Research: Predicting MS from Genes

Jafari N et al. Perspectives on the Use of Multiple Sclerosis Risk Genes for Prediction. Plos One 2011;6(12):e26493.

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 odds ratios of risk SNPs for higher discriminative accuracy in the future.

RESULTS: Individuals with 10 to 12 risk alleles (genetic variants) had a significantly increased risk compared to individuals with the average population risk for developing MS (Odds Ratio 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 single nucleotide polymorphism (SNPs pronounced snips) was 0.64. The area under the curve increases to 0.66 using the well replicated 24 and to 0.69 when including all replicated and novel Single nucleotide polymorphism (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and Odd ratios of 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 Odds ratios of 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.

We have known for many years that an allele of HLA-DR (common in white Northern Europeans) has been associated with a risk of development of MS. 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.

However by investigating more and more of these risk alleles, using such models, it does not dramatically increase the ability to predict whether one will get MS and so simply adding in more and more low risk alleles into the equation does not make it more useful in clinical practice.

Therefore the genetic analysis has so far cost considerable funds but has yet to provide valuable practical information that has therapeutic application.

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 but I’ll leave this for Prof G to discuss.

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