A pre-train and fine-tune framework for adaptive boosting of pre-trained polygenic risk scores

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Abstract
Polygenic risk scores are widely used for predicting genetic risk across complex diseases and traits, and several pre-trained models have been developed. Few approaches leverage these pre-trained polygenic risk scores to further refine predictive performance. Here, we present Adaptive Boosting of pre-trained Polygenic Risk Socres, a fine-tuning framework that refines pre-trained polygenic risk score models through adaptive variable selection and model boosting to identify additional predictive signals that may not be fully captured by the original models. Simulations show that our framework can identify signals orthogonal to pre-trained polygenic risk scores while controlling false discovery rates. Using UK Biobank data, we fine-tune pre-trained polygenic risk scores for binary diseases and continuous traits, and validate the results across three independent datasets: All of Us, eMERGE, and Penn Medicine Biobank. Real data analyses show that Adaptive Boosting of pre-trained Polygenic Risk Scores achieves statistically significant improvements in several scenarios while maintaining competitive performance in others.

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