesions. Neurobiol Dis 2006; 24: 484–491.

  46. 46

    Chernousov MA, Rothblum K, Stahl RC, Evans A, Prentiss L, Carey DJ . Glypican-1 and alpha4(V) collagen are required for Schwann cell myelination. J Neurosci 2006; 26: 508–517.

  47. 47

    Worapamorn W, Haase HR, Li H, Bartold PM . Growth factors and cytokines modulate gene expression of cell-surface proteoglycans in human periodontal ligament cells. J Cell Physiol 2001; 186: 448–456.

  48. 48

    Han SB, Moratz C, Huang NN, Kelsall B, Cho H, Shi CS et al. Rgs1 and Gnai2 regulate the entrance of B lymphocytes into lymph nodes and B cell motility within lymph node follicles. Immunity 2005; 22: 343–354.

  49. 49

    Hunt KA, Zhernakova A, Turner G, Heap GA, Franke L, Bruinenberg M et al. Newly identified genetic risk variants for celiac disease related to the immune response. Nat Genet 2008; 40: 395–402.

  50. 50

    Romanos J, Barisani D, Trynka G, Zhernakova A, Bardella MT, Wijmenga C . Six new coeliac disease loci replicated in an Italian population confirm association with coeliac disease. J Med Genet 2009; 46: 60–63.

  51. 51

    Smyth DJ, Plagnol V, Walker NM, Cooper JD, Downes K, Yang JH et al. Shared and distinct genetic variants in type 1 diabetes and celiac disease. N Engl J Med 2008; 359: 2767–2777.

  52. 52

    Probert L, Eugster HP, Akassoglou K, Bauer J, Frei K, Lassmann H et al. TNFR1 signalling is critical for the development of demyelination and the limitation of T-cell responses during immune-mediated CNS disease. Brain 2000; 123 (Pt 10): 2005–2019.

  53. 53

    Kassiotis G, Kollias G . Uncoupling the proinflammatory from the immunosuppressive properties of tumor necrosis factor (TNF) at the p55 TNF receptor level: implications for pathogenesis and therapy of autoimmune demyelination. J Exp Med 2001; 193: 427–434.

  54. 54

    TNF neutralization in MS: results of a randomized, placebo-controlled multicenter study. The Lenercept multiple sclerosis study group and the university of British Columbia MS/MRI analysis group. Neurology 1999; 53: 457–465.

  55. 55

    Fromont A, De Seze J, Fleury MC, Maillefert JF, Moreau T . Inflammatory demyelinating events following treatment with anti-tumor necrosis factor. Cytokine 2009; 45: 55–57.

  56. 56

    Mohan N, Edwards ET, Cupps TR, Oliverio PJ, Sandberg G, Crayton H et al. Demyelination occurring during anti-tumor necrosis factor alpha therapy for inflammatory arthritis. Arthritis Rheum 2001; 44: 2862–2869.

  57. 57

    Sicotte NL, Voskuhl RR . Onset of multiple sclerosis associated with anti-TNF therapy. Neurology 2001; 57: 1885–1888.

  58. 58

    Patterson N, Hattangadi N, Lane B, Lohmueller KE, Hafler DA, Oksenberg JR et al. Methods for high-density admixture mapping of disease genes. Am J Hum Genet 2004; 74: 979–1000.

  59. 59

    McDonald WI, Compston A, Edan G, Goodkin D, Hartung HP, Lublin FD et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann Neurol 2001; 50: 121–127.

  60. 60

    Wingerchuk DM, Lennon VA, Pittock SJ, Lucchinetti CF, Weinshenker BG . Revised diagnostic criteria for neuromyelitis optica. Neurology 2006; 66: 1485–1489.

  61. 61

    Sole X, Guino E, Valls J, Iniesta R, Moreno V . SNPStats: a web tool for the analysis of association studies. Bioinformatics 2006; 22: 1928–1929.

  62. 62

    Blom G . Statistical Estimates and Transformed Beta-Variables. Wiley: New York, 1958.

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Acknowledgements

We thank the MS patients and controls that participated in this study. We thank L Piccio for her effort in the recruitment of study participants and sample management. We thank RR Lincoln, R Guerrero, H Mousavi, and A Santaniello for sample and database management. We thank R Gomez for recruitment of participants. We also thank J McElroy for insightful discussions. This work was funded by grants from the National Institute of Health (R01 NS046297) and the National Multiple Sclerosis Society (RG3060C8). PLD is a Harry Weaver Neuroscience Scholar Award of the National MS Society.

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Correspondence to J R Oksenberg.

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The authors declare no conflict of interest.

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Supplementary Information accompanies the paper on Genes and Immunity website

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Johnson, B., Wang, J., Taylor, E. et al. Multiple sclerosis susceptibility alleles in African Americans. Genes Immun 11, 343–350 (2010). https://doi.org/10.1038/gene.2009.81

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Keywords

  • multiple sclerosis
  • genetics
  • African American
  • susceptibility
  • EVI5

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