Perspectives on the use of multiple sclerosis risk genes for prediction.

<h4>Objective</h4>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...

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Autores principales:	Naghmeh Jafari, Linda Broer, Cornelia M van Duijn, A Cecile J W Janssens, Rogier Q Hintzen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d984a4a8d8a649179196741c23abbd0e
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spelling	oai:doaj.org-article:d984a4a8d8a649179196741c23abbd0e2021-11-18T07:33:14ZPerspectives on the use of multiple sclerosis risk genes for prediction.1932-620310.1371/journal.pone.0026493https://doaj.org/article/d984a4a8d8a649179196741c23abbd0e2011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22164203/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Objective</h4>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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.Naghmeh JafariLinda BroerCornelia M van DuijnA Cecile J W JanssensRogier Q HintzenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e26493 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naghmeh Jafari Linda Broer Cornelia M van Duijn A Cecile J W Janssens Rogier Q Hintzen Perspectives on the use of multiple sclerosis risk genes for prediction.
description	<h4>Objective</h4>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.<h4>Materials and methods</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.
format	article
author	Naghmeh Jafari Linda Broer Cornelia M van Duijn A Cecile J W Janssens Rogier Q Hintzen
author_facet	Naghmeh Jafari Linda Broer Cornelia M van Duijn A Cecile J W Janssens Rogier Q Hintzen
author_sort	Naghmeh Jafari
title	Perspectives on the use of multiple sclerosis risk genes for prediction.
title_short	Perspectives on the use of multiple sclerosis risk genes for prediction.
title_full	Perspectives on the use of multiple sclerosis risk genes for prediction.
title_fullStr	Perspectives on the use of multiple sclerosis risk genes for prediction.
title_full_unstemmed	Perspectives on the use of multiple sclerosis risk genes for prediction.
title_sort	perspectives on the use of multiple sclerosis risk genes for prediction.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/d984a4a8d8a649179196741c23abbd0e
work_keys_str_mv	AT naghmehjafari perspectivesontheuseofmultiplesclerosisriskgenesforprediction AT lindabroer perspectivesontheuseofmultiplesclerosisriskgenesforprediction AT corneliamvanduijn perspectivesontheuseofmultiplesclerosisriskgenesforprediction AT acecilejwjanssens perspectivesontheuseofmultiplesclerosisriskgenesforprediction AT rogierqhintzen perspectivesontheuseofmultiplesclerosisriskgenesforprediction
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Perspectives on the use of multiple sclerosis risk genes for prediction.

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