A data-adaptive Bayesian regression approach for polygenic risk prediction

Abstract

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 r2 over existing methods. In addition, NeuPred has similar or advantageous computational efficiency compared with the state-of-the-art Bayesian methods. Availability The R package implementing NeuPred is available at https://github.com/shuangsong0110/NeuPred. Supplementary information Supplementary data are available at Bioinformatics online.

Stacked neural network for predicting polygenic risk score

“…LD panels are assembled block-wise, implying that correlations are only considered among variants within the same block. In the continued evolution of PRS methodologies, NeuPred 17 stands out. Utilising neuronised priors, the model further refines predictions by employing a cross-validation strategy for prior selection.…”