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Perspectives on the use of multiple sclerosis risk genes for prediction.
PLoS One 2011; 6(12):e26493Plos

Abstract

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 ORs of risk SNPs for higher discriminative accuracy in the future.

RESULTS

Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 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 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 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 ORs 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.

Authors+Show Affiliations

Department of Neurology, ErasMS Center, Erasmus MC, Rotterdam, The Netherlands.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

22164203

Citation

Jafari, Naghmeh, et al. "Perspectives On the Use of Multiple Sclerosis Risk Genes for Prediction." PloS One, vol. 6, no. 12, 2011, pp. e26493.
Jafari N, Broer L, van Duijn CM, et al. Perspectives on the use of multiple sclerosis risk genes for prediction. PLoS ONE. 2011;6(12):e26493.
Jafari, N., Broer, L., van Duijn, C. M., Janssens, A. C., & Hintzen, R. Q. (2011). Perspectives on the use of multiple sclerosis risk genes for prediction. PloS One, 6(12), pp. e26493. doi:10.1371/journal.pone.0026493.
Jafari N, et al. Perspectives On the Use of Multiple Sclerosis Risk Genes for Prediction. PLoS ONE. 2011;6(12):e26493. PubMed PMID: 22164203.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Perspectives on the use of multiple sclerosis risk genes for prediction. AU - Jafari,Naghmeh, AU - Broer,Linda, AU - van Duijn,Cornelia M, AU - Janssens,A Cecile J W, AU - Hintzen,Rogier Q, Y1 - 2011/12/02/ PY - 2011/05/13/received PY - 2011/09/28/accepted PY - 2011/12/14/entrez PY - 2011/12/14/pubmed PY - 2012/7/21/medline SP - e26493 EP - e26493 JF - PloS one JO - PLoS ONE VL - 6 IS - 12 N2 - 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 ORs of risk SNPs for higher discriminative accuracy in the future. RESULTS: Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 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 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 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 ORs 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/22164203/Perspectives_on_the_use_of_multiple_sclerosis_risk_genes_for_prediction_ L2 - http://dx.plos.org/10.1371/journal.pone.0026493 DB - PRIME DP - Unbound Medicine ER -