Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score.
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.
De Jager
Philip L
PL
Program in Translational NeuroPsychiatric Genomics, Department of Neurology, Brigham and Women's Hospital, Boston, MA 02115, USA. pdejager@rics.bwh.harvard.edu
Chibnik
Lori B
LB
Cui
Jing
J
Reischl
Joachim
J
Lehr
Stephan
S
Simon
K Claire
KC
Aubin
Cristin
C
Bauer
David
D
Heubach
Jürgen F
JF
Sandbrink
Rupert
R
Tyblova
Michaela
M
Lelkova
Petra
P
Havrdova
Eva
E
Pohl
Christoph
C
Horakova
Dana
D
Ascherio
Alberto
A
Hafler
David A
DA
Karlson
Elizabeth W
EW
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