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.
***
Abstract
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.
Labels: genomics, prognosis