Variational Autoencoder-based Model Improves Polygenic Prediction in Blood Cell Traits.

ABSTRACT: Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. In this study, we seek to improve the predictive power of PRS through applying advanced deep learning techniques. We show that the Variational AutoEncoder-based model for PRS construction (VAE-PRS) outperforms currently state-of-the-art methods for biobank-level data in 14 out of 16 blood cell traits, wh

SUBMITTER: Li X 

PROVIDER: S-EPMC11760740 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature