Genome Studies offer low predictive value in detecting MS

Disanto G, Dobson R, Pakpoor J, Elangovan RI, Adiutori R, Kuhle J, Giovannoni G. The refinement of genetic predictors of multiple sclerosis. PLoS One. 2014 May 2;9(5):e96578. doi: 10.1371/journal.pone.0096578. eCollection 2014.

A recent genome wide association study (GWAS) demonstrated that more than 100 genetic variants influence the risk of multiple sclerosis (MS). We investigated what proportion of the general population can be considered at high genetic risk of MS, whether genetic information can be used to predict disease development and how the recently found genetic associations have influenced these estimates. We used summary statistics from GWAS in MS to estimate the distribution of risk within a large simulated general population. We profiled MS associated loci in 70 MS patients and 79 healthy controls (HC) and assessed their position within the distribution of risk in the simulated population. The predictive performance of a weighted genetic risk score (wGRS) on disease status was investigated using receiver operating characteristic statistics. When all known variants were considered, 40.8% of the general population was predicted to be at reduced risk, 49% at average, 6.9% at elevated and 3.3% at high risk of MS. Fifty percent of MS patients were at either reduced or average risk of disease. However, they showed a significantly higher wGRS than HC (median 13.47 vs 12.46, p = 4.08×10-10). The predictive performance of the model including all currently known MS associations (area under the curve = 79.7%, 95%CI = 72.4%-87.0%) was higher than that of models considering previously known associations. Despite this, considering the relatively low prevalence of MS, the positive predictive value was below 1%. The increasing number of known associated genetic variants is improving our ability to predict the development of MS. This is still unlikely to be clinically useful but a more complete understanding of the complexity underlying MS aetiology and the inclusion of environmental risk factors will aid future attempts of disease prediction

There are now 110 identified genetic
variants which influence MS risk outside of the MHC (the “master
switch” of the immune system). These genetic variants have been undoubtedly
beneficial in helping us to understand the disease process underlying
MS, and have implicated different aspects of the immune
system. However, each variant is known to individually exert only a
very small effect on MS risk, which raises the question of how
helpful these genetic variants are in helping us to predict who is
going to get MS?

Using statistical models incorporating
these genetic variants, we tried to estimate what proportion of the
general population is likely to be at a different MS risk compared to
an average baseline risk. We found that about 90% of the general
population are thought to be either at average or reduced risk of MS,
with a higher than average risk in about 10%.

Further, when studying two groups of MS
patients and healthy controls it was found that, based on their
genetic make-up, more MSers were rated as having an increased or high
MS risk compared to controls. However, more than half of MSers were
still considered to be at average or reduced MS risk.

Overall, we found that as the numbers
of known MS associated genetic variants have increased, so has our
ability to predict MS. Nonetheless, this predictive ability is still
poor and very unlikely to be useful clinically. Given that MS
causation is influenced by both genetic and environmental risk
factors, it will be important to determine how (and perhaps
critically, when) these factors interact to influence MS risk in
order to develop a more comprehensive, and accurate, model for MS

CoI This work by TeamG

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