Integrating multi-omics to empower next-generation wheat breeding

This topical collection centers on integrative omics in wheat, emphasizing how genomics, transcriptomics, epigenomics, proteomics, metabolomics, and phenomics, combined with systems biology and computational modeling, are reshaping wheat biology and breeding. The rapid growth of large-scale, high-quality omics datasets offers unprecedented power to elucidate gene function, regulatory networks, and the architecture of complex traits, thereby enabling precision strategies to improve yield, resilience, and grain quality. Against the backdrop of global food security, climate change, and sustainability imperatives, integrative omics provides a holistic view of genotype-phenotype-environment relationships. By connecting molecular layers to field performance, these approaches accelerate the discovery of causal variants and regulatory circuits, refine trait prediction, and inform the rational design of wheat ideotypes with enhanced productivity, stress tolerance, and nutritional value.

We seek innovative studies that integrate multiple omics in wheat or substantially enable such integration, offering a platform to share: original research that dissects complex traits and delivers breeding-ready targets, markers, models, or germplasm; FAIR-compliant data resources and databases (e.g., pangenomes, variation maps, single-cell/spatial omics); computational methods for cross-omics fusion, causal inference, network reconstruction, and genotype–environment modeling; big-data analyses, benchmarks, and interoperable pipelines; and concise reviews, perspectives, or methodologies that synthesize best practices and future directions.

• Genomics, pangenomics, and structural variation analyses in wheat

• Transcriptomics, small RNA profiling, and regulatory network mapping

• Epigenomics, chromatin dynamics, and stress-responsive gene regulation

• Proteomics and metabolomics for functional characterization of key pathways

• Systems biology and network-based modeling of wheat traits

• Integrative multi-omics analyses to dissect traits in wheat

• High-throughput phenotyping and omics-assisted trait prediction

• Data-driven approaches linking omics datasets to wheat breeding pipelines

• Integration of big data and machine learning for trait prediction