Unlocking rice's genetic potential: big data-driven insights from population genomics

Rice, a cornerstone of global food security, faces escalating demand amidst climate-driven agricultural challenges. By integrating data from over 10,000 cultivated and wild rice accessions, researchers have resolved structural variations, domestication trajectories, and adaptive alleles critical for stress resilience and yield improvement. From five major rice-growing regions in China, the newly expanded 6K genomic atlas revealed 3,131 quantitative trait loci associated with 53 phenotypes across 212 datasets under various environmental conditions. Platforms like RiceSuperPIRdb and RiceAltas integrate multidimensional datasets with visualization tools, bridging genetic discovery to breeding applications. Artificial intelligence (AI) models trained on 12,678 hybrid profiles predict heterosis with > 85% accuracy, while platforms such as AutoGP integrate genotype extraction, phenotypic extraction, and genomic selection (GS) models to halve breeding cycles. Yet, petabyte-scale data from initiatives challenge traditional storage and analysis frameworks, necessitating cloud-based architectures and interpretable AI to resolve nonlinear genotype-phenotype relationships. Single-cell multi-omics and 3D genome mapping now dissect spatiotemporal gene regulatory networks, transforming empirical breeding into a predictive science despite enduring bottlenecks in data standardization and equitable resource sharing. This synthesis underscores the pivotal role of population genomics in harnessing rice's evolutionary legacy to meet twenty-first-century food security imperatives.