Revealing the genetic basis of multiple sclerosis: are we there yet?

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For more than 30 years the only genetic factor associated with susceptibility to multiple sclerosis (MS) was the human leukocyte antigen (HLA) region. Recent advancements in genotyping platforms and the development of more effective statistical methods resulted in the identification of 16 more genes by genome-wide association studies (GWAS) in the last three years alone. While the effect of each of these genes is modest compared to that of HLA, this list is expected to grow significantly in the near future, thus defining a complex landscape in which susceptibility may be determined by a combination of allelic variants in different pathways according to ethnic background, disease sub-type, and specific environmental triggers. A considerable overlap of susceptibility genes among multiple autoimmune diseases is becoming evident and integration of these genetic variants with our current knowledge of affected biological pathways will greatly improve our understanding of mechanisms of general autoimmunity and of tissue specificity.

Introduction

Autoimmune disorders arise when physiological tolerance to ‘self’ antigens is lost. Although several mechanisms may be involved in this pathogenic process, dysregulation of T cell and B-cell activation and of pathways leading to inflammation are logical candidates. Multiple sclerosis (MS) is a common inflammatory disorder of the central nervous system (CNS) characterized by myelin loss, gliosis, varying degrees of axonal pathology, and progressive neurological dysfunction. A large body of research supports a multifactorial etiology, with an underlying genetic susceptibility likely acting in concert with undefined environmental exposures [1, 2, 3]. Although the exact pathogenic mechanisms underlying MS remain unknown, it has been proposed that lymphocytes activated in the periphery by a microbial mimic home to the CNS become attached to receptors on endothelial cells, and then proceed to cross the blood–brain barrier (BBB) directly into the interstitial matrix. T cells are then reactivated in situ by fragments of myelin antigens exposed in the context of human leukocyte antigen (HLA) molecules on the surface of antigen presenting cells (macrophages, microglia, and perhaps, astrocytes). Reactivation induces the release of proinflammatory cytokines that open further the BBB and stimulate chemotaxis, resulting in a second, larger wave of inflammatory cell recruitment and leakage of pathogenic antibodies and other plasma proteins into the nervous system.

Susceptibility to MS has been associated with multiple factors including genetics, epigenetics, and the environment. While the modest concordance rate in monozygotic twins (30–35%) suggests that environmental factors are major players in MS, increased heritability within families and the decrease in risk with degree of relatedness all argue in favor of genetic factors. Modern genomics developments have made available miniaturization and automation of genotyping platforms, and more than 200 genome-wide association studies (GWAS) have been performed in different diseases to date [4, 5•] including 31 studies in seven common AID (seven in MS alone). In this chapter the findings on these studies will be summarized, and hypotheses about the possible pathogenic mechanisms implicated in MS will be elaborated.

The HLA region has been associated with hundreds of human diseases, including most autoimmune diseases (e.g. B27 with Ankylosing spondylitis, DR3 with Grave's disease, Myasthenia Gravis, systemic lupus erythematosus (SLE), and Type 1 Diabetes (T1D); DR15 with MS) (for a review, see [6]). For most of these diseases, however, it has not yet been possible to show the molecular mechanisms underlying disease association with a particular HLA molecule(s). One possible explanation for this shortcoming is that it has often been difficult to unequivocally ascertain the primary disease-risk HLA gene(s) because of the remarkably strong linkage disequilibrium across the HLA [7]. There is still debate, for example, as to whether the HLA-DRB1*15:01 association explains the entire HLA-class II genetic linkage signal in MS [8, 9, 10, 11, 12] and whether susceptibility genes also exist within the class III region [13] and/or are telomeric to the class I region [14, 15, 16]. In addition, MS could result from the combination of different HLA molecules expressed at various loci (class I and/or class II) rather than the result of one HLA variant only. The situation has been complicated by the fact that susceptibility to MS is clearly polygenic and particular HLA allele(s), in combination with other genetic variants and environmental factors, may be required to develop the disease. Moreover, disease-relevant autoantigen(s) are largely unknown, which prevents a thorough three-dimensional analysis of HLA–peptide interactions. Last, but not least, MS is phenotypically very heterogeneous, in terms of clinical presentation, age at onset, comorbidities with other autoimmune disorders, and severity, and it is likely that different alleles, or allelic combinations at different loci, will predispose to different forms of the disease [17].

The strategy for a genetic linkage study requires the collection of family pedigrees with more than one affected member to track the inheritance and use highly polymorphic genetic markers to identify discrete chromosomal segments that deviate from independent segregation and cosegregate with the trait. Linkage screens with different levels of resolution and genome coverage have been completed in more than 30 datasets of familial MS cases [18]. Each of these studies suggested multiple chromosomal regions with potential involvement in disease susceptibility, consistent with the long-held view that MS is a polygenic disorder. However, only the HLA-class II locus on chromosome 6p21.3 has ever exceeded the threshold for formal statistical significance (a LOD > 3 is considered proof of linkage) and no other region of statistically confident linkage has ever been observed. Using the values of HLA allele sharing by descent in sibships, it has been estimated that the HLA locus accounts for 20–60% of the genetic susceptibility in MS [19]. Even at the higher end of this estimate, much of the heritability of MS remains to be explained.

An International Multiple Sclerosis Genetics Consortium (IMSGC) reported in late 2005 the results of a linkage screen in 730 multi-case MS families using more than 4500 SNPs [20]. The peak logarithm of the odds (LODs) score of 11.7 found in the HLA illustrates the substantially greater power achieved by this high-density screen compared with earlier efforts. Strikingly, however, although numerous regions on multiple chromosomes revealed possible linkage signals of interest, no other locus reached genome-wide significance. These data indicate that any susceptibility allele common in the population and outside the HLA region is likely to increase MS risk by a factor of less than of 2. Similar findings have been reported for other autoimmune diseases.

The aim of a GWAS is to characterize the genetic architecture of a complex genetic trait through the identification of disease variants against the background of random variation seen in a population as a whole. In a typical study, hundreds of thousands of markers covering a significant portion of the common variation in the population are tested simultaneously in cases and controls and the allelic frequencies of each marker are compared between the two groups. Compared to linkage, association studies have greater statistical power to detect common genetic variants that confer a modest risk for a disease [21]. Although population stratification and the introduction of phenocopies due to poor ascertainment of study participants are important confounding factors in association studies, inadequate sample size is undoubtedly the main reason why most published claims of association are suspected type I errors [22]. Hence, the identification of genes influencing the development of MS needs to rely primarily on association-based methods and must involve very large patient cohorts.

A total of seven positive GWAS have been reported to date in MS. The classic HLA-DRB1 risk locus stood out in all studies with remarkably strong statistical significance (e.g. P < 1 × 10−32 in [23]). These studies were followed by extensive replications efforts of the top hits [24, 25, 26, 27, 28] that together with a comprehensive replicated meta-analysis [29] provided robust evidence for approximately a dozen non-HLA loci affecting disease susceptibility (Table 1).

It is important to note that these markers may not represent necessarily the causal disease variant themselves, explaining in part the very modest independent odd values for each allele. Additional follow-up experiments refined some of the association signals and revealed early mechanistic insights into the functional role of the identified genes, most notable a change in the soluble versus membrane-bound ratio for the IL2 and IL7 receptors [30••, 31••, 32••] and diminished expression of CD58 mRNA [31••]. It is noteworthy that some of the MS allelic variants were also proposed to be involved in other autoimmune diseases, suggesting common mechanisms underlying different autoimmune conditions [28, 33••, 34••, 35]. For example, the IL2RA-mediated susceptibility effect is shared among MS, T1D, Graves’ disease, and rheumatoid arthritis (RA) [32••]. Interestingly, the direction of the association is not consistent across diseases as the IL2RA allelic variant associated with susceptibility to MS appears to confer resistance to T1D. A second allele confers susceptibility to both diseases, whereas yet a third allele is associated with susceptibility to T1D only [32••, 36]. Similarly, while the minor allele of the R620W polymorphism in PTPN22 has been associated with susceptibility to T1D, RA [37, 38, 39, 40], and SLE [41, 42], it appears to confer protection to CD [43]. Functional studies aimed at detecting tissue (or cell) specific variation in the expression or function of the target gene may contribute to elucidate the pathogenic mechanisms operating in each AID [36].

Altogether, the GWAS data seem to support the long-held view that MS susceptibility is conferred by the action of common (i.e. those with a risk allele frequency of >5%) sequence allelic variants in multiple genes [44]. Even with this expanding roster of risk loci, much of the heritability of MS remains unexplained. For example, the sorting and classification of duplications or deletions of genomic segments generating copy number variants (CNVs) lag behind. CNVs are a major source of human genetic diversity and have been shown to influence rare genomic disorders as well as complex traits and diseases [45, 46, 47]. Recent studies have reported associations between CNV and several autoimmune conditions such as SLE, psoriasis, CD, RA, and T1D [48].

The second phase of the Wellcome Trust Case Control Consortium (WTCCC2), a massive collaborative project that is genotyping a total of 120,000 samples in MS and 12 other diseases (including ankylosing spondylitis, ulcerative colitis, and psoriasis), is now near completion [49]. The MS component of this project includes 10,000 cases genotyped with a high-SNP/CNV density platform. Such study will be adequately powered to identify common risk alleles with odds ratio as modest as 1.2 [50, 51] thus practically guaranteeing identification of the complete set of common variants involved in MS.

In most GWAS the number of markers in which the evidence for association exceeds the genome-wide significance threshold is small, and markers that do not exceed this threshold are generally neglected. A plausible strategy to increase the prior odds of finding a true significant marker is to incorporate prior biological knowledge into the GWAS data in the form of gene ontologies or pathways [52•, 53, 54, 55, 56]. The advantage of these methods is that even if markers in individual genes do not reach genome-wide significance, several modest associations in genes from the same biological pathway may highlight collectively its involvement in a disease process. Building on this rationale we merged statistical evidence from GWA analyses with experimental evidence of protein interaction from yeast two hybrid or chromatin immuno-precipitation (ChIP) studies to discover sub-networks (or modules) of interacting proteins associated with MS susceptibility [57]. Through this approach we were able to identify novel susceptibility pathways in MS, such as axon guidance and glutamate metabolism. We also uncovered genetic overlaps between MS and Alzheimer's disease and bipolar disorder. In addition, the presence of common variants in the MHC region between MS, RA, and T1D, but not T2D or CD with MHC alleles was highlighted.

In order to systematically study genetic commonalities among autoimmune disorders we used publicly available information [5•, 58] to extract all moderately significant (P < 10−4) associations from studies in celiac disease (CeD), Crohn's disease (CD,) MS, psoriasis, RA, SLE, and T1D (plus those in T2D). Altogether, 1201 genes with modest evidence for association in at least one of these autoimmune disorders were identified. We then used a network-based approach to visualize all the reported associations at once and identified 71 non-MHC genes shared by at least two diseases (Figure 1), seven by three diseases, and only two by four diseases (Table 2) [33••]. This selected list of potentially ‘general’ autoimmunity genes includes PTPN22, a tyrosine phosphatase strongly associated with T1D (aggregate P < 10−226), RA (aggregate P < 10−90), and to a lesser extent with CD (aggregate P < 10−8). TNFAIP3 is highly associated with RA (aggregate P < 10−20), SLE (aggregate P < 10−11), Ps (aggregate P < 10−11), and moderately associated with CD (aggregate P < 10−5). Other general autoimmunity genes are IL23R, KIAA1109, and CTLA4. Altogether, the data presented here suggest that genes involved in activation, proliferation, and homeostasis of cells involved in adaptive immune responses are more likely to represent general autoimmunity genes. This is further supported by the observation that a large proportion of these genes physically interact among each other (S Baranzini, unpublished observation), thus possibly taking part in the same or highly overlapping biological pathways. A corollary to this observation indicates that associations unique to each disease would be responsible for attracting immune responses to specific tissues, although more functional studies in animal models will be needed to confirm this.

Section snippets

The next steps in MS genetics research

Despite the success of GWA studies in identifying novel susceptibility loci that withstood the challenge of independent replication, many questions remain concerning the genetic architecture of MS [59]. Noteworthy, all of the reported associations are derived from microarray-based studies, where only common variants (minor allele frequency >10%) are interrogated in GWAS. Thus, the almost certain influence of evolutionarily younger (rare) variants has not been adequately evaluated. With the

Conclusions

The genetic bases of autoimmune diseases are just starting to be uncovered. While several bona-fide susceptibility genes have been identified in most common traits, technological advances in high throughput genotyping platforms and more affordable second generation sequencing methods will contribute to significantly expand these lists. In addition, structural variants (insertion/deletion polymorphisms, CNVs, etc.) are also likely to play a significant role in determining susceptibility to AID.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

  • • of special interest

  • •• of outstanding interest

Acknowledgements

SEB is a Harry Weaver Neuroscience Scholar of the National Multiple Sclerosis Society (NMSS). This work was also supported by grants from the NMSS (RG4051A2, RG2901D9), and from the National Institute for Neurological Disorders and Stroke (RO1NS049477).

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