Intensive rice breeding over the past 50 y has dramatically increased productivity especially in the indica subspecies, but our knowledge of the genomic changes associated with such improvement has been limited. In this study, we analyzed low-coverage sequencing data of 1,479 rice accessions from 73 countries, including landraces and modern cultivars. We identified two major subpopulations, indica I (IndI) and indica II (IndII), in the indica subspecies, which corresponded to the two putative heterotic groups resulting from independent breeding efforts. We detected 200 regions spanning 7.8% of the rice genome that had been differentially selected between IndI and IndII, and thus referred to as breeding signatures. These regions included large numbers of known functional genes and loci associated with important agronomic traits revealed by genome-wide association studies. Grain yield was positively correlated with the number of breeding signatures in a variety, suggesting that the number of breeding signatures in a line may be useful for predicting agronomic potential and the selected loci may provide targets for rice improvement.) is one of the most important cereal crops in the world. There have been landmark achievements in rice improvement over the past 50 y, especially in the indica subspecies. A major breakthrough resulted from the independent development of a series of semidwarf varieties in China and by the International Rice Research Institute (IRRI) in the 1950s and 1960s, leading to the "green revolution" in rice. Since then, semidwarfness has been a basic characteristic for almost all modern varieties. Based on semidwarf varieties, improvement for other traits, such as abiotic stress resistance, broad-spectrum resistances to biotic stresses, and better grain quality, has also been achieved. Another major breakthrough stemmed from the exploitation of hybrid vigor in China (1), resulting in the largescale adoption of hybrid rice since the 1970s. Jointly, these breakthroughs have greatly increased rice productivity in the past several decades globally.Genomic studies in recent years have identified a large number of loci that were under selection during rice domestication (2). However, there has been very limited study to identify loci or genomic regions that have been under selection due to breeding. Next-generation sequencing technologies have enabled sequencing of a large number of rice accessions at relatively low cost, providing opportunities to inspect the genomic regions selected in the history of crop improvement. Meanwhile, genome-wide association studies (GWAS) have provided an effective approach to analyze the genetic architecture of complex traits and allow identification of candidate genes for further improvement of agronomically important traits (3,4).In this study, we analyzed low-coverage sequencing data of 1,479 rice accessions, which revealed a large number of differentially selected regions associated with breeding efforts between two major subpopulations in indica. These selected regi...

Rice Variation Map (RiceVarMap, http:/ricevarmap.ncpgr.cn) is a database of rice genomic variations. The database provides comprehensive information of 6 551 358 single nucleotide polymorphisms (SNPs) and 1 214 627 insertions/deletions (INDELs) identified from sequencing data of 1479 rice accessions. The SNP genotypes of all accessions were imputed and evaluated, resulting in an overall missing data rate of 0.42% and an estimated accuracy greater than 99%. The SNP/INDEL genotypes of all accessions are available for online query and download. Users can search SNPs/INDELs by identifiers of the SNPs/INDELs, genomic regions, gene identifiers and keywords of gene annotation. Allele frequencies within various subpopulations and the effects of the variation that may alter the protein sequence of a gene are also listed for each SNP/INDEL. The database also provides geographical details and phenotype images for various rice accessions. In particular, the database provides tools to construct haplotype networks and design PCR-primers by taking into account surrounding known genomic variations. These data and tools are highly useful for exploring genetic variations and evolution studies of rice and other species.

Rice (Oryza sativa) is an important dietary source of both essential micronutrients and toxic trace elements for humans. The genetic basis underlying the variations in the mineral composition, the ionome, in rice remains largely unknown. Here, we describe a comprehensive study of the genetic architecture of the variation in the rice ionome performed using genome-wide association studies (GWAS) of the concentrations of 17 mineral elements in rice grain from a diverse panel of 529 accessions, each genotyped at ;6.4 million single nucleotide polymorphism loci. We identified 72 loci associated with natural ionomic variations, 32 that are common across locations and 40 that are common within a single location. We identified candidate genes for 42 loci and provide evidence for the causal nature of three genes, the sodium transporter gene Os-HKT1;5 for sodium, Os-MOLYBDATE TRANSPORTER1;1 for molybdenum, and Grain number, plant height, and heading date7 for nitrogen. Comparison of GWAS data from rice versus Arabidopsis (Arabidopsis thaliana) also identified well-known as well as new candidates with potential for further characterization. Our study provides crucial insights into the genetic basis of ionomic variations in rice and serves as an important foundation for further studies on the genetic and molecular mechanisms controlling the rice ionome.

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Rice cultivars have been adapted to favorable ecological regions and cropping seasons. Although several heading date genes have separately made contributions to this adaptation, the roles of gene combinations are still unclear. We employed a map-based cloning approach to isolate a heading date gene, which coordinated the interaction between Ghd7 and Ghd8 to greatly delay rice heading. We resequenced these three genes in a germplasm collection to analyze natural variation. Map-based cloning demonstrated that the gene largely affecting the interaction between Ghd7 and Ghd8 was Hd1. Natural variation analysis showed that a combination of loss-of-function alleles of Ghd7, Ghd8 and Hd1 contributes to the expansion of rice cultivars to higher latitudes; by contrast, a combination of pre-existing strong alleles of Ghd7, Ghd8 and functional Hd1 (referred as SSF) is exclusively found where ancestral Asian cultivars originated. Other combinations have comparatively larger favorable ecological scopes and acceptable grain yield. Our results indicate that the combinations of Ghd7, Ghd8 and Hd1 largely define the ecogeographical adaptation and yield potential in rice cultivars. Breeding varieties with the SSF combination are recommended for tropical regions to fully utilize available energy and light resources and thus produce greater yields.