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DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi-Trait Genomic Prediction and Adaptive Marker

Yuexin Ma1,2, Xiang Li1,3, Xiaohao Ji4

  • 1State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Taian, Shandong, China.

Plant Biotechnology Journal
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

Genomic selection (GS) accelerates plant breeding using deep learning. DNAwhisper, a new framework, improves prediction accuracy and identifies key genetic markers for complex traits.

Keywords:
deep learninggenomic selectionmarker prioritisationmulti‐trait genomic predictionpre‐training

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Area of Science:

  • Plant breeding and genetics
  • Bioinformatics and computational biology
  • Genomics and quantitative genetics

Background:

  • Genomic selection (GS) is vital for enhancing genetic gain in plant breeding.
  • Deep learning models offer potential for capturing complex genetic interactions, but face challenges with high-dimensional, noisy genomic data.
  • Existing methods struggle to effectively identify informative markers for complex trait prediction.

Purpose of the Study:

  • To introduce DNAwhisper, a novel deep learning framework for multi-trait prediction and adaptive marker prioritization in genomic selection.
  • To address limitations of current deep learning applications in GS by improving feature learning and marker identification.
  • To enhance the interpretability of genomic predictions and facilitate the discovery of genetic architecture.

Main Methods:

  • Developed DNAwhisper, a deep learning framework featuring a cascaded GFIformer architecture with shared parameters across marker blocks for adaptive feature compression.
  • Implemented pre-training on population genetic structure to regularize feature learning and establish generalizable latent representations.
  • Utilized trait-guided deep supervision to extract multi-resolution importance scores from a hierarchical pyramid for marker prioritization.

Main Results:

  • DNAwhisper achieved 3.0% to 10.0% higher prediction accuracy compared to baseline models across maize, wheat, tomato, and grape datasets.
  • The framework successfully prioritized informative markers and identified major quantitative trait loci (QTLs) and epistatic interactions.
  • Demonstrated identification of key genes like VGT1 and ZCN8 involved in maize flowering traits.

Conclusions:

  • DNAwhisper offers a robust deep learning strategy for improving prediction accuracy in genomic selection.
  • The framework enhances interpretability by providing multi-resolution genomic region importance scores, aiding marker prioritization.
  • DNAwhisper provides a novel approach for dissecting the genetic architecture of complex traits in plants.