title: The refinement of genetic predictors of multiple sclerosis.
abstract: A recent genome wide association study (GWAS) demonstrated that more than 100 genetic variants influence the risk of multiple sclerosis (MS). We investigated what proportion of the general population can be considered at high genetic risk of MS, whether genetic information can be used to predict disease development and how the recently found genetic associations have influenced these estimates. We used summary statistics from GWAS in MS to estimate the distribution of risk within a large simulated general population. We profiled MS associated loci in 70 MS patients and 79 healthy controls (HC) and assessed their position within the distribution of risk in the simulated population. The predictive performance of a weighted genetic risk score (wGRS) on disease status was investigated using receiver operating characteristic statistics. When all known variants were considered, 40.8% of the general population was predicted to be at reduced risk, 49% at average, 6.9% at elevated and 3.3% at high risk of MS. Fifty percent of MS patients were at either reduced or average risk of disease. However, they showed a significantly higher wGRS than HC (median 13.47 vs 12.46, p = 4.08×10(-10)). The predictive performance of the model including all currently known MS associations (area under the curve = 79.7%, 95%CI = 72.4%-87.0%) was higher than that of models considering previously known associations. Despite this, considering the relatively low prevalence of MS, the positive predictive value was below 1%. The increasing number of known associated genetic variants is improving our ability to predict the development of MS. This is still unlikely to be clinically useful but a more complete understanding of the complexity underlying MS aetiology and the inclusion of environmental risk factors will aid future attempts of disease prediction.
authors.affiliation: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, London, United Kingdom.
authors.forename: Giulio
authors.initials: G
authors.lastname: Disanto
authors.affiliation: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, London, United Kingdom.
authors.forename: Ruth
authors.initials: R
authors.lastname: Dobson
authors.affiliation: Oxford medical school, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom.
authors.forename: Julia
authors.initials: J
authors.lastname: Pakpoor
authors.affiliation: Oxford medical school, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom.
authors.forename: Ramyiadarsini I
authors.initials: RI
authors.lastname: Elangovan
authors.affiliation: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, London, United Kingdom.
authors.forename: Rocco
authors.initials: R
authors.lastname: Adiutori
authors.affiliation: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, London, United Kingdom.
authors.forename: Jens
authors.initials: J
authors.lastname: Kuhle
authors.affiliation: Queen Mary University of London, Blizard Institute, Barts and The London School of Medicine and Dentistry, London, United Kingdom.
authors.forename: Gavin
authors.initials: G
authors.lastname: Giovannoni
mesh_terms: D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D014644:Genetic Variation; D055106:Genome-Wide Association Study; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000453:epidemiology / Q000235:genetics*; D012307:Risk Factors