For the six quantitative traits, both PRS-CS and PRS-CS-auto consistently outperformed all alternative methods examined. The relative improvement in prediction accuracy for PRS-CS compared to LDpred ranged from 8.01% for LDL and 8.75% for BMI, to 27.75% for height and 32.05% for cholesterol, with an average improvement of 18.17%. The average improvement of PRS-CS-auto relative to LDpred across the six quantitative traits was 11.41%. The average improvements of PRS-CS and PRS-CS-auto relative to P+T were 48.16% and 38.62%, respectively. We note that LDpred was the best method after PRS-CS and PRS-CS-auto for all quantitative traits except height, for which its prediction accuracy was lower than LDpred-inf and P+T. This is theoretically expected and consistent with a recent study, which also observed that for highly polygenic traits, LDpred-inf often outperforms LDpred24.
