Fig. 2: Performance of the different risk scores including covariates.
From: Multi-PGS enhances polygenic prediction by combining 937 polygenic scores
Comparison between the per-disorder attention-deficit/hyperactivity disorder (ADHD), affective disorder (AFF), anorexia nervosa (AN), autism spectrum disorder (ASD), bipolar disorder (BD) and schizophrenia (SCZ) single GWAS PGS (specific details on SD2) and the multi-PGS trained with 937 PGS in terms of A liability adjusted R2 and B log odds ratios of the top risk score quintile compared to the middle risk score quintiles. All models included sex, age and first 20 PCs as covariates for training and calculating the risk score on the test set in a fivefold cross-validation scheme. The MultiPGS_lasso and MultiPGS_xgboost were trained with lasso regression and XGBoost respectively, using the 937 PGS and the covariates as explanatory variables. The MultiPGS_lassoPGS_xgboostCOV was generated with lasso regression, combining the 937 PGS and the predicted values of an XGBoost model that included only the covariates. 95% confidence intervals were calculated from 10,000 bootstrap samples of the mean adjusted R2or logOR, where the adjusted R2 was the variance explained by the full model after accounting for the variance explained by a logistic regression covariates-only model as R2adjusted = (R2full - R2cov)/(1 − R2cov). Prevalences used for the liability are shown beneath each disorder label and case-control ratios are available on SD2. All association logOR for all quintiles are available in SF6.
