The genetics of multiple sclerosis: An update 2010

Molecular and Cellular Probes 24 (2010) 237e243

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The genetics of multiple sclerosis: An update 2010 Sabine Hoffjan*, Denis A. Akkad Department of Human Genetics, Ruhr-University, Universitätsstrasse 150, 44801 Bochum, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 February 2010 Accepted 28 April 2010 Available online 5 May 2010

Multiple sclerosis (MS) is a chronic neuro-inflammatory autoimmune disease believed to arise from complex interactions of both environmental and genetic factors. The successful accomplishment of genome-wide association studies (GWAS), analyzing >100.000 single nucleotide polymorphism markers simultaneously based on chip technology, has recently brought interesting new insights into the genetic background of this complex disease. To date, six GWAS have been performed for MS; even though study design and results vary substantially between experiments, some new susceptibility genes have been identified and replicated using this approach. For example, nucleotide variation in the interleukin 7 receptor (IL7RA), the interleukin 2 receptor (IL2RA), the CD58 and the c-type lectin domain family 16 member A (CLEC16A) genes has been consistently associated with MS in several populations. There appears to be substantial overlap between susceptibility variants for different autoimmune diseases, suggesting that at least part of the genetic background may be shared among autoimmune disorders. Regarding phamacogenomics, results from GWAS for treatment response to interferon beta (IFNb) in MS suggest that genes that code for neurotransmitter-gated channels might play a role in the drug response. In particular, GPC5 has already been confirmed to be an IFNb response gene in an independent study. Future prospects include, among others, more sophisticated analyses of GWAS data, advances in the ‘one SNP at a time’ approach towards pathway and network-based analyses, next-generation sequencing techniques as well as studies of gene/gene and gene/environment interactions. Ó 2010 Elsevier Ltd. All rights reserved.

Keywords: Multiple sclerosis Multifactorial Polymorphism Genome-wide association Pharmacogenomics Pharmacogenetics

1. Introduction Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system (CNS) characterized by inflammation, demyelination and primary or secondary axonal degeneration [1]. Prevalence rates for MS vary between 2 and 160 per 100,000 in different countries [2], and more than 2 million individuals are affected by this disease worldwide. Clinical features include various neurological dysfunctions, such as visual and sensory problems, limb weakness or gait disturbance [3]. The clinical course can be subdivided into different subtypes: relapsing-remitting MS (rrMS) is characterized by relapses with full recovery; secondary progressive

Abbreviations: AID, autoimmune diseases; CD, Crohn’s disease; EAE, experimental autoimmune encephalomyelitis; EDSS, extended disability status score; GWAS, genome-wide association study; HLA, human leukocyte antigen; IFN, interferon; IL, interleukin; JIA, juvenile idiopathic arthritis; LD, linkage disequilibrium; MS, multiple sclerosis; ppMS, primary progressive MS; RA, rheumatoid arthritis; rrMS, relapsing-remitting MS; SLE, systemic lupus erythematosus; SNP, single nucleotide polymorphism; spMS, secondary progressive MS; T1D, type 1 diabetes. * Corresponding author. Tel.: þ49 234 32 23823; fax: þ49 234 32 14196. E-mail address: [email protected] (S. Hoffjan). 0890-8508/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.mcp.2010.04.006

MS (spMS) means that an initial relapsing-remitting course is followed by chronic progression; and primary progressive MS (ppMS) is characterized by chronic disease progression from the beginning of the disease without clinical remission [4]. Like most other autoimmune-mediated diseases, MS belongs to the large group of multifactorial disorders which are believed to arise from complex interactions of both environmental and genetic factors (Fig. 1). Typical features for complex genetic diseases include modest heritability without a classic Mendelian mode of transmission and heterogeneity, which means that variation in a large number of genes contributes to the overall susceptibility. Gene/gene interactions (so-called epistatic effects) are also believed to play an important role for the pathogenesis of complex diseases [1]. Twin studies have revealed higher concordance rates (w25e30%) in monozygotic twins as compared with dizygotic twins (w5%) [5], suggesting a strong genetic component. On the other hand, concordance rates of only w30% in monozygotic twins that share identical genomes indicate that additional factors also play an important role in the susceptibility to disease. Several environmental factors have been proposed to influence the risk of MS, including, for example, virus infections, geographical distribution, diet and exposure to sunlight, although the link of any of these factors to the pathogenesis of disease is still under dispute [1].

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g en e t i c variation

environmental factors

gene expression

infections, diet, sun exposure and others

locus 1, 2, 3…

intrinsic factors age, sex, general condition and others

e pist atic effects gene/gene interactions

# individuals

phenotype

symptoms

Fig. 1. Schematic diagram of the complex pathogenesis of multifactorial diseases such as multiple sclerosis. Disease susceptibility as well as phenotypic representation is presumably influenced by variation in numerous genes in complex interactions with environmental factors. Additionally, epistatic effects (e.g. geneegene interactions) and intrinsic factors of the respective individual are also believed to play an important role for pathogenesis.

2. Genetic studies in MS Identifying susceptibility genes for complex diseases, such as MS, has been a major challenge in the past two decades. Even though we are still far from knowing the full set of genes influencing the pathogenesis of MS, remarkable success has been made recently, particularly through the successful development of genome-wide association studies (GWAS), analyzing >100.000 single nucleotide polymorphism (SNP) markers simultaneously based on chip technology. This approach combines the advantages of the two experimental approaches that had previously dominated, namely genomewide linkage screens and candidate gene association studies of MS. 2.1. Linkage studies Genome-wide linkage screens usually investigated several hundred microsatellite markers spaced evenly throughout the genome in large families, in order to identify markers or alleles that are co-inherited (i.e. linked) with MS. Positional cloning techniques were then used to identify the gene influencing disease susceptibility in the linked region. The main advantage of linkage screens was that they were performed on a genome-wide basis, which means that susceptibility regions could be detected without prior knowledge about gene function. On the other hand, these approaches were timeconsuming as well as costly, and they often lacked sufficient “power” to detect loci with only a moderate influence on susceptibility [6]. Linkage scans for MS have been performed in many populations from different ethnicities [7]. The only region that was consistently linked to MS with statistical levels reaching genome-wide significance was the human leukocyte antigen (HLA) region on chromosome 6p21.3. Estimations revealed that the HLA region accounts for w20e60% of genetic susceptibility in MS [8]. In almost all populations studied, an association was seen for the DRB1*1501 allele, residing on a large, extended haplotype. Yet, the complex architecture of the HLA region, with a high degree of linkage disequilibrium (LD) and large

haplotype blocks (haploblocks), makes it difficult to pin-point the causative gene(s) or variation(s) in this region. Nevertheless, before GWAS, the HLA region comprised the only indisputable susceptibility region linked to MS. 2.2. Candidate gene association studies Such association studies refer to the investigation of known genes whose biological functions suggest that they could play a role in the pathophysiology of MS. In this approach, allele and genotype frequencies of markers, mostly SNPs, are compared between unrelated MS patients (cases) and control subjects, in order to detect significant differences between the two groups to infer an association with the disease. Candidate gene studies with SNPs were believed to have a greater ability to detect common alleles with a modest effect on disease susceptibility than linkage studies [6], but they relied on the choice of suitable candidate genes, and, thus, were always dependent on the underlying hypotheses. A large number of candidate gene studies for MS have been published to date, but again, apart from HLA, the results were hardly consistent among studies. Sample sizes were often small, increasing the risk for false positive results, and additional problems, such as population stratification and adjustment for multiple testing, had to be addressed for this approach [9]. Thus, many reported associations failed to be replicated in other studies. 2.3. GWAS As a new and important step in the genetic research concerning complex diseases, association studies on a genome-wide level have become feasible in the last three years, primarily due to the development of new laboratory and analytical methods. Chip-based technology now allows the efficient genotyping of several hundred thousand SNPs throughout the genome simultaneously. Using this approach, many new susceptibility loci have been discovered not

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only for MS, but also for a wide variety of other complex diseases (a database on GWAS provided by the NIH can be accessed through http://www.genome.gov/26525384). Nevertheless, there is still an on-going debate about the tools to be used to analyse and interpret the large amount of data generated by GWAS. For example, given the many tests that are performed simultaneously, the issue of correcting for multiple testing has to be carefully addressed. The level for genome-wide significance has been set at p < 5  108, representing Bonferroni correction for 1 million tests [10]. Yet, there are far more association results with p-values in the ‘grey zone’ between p < 0.05 and this genome-wide threshold, leaving open the question as to how to deal with these suggestive results. Furthermore, until now, the SNPs have only been analyzed without including gene/gene or gene/environment interactions [11]. Also, GWAS generally identify only common variations (with minor allele frequencies exceeding 5%). Yet, complex diseases are most likely to be caused by a complex interaction of both common variations with low penetrance and rare variants with high penetrance [12]. Thus, even though GWAS have already brought substantial new insights into the genetic background of complex diseases, such as MS, more sophisticated analytical tools still need to be developed. Six GWAS of MS have been published to date, followed by one meta-analysis (see Table 1). In accordance with preceding results, markers in the HLA region yielded highly significant association results in virtually all GWAS. Since HLA was already known as MS susceptibility region before, the following results will concentrate on the non-HLA genes that were discovered as MS susceptibility genes via GWAS recently.

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3. MS susceptibility genes identified or confirmed via GWAS 3.1. Interleukin 7 receptor alpha chain (IL7RA) The first GWAS reported a highly significant association of MS with a non-synonymous coding SNP (T244I) in the IL7RA gene located on chromosome 5p13 (p ¼ 2.94  107) [13]. Strikingly, the same SNP was almost simultaneously identified in a different study which used genomic convergence to select candidate genes that were differentially expressed between MS patients and controls [14]. In this study, a significant association of the T244I SNP with MS was reported in 760 US families of European descent and was subsequently replicated in three independent European populations (overall p-value: 2.9  107) [14]. The association of this variation with MS has since then been confirmed in several additional Caucasian populations [15e19]. Clearly, to date, IL7RA represents the most consistently replicated susceptibility gene for MS apart from the HLA region. Yet, a recent study of African Americans did not provide evidence for an association in this ethnic group [20]. The MS-associated SNP (T244I) is located in an important transmembrane domain of the IL7RA chain and presumably affects differential splicing of the gene [14]. Transcripts that include exon 6 encode the membrane-bound form of the receptor, and transcripts without exon 6 encode the soluble form (sIL7R). It was postulated that the T244I SNP might lead to increased skipping of exon 6 and, thus, to an increased production of the soluble form of the receptor in patients carrying the risk allele [14]. Considering the suggestion

Table 1 Genome-wide association studies for MS. ID

Reference

Study population

Gene chip

Original sample

Replication sample

Phenotypes

Most significant non-HLA associations in the original genome scan/after replication

1

Hafler et al., 2007 [13]

UK/US (International MS Genetic Consortium)

500 K

931 trios

1540 trios, 2322 cases and 5418 controls

MS

2

Comabella et al., 2008 [65]

Spanish

500 K

275 cases and 275 controls (US)

MS

3

Aulchenko et al., 2008 [66]

Dutch

500 K

Baranzini et al., 2009

European (Gene MSA consortium)

500 K

2634 cases and 2930 controls e

MS

4

242 cases and 242 controls (pooled analysis) 45 cases and 195 controls 978 cases and 883 controls

IL2RA rs12722489: p ¼ 2.96  108 rs2104286: p ¼ 2.16  107 IL7RA rs6897932: p ¼ 2.94  107 13q31.3 (unnotated region) rs1327328 : p ¼ 7  104

5

The Australia and New Zealand MS Genetics consortium, 2009 [32]

Australian/New Zealand (ANZgene)

370 K

1618 cases and 3413 controls (UK/US)

2256 cases and 2310 controls

6

Jakkula et al., 2010 [67]

Finnish

300 K

68 cases and 136 controls from a high-risk isolate

3859 cases and 9110 controls

MS

GWAS 1, 4 and 860 additional US cases

500 K

2624 cases and 7220 controls

2215 cases and 2116 controls

MS

TNFRSF1A rs1800693: p ¼ 1.59  1011 IRF8 rs17445836: p ¼ 3.73  109 CD6 rs17824933: p ¼ 3.79  109

Pharmacogenetic studies: 8 Buyn et al., 2008 [53]

Spanish/French

100 K (pooled DNA samples)

Spanish

500 K (pooled DNA samples)

Joint analysis: original sample plus 81 additional rrMS patients 94 rrMS patients (49 responders and 45 non-responders)

Response to IFNb

9

206 rrMS patients (99 responders and 107 non-responders) 106 rrMS patients (53 responders and 53 non-responders)

HALPN1 rs4466137: p ¼ 0.004 GPC5 rs10492503: p ¼ 0.007 GRIA3 rs12557782: p ¼ 0.002 CIT rs7308076: p ¼ 0.003

Meta-analysis: 7 De Jager et al., 2009 [68]

Comabella et al., 2009 [55]

MS Age of onset MS severity MS

Response to IFNb

KIF1B rs10492972: p ¼ 4  104 GPC5 rs9523762: p ¼ 1  105 12q13e14 rs703842: p ¼ 5.4  1011 20q13 rs6074022: p ¼ 1.3  107 STAT3 rs744166: p ¼ 2.75  1010

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that this SNP in IL7RA is the causative variation, a recent follow-up study investigated variations in other genes functionally related to IL7RA [21]. The authors reported significant associations with MS for variations in the IL7 and SOCS1 (suppressor of cytokine signalling 1) genes, additionally emphasizing the role of the IL7/IL7RA pathway for MS susceptibility [21]. Since IL7 and its receptor play an important role in T-cell genesis, peripheral survival, expansion and memory T-cell development, therapeutic targeting of the IL7/ IL7R-axis has already been suggested for various diseases [22]. Yet, the specific consequences of the functional variation in the IL7RA gene or other genes related to this pathway linked to MS susceptibility need to be elucidated further before these results can become clinically relevant. 3.2. Interleukin 2 receptor alpha chain (IL2RA) The strongest association results from the first GWAS (even stronger than for IL7RA) were observed for two SNPs within intron 2 of the IL2RA gene located on chromosome 10p15 (p ¼ 2.96  108 and p ¼ 2.16  107, respectively). Yet, replication of these results was more complicated than for IL7RA. Several groups reported an association of SNPs within IL2RA with MS [16,19,23e25], but the associated variations varied across populations, and it has not yet been possible to pin-point the exact causative variation(s) in this gene or to detect functional relevance. A fine-mapping study in a large Australian population suggested allelic heterogeneity at the IL2RA locus and the existence of at least two independent susceptibility alleles [24]. Variation in IL2RA has also been associated with other autoimmune diseases, including type 1 diabetes (T1D) [26], rheumatoid arthritis (RA) [27], juvenile idiopathic arthritis (JIA) [28] and Graves’ disease [29] (Table 2). Interestingly, two independent effects at the IL2RA locus were also described and replicated for T1D [26]. Taken together, further research is needed to elucidate the role of variation in IL2RA in relation to MS and other autoimmune diseases, particularly since monoclonal antibodies against IL2RA are already in clinical use to be evaluated as a potential MS therapy in small clinical trials [30].

Table 2 MS-associated loci showing overlapping association with other autoimmune diseases. Gene

Gene name

IL2RA

Interleukin 2 receptor alpha

Location

Autoimmune disease

10p15-p14 T1D RA JIA GD IL7RA Interleukin 7 5p13 T1D receptor alpha chronic inflammatory arthropathies CD58 CD58 molecule 1p13 RA CLEC16A C-type lectin 16p13.13 T1D domain family CD 16, member A Addison’s disease JIA, RA AITD CD226 CD226 antigen 18q22.3 T1D Wegener’s granulomatosis TNFRSF1A Tumor necrosis 12p13.2 IBD factor receptor Persistent palindromic superfamily, rheumatism member 1A

Reference [26] [27] [28] [29] [69] [70] [71] [34e36] [38] [41] [39] [40] [35,72] [43] [73] [74]

A more comprehensive review discussing the shared genetic factors of immunerelated diseases is given in [45]. T1D: type 1 diabetes, RA: rheumatoid arthritis, JIA: juvenile idiopathic arthritis, GD: Graves’ disease, CD: Crohn’s disease, AITD: autoimmune thyroid disease, IBD: inflammatory bowel disease.

3.3. CD58 A SNP in the CD58 gene, encoding the co-stimulatory molecule LFA-3, was among the variations that rendered suggestive evidence for an association with MS in the first GWAS, with a p-value of 1.9  105 [13]. Given the important role of CD58 in co-stimulating and enhancing T-cell receptor signalling, the gene was further evaluated using re-sequencing and fine-mapping techniques [31]. The SNP most strongly associated with MS was rs2300747 in intron 1 of the CD58 gene (p ¼ 1.1  106), and the respective G allele was found to have a protective effect on MS development. Functional analyses revealed that the G allele was associated with increased mRNA expression in cell lines as well as in mononuclear cells from MS patients. Furthermore, CD58 mRNA levels are elevated in MS subjects during clinical remission, again supporting the hypothesis of a protective effect [31]. Association of a variation in the CD58 gene with MS was also seen in the Australian GWAS [32], and in a recent meta-analysis CD58 was affirmed as a significant risk gene for MS [33]. Thus, after IL7RA and IL2RA, CD58 belongs to the best replicated susceptibility genes for MS in Caucasians. Interestingly, an association was not detectable in African Americans [20]. 3.4. CLEC16A An association with SNPs in intron 19 of the CLEC16A gene (also called KIAA0350), located on chromosome 16p13, was originally reported for T1D from two independent GWAS [34,35]. In the first GWAS for MS, a SNP in intron 22 of this gene showed suggestive evidence for an association with disease (p ¼ 3.83  106) [13]. Subsequent additional evaluations revealed an association of a SNP in intron 19 with MS in the Sardinian population [36] and of another perfectly linked SNP in intron 19 in a large Caucasian replication cohort [37]. A recent meta-analysis confirmed, genomewide, a significant association of the intron 22 SNP in the CLEC16A gene with MS [33]; even in the African American population some evidence for association was identified (p ¼ 0.028) [20]. Interestingly, apart from T1D, variation in CLEC16A has also been associated with several other autoimmune diseases, including Crohn’s disease (CD) [38], JIA [39], autoimmune thyroid disease [40] and Addison’s disease [41] (Table 2). 3.5. CD226 As for CLEC16A, an association with a coding SNP in the CD226 gene (Gly307Ser) was first reported for T1D [35]. Subsequently, this SNP was found to be significantly associated with MS in three cohorts [37,42,43]. CD226 (also known as DNAX accessory molecule 1, DNAM1) belongs to the immunoglobulin supergene family of receptors and is involved in adhesion and co-stimulation of T cells [44]. Interestingly, anti-CD226 antibodies delayed the onset and reduced the severity of experimental autoimmune encephalomyelitis (EAE) in the mouse model of MS [44], again pointing to a potential role of this molecule in the pathogenesis of MS. Nonetheless, the exact functional consequence of the Gly307Ser exchange within the CD226 protein still needs to be elucidated. 4. Overlapping association for autoimmune diseases The examples of CLEC16A, IL2RA and CD226 highlight that there seems to be substantial overlap between risk loci for different autoimmune diseases (AIDs). Indeed, there has been growing evidence over the past years that at least part of the genetic background may be shared among them [45]. AIDs arise from a failure in self-tolerance of the immune system, which becomes unable to distinguish between harmless self-antigens and potentially harmful

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non-self antigens. As a result, normal tissue is destroyed by the own immune system. In MS, self-destruction is mainly concentrated on the myelin sheaths in the CNS. Other AIDs include T1D, RA, systemic lupus erythematosus (SLE) and CD, among others. It has been known for many years from epidemiological studies that patients with one AID have an increased susceptibility to also develop other AIDs [46,47]. Furthermore, most AIDs show substantial linkage to the HLA region, although it has not yet been possible to pinpoint the exact causal genes in this highly linked region for any specific disease [48], even after >35 years of investigations of this phenomenon. Results from GWAS have further added to the concept of a shared genetic background for AIDs [49]. In addition to IL2RA, CLEC16A and CD226, several other genes have been identified that show evidence for an overlapping association with different AIDs [49] (the most significantly MS-associated genes are detailed in Table 2). A recent review evaluated data from 22 primary GWAS, 6 non-synonymous SNP scans and 28 replication and meta-analysis papers for 11 immune-related disorders, and found that the genes shared among these AIDs were involved in three key pathways: T-cell differentiation, immune-cell signalling and innate immune response [45]. In another recent analysis of GWAS data for 7 common AIDs (including MS), ‘disease gene networks’ were visualized that highlighted the complex architecture of autoimmunity with both shared and specific susceptibility genes for each AID [50]. Certainly, the complex genetic background of autoimmunity is only just beginning to be discovered. Yet, additional GWAS will need to be performed to support these preliminary comparative findings. Furthermore, analyses of structural variations, such as insertions/deletions and copy number differences, are also now being evaluated on a genome-wide level, such that the complex architecture should become much clearer in the near future. Taken together, more profound knowledge regarding the genetic basis of autoimmune disorders, in the future, might facilitate the cross-utilization of certain drugs among AIDs and might be the basis for the development of new therapeutic approaches. 5. Pharmacogenomic studies for MS Recent evidence indicates that the clinical response to MS medication varies substantially among individual patients and that the principle of ‘one drug fits all‘ does not apply [51]. Pharmacogenomic approaches aim at identifying genetic variations that affect the response to certain drugs and thus may help the physician to decide which patients may benefit from a certain therapy and which patients are prone to develop adverse side effects. Thus far, most pharmacogenomic studies of MS have focussed on the response to recombinant interferon beta (IFNb), a standard therapy for rrMS. IFNb shows anti-viral, anti-proliferative and proapoptotic activity and, in some patients, effectively reduces clinical symptoms and slows disease progression [51]. However, up to 50% of patients do not response adequately to IFNb, and some even develop serious side effects [51]. Thus, effectively selecting potential responders and non-responders before initiating this costly therapy would be preferred by patients and clinicians. Until recently, most pharmacogenomic studies of MS had followed a candidate gene design (see above), comparing variation, for example, in HLA or IFN receptor genes between responders and non-responders to IFNb therapy. In few studies, treatment response to glatirameracetat was evaluated. Even though some significant associations were reported, replication of these results in additional cohorts was rarely achieved (reviewed in [52]). One main reason for this general lack of replication is that no consensus criteria for defining responders and non-responders have been defined yet, and there is often a considerable ‘grey zone’ between these two clinical entities [51]. Furthermore, it may be difficult to distinguish between actual responders to medication and patients

241

with a naturally benign course of the disease who would not have experienced relapses even without therapy. Including a placebogroup in a prospective study would help overcome the latter problem, but, for ethical reasons, this is rarely practiced. Recently, the first two GWAS studies for treatment response to IFNb in MS have been published. Even though the general problems of accurately defining clinical response still remain the same, some interesting new findings have emerged from these studies. In 2007, Byun et al. [53] analyzed a cohort of 206 Spanish and French patients with rrMS treated with IFNb over a period of 2 years, including 99 responders and 107 non-responders, with 100 K GeneChips in a pooled DNA approach. Responders had no relapses and no increase in EDSS over the follow-up period, whereas non-responders had at least two relapses or an increase in EDSS of at least one point. Following validation by individual genotyping, 35 SNPs showed significantly different genotype frequencies between responders and non-responders, with the two genes most strongly implicated in the response to IFNb being the hyaloronan proteoglycan link protein (HAPLN1) and the glypican 5 (GPC5) genes [53] (Table 1). Remarkably, a replication study in a separate Spanish cohort of 199 rrMS patients already confirmed GPC5 as an IFNb response gene while replication failed for HAPLN1 [54]. Gene ontology analysis also revealed that SNPs showing differences between responders and non-responders were likely to be related to ion channels and signal transduction pathways, for example, glutamate receptor genes [53]. A second GWAS analyzed 53 responders and 53 non-responders with 500 K microchips, again in a pooling-based approach [55]. After applying several selection criteria, 383 SNPs were individually genotyped in an independent cohort of 49 responders and 45 non-responders and 7 SNPs located in genes remained significant in this validation cohort. The most significant result was found for a SNP in intron 2 of the GRIA3 gene which encodes an AMPA-type glutamate receptor and is located on the X chromosome; a positive association with treatment response was only seen in women but not in men [55]. Thus, there is first evidence from GWAS for a potential correlation between genes that code for neurotransmitter-gated channels and a response to IFNb treatment. However, further replication studies in independent and large cohorts are needed to verify these preliminary results and e even though there is legitimate hope for a more personalized therapy some time in the future e a lot of work still needs to be done before these findings can become clinically relevant. 6. Future prospects 6.1. GWAS Taken together, GWAS have successfully identified numerous loci influencing MS susceptibility or treatment response, but we are still far from understanding the full genetic architecture of this complex disease. Starting from the encouraging results of GWAS, several further steps forward are required e and feasible e in the near future [11]. First of all, both computational methods and finemapping or re-sequencing efforts are needed in order to refine the association signals gained by GWAS. These follow-up studies, which first concentrated only on the few highly significant results, will have to be extended to the more moderate association signals from GWAS, in order to cover the majority of true associations and identify significant associations that may have been overlooked in the initial reports [56]. Another important step forward from initial GWAS includes the evaluation of both gene/gene (G  G) and gene/environment (G  E) interactions. These investigations will certainly be challenging, since they require more efficient computational approaches as well as precise phenotyping and thorough measurements of environmental influences in large data sets. However, given the complex genetic

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structure of MS, the full picture will probably not emerge if G  G and G  E interaction effects are not incorporated in the (genetic) analyses [11]. A first attempt to analyse GWAS data in a more comprehensive way, apart from the ‘one SNP at a time’ approach, was recently presented as a so-called pathway and network-based analysis [57]. Using this approach, the authors re-evaluated data from two GWAS of MS, taking into account all SNPs with nominal evidence of association (p < 0.05) on the basis of a human protein interaction network, and searched for sub-networks comprising genes associated with MS that belong to specific biological pathways. Apart from HLA, they identified clusters of immuno-relevant genes as well as two neural clusters, one of which included mainly axon guidance molecules and several glutamate receptor and glutamate-related genes [57]. Additional information on biological function might be derived from larger scale expression studies based on the use of microarray technology [58], functional studies on associated variations as well as complementary research in animal models of EAE. 6.2. Next-generation sequencing To date, GWAS have captured almost exclusively common variation (with MAF >5%). However, low frequency and rare variants could also contribute substantially to MS heritability [12]. A more comprehensive evaluation of rare variation has become technically feasible with next-generation sequencing methods [59]. In fact, the ‘1000 genomes project’ has already started and aims at sequencing the genomes of at least one thousand people from around the world to create the most detailed and medically useful picture of human genetic variation to date (http://www.1000genomes.org). Furthermore, the first reports of whole-genome sequencing in patients with rare diseases have just been published. For example, in a family with a recessive form of Charcot-Marie-Tooth disease, the whole genome of the proband was sequenced, all potential functional variants in genes likely to be related to the disease were identified and genotyped in affected family members; and, eventually mutations in the SH3TC2 gene were validated as being causative for the disease in this family [60]. In another study, the whole-genome sequences of a family quartet (siblings, both affected by two rare recessive diseases, Miller syndrome and primary ciliary dyskinesia, and their parents) were analyzed [61]. Even though substantial technical, analytical, financial as well as ethical hurdles [62] still need to be overcome, whole-genome sequencing is likely to provide promising new analytical possibilities also for MS research in the future. Additionally, the influence of structural variation such as copy number variation (CNV) on disease susceptibility will have to be investigated [12]. Thus, a more complete picture of the genetic architecture of MS, including both common variations with modest effects and rare variants with potentially large effects on disease susceptibility as well as structural variation, will hopefully be composed in the near future. Additionally, an integrated approach including proteomics appears warranted. 6.3. Prediction programs In the future, a better understanding of the genetic background of MS might assist in predicting susceptibility to MS either as part of a diagnostic algorithm or to identify high-risk individuals for prospective studies. Recently, De Jager et al. [63] described a first attempt to predict MS based on a weighted risk score, including 16 MS susceptibility loci. These authors were able to modestly predict MS risk in three independent large cohorts [63]. Additional to GWAS and algorithm based risk identification, gene expression analyses were used to identify 108 biological markers that may predict the conversion of clinically isolated syndrome, the earliest potential clinical manifestation of MS, to clinically definite MS [64].

Although very preliminary at this stage, these results indicate that prediction programs based (among others) on genetic variation will probably be available in the future. Taken together, further research on the genetics of MS should provide a significant step forward from pure association results to biologically relevant genetic variation and potential new therapeutic targets.

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