Environment-Aware DNA Language Model for Stress-Responsive Genomic Prioritization in Maize

Abstract

Abiotic stresses such as heat and drought severely reduce maize productivity, yet identifying genomic regions that confer stress resilience remains a challenge. Inspired by advances in Large Language Models (LLMs), Genomic Foundation Models (GFMs) have recently emerged as a promising approach for capturing regulatory patterns through large-scale pre-training on DNA sequences. However, their application to plant stress-response analysis remains unexplored. This study presents an environment-aware DNA-LLM that adapts AgroNT, a transformer-based GFM pre-trained on diverse plant genomes, by incorporating stress-specific prompt tokens. Through parameter-efficient fine-tuning, the model learns stress-conditioned sequence representations that form distinct clusters in the embedding space across environmental contexts. By combining stress-induced shifts in these sequence representations relative to control conditions with transformer attention patterns, we prioritized putative heat- and drought-responsive genomic regions associated with grain yield in the Genomes-to-Fields (G2F) panel. Prioritized regions were supported by spatiotemporal differential gene-expression evidence and overlap with stress-associated quantitative trait loci. They were further characterized through transcription-factor family analysis and regulatory motif enrichment. Attention-guided analysis additionally identified stress-associated motifs enriched within model-emphasized sequence regions. Overall, the prioritized loci were proximal to genes involved in transcriptional regulation, signaling, and metabolic pathways relevant to abiotic-stress adaptation, demonstrating the potential of stress-conditioned transformer-based sequence modeling for environment-aware genome-to-phenome analysis.