title: Perspectives on the use of multiple sclerosis risk genes for prediction.
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.
  
  
  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.
  
  
  CONCLUSIONS
  Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.
authors.affiliation: Department of Neurology, ErasMS Center, Erasmus MC, Rotterdam, The Netherlands.
authors.forename: Naghmeh
authors.initials: N
authors.lastname: Jafari
authors.forename: Linda
authors.initials: L
authors.lastname: Broer
authors.forename: Cornelia M
authors.initials: CM
authors.lastname: van Duijn
authors.forename: A Cecile J W
authors.initials: AC
authors.lastname: Janssens
authors.forename: Rogier Q
authors.initials: RQ
authors.lastname: Hintzen
mesh_terms: D000328:Adult; D000368:Aged; D019540:Area Under Curve; D018968:CD58 Antigens / Q000235:genetics; D016022:Case-Control Studies; D018797:Cell Cycle Proteins; D005189:False Positive Reactions; D005260:Female; D020690:GTPase-Activating Proteins; D005787:Gene Frequency; D020022:Genetic Predisposition to Disease; D005838:Genotype; D059811:HLA-DRB1 Chains / Q000235:genetics; D006801:Humans; D020381:Interleukin-17; D053645:Interleukin-2 Receptor alpha Subunit / Q000235:genetics; D037181:Lectins, C-Type / Q000235:genetics; D008297:Male; D008875:Middle Aged; D009004:Monosaccharide Transport Proteins / Q000235:genetics; D009103:Multiple Sclerosis / Q000175:diagnosis* / Q000235:genetics*; D009687:Nuclear Proteins / Q000235:genetics; D020641:Polymorphism, Single Nucleotide; D011237:Predictive Value of Tests; D020395:Receptors, Interleukin-7 / Q000235:genetics; D015203:Reproducibility of Results; D012306:Risk
chemical_list: D018968:CD58 Antigens; C522505:CLEC16A protein, human; D018797:Cell Cycle Proteins; C506399:EVI5 protein, human; D020690:GTPase-Activating Proteins; D059811:HLA-DRB1 Chains; C508017:IL2RA protein, human; C515934:Il17a protein, rat; D020381:Interleukin-17; D053645:Interleukin-2 Receptor alpha Subunit; D037181:Lectins, C-Type; D009004:Monosaccharide Transport Proteins; D009687:Nuclear Proteins; D020395:Receptors, Interleukin-7