Original Article

Genes and Immunity (2012) 13, 21–28; doi:10.1038/gene.2011.44; published online 30 June 2011

A cytokine gene screen uncovers SOCS1 as genetic risk factor for multiple sclerosis

K Vandenbroeck1,2, J Alvarez3, B Swaminathan1, I Alloza1, F Matesanz4, E Urcelay5, M Comabella6, A Alcina4, M Fedetz4, M A Ortiz5, G Izquierdo7, O Fernandez8, N Rodriguez-Ezpeleta3, C Matute9, S Caillier10, R Arroyo5, X Montalban6, J R Oksenberg10, A Antigüedad11 and A Aransay3

  1. 1Neurogenomiks Laboratory, Department of Neuroscience, University of the Basque Country UPV/EHU, Leioa, Spain
  2. 2IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
  3. 3CIC bioGUNE, Parque Tecnológico de Bizkaia, Derio, Spain
  4. 4Instituto de Parasitología y Biomedicina ‘López Neyra’, Consejo Superior de Investigaciones Científicas, Granada, Spain
  5. 5Immunology and Neurology Department, Hospital Clínico S Carlos, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), Madrid, Spain
  6. 6Centre d’Esclerosi Múltiple de Catalunya, CEM-Cat, Unitat de Neuroimmunologia Clínica, Hospital Universitari Vall d'Hebron, Barcelona, Spain
  7. 7Unidad de Esclerosis Múltiple, Hospital Virgen Macarena, Sevilla, Spain
  8. 8Servicio de Neurología, Instituto de Neurociencias Clínicas del Hospital Regional Universitario Carlos Haya de Málaga, Málaga, Spain
  9. 9Neurotek Laboratory, Department of Neuroscience, University of the Basque Country UPV/EHU, Leioa, Spain
  10. 10Department of Neurology, University of California, San Francisco, CA, USA
  11. 11Servicio de Neurología, Hospital de Basurto, Bilbao, Spain

Correspondence: Dr K Vandenbroeck, Neurogenomiks Laboratory, Department of Neuroscience, Universidad del País Vasco (UPV/EHU), Edificio 205, Planta–1, Parque Tecnológico de Bizkaia, 48170 Zamudio (Bizkaia), Spain. E-mail: k.vandenbroeck@ikerbasque.org

Received 17 March 2011; Revised 5 May 2011; Accepted 11 May 2011; Published online 30 June 2011.

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Abstract

Cytokine and cytokine receptor genes, including IL2RA, IL7R and IL12A, are known risk factors for multiple sclerosis (MS). Excitotoxic oligodendroglial death mediated by glutamate receptors contributes to demyelinating reactions. In the present study, we screened 368 single-nucleotide polymorphisms (SNPs) in 55 genes or gene clusters coding for cytokines, cytokine receptors, suppressors of cytokine signaling (SOCS), complement factors and glutamate receptors for association with MS in a Spanish–Basque resident population. Top-scoring SNPs were found within or nearby the genes coding for SOCS-1 (P=0.0005), interleukin-28 receptor, alpha chain (P=0.0008), oncostatin M receptor (P=0.002) and interleukin-22 receptor, alpha 2 (IL22RA2; P=0.003). The SOCS1 rs243324 variant was validated as risk factor for MS in a separate cohort of 3919 MS patients and 4003 controls (combined Cochran–Mantel–Haenszel P=0.00006; odds ratio (OR)=1.13; 95% confidence interval (CI)=1.07–1.20). In addition, the T allele of rs243324 was consistently increased in relapsing-remitting/secondary progressive versus primary-progressive MS patients, in each of the six data sets used in this study (PCMH=0.0096; OR=1.24; 95% CI 1.05–1.46). The association with SOCS1 appears independent from the chr16MS risk locus CLEC16A.

Keywords:

multiple sclerosis; SOCS1; cytokine; genetics; single-nucleotide polymorphism

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Introduction

Multiple sclerosis (MS) is a chronic (CH) inflammatory demyelinating disorder of the central nervous system of unknown etiology, and represents the most common cause of non-traumatic neurological disability in young adults. The observed rates of familial aggregation of MS reflected in the increased risk of siblings, second- and third-degree relatives to develop the disease, as well as twin studies, collectively reject a Mendelian trait as the driving force for susceptibility, but are reconcilable with a polygenic, multifactorial mechanism.1 Although the human leukocyte antigen (HLA) gene cluster on chromosome 6p21.3 has been known as susceptibility locus since the early 1970s, it was not until 2007, when the first non-HLA genetic risk factors were unequivocally identified via a genome-wide association study (GWAS).2 Subsequently, seven more GWAS have led to the identification of around 15 validated non-HLA risk loci for MS, including among others IL2RA, IL7R, CD58, EVI5 and CD40.1 In addition, a meta-analysis of GWAS3 identified the additional loci CD6, TNFRSF1A and IRF8, and three further loci with suggestive evidence arising from this study were subsequently validated as genuine MS risk factors, that is, IL12A, MPHOSPH9 and RSG1.4 All non-HLA MS-susceptibility alleles known so far are relatively common in the population and contribute only modestly to overall risk (odds ratios (OR) of 1.1–1.3).

In the present study, we report the results of a haptag screen primarily focusing on cytokine, cytokine receptor genes and associated signal transduction factors that also covered a small selection of ionotropic glutamate receptors and transporters. The latter category of genes was included based on the observation that glutamate-mediated glial injury contributes to white matter pathology as seen in MS.5 The study was performed in two phases, with a primary screen of 368 single-nucleotide polymorphism (SNPs) in a Spanish Basque resident case–control collection of 462 MS patients and 470 controls. Four top-scoring SNPs were subsequently analyzed in a validation cohort composed of five independent sample collections from European and Northern-American origin, which together included 3919 MS patients and 4003 controls. One SNP, rs243324, located in the 5′-regulatory region of suppressor of cytokine signaling-1 (SOCS1) was identified and validated as novel risk factor of MS.

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Results

Details of sample collections used in the present study are provided in Table 1 . The primary screen of 384 SNPs was performed in the Bilbao collection. A total of 368 SNPs (mean call frequency, 97.25%) was successfully genotyped for 462 cases and 470 controls (genotyping success rate of 97.23%). Risk alleles and their frequencies, P-values for disease association and ORs of the 20 most strongly associated SNPs (Pless than or equal to0.021), arising from the primary screen, are represented in Table 2. The most strongly associated SNPs were found in or nearby the genes coding for SOCS1, interleukin-28 receptor, alpha chain (IL28RA), oncostatin M receptor (OSMR), interleukin-7 receptor (IL-7R) and interleukin-22 receptor, alpha 2 (IL22RA2) with ORs of around 1.37 and uncorrected P-values of 0.003–0.0005. Experimentwise, none of these associations withstood Bonferroni correction. Combined single-marker and haplotype analyses highlighted SOCS1 and IL28RA as most strongly associated gene loci (corrected P-values <0.05; Table 3 ). Of seven IL7R tagSNPs typed, rs6897932 emerged as the single most strongly associated one, in confirmation with earlier reports.2, 6, 7 Detection of this established risk SNP may attest to the suitability of this case–control collection to uncover new risk loci with similar or higher OR.




The four new top-scoring SNPs that have not been reported before (IL7R rs6897932 was excluded) were analyzed in the validation cohort comprising five independent collections totaling 3919 MS patients and 4003 controls (Table 1). Each population-specific data set was considered as a distinct stratum for testing marker association by means of the Cochran–Mantel–Haenszel test. Table 4 summarizes the results of the validation and combined analyses. SOCS1 rs243324 emerged as the only SNP significantly associated in the validation cohort (validation PCMH=0.0027; combined PCMH=0.00006). Similar to the Bilbao data, the rs243324 T allele emerged as the significantly associated risk allele in the Madrid (P=0.0004; OR=1.30 (95% confidence interval (CI) 1.12–1.51)) and Andalucía (P=0.045; OR=1.13 (95% CI 1.00–1.27)) data sets, whereas nonsignificant similar trends were found in the remaining three collections (data not shown; Breslow–Day P-value of 0.14 for the full-validation cohort). IL28RA rs1416834 displayed a G allele association pattern opposite to that seen in the Bilbao data set, in both the Barcelona (P=0.05; OR=0.86 (95% CI 0.75–1.00)) and Madrid (P=0.004; OR=0.79 (95% CI 0.68–0.93)) collections. Owing to this heterogeneity effect, the Breslow–Day test for rs141684 in combined primary and validation data sets was highly significant (P=0.0002; Table 4). OSMR rs3805558 did not show significant association in any of the individual or combined validation collections. The A allele of IL22RA2 rs202573 was significantly associated in the Bilbao (Table 2) and Andalucía (P=0.04, OR=1.14 (95% CI 1.00–1.29)) data sets, but was not significant in any of the remaining or the combined validation data sets.


As these SNPs were selected as haplotype-tagging markers, they are unlikely to represent the ultimate causative variants. As linkage disequilibrium (LD) architecture varies between populations from European or African ancestry (http://www.hapmap.org), we also analyzed the association patterns of the top-scoring SNPs upon exclusion of the African-American data set. This increased the strength of the association for SOCS1 rs243324 in both the validation (PCMH=0.0005; OR=1.16 (95% CI 1.07–1.26)) and combined (including Bilbao data set) cohorts (PCMH=0.000004; OR=1.19 (95% CI 1.11–1.29)), whereas it did not reinforce association of the other SNPs.

SOCS1 is located on chromosome 16 at a distance of around 70kb from the confirmed MS risk gene CLEC16A.2, 8 We investigated LD between a selection of SNPs in this gene that recently emerged as good markers for association with MS;3, 8 that is, rs11865121, rs12708716, its proxy rs2903692 and/or rs6498169, and the most strongly associated SNP in SOCS1, rs243324. This exercise was performed in the Bilbao, Madrid and Andalucía collections (Table 5 ). None of the CLEC16A SNPs were significantly associated with MS in the Bilbao cohort, whereas rs2903692 was associated in both the Madrid and Andalucía collections. Analysis of the LD patterns of each of these CLEC16A SNPs with SOCS1 rs243324, confirmed that SOCS1 is an independent MS risk locus (D′=0.25–0.31; r2=0.029–0.063) in each of the three data sets. This is likely to be related to the presence of an area showing a recombination rate of up to 8cM/Mb separating both the CLEC16A and SOCS1 LD blocks.


rs243324 is located in the SOCS1 5′-gene-flanking region at 5kb upstream from the transcription initiation site. In a recent study, SOCS1 appeared to be differentially expressed in immune cell infiltrates in lesions of relapsing-remitting (RR) compared with chronic (CH) forms of experimental autoimmune encephalomyelitis (EAE).9 We assessed, therefore, whether allelic patterns for SOCS1 rs243324 differ between the RR/secondary progressive (SP) and primary progressive (PP) forms of MS. Table 6 shows that the frequency of the T allele is on average 5.2 (±1.9 s.d.)% higher in RR/SP than in PP MS. This effect was homogenous over the six data sets included in the present study (Breslow–Day P-value of 0.96). Even if it did not reach significance in any individual data set because of relative low numbers of PP patients in each stratum, the effect in the combined data set was significant (Cochran–Mantel–Haenszel P=0.0096; OR=1.24 (95% CI 1.05–1.46)). Thus, SOCS1 may merit further scrutiny as genetic marker for disease course of MS. Analysis of SOCS1 rs243324 in the individual and combined gender-stratified study cohorts revealed absence of any significant gender effects (data not shown).


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Discussion

Over the last 4 years, GWAS studies have rapidly and successfully outperformed candidate gene studies as primary gene-hunting tool for MS risk. The unequivocal identification via GWAS of susceptibility loci such as IL7R, IL2RA and IL12A has authenticated cytokine and cytokine receptor genes as a genuine class of MS risk loci.2, 4, 6, 7 Hence, the present study was designed with as primary goal the scrutiny through a haptag approach of those cytokine and cytokine receptor genes that have not systematically been investigated in pre-GWAS MS candidate gene studies. Thus were excluded from the present study cytokine gene loci such as IFNG, IL4, IL1A/IL1B all of which have been analyzed in extenso before.10 In addition, our screen included a selection of genes coding for ionotropic glutamate receptors and transporters, which are known to regulate brain glutamate levels implied in the pathological mechanism driving neuroaxonal cell death.5, 11 Glutamate levels in the brains of MS patients are at least partially controlled by common genetic polymorphisms, and SNPs in genes belonging to the glutamatergic system have been associated with responsiveness to interferon-β therapy in MS.12, 13, 14

One SNP, rs243324, located in the 5′-regulatory region of SOCS1, emerged from the combined primary screen and validation study in >4300 MS patients and >4400 controls as a new marker for susceptibility to MS (P=6.01E-05). SOCS1 functions canonically to impede Type I and II interferon signaling by binding predominantly to phosphorylated, IFNAR1- or IFNGR-associated JAK proteins through its SH2 domain, thus abrogating STAT activation and dampening ensuing expression of, among others, the immunoregulatory major histocompatibility complex class I and II, and CD40 genes.15 A role for SOCS1 in demyelinating conditions has been inferred mostly from EAE studies. Upregulation of SOCS1 mRNA in EAE is primarily restricted to infiltrating mononuclear cells,16 whereas spinal chord SOCS1 mRNA levels are higher at the peak stage of CH EAE (C57BL/6J; myelin oligodendrocyte glycoprotein-induced compared with RR-EAE (SJL/J; bovine myelin-induced).17 Transgenic mice overexpressing SOCS1 in oligodendrocytes develop EAE with accelerated onset accompanied by enhanced early inflammation; an effect related to decreased responsiveness of oligodendrocytes to early protective effects of interferon-γ.18 Administration of the SOCS1 mimetic, tyrosine kinase-inhibitor peptide, suppressed development of acute EAE in New-Zealand White mice, protected SJL/J mice with RR-EAE against relapse,19 and reduces disease severity in C57BL/6 mice with CH-EAE.9 T-cell-specific SOCS1-deficient mice are essentially resistant to EAE because of preferential differentiation of CD4(+) T cells into Th1 rather than Th17, the Th type known to be essential for EAE.20 Berard et al.9 analyzed expression of SOCS1 in the CH and RR forms of EAE, induced in the same mouse strain (C57BL/6) using the same myelin antigen (myelin oligodendrocyte glycoprotein). In both models, SOCS1 was predominantly expressed in macrophages. The proportion of Mac-1+ macrophages expressing SOCS1 in lesions at the peak stage of RR disease was significantly higher than that observed in CH-EAE, and coincided with reduced expression of the macrophage effector molecule inducible nitric oxide synthase.9 Collectively, this data indicates that increased macrophage SOCS1 expression at the RR peak stage may lead to disengagement of pro-inflammatory effector pathways, thus promoting remission in the RR–EAE model. With this data in mind, we compared SOCS1 rs243324 allele counts in PP versus RR/SP MS patients. Though the present study was not a priori designed to assess the impact of genetic variation upon clinical course of MS, the total number of PP (n=347) versus RR/SP (n=3545) patients available via the six sample collections (Table 1) was sufficiently high to allow 73–81% power for detection of genetic effects conferred by rs243324 with OR=1.25, under additive or multiplicative models, respectively. This analysis showed that the rs243324T allele is consistently increased in RR/SP versus PP patients (Table 6). Thus, even if independent confirmation is required to firmly validate these findings, and further functional studies are indispensable to assess the impact of rs243324 on SOCS1 expression, the SOCS1 locus appears to emerge from this study as a potential marker for clinical course of MS. Functional polymorphisms altering transcriptional activity of the SOCS1 gene have been reported,21, 22 and one of these, rs33977706, located at position −820, occurs in moderate LD with rs243324 (r2=0.19, D′=0.80; http://www.broadinstitute.org/mpg/snap/ in 1000 Genomes Pilot 1 SNP data set).

A recent study analyzed a number of genes with functional relationships to the validated MS locus IL7RA, and identified a series of SNPs upstream from the SOCS1 locus associated with susceptibility to MS.23 The most strongly associated SNP arising from the combined data sets used in that study, rs441349, is located in the intergenic area between protamine-2 (PRM2) and PRM1, at a distance of 16.8kb centromeric from rs243324 and at 21.8kb from the SOCS1 transcription initiation site.23 We typed rs441349 in both the Madrid and Andalucía collections (data not shown), and found that, similar to Zuvich et al.,23 the C allele constituted the risk allele associated with MS (PCMH=0.01). The effect of rs441349 was less strong than that of rs243324 (PCMH=0.0002) when both SNPs were considered jointly in these Spanish data sets. The LD between both SNPs is moderate (D′=0.765; r2=0.11). Haplotype analysis revealed that the rs243324–rs441349 haplotype TC describes the association with MS marginally better than rs243324 alone, but much better than rs441349 alone (10000 permuted P=0.0001 (TC haplotype), 0.0004 (rs243324) and 0.026 (rs441349)). An extensive fine mapping of the SOCS1 gene area including a cluster of four small genes composed of PRM1-3 and transition protein-2, is currently being performed. Protamines and transition protein-2 facilitate condensation of genetic material within the developing spermatid.24 Though no functional associations with autoimmunity/inflammation/MS seem to have been reported (PubMed search), the coordinate expression of this multigene locus24 may warrant joint investigation in functional studies centering upon SOCS1.

IL22RA2 encodes a soluble, secreted receptor for IL-22. A SNP located at a distance of 8.9kb at 3′ from the IL22RA2 gene, rs276474, was recently found to be associated with MS in a combined Swedish and Norwegian cohort.25 In the present study, the IL22RA2 SNP rs202573 located in the fifth intron was associated in the individual Bilbao and Andalucía collections, but was not significant in the total cohort. Both SNPs are located 17.6kb apart and occur in linkage equilibrium (r2=0.013, D′=0.20; SNAP 1000 Genomes Pilot 1 SNP data set). rs1416834, located in the fourth intron of IL28RA, showed an allelic pattern of association in the Bilbao collection opposite to that seen in the Madrid and Barcelona collections. Interestingly, a recent GWAS identified rs4649203 in the promoter region of IL28RA as novel susceptibility locus for psoriasis.26 rs1416834 and rs4649203 are separated by a 33-kb interval that contains a 15-cM/Mb recombination hotspot encompassing the area around the second exon, and, hence, are not in LD (r2=0.033, D′=0.28; SNAP 1000 Genomes Pilot 1 SNP data set). Although it is possible that the association of IL28RA rs1416834 with MS represents a spurious finding, it cannot be excluded that its allelic ‘flip-flop’ association pattern with MS risk is due to sample or population variation in inter-locus correlation with the real causative variant.27, 28 Given the fact that MS and psoriasis share the TYK2-susceptibility locus,23, 26, 29 fine mapping of the IL28RA region is indicated to verify whether this locus represents a second shared genetic determinant.

In conclusion, this study has identified and validated SOCS1 as risk factor for MS. Together with other validated risk factors such as CD6, IRF5 and IRF8,3, 30, 31 it is part of an increasing series of MS-susceptibility genes of which the products have established roles in defense against infectious pathogens and in the innate/adaptive immune response. The SOCS1 rs243324 association follows the principle of what is emerging as the gamut of hitherto identified risk SNPs in multifactorial conditions, that is, low penetrance, high frequency of the risk allelic variant in unaffected subjects and low OR of 1.1–1.3. To this, the potential existence of numerous epistatic, epigenetic and random effects, as well as etiological heterogeneity should be added.1 The individual value of the SOCS1 SNP in predictive modeling is, thus, likely to be limited, but may contribute to strengthen aggregate models of multiple genetic variants that estimate well-defined clinical parameters of MS. Finally, the identification of SOCS1 as MS risk factor may actuate targeted drug development programs. The availability of the SOCS1 mimetic peptide tyrosine kinase-inhibitor peptide with demonstrated beneficial effects in EAE is illustrative for the effectiveness of this approach.9, 19

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Patients and methods

Patients and controls

Demographic and clinical details of sample collections used in the present study are provided in Table 1. The original screen was performed in the Bilbao collection including 462 MS patients residing in the Spanish Basque country (Hospital de Basurto) and 470 healthy controls provided by the Basque Biobank of the Fundación Vasca de Innovación e Investigación Sanitaria. The validation cohort consisted of 3919 MS patients and 4003 healthy control subjects contributed by the following centers; Barcelona (Hospital Vall d’Hebron), Madrid (Hospital Clínico S Carlos), Andalucía (Hospital Virgen Macarena, Sevilla; Hospital Carlos Haya, Málaga; Hospital Clínico, Hospital Virgen de las Nieves and Blood Bank, Granada) and University of California San Francisco (African-American and white collections). All patients were ascertained to have definite MS according to the Poser or McDonald criteria.32, 33 Patients and controls were included on the basis of informed consent, and the study was approved by local Ethics Committees.

Selection of SNPs

Haplotype-tagging SNPs were determined using the Multimarker Tagger algorithm implemented in HapMap on the CEU cohort (r2 cutoff 0.8; minimum allele frequency 0.2; HapMap Release #19). Wherever possible, non-synonymous SNPs were force-included in the haptag selection (34 non-synonymous SNPs). The following genes or gene clusters were included in the analysis: IL22RA1–IL28RA (chr 1; 9 SNPs); GRIK3 (chr 1; 13 NPs); IL23R–IL12RB2 (chr 1; 22 SNPs); IL19–IL20–IL24 (chr 1; 5 SNPs); IL1RL2–IL1RL1–IL18R1–IL18RAP (chr 2; 16 SNPs); IL1F7–IL1F9–IL1F6–IL1F8–IL1F5–lL1F10–IL1RN (Chr 2; 30 SNPs); IL17RE–IL17RC (Chr 3; 4 SNPs); IL17RB (Chr 3; 4 SNPs); IL12A (Chr 3; 3 SNPs); IL21 (Chr 4; 3 SNPs); IL15 (Chr 4; 4 SNPs); GRIA2 (Chr 4; 3 SNPs); IL7R (Chr 5; 7 SNPs); SLC1A3 (Chr 5; 2 SNPs); LIFR (Chr 5; 4 SNPs); OSMR (Chr 5; 14 SNPs); C9 (Chr 5; 5 SNPs); C6 (Chr 5; 7 SNPs); IL31RA–IL6ST (Chr 5; 13 SNPs); IL13 (Chr 5; 2 SNPs); IL9 (Chr 5; 2 SNPs); IL17B (Chr 5; 2 SNPs); GRIA1 (Chr 5; 22 SNPs); IL17A–IL17F (Chr 6; 11 SNPs); IL20RA (Chr 6; 10 SNPs); IL22RA2 (Chr 6; 4 SNPs); IL7 (Chr 8; 2 SNPs); C5 (Chr 9; 6 SNPs); IL15RA (Chr 10; 6 SNPs); SLC1A2 (Chr 11; 14 SNPs); IL18BP (Chr 11; 2 SNPs); GRIA4 (Chr 11, 11 SNPs); IL18 (Chr 11, 3 SNPs); IL23A (Chr 12; 2 SNPs); IL22 (Chr 12; 2 SNPs); SOCS2 (Chr 12; 4 SNPs); IL31 (Chr 12; 3 SNPs); IL17D (Chr 13; 5 SNPs); IL25 (Chr 14; 3 SNPs); IL16 (Chr 15; 8 SNPs); IL32 (Chr 16; 4 SNPs); SOCS1 (Chr 16; 3 SNPs); IL21R (Chr 16; 8 SNPs); IL17C (CHr 16; 2 SNPs); SOCS3 (Chr 17; 2 SNPs); EBI3 (Chr 19; 2 SNPs); TMED1 (Chr 19; 1 SNP); IL12RB1 (Chr 19; 3 SNPs); IL28B–IL28A–IL29 (Chr 19; 12 SNPs); IL11 (Chr 19; 4 SNPs); GRIK1 (Chr 21; 13 SNPs), IL17RA (Chr 22; 6 SNPs); MIF (Chr 22; 8 SNPs); LIF (Chr 22; 3 SNPs); OSM (Chr 22; 4 SNPs).

Illumina screen

A customized Bead Array Matrix SNP panel was manufactured at Illumina Customer Service (San Diego, CA, USA). SNPs were characterized for each sample in multiplexes of 384 following the Golden Gate protocol of Illumina. Two hundred fifty nanograms (5μl at 50ngμl−1) of total genomic DNA were used per genotyping reaction. Bead Arrays Matrixes were resolved at the Illumina's BeadStation-500GX system. Intensity files obtained from the BeadStation-500GX were decoded into genotyping data with the BeadStudio v 3.0 software (Illumina).

Validation of top-scoring SNPs and genotyping of CLEC16A SNPs

The top-scoring SNPs were replicated in the validation collections using the following Taqman SNP Genotyping Assays (Applied Biosystems, Carlsbad, CA, USA): C_1004275_10 (SOCS1, rs243324) C_68971_10 (IL28RA, rs1416834), C_27503774_10 (OSMR, rs3805558) and C_629495_10 (IL22RA2, rs202573). The CLEC16A SNPs rs2903692 and rs6498169 were genotyped in the Madrid and Andalucía collections using Taqman SNP Genotyping Assays C_15941578_10 and C_29080959_10, respectively. The Taqman assays were performed according to the manufacturer's instructions. The CLEC16A SNPs rs11865121, rs12708716 and rs6498169 were typed in the Bilbao collection using the iPLEX Sequenom MassARRAY platform of the Spanish National Genotyping Center (CEGEN, Santiago de Compostela, http://www.cegen.org). All genotyping success rates were >95%. The concordance between genotyping procedures (Golden Gate versus TaqMan) was tested on a subset of samples and amounted to 98.9%.

Data management and statistical analysis

Raw data were analyzed for comparison of allele and genotype counts using PLINK version 1.05.34 The Cochran–Mantel–Haenszel test implemented in PLINK was used to calculate an average OR arising from the combination of distinct case–control data sets. Heterogeneity of ORs was assessed through the Breslow–Day test, also implemented in PLINK. A χ2-test was used to ascertain that genotype distributions fulfilled the criteria of Hardy–Weinberg equilibrium. Haplotype analysis was performed with Haploview 4.2 (ref. 35). Unless otherwise indicated, P-values are uncorrected. Power was calculated using the CATS power calculator at http://www.sph.umich.edu/csg/abecasis/CaTS/.36 Linkage disequilibrium patterns between SNPs were analyzed with the SNP Annotation and Proxy Search tool at http://www.broadinstitute.org/mpg/snap/.37

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Conflict of interest

The authors declare no conflict of interest.

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Acknowledgements

We express our gratitude to all patients and control subjects participating in this study. We thank the Basque Biobank of the Basque Foundation for Health Innovation and Research for supplying DNA samples. This study was supported by grants to K Vandenbroeck from the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreement no 212877 (UEPHA*MS; www.reem.es/uepha-ms/), and from the Gobierno Vasco (Ref. IT512-10; Convocatoria ‘Grupos de Investigación 2010–2015’); by grants to A Aransay from the Gobierno Vasco (Ref. MV-2005-1-13; ‘Convocatoria de Programas de Perfeccionamiento y Movilidad del Personal Investigador 2005’), the Department of Industry, Tourism and Trade of the Government of the Autonomous Community of the Basque Country (Etortek Research Programs 2005–2006) and from the Innovation Technology Department of the Bizkaia County; by project grant FIS PI10/1985 to E Urcelay; by grants to A Alcina from the Fondos Europeos de Desarrollo Regional (FEDER), Ministerio de Ciencia e Innovación (SAF2009-11491) and Junta de Andalucía (P07-CVI-02551); and by grant FIS PI/081636 to F Matesanz.