We searched PubMed for articles published between 1993 and December, 2013, with terms such as “multiple sclerosis”, “gene*”, “genomewide association”, “linkage”, and “association”. We also identified references from relevant articles and the online Catalog of Published Genome-Wide Association Studies. We restricted our search to articles published in English. The final reference list was generated on the basis of relevance to the topics covered in this Review.
ReviewMultiple sclerosis genetics
Introduction
Multiple sclerosis is an inflammatory demyelinating disease of the CNS that results in chronic progressive disability for the majority of people with the disorder. Most patients are unemployed within 15 years of diagnosis and rates of depression, suicide, and divorce are substantially increased compared with the healthy population.1 Half of all patients need assistance with mobility within 20 years of diagnosis, and 50% of patients eventually develop substantial cognitive deficits.1 The disease most often starts between 20 and 40 years of age, and affects women more frequently than men.2 According to the Atlas of MS database, worldwide about 2·5 million people have multiple sclerosis, and figures from the Multiple Sclerosis International Federation suggest that in Europe alone the disease costs more than €15 billion each year in terms of direct health-care costs and lost productivity. As is the case for many other autoimmune diseases, evidence suggests that the incidence of multiple sclerosis is increasing.3 Although the precise aetiology of multiple sclerosis remains unknown, in the past few years the identification of genetic variants affecting the development of the disease has grown almost exponentially. In this Review we outline the basic epidemiological foundations underpinning the genetic analysis of multiple sclerosis, describe some of the landmark findings from the past, summarise recent findings, and consider what the future might hold. Like pieces in a jigsaw puzzle, each of these associated variants provides a clue to aetiology. The more pieces we find the more likely it is that they will fit together in meaningful ways to reveal the crucial mechanisms underlying the development of this enigmatic disease.
Section snippets
Epidemiology
Two features have consistently emerged from the extensive epidemiological analysis of multiple sclerosis: first, that the disease clusters in families,4, 5 and second, that the disease varies greatly in frequency worldwide.6, 7 Although neither of these findings necessarily implies an exclusive role for either genetic or environmental factors, supplementary studies in informative subgroups (eg, twins,8 adoptees,9 conjugal pairs,10 and migrant individuals11) suggest that familial clustering is
Linkage
Although segregation analysis has little power to establish the relative importance of the two main allelic models, linkage analysis can be useful.21 Families with multiple sclerosis rarely include more than three or four affected individuals and large, extended families with many cases of the disease are extremely uncommon.22 Furthermore, the absence of any linkage in the few larger-than-average families that have been reported22, 23, 24, 25 suggests that—unlike many other complex traits—rare,
The major histocompatibility complex
Although associations between multiple sclerosis and variation in the genes encoding human leucocyte antigens (HLAs) contained within the major histocompatibility complex have been recognised for several decades,29, 30 the extreme polymorphism and extensive linkage disequilibrium (ie, correlation between linked variants) that characterise this gene-dense region31 make the identification of relevant variants driving these associations difficult. However, in the past few years the advent of
Genome-wide association studies—a new era in complex genetics
Despite decades of candidate-gene-based efforts, little progress was made in the identification of relevant, genuinely associated risk alleles outside the major histocompatibility complex before the advent of genome-wide association studies. The only real progress was the identification of association with the SNP rs6897932 from the IL7R gene, which was suggested by combining information from many data sources (ie, genomic convergence45) and confirmed by typing large numbers of cases and
Immunochip follow-up
The ability of genome-wide association studies to robustly screen most common variation by direct typing of just a few hundred thousand SNPs is crucially dependent on the extensive correlation between tightly linked variants (linkage disequilibrium) that is a characteristic feature of the human genome. Unfortunately, this same feature limits our ability to understand the results of such studies. Rather than individual associated variants, genome-wide association studies necessarily identify
110 established variants associated with multiple sclerosis
Collectively these studies have identified 110 variants outside the major histocompatibility complex that are confidently associated with susceptibility to multiple sclerosis (appendix). According to the Variant Effect Predictor tool71 on Ensembl (release 72), 15 of the 110 SNPs are themselves coding variants and a further 35 are in tight linkage disequilibrium (r2>0·8) with coding variants (appendix). However, among the implicated coding variants, only 14 are missense and just 7 of these are
Secondary phenotypes
Analysis of clinical features in families in which more than one member has multiple sclerosis suggests that genetic factors probably affect the course of the disease.86 In this context, three genome-wide association studies51, 87, 88 have specifically investigated clinical features as their primary endpoint, but unfortunately no genome-wide significant association emerged from these modestly powered efforts. However, genes for calcium and glutamate signalling were enriched among the
Missing heritability
The associated loci identified so far account for only about a quarter of the heritability reported in multiple sclerosis, leaving an obvious question about what determines the remainder. Much of the remainder (perhaps half) is probably so-called phantom heritability—ie, resulting from as-yet-undefined interactions between risk factors.18 The remainder probably relates to risk alleles that are yet to be discovered,70 some of which will be common, and might emerge in larger genome-wide
Conclusions and future directions
Each of the associated genetic variants identified so far has the potential to provide crucial insight into aetiology of multiple sclerosis, and thereby promote the development of a rational therapy that is both safe and effective. The discovery that most, if not all, of these variants seem to exert their effects by affecting tissue-specific gene expression has exposed just how little is known about the way in which regulatory information is encoded in the genome—an information gap that
Search strategy and selection criteria
References (113)
- et al.
Multiple sclerosis
Lancet
(2008) - et al.
Sex ratio of multiple sclerosis in Canada: a longitudinal study
Lancet Neurol
(2006) Inflation of sibling recurrence-risk ratio, due to ascertainment bias and/or overreporting
Am J Hum Genet
(1998)- et al.
Histocompatibility determinants in multiple sclerosis, with special reference to clinical course
Lancet
(1973) - et al.
A statistical method for predicting classical HLA alleles from SNP data
Am J Hum Genet
(2008) - et al.
Genetic dissection of the human leukocyte antigen region by use of haplotypes of Tasmanians with multiple sclerosis
Am J Hum Genet
(2002) - et al.
Evidence for VAV2 and ZNF433 as susceptibility genes for multiple sclerosis
J Neuroimmunol
(2010) - et al.
Genome-wide association study in a high-risk isolate for multiple sclerosis reveals associated variants in STAT3 gene
Am J Hum Genet
(2010) - et al.
Five years of GWAS discovery
Am J Hum Genet
(2012) - et al.
MGAT5 alters the severity of multiple sclerosis
J Neuroimmunol
(2010)
McAlpine's multiple sclerosis
Age-adjusted recurrence risks for relatives of patients with multiple sclerosis
Brain
Modelling genetic susceptibility to multiple sclerosis with family data
Neuroepidemiology
Latitude is significantly associated with the prevalence of multiple sclerosis: a meta-analysis
J Neurol Neurosurg Psychiatry
High nationwide prevalence of multiple sclerosis in Sweden
Mult Scler
Twin concordance and sibling recurrence rates in multiple sclerosis
Proc Natl Acad Sci USA
A genetic basis for familial aggregation in multiple sclerosis
Nature
Conjugal multiple sclerosis: population-based prevalence and recurrence risks in offspring
Ann Neurol
Multiple sclerosis among immigrants in Greater London
BMJ
Multiple sclerosis in stepsiblings: recurrence risk and ascertainment
J Neurol Neurosurg Psychiatry
HLA-DRB1 and multiple sclerosis in Malta
Neurology
What role for genetics in the prediction of multiple sclerosis?
Ann Neurol
Genetic epidemiology of multiple sclerosis
Epidemiol Rev
Risk for relatives of patients with multiple sclerosis in central Sardinia, Italy
Neuroepidemiology
The mystery of missing heritability: Genetic interactions create phantom heritability
Proc Natl Acad Sci USA
Modelling the effects of penetrance and family size on rates of sporadic and familial disease
Hum Hered
Genome-wide association studies: theoretical and practical concerns
Nat Rev Genet
The future of genetic studies of complex human diseases
Science
A genome-wide scan in forty large pedigrees with multiple sclerosis
J Hum Genet
Genome-wide linkage screen of a consanguineous multiple sclerosis kinship
Mult Scler
A linkage study in two families with multiple sclerosis and healthy members with oligoclonal CSF immunopathy
Mult Scler
A genome scan in a single pedigree with a high prevalence of multiple sclerosis
J Neurol Neurosurg Psychiatry
HLA class II-associated genetic susceptibility in multiple sclerosis: a critical evaluation
Tissue Antigens
Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis
Nature
A high-density screen for linkage in multiple sclerosis
Am J Hum Genet
Multiple sclerosis: association with HL-A3
Tissue Antigens
Variation analysis and gene annotation of eight MHC haplotypes: the MHC Haplotype Project
Immunogenetics
A high-resolution HLA and SNP haplotype map for disease association studies in the extended human MHC
Nat Genet
HLA*IMP—an integrated framework for imputing classical HLA alleles from SNP genotypes
Bioinformatics
Fine-mapping the genetic association of the major histocompatibility complex in multiple sclerosis: HLA and non-HLA effects
PLoS Genet
Genes in the HLA class I region may contribute to the HLA class II-associated genetic susceptibility to multiple sclerosis
Tissue Antigens
A second major histocompatibility complex susceptibility locus for multiple sclerosis
Ann Neurol
Meta-analysis of genome scans and replication identify CD6, IRF8 and TNFRSF1A as new multiple sclerosis susceptibility loci
Nat Genet
Dissection of the HLA association with multiple sclerosis in the founder isolated population of Sardinia
Hum Mol Genet
Complex interactions among MHC haplotypes in multiple sclerosis: susceptibility and resistance
Hum Mol Genet
Heterogeneity at the HLA-DRB1 locus and risk for multiple sclerosis
Hum Mol Genet
Epistasis among HLA-DRB1, HLA-DQA1, and HLA-DQB1 loci determines multiple sclerosis susceptibility
Proc Natl Acad Sci USA
A polymorphism in the HLA-DPB1 gene is associated with susceptibility to multiple sclerosis
PLoS One
Genomic convergence: identifying candidate genes for Parkinson's disease by combining serial analysis of gene expression and genetic linkage
Hum Mol Genet
Interleukin 7 receptor alpha chain (IL7R) shows allelic and functional association with multiple sclerosis
Nat Genet
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