A data-adaptive Bayesian regression approach for polygenic risk prediction

Motivation: Polygenic risk score (PRS) has been widely exploited for genetic risk prediction due to its accuracy and conceptual simplicity. We introduce a unified Bayesian regression framework, NeuPred, for PRS construction, which accommodates varying genetic architectures and improves overall prediction accuracy for complex diseases by allowing for a wide class of prior choices. To take full advantage of the framework, we propose a summary-statistics-based cross-validation strategy to automatically select suitable chromosome-level priors, which demonstrates a striking variability of the prior preference of each chromosome, for the same complex disease, and further significantly improves the prediction accuracy. Results: Simulation studies and real data applications with seven disease datasets from the Wellcome Trust Case Control Consortium cohort and eight groups of large-scale genome-wide association studies demonstrate that NeuPred achieves substantial and consistent improvements in terms of predictive r(2) over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods. Availability and implementation: The R package implementing NeuPred is available at https://github.com/shuang song0110/NeuPred.

Automated summary

The study introduced a unified Bayesian regression framework NeuPred for constructing PRS, which accommodates varying genetic architectures and improves prediction accuracy for complex diseases. An automatic chromosome-level prior selection strategy based on summary statistics was proposed, demonstrating significant improvement in accuracy. NeuPred showed substantial and consistent improvements in predictive r(2) over existing methods, while maintaining similar or advantageous computational efficiency.