This study investigated the extent of MS prediction by genetic risk models, using empirical and simulation data on the most updated genetic information for MS. First, we showed that the predictive performance of testing multiple genes can be enhanced by using a combination of individual MS risk alleles. As expected, HLA-DR
influences the ability to predict MS considerably due to its high OR. However, even without HLA-DR
there was an increased, but small, risk for developing MS in people with 8 to 10 risk alleles. This underlines the current insight that multiple genes exert a small effect on developing MS on top of the major influence of HLA-DR 
Next, after validating the genetic risk models with simulated genotype and MS status in a population of 100,000 individuals, we estimated that the predictive value as reflected in AUCs would be 0.66 when all 24 well replicated GWA derived polymorphisms were considered. Moreover, we showed that including the 29 novel risk genes increased the AUC only slightly to 0.69, illustrating that even more than doubling the number of risk SNPs does not increase the AUC sufficiently to make it useful in clinical practice. The AUC of 0.69 is comparable to other risk prediction models in MS 
. In 2009, De Jager and colleagues investigated the prediction of 16 MS susceptibility loci using weighted genetic risk scores in three cohorts 
. They demonstrated a consistent discriminatory ability in three independent samples (AUC varying 0.64–0.70). Gourraud and colleagues also investigated the aggregation of genetic MS risk markers in individuals by comparing multiple and single case families 
. They showed that a greater genetic burden in siblings of MS patients was associated with an increased MS risk (OR 2.1, p
0.001). However, the AUC for genetic burden differences between probands and siblings was only 0.57, indicating that the available genetic data is not sufficient to achieve case-control prediction of MS. They also used 16 MS susceptibility loci, partly matching with those of De Jager et al.
Before interpreting the clinical relevance of our findings, a methodological issue needs to be disclosed. We assumed that genetic variants inherited independently and that the combined effect of the genetic variants on disease risk followed a multiplicative risk model of independent effects (i.e., no statistical interaction terms were included in the model). Although so far no studies have demonstrated gene-gene interactions with MS risk, it is still possible that these will be discovered in future studies in larger populations. However, gene-gene interactions only improve the MS risk predictions if their effect sizes are substantially high (e.g., OR>5). When interaction effects are smaller, their effects on the predictive accuracy will be comparable with that of single gene effects, because by definition their frequencies are lower.
With the current model including 53 variants, we are still not able to differentiate with reasonable accuracy between individuals who will develop MS and those who will not (AUC 0.69). This makes our model not clinically useful. So the question is raised how to improve MS prediction.
We demonstrated in the simulation study that in order to obtain higher AUCs, a considerable number of additional common genetic variants or stronger associated variants with high ORs () need to be identified. The per-allele OR of the polymorphisms identified in GWA studies ranges from 1.08 to 2.1. When future GWA studies will identify polymorphisms with per-allele ORs around 1.1, the predictive ability of the genetic risk model can theoretically be improved beyond that of the existing models. Yet, even small improvements to 0.70 still require the discovery of 20 new statistically significant variants. Despite the increase it is still not clinically applicable. Because even in a disease that is readily treatable and even preventable like coronary heart disease (as presented in the Framingham Risk score) an AUC of about 0.80 is used 
. For MS there is still no cure or preventive treatment available, and so a higher predictive accuracy is desirable to prevent false positives. We have shown that to pursue an AUC of 0.85, we have to include 50 new variants with ORs of 1.4 or a few common variants (minor allele frequency >30%) with high ORs (). This may prove to be difficult, because the common genetic variants with high ORs may already have been identified, which would imply that even higher numbers of common genetic variants with relatively smaller ORs or many exceedingly rare variants (minor allele frequency <1%) with high ORs, will be needed. This seems not feasible. To note, unlike HLA-DR
most of the genetic risk factors identified so far have only a slight effect on susceptibility to MS (with ORs that range from 1.1 to 1.2) 
. However, more high risk genetic MS risk variants can be expected in near future 
. With novel techniques such as next generation sequencing we can expect new rare variants with high ORs to be discovered 
. This approach has already been proven successful in rare Mendelian disorders and can potentially also identify rare variants explaining the high recurrence rate of MS within families 
. Also, this technique potentially allows us to find the causal variants for MS which will most likely have higher ORs than those found in GWA studies.
Another approach to improve MS prediction could be combining genetic with nongenetic risk factors such as infection with Epstein-Barr virus (EBV), smoking, and serum vitamin D concentrations 
. It is likely that risk prediction models combined with nongenetic factors will perform better as ORs for SNPs tend to be smaller than ORs based on nongenetic factors (e.g. infectious mononucleosis 
). De Jager and colleagues showed an enhanced discriminatory ability of 16 susceptibility genes by the inclusion of sex (AUC increasing from 0.70 to 0.74) and smoking and immune response to EBV (AUC increasing from 0.64 to 0.68). Others have performed studies combining the effects of HLA-DR
and non-genetic factors like smoking and anti EBV serum levels 
. Also, integration of transcriptional, proteomics, and clinical factors will probably improve the prediction model and with that our understanding of MS genetics 
. However, the added value of the SNPs might then be questioned. For other diseases it has been shown that the AUC does not improve a lot when adding SNPs to clinical risk factors. It should be noted though, that in these studies only small numbers of SNPs were added to the clinical risk factors.
Even if we can improve the prediction of MS in the future the question remains what the clinical implications of such predictive risk models would be. The discriminative accuracy that is required in preventive or clinical care depends on the goal of testing, the availability of (preventive) treatment, and the adverse effects of false-positive and false-negative test results.
Although the early results from GWA studies have not yet been used clinically, at least a partial goal of understanding the genetic basis of MS is to investigate the use of these variants to predict disease risk, so that environmental changes or therapeutic interventions can be initiated before the inflammatory demyelinating process progresses or even starts. Also, by better mapping the genetic of MS, we hope to improve our understanding of the pathofysiology of MS. This could help us finding better and new therapeutic drugs. By combining family history with a quantitative measure of genetic risk, a screening method might eventually be implemented that could identify clinically silent evidence of disease among first-degree relatives of MS patients, who have 20–50 times higher risk of developing MS 
. However, the absolute risk is only 2–5% and therefore the models could be more useful in high risk populations with individuals who have had clinically isolated syndrome suggesting MS. These patients present with a neurological disability during their productive years of life and face the possibility of a chronic disease. Thus, they yearn for more clarity about their future. But also improving the risk prediction would enable us to distinguish individuals at risk to start early treatment for reducing the accumulation of neurological disability 
Given the possible clinical consequences of false-positivity within these patients, the required prediction AUCs for the pre-symptomatic diagnosis is considerably higher than an AUC intended for clinically isolated syndrome. It has been suggested that identified genetic variants have stronger effects in multiplex families 
. It is of note that the ORs assessed up to now in GWA studies and validation studies are generally derived from datasets on sporadic cases. In a multiplex family setting, with potential stronger effects for individual risk variants, our estimates may prove to be conservative.
In conclusion, our analyses show that prediction of MS risk based on low susceptibility variants theoretically can improve prediction of disease when more variants are being discovered. However, the discriminatory ability in a clinical setting will be limited.
The empirical study was conducted with the approval of the institutional medical ethics committee of Erasmus MC, Rotterdam, The Netherlands.