What role for genetics in the prediction of multiple sclerosis?

Prediction of treatment response in rheumatoid arthritis patients using genome-wide SNP data

Although a number of treatments are available for rheumatoid arthritis (RA), each of them shows a significant nonresponse rate in patients. Therefore, predicting a priori the likelihood of treatment response would be of great patient benefit. Here, we conducted a comparison of a variety of statistical methods for predicting three measures of treatment response, between baseline and 3 or 6 months, using genome-wide SNP data from RA patients available from the MAximising Therapeutic Utility in Rheumatoid Arthritis (MATURA) consortium. Two different treatments and 11 different statistical methods were evaluated. We used 10-fold cross validation to assess predictive performance, with nested 10-fold cross validation used to tune the model hyperparameters when required. Overall, we found that SNPs added very little prediction information to that obtained using clinical characteristics only, such as baseline trait value. This observation can be explained by the lack of strong genetic effects and the relatively small sample sizes available; in analysis of simulated and real data, with larger effects and/or larger sample sizes, prediction performance was much improved. Overall, methods that were consistent with the genetic architecture of the trait were able to achieve better predictive ability than methods that were not. For treatment response in RA, methods that assumed a complex underlying genetic architecture achieved slightly better prediction performance than methods that assumed a simplified genetic architecture.

Autoimmune aspects of psoriasis: Heritability and autoantigens

Chronic immune-mediated disorders (IMDs) constitute a major health burden. Understanding IMD pathogenesis is facing two major constraints: Missing heritability explaining familial clustering, and missing autoantigens. Pinpointing IMD risk genes and autoimmune targets, however, is of fundamental importance for developing novel causal therapies. The strongest association of all IMDs is seen with human leukocyte antigen (HLA) alleles. Using psoriasis as an IMD model this article reviews the pathogenic role HLA molecules may have within the polygenic predisposition of IMDs. It concludes that disease-associated HLA alleles account for both missing heritability and autoimmune mechanisms by facilitating tissue-specific autoimmune responses through autoantigen presentation.

The burden of multiple sclerosis variants in continental Italians and Sardinians

BACKGROUND

Recent studies identified > 100 non-HLA (human leukocyte antigen) multiple sclerosis (MS) susceptibility variants in Northern European populations, but their role in Southern Europeans is largely unexplored.

OBJECTIVE

We aimed to investigate the cumulative impact of those variants in two Mediterranean populations: Continental Italians and Sardinians.

METHODS

We calculated four weighted Genetic Risk Scores (wGRS), using up to 102 non-HLA MS risk variants and 5 HLA MS susceptibility markers in 1691 patients and 2194 controls from continental Italy; and 2861 patients and 3034 controls from Sardinia. We then assessed the differences between populations using Nagelkerke's R(2) and the area under the Receiver Operating Characteristic (ROC) curves.

RESULTS

As expected, the genetic burden (mean wGRS value) was significantly higher in MS patients than in controls, in both populations. Of note, the burden was significantly higher in Sardinians. Conversely, the proportion of variability explained and the predictive power were significantly higher in continental Italians. Notably, within the Sardinian patients, we also observed a significantly higher burden of non-HLA variants in individuals who do not carry HLA risk alleles.

CONCLUSIONS

The observed differences in MS genetic burden between the two Mediterranean populations highlight the need for more genetic studies in South Europeans, to further expand the knowledge of MS genetics.

Combinations of susceptibility genes are associated with higher risk for multiple sclerosis and imply disease course specificity

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system that predominantly affects young adults. The genetic contributions to this multifactorial disease were underscored by a genome wide association study (GWAS) conducted by the International Multiple Sclerosis Genetic Consortium in a multinational cohort prompting the discovery of 57 non-MHC MS-associated common genetic variants. Hitherto, few of these newly reported variants have been replicated in larger independent patient cohorts. We genotyped a cohort of 1033 MS patients and 644 healthy controls with a consistent genetic background for the 57 non-MHC variants reported to be associated with MS by the first large GWAS as well as the HLA DRB1*1501 tagging SNP rs3135388. We robustly replicated three of the 57 non-MHC reported MS-associated single nucleotide polymorphisms (SNPs). In addition, our study revealed several genotype-genotype combinations with an evidently higher degree of disease association than the genotypes of the single SNPs. We further correlated well-defined clinical phenotypes, i.e. ataxia, visual impairment due to optic neuritis and paresis with single SNPs and genotype combinations, and identified several associations. The results may open new avenues for clinical implications of the MS associated genetic variants reported from large GWAS.

Burden of risk variants correlates with phenotype of multiple sclerosis

BACKGROUND

More than 100 common variants underlying multiple sclerosis (MS) susceptibility have been identified, but their effect on disease phenotype is still largely unknown.

OBJECTIVE

The objective of this paper is to assess whether the cumulative genetic risk score of currently known susceptibility variants affects clinical presentation.

METHODS

A cumulative genetic risk score was based on four human leukocyte antigen (HLA) and 106 non-HLA risk loci genotyped or imputed in 842 Belgian MS patients and 321 controls. Non-parametric analyses were applied.

RESULTS

An increased genetic risk is observed for MS patients, including subsets such as oligoclonal band-negative and primary progressive MS patients, compared to controls. Within the patient group, a stronger association between HLA risk variants and the presence of oligoclonal bands, an increased immunoglobulin G (IgG) index and female gender was apparent. Results suggest an association between a higher accumulation of non-HLA risk variants and increased relapse rate as well as shorter relapse-free intervals after disease onset.

CONCLUSION

MS patients display a significantly increased genetic risk compared to controls, irrespective of disease course or presence of oligoclonal bands. Whereas the cumulative burden of non-HLA risk variants appears to be reflected in the relapses of MS patients, the HLA region influences intrathecal IgG levels.

Multiple sclerosis risk loci correlate with cervical cord atrophy and may explain the course of disability

Genome-wide association studies (GWAS) underscore the genetic basis of multiple sclerosis (MS); however, only few of the newly reported genetic variations relevant in MS have been replicated or correlated for clinical/paraclinical phenotypes such as spinal cord atrophy in independent patient cohorts. We genotyped 141 MS patients for 58 variations reported to reach significance in GWAS. Expanded disability status scale (EDSS) and disease duration (DD) are available from regular clinical examinations. MRI included sagittal high-resolution 3D T1-weighted magnetization-prepared rapid acquisition gradient echo of the cervical cord region used for volumetry. Due dependency of mean upper cervical cord area (MUCCA) with EDSS and/or DD, correction operations were performed compensating for EDSS/DD. We assessed each MS risk locus for possible MUCCA association. We identified twelve risk loci that significantly correlated with MUCCA. For nine loci-BATF, CYP27B1, IL12B, NFKB1, IL7, PLEK, EVI5, TAGAP and nrs669607-patients revealed significantly higher degree of atrophy; TYK2, RGS1 and CLEC16A revealed inverse effects. The weighted genetic risk score over the twelve loci showed significant correlation with MUCCA. Our data reveal a risk gene depending paraclinical/clinical phenotype. Since MUCCA clearly correlates with disability, the candidates identified here may serve as prognostic markers for disability progression.

Noise in multiple sclerosis: unwanted and necessary

As our knowledge about the etiology of multiple sclerosis (MS) increases, deterministic paradigms appear insufficient to describe the pathogenesis of the disease, and the impression is that stochastic phenomena (i.e. random events not necessarily resulting in disease in all individuals) may contribute to the development of MS. However, sources and mechanisms of stochastic behavior have not been investigated and there is no proposed framework to incorporate nondeterministic processes into disease biology. In this report, we will first describe analogies between physics of nonlinear systems and cell biology, showing how small-scale random perturbations can impact on large-scale phenomena, including cell function. We will then review growing and solid evidence showing that stochastic gene expression (or gene expression “noise”) can be a driver of phenotypic variation. Moreover, we will describe new methods that open unprecedented opportunities for the study of such phenomena in patients and the impact of this information on our understanding of MS course and therapy.

Multiple sclerosis genetics

Genome-wide association studies have revolutionised the genetic analysis of multiple sclerosis. Through international collaborative efforts involving tens of thousands of cases and controls, more than 100 associated common variants have now been identified. These variants consistently implicate genes associated with immunological processes, overwhelmingly lie in regulatory rather than coding regions, and are frequently associated with other autoimmune diseases. The functional implications of these associated variants are mostly unknown; however, early work has shown that several variants have effects on splicing that result in meaningful changes in the balance between different isoforms in relevant tissues. Including the well established risk attributable to variants in genes encoding human leucocyte antigens, only about a quarter of reported heritability can now be accounted for, suggesting that a substantial potential for further discovery remains.

Genetic burden in multiple sclerosis families

A previous study using cumulative genetic risk estimations in multiple sclerosis (MS) successfully tracked the aggregation of susceptibility variants in multi-case and single-case families. It used a limited description of susceptibility loci available at the time (17 loci). Even though the full roster of MS risk genes remains unavailable, we estimated the genetic burden in MS families and assess its disease predictive power using up to 64 single-nucleotide polymorphism (SNP) markers according to the most recent literature. A total of 708 controls, 3251 MS patients and their relatives, as well as 117 twin pairs were genotyped. We validated the increased aggregation of genetic burden in multi-case compared with single-case families (P=4.14e-03) and confirm that these data offer little opportunity to accurately predict MS, even within sibships (area under receiver operating characteristic (AUROC)=0.59 (0.55, 0.53)). Our results also suggest that the primary progressive and relapsing-type forms of MS share a common genetic architecture (P=0.368; difference being limited to that corresponding to ± 2 typical MS-associated SNPs). We have confirmed the properties of individual genetic risk score in MS. Comparing with previous reference point for MS genetics (17 SNPs), we underlined the corrective consequences of the integration of the new findings from GWAS and meta-analysis.

Progress in multiple sclerosis genetics

A genetic component in the susceptibility to multiple sclerosis (MS) has long been known, and the first and major genetic risk factor, the HLA region, was identified in the 1970’s. However, only with the advent of genome-wide association studies in the past five years did the list of risk factors for MS grow from 1 to over 50. In this review, we summarize the search for MS risk genes and the latest results. Comparison with data from other autoimmune and neurological diseases and from animal models indicates parallels and differences between diseases. We discuss how these translate into an improved understanding of disease mechanisms, and address current challenges such as genotype-phenotype correlations, functional mechanisms of risk variants and the missing heritability.

Power and predictive accuracy of polygenic risk scores

Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

Recently there has been much interest in combining multiple genetic markers into a single score for predicting disease risk. Even if many of the individual markers have no detected effect, the combined score could be a strong predictor of disease. This has allowed researchers to demonstrate that some diseases have a strong genetic basis, even if few actual genes have been identified, and it has also revealed a common genetic basis for distinct diseases. These analyses have so far been performed opportunistically, with mixed results. Here I derive formulae based on the heritability of disease and size of the study, allowing researchers to plan their analyses from a more informed position. I show that discouraging results in some previous studies were due to the low number of subjects studied, but a modest increase in study size would allow more successful analysis. However, I also show that, for genetics to become useful for predicting individual risk of disease, hundreds of thousands of subjects may be needed to estimate the gene effects. This is larger than most existing studies, but will become more common in the near future, so that gene scores will become more useful for predicting disease than has appeared to date.

Uncovering the genetic architecture of multiple sclerosis

Multiple sclerosis (MS) is a disease in which genetic, environmental, and stochastic factors interact to trigger an inflammatory disease of the CNS that also has a neurodegenerative component. Over the past 3 years, progress in high-throughput technologies and analysis methods has synergized with the collaborative efforts of investigators studying MS genetics to enable the discovery of more than a dozen genes involved in making individuals susceptible to MS. These genes are beginning to suggest molecular pathways that may be particularly vulnerable to genetic variation in MS. Soon, a comprehensive map of common genetic variants affecting MS susceptibility will be assembled, and communal efforts will need to focus on the more challenging issue of understanding the genetic architecture of disease course and treatment response in MS. Early efforts integrating different dimensions of information, including genomics, imaging, transcriptomics, and proteomics, with precise phenotypic data from clinicians illustrate the way forward for prognostic algorithms in MS and suggest that these approaches will yield a new series of insights in the next decade.

Multiple sclerosis

Multiple sclerosis (MS) is a multifocal demyelinating disease with progressive neurodegeneration caused by an autoimmune response to self-antigens in a genetically susceptible individual. While the formation and persistence of meningeal lymphoid follicles suggest persistence of antigens to drive the continuing inflammatory and humoral response, the identity of an antigen or infectious agent leading to the oligoclonal expansion of B and T cells is unknown. In this review we examine new paradigms for understanding the immunopathology of MS, present recent data defining the common genetic variants underlying disease susceptibility, and explore how improved understanding of immune pathway disruption can inform MS prognosis and treatment decisions.

The immunogenetic architecture of autoimmune disease

The development of most autoimmune diseases includes a strong heritable component. This genetic contribution to disease ranges from simple Mendelian inheritance of causative alleles to the complex interactions of multiple weak loci influencing risk. The genetic variants responsible for disease are being discovered through a range of strategies from linkage studies to genome-wide association studies. Despite the rapid advances in genetic analysis, substantial components of the heritable risk remain unexplained, either owing to the contribution of an as-yet unidentified, "hidden," component of risk, or through the underappreciated effects of known risk loci. Surprisingly, despite the variation in genetic control, a great deal of conservation appears in the biological processes influenced by risk alleles, with several key immunological pathways being modified in autoimmune diseases covering a broad spectrum of clinical manifestations. The primary translational potential of this knowledge is in the rational design of new therapeutics to exploit the role of these key pathways in influencing disease. With significant further advances in understanding the genetic risk factors and their biological mechanisms, the possibility of genetically tailored (or "personalized") therapy may be realized.

Perspectives on the use of multiple sclerosis risk genes for prediction

Objective

A recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models.

Materials and Methods

Genotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future.

Results

Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02–3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85.

Conclusion

Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.

Perspectives on the use of multiple sclerosis risk genes for prediction

OBJECTIVE

A recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models.

MATERIALS AND METHODS

Genotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future.

RESULTS

Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02-3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85.

CONCLUSION

Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.

Combinatorial antibody library from multiple sclerosis patients reveals antibodies that cross-react with myelin basic protein and EBV antigen

Multiple sclerosis (MS) is a widespread neurodegenerative autoimmune disease with unknown etiology. It is increasingly evident that, together with pathogenic T cells, autoreactive B cells are among the major players in MS development. The analysis of myelin neuroantigen-specific antibody repertoires and their possible cross-reactivity against environmental antigens, including viral proteins, could shed light on the mechanism of MS induction and progression. A phage display library of single-chain variable fragments (scFvs) was constructed from blood lymphocytes of patients with MS as a potential source of representative MS autoantibodies. Structural alignment of 13 clones selected toward myelin basic protein (MBP), one of the major myelin antigens, showed high homology within variable regions with cerebrospinal fluid MS-associated antibodies as well as with antibodies toward Epstein-Barr latent membrane protein 1 (LMP1). Three scFv clones showed pronounced specificity to MBP fragments 65-92 and 130-156, similar to the serum MS antibodies. One of these clones, designated E2, in both scFv and full-size human antibody constructs, was shown to react with both MBP and LMP1 proteins in vitro, suggesting natural cross-reactivity. Thus, antibodies induced against LMP1 during Epstein-Barr virus infection might act as inflammatory trigger by reacting with MBP, suggesting molecular mimicry in the mechanism of MS pathogenesis.

Multiple sclerosis: where will we be in 2020?

The past decade has seen a surge of research interest in multiple sclerosis and an accelerated expansion of investigative efforts into multiple sclerosis therapeutics. Investigators have continued dissecting the complex immunological perturbations that may contribute to the disease and made major advances in understanding the genetics of multiple sclerosis. This article addresses current investigative issues and offers predictions about where the understanding and treatment of multiple sclerosis may stand at the end of the 21st century's second decade.

Genomic medicine and neurological disease

"Genomic medicine" refers to the diagnosis, optimized management, and treatment of disease--as well as screening, counseling, and disease gene identification--in the context of information provided by an individual patient's personal genome. Genomic medicine, to some extent synonymous with "personalized medicine," has been made possible by recent advances in genome technologies. Genomic medicine represents a new approach to health care and disease management that attempts to optimize the care of a patient based upon information gleaned from his or her personal genome sequence. In this review, we describe recent progress in genomic medicine as it relates to neurological disease. Many neurological disorders either segregate as Mendelian phenotypes or occur sporadically in association with a new mutation in a single gene. Heritability also contributes to other neurological conditions that appear to exhibit more complex genetics. In addition to discussing current knowledge in this field, we offer suggestions for maximizing the utility of genomic information in clinical practice as the field of genomic medicine unfolds.

Varicella zoster virus and relapsing remitting multiple sclerosis

Multiple sclerosis (MS) is an immune-mediated disorder; however, little is known about the triggering factors of the abnormal immune response. Different viruses from the herpes family have been mentioned as potential participants. Here, we review the evidences that support the association of varicella zoster virus (VZV) with MS. Epidemiological studies from geographical areas, where incidence of MS has increased in recent decades, pointed out a high frequency of varicella and zoster in the clinical antecedents of MS patients, and also laboratory investigations have found large quantities of DNA from VZV in leucocytes and cerebrospinal fluid of MS patients restricted to the ephemeral period of MS relapse, followed by disappearance of the virus during remission. The above observations and the peculiar features of VZV, mainly characterized by its neurotropism and long periods of latency followed by viral reactivation, support the idea on the participation of VZV in the etiology of MS. However, as with reports from studies with other viruses, particularly Epstein Barr virus, conflicting results on confirmatory studies about the presence of viral gene products in brain tissue indicate the need for further research on the potential participation of VZV in the etiology of MS.

Aggregation of multiple sclerosis genetic risk variants in multiple and single case families

OBJECTIVE

Multiple sclerosis (MS) is a multifactorial neurologic disease characterized by modest but tractable heritability. Genome-wide association studies have identified and/or validated multiple polymorphisms in approximately 16 genes associated with susceptibility. We aimed at investigating the aggregation of genetic MS risk markers in individuals by comparing multiple- and single-case families.

METHODS

A weighted log-additive integrative approach termed MS genetic burden (MSGB) was used to account for the well-established genetic variants from previous association studies and meta-analyses. The corresponding genetic burden and its transmission was analyzed in 1,213 independent MS families (810 sporadic and 403 multicase families).

RESULTS

MSGB analysis demonstrated a higher aggregation of susceptibility variants in multicase compared to sporadic MS families. In addition, the aggregation of non-major histocompatibility complex single nucleotide polymorphisms depended neither on gender nor on the presence or absence of HLA-DRB1*15:01 alleles. Interestingly, although a greater MSGB in siblings of MS patients was associated with an increased risk of MS (odds ratio, 2.1; p = 0.001), receiver operating characteristic curves of MSGB differences between probands and sibs (area under the receiver operator curves, 0.57 [95% confidence interval, 0.53-0.61]) show that case-control status prediction of MS cannot be achieved with the currently available genetic data.

INTERPRETATION

The primary interest in the MSGB concept resides in its capacity to integrate cumulative genetic contributions to MS risk. This analysis underlines the high variability of family load with known common variants. This novel approach can be extended to other genetically complex diseases. Despite the emphasis on assembling large case-control datasets, multigenerational, multiaffected families remain an invaluable resource for advancing the understanding of the genetic architecture of complex traits.

Genes associated with multiple sclerosis: 15 and counting

Evaluation of: The International Multiple Sclerosis Genetics Consortium (IMSGC). IL12A, MPHOSPH9/CDK2AP1 and RGS1 are novel multiple sclerosis susceptibility loci. Genes Immun. 11(5), 397-405 (2010). Multiple sclerosis (MS) develops in genetically susceptible populations as a result of environmental exposures, and discovering these genetic and/or environmental factors will provide fundamental new insights into the pathogenesis, diagnosis and treatment of this disabling disease. With the introduction of genome-wide association studies, the number of genes found to be associated with MS has increased rapidly. In all of these genes, in a study by the International Multiple Sclerosis Genetics Consortium, the classic MS risk locus, HLA-DRB1, stood out with remarkably strong statistical significance, but they also identified 12 other loci and/or genes associated with MS. However, all of these alleles have a very modest odds ratio and they explain approximately 3% of the variance in MS risk. Recently, the International Multiple Sclerosis Genetics Consortium provided evidence for three new loci that show significant association at a genome-wide level: RGS1, IL12A and MPHOSPH9/CDK2AP1. In this article, we will review the three newly discovered susceptibility loci and the implications of genome-wide association studies in MS on clinical practice.

Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data

BACKGROUND

Multiple sclerosis (MS) is the most common cause of chronic neurologic disability beginning in early to middle adult life. Results from recent genome-wide association studies (GWAS) have substantially lengthened the list of disease loci and provide convincing evidence supporting a multifactorial and polygenic model of inheritance. Nevertheless, the knowledge of MS genetics remains incomplete, with many risk alleles still to be revealed.

METHODS

We used a discovery GWAS dataset (8,844 samples, 2,124 cases and 6,720 controls) and a multi-step logistic regression protocol to identify novel genetic associations. The emerging genetic profile included 350 independent markers and was used to calculate and estimate the cumulative genetic risk in an independent validation dataset (3,606 samples). Analysis of covariance (ANCOVA) was implemented to compare clinical characteristics of individuals with various degrees of genetic risk. Gene ontology and pathway enrichment analysis was done using the DAVID functional annotation tool, the GO Tree Machine, and the Pathway-Express profiling tool.

RESULTS

In the discovery dataset, the median cumulative genetic risk (P-Hat) was 0.903 and 0.007 in the case and control groups, respectively, together with 79.9% classification sensitivity and 95.8% specificity. The identified profile shows a significant enrichment of genes involved in the immune response, cell adhesion, cell communication/signaling, nervous system development, and neuronal signaling, including ionotropic glutamate receptors, which have been implicated in the pathological mechanism driving neurodegeneration. In the validation dataset, the median cumulative genetic risk was 0.59 and 0.32 in the case and control groups, respectively, with classification sensitivity 62.3% and specificity 75.9%. No differences in disease progression or T2-lesion volumes were observed among four levels of predicted genetic risk groups (high, medium, low, misclassified). On the other hand, a significant difference (F = 2.75, P = 0.04) was detected for age of disease onset between the affected misclassified as controls (mean = 36 years) and the other three groups (high, 33.5 years; medium, 33.4 years; low, 33.1 years).

CONCLUSIONS

The results are consistent with the polygenic model of inheritance. The cumulative genetic risk established using currently available genome-wide association data provides important insights into disease heterogeneity and completeness of current knowledge in MS genetics.

Familial prevalence of autoimmune disorders in multiple sclerosis in Northern Greece

Objective: The objective of this study was to evaluate for up to 7 years the prevalence of autoimmune disorders among naïve (untreated) multiple sclerosis family members compared with a contemporary general control population in Northern Greece, in a prospective case-control study, and to examine the possible relationship between immunomodulatory treatment and the appearance of additional autoimmune disorders.

Methods: The patients and controls enrolled comprised 1383 patients with definite MS and 4392 relatives in their families and a total of 452 controls families with 1652 members.

Results: At baseline, 891 multiple sclerosis families with 3112 members (73 multiplex multiple sclerosis families with 292 members and 818 simplex families with 2820 members) and 355 control families with 1580 members were examined regarding whether they had any of 12 autoimmune diseases. The baseline affected multiplex plus simplex multiple sclerosis families, the family members and the coexistent additional autoimmune disorders were higher compared with controls. There was an increase in longitudinally affected multiple sclerosis families, multiple sclerosis family members and coexistent additional autoimmune disorders compared with respective findings at the baseline observation. Comparison analysis between two time point observations (after a mean 7.1 ± 2.2 years) for each autoimmune disorder in overall multiple sclerosis family members revealed increased rates for longitudinal autoimmune Hashimoto’s thyroiditis, Graves’ disease, insulin-dependent diabetes mellitus, psoriasis and vitiligo ( p = 0.02, p = 0.006, p = 0.0004, p = 0.05, and p = 0.05, respectively). Some 145 newly developed, longitudinally definite autoimmune cases were recognized in multiplex plus simplex multiple sclerosis families; 116 (80%) of these disorders were observed in patients with multiple sclerosis treated with immunomodulatory medications, and 68 of these 116 (58.6%) cases exhibited baseline positive autoreactive antibodies. Binary logistic regression analysis revealed that immunotherapy predisposes to autoimmunity (odds ratio 2.8, p < 0.001) independently of the presence of baseline autoantibodies and patients’ gender.

Conclusions: There is a longitudinally increased frequency of additional autoimmune disorders among multiple sclerosis family members, probably related to immunomodulatory therapy.