Multiple Sclerosis is a complex disease caused by interactions between genes and the environment. We now know a fair amount about how genetic variants influence the risk of developing MS, but we still know very little about how genes influence the course of the disease. Are there specific gene variants which are associated with more relapses and faster progression of disability?
To answer this important question, Bruce Taylor and colleagues sequenced the entire genomes of 127 people who had experienced a first clinical episode suggestive of MS. The authors then focussed on a specific set of 116 gene variants known to increase someone’s risk of developing MS in the first place. Participants were followed up for 5 years to assess the number of relapses, the rate of disability progression, and conversion to clinically-definite MS. The aim of the study was to see if there was any association between these 116 single nucleotide polymorphisms (SNPs = gene variants) and the course of the disease over a 5 year period.
The authors divided up the SNPs into two categories – those located in the human leukocyte antigen (HLA) genes, which code for the proteins the immune system uses to recognise foreign particles, and those not. 7 non-HLA SNPs were associated with conversion to MS and/or risk of relapse. But – and this is an important but – none of these associations were individually statistically significant. Combining these 7 SNPs into a composite genetic risk score was predictive of conversion to MS and risk of relapse, suggesting that while the effects of individual gene variants on the course of MS is small, the cumulative effect of lots of these variants is important. People with 5 or more ‘risk’ gene variants were around 6x more likely to develop clinically-definite MS over the study period compared to patients with 2 or fewer ‘risk’ variants.
A different set of 7 non-HLA SNPs was associated with disability progression. Again, none of these associations were individually significant, but were significant when combined into a genetic risk score. People with 2 or fewer ‘risk’ SNPs had, on average, a yearly increase in EDSS score of 0.14, compared to 0.62 for patients with 6 or 7 risk SNPs.
Of the 6 HLA SNPs assessed, the only significant association was between HLA B*44:02 and risk of relapse. This variant was actually protective – it was associated with a lower risk of relapse than the general cohort.
There are some very interesting observations made in this study. A particularly striking finding was the complete dichotomy between genes associated with conversion to MS or relapse and genes associated with disability progression. This strengthens the argument that the pathways involved in relapses and disability progression are quite separate. Another interesting observation is that variants in a key MS risk gene, HLA-DRB1*15, were not associated with the clinical outcomes assessed in this study. This is intriguing because earlier studies have reported an association between HLA-DRB1*15 variants and the risk of progressive disease – ethnic differences between the study populations may explain this discrepancy.
For me the main message of this study is that gene variants probably do influence the course of MS – while the contributions of individual SNPs might be small, the sum total of a person’s ‘risk’ SNPs is likely to influence how the disease progresses. The problem with this study, and with other studies aiming to do the same thing, is that it is incredibly difficult to find statistically significant associations between gene variants and clinical outcomes. This is because of two factors – because the effects of individual variants is so small, and because these studies have to look at so many gene variants. The problem with testing so many gene variants is that it increases your chance of seeing false positives, or spurious associations. Why is this?
The results of statistical tests of association are usually given as p values – this expresses the probability that the association is not real, and is just due to chance. Normally researchers call a result statistically significant if the p value is less than 0.05, which means that there is less than a 5% chance that the result is not real. However, if you do 20 statistical tests and get 20 p values of 0.05, there is a good chance that one of these associations will be a false positive (as 20 x 5% = 100%). In this study, the researchers looked at 116 gene variants – if they accepted a p value of 0.05 as statistically significant, they would get lots of spurious associations between gene variants and outcomes. So, to adjust for this, researchers adjust the p value threshold they count as significant depending on the number of statistical tests they are doing. This is why so many of the associations reported in this study are not individually significant.
So this study suggests that the genetic influence on the course of MS is, predictably, polygenic – i.e. a product of lots of ‘risk’ gene variants, none of which are individually sufficient to cause the disease. As well as being a product of multiple genetic factors, the course of the disease is clearly influenced by all kinds of environmental factors, such as the disease-modifying therapies (DMTs) used, vitamin D exposure, and potentially exposure to infectious agents like EBV.
We are still a long way from being able to use this kind of genetic information in a clinical setting. It would be great if we could use SNP data to predict who will benefit from different types of DMT and who would benefit from early aggressive treatment. Whilst this study does not provide this kind of information just yet, it demonstrates that genomics could play an important role in caring for people with MS in the future.
Background The genetic drivers of multiple sclerosis (MS) clinical course are essentially unknown with limited data arising from severity and clinical phenotype analyses in genome-wide association studies.
Methods Prospective cohort study of 127 first demyelinating events with genotype data, where 116 MS risk-associated single nucleotide polymorphisms (SNPs) were assessed as predictors of conversion to MS, relapse and annualised disability progression (Expanded Disability Status Scale, EDSS) up to 5-year review (ΔEDSS). Survival analysis was used to test for predictors of MS and relapse, and linear regression for disability progression. The top 7 SNPs predicting MS/relapse and disability progression were evaluated as a cumulative genetic risk score (CGRS).
Results We identified 2 non-human leucocyte antigen (HLA; rs12599600 and rs1021156) and 1 HLA (rs9266773) SNP predicting both MS and relapse risk. Additionally, 3 non-HLA SNPs predicted only conversion to MS; 1 HLA and 2 non-HLA SNPs predicted only relapse; and 7 non-HLA SNPs predicted ΔEDSS. The CGRS significantly predicted MS and relapse in a significant, dose-dependent manner: those having ≥5 risk genotypes had a 6-fold greater risk of converting to MS and relapse compared with those with ≤2. The CGRS for ΔEDSS was also significant: those carrying ≥6 risk genotypes progressed at 0.48 EDSS points per year faster compared with those with ≤2, and the CGRS model explained 32% of the variance in disability in this study cohort.
Conclusions These data strongly suggest that MS genetic risk variants significantly influence MS clinical course and that this effect is polygenic.