Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score

Abstract

Background Predicting susceptibility to multiple sclerosis may have important clinical applications either as part of a diagnostic algorithm or as a tool with which to identify high-risk individuals for prospective studies. Here, we examine the utility of an aggregate measure of risk of multiple sclerosis (MS) based on genetic susceptibility loci. Secondarily, we assess the added effect of environmental risk factors that have been associated with susceptibility for MS. Methods We created a weighted genetic risk score (wGRS) that includes 16 MS susceptibility loci. We tested our model using data from (1) 2215 MS cases and 2189 controls (derivation samples), (2) a validation set of 1340 cases and 1109 controls taken from several MS therapeutic trials (TT samples), and (3) a second validation set of 143 cases and 281 controls from the U.S. Nurses’ Health Studies I and II (NHS) for whom we also have information regarding exposure to smoking and Epstein-Barr Virus (EBV). Findings . Patients with wGRS > 1.25 standard deviations from the mean had a significantly higher odds ratio for MS in all datasets. The area under the curve for a purely genetic model was 0.70 and for a gender + genetic model was 0.74 in the derivation samples (P <0.0001), 0.64 and 0.72 in the TT cohort (P <0.0001). Similarly, consideration of smoking and immune response to EBV enhanced the AUC of 0.64 for the genetic model to 0.68 in the NHS cohort (P =0.02). The wGRS does not appear to be correlated with conversion of a clinically isolated syndrome to MS. Interpretation The current combination of 16 susceptibility alleles into a wGRS modestly predicts MS risk and shows consistent discriminatory ability in independent subject samples and is enhanced by considering non-genetic risk factors.

Genetics can contribute to the prognosis of Brugada syndrome: a pilot model for risk stratification

“…33 Mann-Whitney test was used for weighted genetic risk score comparisons. The contribution of weighted genetic risk score to MAE prediction was evaluated generating a receiver operating characteristic curve by plotting the sensitivity of continuous weighted genetic risk score against 1 Àspecificity and calculating the area under the curve as measure of performance.…”

“…The weight of each risk allele and the presence of SCN5A mutation was calculated as function of its odds ratio obtained with w 2 -test 33 (Table 4). Using this model, patients experiencing MAE had mean genetic risk score ¼ 6.36 ± 2.5, while event-free patients ¼ 2.87±1.99 (Mann-Whitney P ¼ 1.5 Â 10 À5 ; Figure 3a).…”

Random Forests for Genetic Association Studies

“…At this time much of that work has centered on summing marginal effects to derive an overall risk score (e.g. De Jager et al [2009]; The International Schizophrenia Consortium [2009]). While initially valuable such methods do not adequately take into account the joint effect of multiple SNPs.…”

Epitope spreading as an early pathogenic event in pediatric multiple sclerosis

“…We also generated receiver operating characteristic curves by plotting the sensitivity against 1 2 specificity and calculating the area under the curve (AUC) for each population. The AUC is a measure of the ability of the model to discriminate between ADS-mono and ADS-MS. 15 Receiver operating characteristic analysis of the classifier built using the IgG reactivities in the training set produced an AUC of 0.952 ( figure 2A and table e-5). Because the LOOCV was performed on the same sample cohort (training set) used to generate the model, performance of the classifier may reflect overfitting of the model.…”