A Deep Autoencoder Compression-Based Genomic Prediction Method for Whole-Genome Sequencing Data.

ABSTRACT: Genomic prediction using whole-genome sequencing (WGS) data is challenged by the imbalance between a limited sample size (n) and an extensive number of single-nucleotide polymorphisms (SNPs) (p), where n ≪p. The high dimensionality of WGS data also increases computational demands, limiting its practical application. In this study, we introduce DAGP, a novel method that integrates deep autoencoder compression to reduce WGS data dimensionality by over 99% while preserving essential genetic information. This compression significantly improves computational efficiency, facilitating the effective use of high-dimensional genomic data. Our results demonstrated that DAGP, when combined with the genomic best linear unbiased prediction (GBLUP) method, maintained predictio

SUBMITTER: Song H 

PROVIDER: S-EPMC12650564 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature