Unsupervised representation learning improves genomic discovery and risk prediction for respiratory and circulatory functions and diseases.

ABSTRACT: High-dimensional clinical data are becoming more accessible in biobank-scale datasets. However, effectively utilizing high-dimensional clinical data for genetic discovery remains challenging. Here we introduce a general deep learning-based framework, REpresentation learning for Genetic discovery on Low-dimensional Embeddings (REGLE), for discovering associations between genetic variants and high-dimensional clinical data. REGLE uses convolutional variational autoencoders to compute a non-linear, low-dimensional, disentangled embedding of the data with highly heritable individual components. REGLE can incorporate expert-defined or clinical features and provides a framework to create accurate disease-specific polygenic risk scores (PRS) in datasets which have minimal expert phenotypin

SUBMITTER: Yun T 

PROVIDER: S-EPMC10168505 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature