title: Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score.
abstract: BACKGROUND
  Prediction of susceptibility to multiple sclerosis (MS) might have important clinical applications, either as part of a diagnostic algorithm or as a means to identify high-risk individuals for prospective studies. We investigated the usefulness of an aggregate measure of risk of MS that is based on genetic susceptibility loci. We also assessed the added effect of environmental risk factors that are associated with susceptibility for MS.
  
  
  METHODS
  We created a weighted genetic risk score (wGRS) that includes 16 MS susceptibility loci. We tested our model with data from 2215 individuals with MS and 2189 controls (derivation samples), a validation set of 1340 individuals with MS and 1109 controls taken from several MS therapeutic trials (TT cohort), and a second validation set of 143 individuals with MS and 281 controls from the US Nurses' Health Studies I and II (NHS/NHS II), for whom we also have data on smoking and immune response to Epstein-Barr virus (EBV).
  
  
  RESULTS
  Individuals with a wGRS that was more than 1.25 SD from the mean had a significantly higher odds of MS in all datasets. In the derivation sample, the mean (SD) wGRS was 3.5 (0.7) for individuals with MS and 3.0 (0.6) for controls (p<0.0001); in the TT validation sample, the mean wGRS was 3.4 (0.7) for individuals with MS versus 3.1 (0.7) for controls (p<0.0001); and in the NHS/NHS II dataset, the mean wGRS was 3.4 (0.8) for individuals with MS versus 3.0 (0.7) for controls (p<0.0001). In the derivation cohort, the area under the receiver operating characteristic curve (C statistic; a measure of the ability of a model to discriminate between individuals with MS and controls) for the genetic-only model was 0.70 and for the genetics plus sex model was 0.74 (p<0.0001). In the TT and NHS cohorts, the C statistics for the genetic-only model were both 0.64; adding sex to the TT model increased the C statistic to 0.72 (p<0.0001), whereas adding smoking and immune response to EBV to the NHS model increased the C statistic to 0.68 (p=0.02). However, the wGRS does not seem to be correlated with the conversion of clinically isolated syndrome to MS.
  
  
  CONCLUSIONS
  The inclusion of 16 susceptibility alleles into a wGRS can modestly predict MS risk, shows consistent discriminatory ability in independent samples, and is enhanced by the inclusion of non-genetic risk factors into the algorithm. Future iterations of the wGRS might therefore make a contribution to algorithms that can predict a diagnosis of MS in a clinical or research setting.
authors.affiliation: Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA. pdejager@rics.bwh.harvard.edu
authors.forename: Philip L
authors.initials: PL
authors.lastname: De Jager
authors.forename: Lori B
authors.initials: LB
authors.lastname: Chibnik
authors.forename: Jing
authors.initials: J
authors.lastname: Cui
authors.forename: Joachim
authors.initials: J
authors.lastname: Reischl
authors.forename: Stephan
authors.initials: S
authors.lastname: Lehr
authors.forename: K Claire
authors.initials: KC
authors.lastname: Simon
authors.forename: Cristin
authors.initials: C
authors.lastname: Aubin
authors.forename: David
authors.initials: D
authors.lastname: Bauer
authors.forename: Jürgen F
authors.initials: JF
authors.lastname: Heubach
authors.forename: Rupert
authors.initials: R
authors.lastname: Sandbrink
authors.forename: Michaela
authors.initials: M
authors.lastname: Tyblova
authors.forename: Petra
authors.initials: P
authors.lastname: Lelkova
authors.forename: Eva
authors.initials: E
authors.lastname: Havrdova
authors.forename: Christoph
authors.initials: C
authors.lastname: Pohl
authors.forename: Dana
authors.initials: D
authors.lastname: Horakova
authors.forename: Alberto
authors.initials: A
authors.lastname: Ascherio
authors.forename: David A
authors.initials: DA
authors.lastname: Hafler
authors.forename: Elizabeth W
authors.initials: EW
authors.lastname: Karlson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D000483:Alleles; D002648:Child; D002675:Child, Preschool; D015331:Cohort Studies; D004777:Environment; D005260:Female; D005838:Genotype; D006801:Humans; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000453:epidemiology* / Q000235:genetics*; D016017:Odds Ratio; D020641:Polymorphism, Single Nucleotide / Q000235:genetics; D011237:Predictive Value of Tests; D040641:Quantitative Trait Loci; D018570:Risk Assessment; D012307:Risk Factors