Informatics Driven Strategies for Crop Improvement

Rapid advances in genome sequencing and high-throughput phenotyping now enable population-scale genomic and multi-omics datasets across diverse crops. Yet converting these data into actionable biological insight and breeding decisions remains a major bottleneck. Challenges include integrating heterogeneous data types (SNPs/structural variants, transcriptomics, methylation, metabolomics), variable data quality across environments, complex genetic architectures (epistasis, pleiotropy, G×E), and limited functional validation. Integrative bioinformatics, combining robust variant discovery, association mapping, multi-omics integration, predictive modeling, and causal inference can bridge this gap by linking genetic variation to gene function and field performance. This Research Topic highlights analytical frameworks, resources, and reproducible pipelines that accelerate trait discovery, validation, and deployment in modern breeding programs.This Research Topic aims to showcase methods and applications that move from genome-scale data to trait improvement, emphasizing (1) integrative analyses that link genotype to phenotype across environments, (2) computational approaches that enable prediction and decision-making in breeding, and (3) biological validation strategies that convert candidate loci into deployable markers or targets. We welcome studies in major and underutilized crops that provide clear translational value for genetic improvement.This Research Topic welcomes studies that translate plant genomic and computational analyses into actionable crop-improvement outcomes, spanning discovery-to-deployment. Submissions should connect genetic/omic variation → biological mechanism or prediction → breeding-relevant interpretation, ideally with evidence across populations and/or environments. Suitable themes include variant discovery (SNPs/indels/SVs), pan-genomes/graph genomes, GWAS/QTL and meta-analyses, genomic prediction/selection, multi-omics integration (e.g., eQTL/TWAS, methylome, metabolome), network and systems biology, phenomics + field analytics, G×E modeling, causal gene prioritization, marker/assay development, and functional validation (e.g., NILs, expression assays, gene editing) that strengthens claims of utility for improvement. Topic editors: Vincent Pamugas Reyes, Jagesh Kumar Tiwari, Fengli Zhao, Stefania Pilati Source: https://www.frontiersin.org/research-topics/79440/informatics-driven-strategies-for-crop-improvement