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

SUBMITTER: Song S 

PROVIDER: S-EPMC8963326 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature