Fig. 5: Examples of the prediction accuracy of multi-PGS vs. top predictive single-GWAS-PGS on register-based phenotypes. | Nature Communications

Fig. 5: Examples of the prediction accuracy of multi-PGS vs. top predictive single-GWAS-PGS on register-based phenotypes.

From: Multi-PGS enhances polygenic prediction by combining 937 polygenic scores

Fig. 5: Examples of the prediction accuracy of multi-PGS vs. top predictive single-GWAS-PGS on register-based phenotypes.The alternative text for this image may have been generated using AI.

Comparison between a per-phenotype single GWAS PGS (the top-ranked PGS with largest weight from the lasso multi-PGS model on each outcome, details on SD4) and the multi-PGS trained with 937 PGS in terms of adjusted R2. The set of outcomes includes a other outcomes with available GWAS, b outcomes with no available GWAS, c continuous phenotypes from the MBR and d Case−case predictions. All models included sex, age and first 20 PCs for training the different PGS weights and calculating the risk score on the test set in a fivefold cross-validation scheme. CI were calculated from 10,000 bootstrap samples of the mean adjusted R2, 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). The number next to the multiPGS bar indicates the number of non-zero lasso mean weights for the 5 cross-validation subsets.

Back to article page