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

Hailiang Song^1,2, Tian Dong^1,2, Wei Wang^1,2

¹Fisheries Science Institute, Beijing Academy of Agriculture and Forestry Sciences & Beijing Key Laboratory of Fisheries Biotechnology, Beijing 100068, China.

|November 27, 2025

Summary

Genomic prediction using whole-genome sequencing (WGS) data is improved by DAGP, a novel deep autoencoder compression method. DAGP significantly reduces dimensionality, enhancing computational efficiency and maintaining prediction accuracy for genomic selection.

Keywords:

data compression deep autoencoder genomic prediction whole-genome sequencing

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Genomic prediction using whole-genome sequencing (WGS) data faces challenges due to high dimensionality (p) and limited sample size (n), where n ≪ p.
The computational burden of high-dimensional WGS data restricts its practical application in genomic selection.
Existing methods struggle to efficiently handle the vast amount of genetic information in WGS data.

Purpose of the Study:

To introduce DAGP, a novel deep autoencoder compression method for reducing WGS data dimensionality.
To evaluate the computational efficiency and prediction accuracy of DAGP in genomic prediction.
To assess the performance of DAGP when integrated with various genomic prediction models.

Main Methods:

Developed DAGP, a deep autoencoder model to compress WGS data, reducing dimensionality by over 99% while retaining essential genetic information.
Integrated DAGP with Genomic Best Linear Unbiased Prediction (GBLUP), Bayesian, and machine learning models for genomic prediction.
Validated the method on sturgeon (50K markers) and maize (20K markers) datasets, comparing prediction accuracy with traditional WGS-based GBLUP.

Main Results:

DAGP achieved over 99% dimensionality reduction of WGS data, significantly enhancing computational efficiency.
Genomic prediction accuracy using DAGP combined with GBLUP was comparable to using full WGS data, even at reduced marker densities.
Integration of DAGP with Bayesian and machine learning models resulted in improved genomic prediction accuracy, with average gains of 6.05% and 5.35% over WGS-based GBLUP, respectively.

Conclusions:

DAGP offers an efficient and scalable solution for genomic prediction with high-dimensional WGS data.
The method preserves crucial genetic information during compression, enabling accurate genomic predictions.
DAGP facilitates the practical application of WGS data in genomic selection across various species, improving both computational feasibility and predictive performance.