Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score
Philip L De Jager, Lori B Chibnik, Jing Cui, Joachim Reischl, Stephan Lehr, K Claire Simon, Cristin Aubin, David Bauer, Jürgen F Heubach, Rupert Sandbrink, Michaela Tyblova, Petra Lelkova, Steering committee of the BENEFIT study, Steering committee of the BEYOND study, Steering committee of the LTF study, Steering committee of the CCR1 study, Eva Havrdova, Christoph Pohl, Dana Horakova, Alberto Ascherio, David A Hafler, Elizabeth W Karlson, M S Freedman, G Edan, H-P Hartung, C H Polman, L Kappos, X Montalbán, D Miller, P O'Connor, H-P Hartung, G Comi, M Filippi, L Kappos, B G W Arnason, S Cook, D S Goodin, D Jeffery, A Traboulsee, G C Ebers, D Langdon, D S Goodin, A T Reder, F Zipp, S Schimrigk, H-P Hartung, M Filippi, J Hillert, Philip L De Jager, Lori B Chibnik, Jing Cui, Joachim Reischl, Stephan Lehr, K Claire Simon, Cristin Aubin, David Bauer, Jürgen F Heubach, Rupert Sandbrink, Michaela Tyblova, Petra Lelkova, Steering committee of the BENEFIT study, Steering committee of the BEYOND study, Steering committee of the LTF study, Steering committee of the CCR1 study, Eva Havrdova, Christoph Pohl, Dana Horakova, Alberto Ascherio, David A Hafler, Elizabeth W Karlson, M S Freedman, G Edan, H-P Hartung, C H Polman, L Kappos, X Montalbán, D Miller, P O'Connor, H-P Hartung, G Comi, M Filippi, L Kappos, B G W Arnason, S Cook, D S Goodin, D Jeffery, A Traboulsee, G C Ebers, D Langdon, D S Goodin, A T Reder, F Zipp, S Schimrigk, H-P Hartung, M Filippi, J Hillert
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).
Findings: 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.
Interpretation: 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.
Conflict of interest statement
Conflict of Interest
Dr. Cook has received honoraria and consulting fees from Merck-Serono and Bayer Healthcare.
Dr. Filippi has received grant/research support from TEVA, Merck-Serono, Bayer Schering Pharma, Biogen IDEC, and GENMAB. He has been a consultant or received speaker fees from: TEVA, Merck-Serono, Bayer Schering Pharma, Biogen IDEC, and GENMAB.
Dr. Freedman has received honoraria or consulting fees from Merck-Serono, Novartis, Bayer Healthcare, TEVA, Biogen IDEC, and Sanofi Aventis.
Dr. Havrdova received speaker’s honoraria from Biogen IDEC, TEVA, Novartis, Merck and Bayer Schering Pharma.
Dr. Langdon has received honoraria from Merck-Serono, Novartis, Bayer Healthcare, Sanofi-Aventis, Hoffman LaRoche. She performs contract work for Merck-Serono, and Bayer Healthcare
Dr. O’Connor has received consulting fees from TEVA, Sanofi-Aventis, Novartis, Bayer. He receives grant support from Bayer, Biogen IDEC, Novartis, and Sanofi Aventis.
Dr. Polman has received consulting and/or lecturing fees from Biogen IDEC, Bayer Schering Pharma AG, TEVA, Serono, Novartis, GlaxoSmithKline, UCB, AstraZeneca, Roche, and antisense therapeutics. He receives grant support from Biogen IDEC, Bayer Schering Pharma, TEVA, Serono, Novartis, GlaxoSmithKline.
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Source: PubMed