Genomic selection (GS) is critical for accelerating genetic gain in modern plant breeding. Deep learning approaches offer powerful non-linear representation capabilities for modelling non-additive effects. However, their application in GS remains restricted, as high-dimensional, low-sample and noisy data hinder the identification of informative markers. The present study proposes DNAwhisper, a deep learning framework designed for multi-trait prediction and adaptive marker prioritisation. The framework integrates a cascaded architecture, GFIformer, employing shared network parameters across partitioned marker blocks to adaptively compress genetic features within a hierarchical pyramid. Pre-training on population genetic structure regularises feature learning to establish a generalisable latent representation. During trait modelling, importance scores for aggregated genomic regions at multi-resolutions are extracted from the distinct pyramid levels under trait-guided deep supervision, enhancing interpretability and supporting marker prioritisation. DNAwhisper was evaluated on maize, wheat, tomato and grape datasets for marker prioritisation and phenotypic prediction, achieving prediction accuracy approximately 3. 0% to 10. 0% higher than the baseline model. Furthermore, DNAwhisper identifies major QTLs (e. g. , VGT 1 VGT1, ZCN 8 ZCN8) and epistatic signals within the gibberellin metabolic pathway across maize flowering traits. This framework provides a new strategy for dissecting the genetic architecture of complex traits.
Ma et al. (Fri,) studied this question.