Asian rice, Oryza sativa is a cultivated, inbreeding species that feeds over half of the world's population. Understanding the genetic basis of diverse physiological, developmental, and morphological traits provides the basis for improving yield, quality and sustainability of rice. Here we show the results of a genome-wide association study based on genotyping 44,100 SNP variants across 413 diverse accessions of O. sativa collected from 82 countries that were systematically phenotyped for 34 traits. Using cross-population-based mapping strategies, we identified dozens of common variants influencing numerous complex traits. Significant heterogeneity was observed in the genetic architecture associated with subpopulation structure and response to environment. This work establishes an open-source translational research platform for genome-wide association studies in rice that directly links molecular variation in genes and metabolic pathways with the germplasm resources needed to accelerate varietal development and crop improvement.

A total of 2414 new di-, tri- and tetra-nucleotide non-redundant SSR primer pairs, representing 2240 unique marker loci, have been developed and experimentally validated for rice ( Oryza sativa L.). Duplicate primer pairs are reported for 7% (174) of the loci. The majority (92%) of primer pairs were developed in regions flanking perfect repeats ≥ 24 bp in length. Using electronic PCR (e-PCR) to align primer pairs against 3284 publicly sequenced rice BAC and PAC clones (representing about 83% of the total rice genome), 65% of the SSR markers hit a BAC or PAC clone containing at least one genetically mapped marker and could be mapped by proxy. Additional information based on genetic mapping and “nearest marker” information provided the basis for locating a total of 1825 (81%) of the newly designed markers along rice chromosomes. Fifty-six SSR markers (2.8%) hit BAC clones on two or more different chromosomes and appeared to be multiple copy. The largest proportion of SSRs in this data set correspond to poly(GA) motifs (36%), followed by poly(AT) (15%) and poly(CCG) (8%) motifs. AT-rich microsatellites had the longest average repeat tracts, while GC-rich motifs were the shortest. In combination with the pool of 500 previously mapped SSR markers, this release makes available a total of 2740 experimentally confirmed SSR markers for rice, or approximately one SSR every 157 kb.

The population structure of domesticated species is influenced by the natural history of the populations of predomesticated ancestors, as well as by the breeding system and complexity of the breeding practices exercised by humans. Within Oryza sativa, there is an ancient and well-established divergence between the two major subspecies, indica and japonica, but finer levels of genetic structure are suggested by the breeding history. In this study, a sample of 234 accessions of rice was genotyped at 169 nuclear SSRs and two chloroplast loci. The data were analyzed to resolve the genetic structure and to interpret the evolutionary relationships between groups. Five distinct groups were detected, corresponding to indica, aus, aromatic, temperate japonica, and tropical japonica rices. Nuclear and chloroplast data support a closer evolutionary relationship between the indica and the aus and among the tropical japonica, temperate japonica, and aromatic groups. Group differences can be explained through contrasting demographic histories. With the availability of rice genome sequence, coupled with a large collection of publicly available genetic resources, it is of interest to develop a population-based framework for the molecular analysis of diversity in O. sativa.

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute's (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline. Genomic selection is a promising breeding technique that aims to improve the efficiency and speed of the breeding process. While it has been shown to be effective in crops such as wheat and corn, it has not yet been applied to rice breeding. Genome-wide association studies (GWAS), by contrast, are used to identify genes or QTLs that underlie traits of importance to breeding such as yield, flowering time, or plant height, and has been performed successfully in rice. Here, we experiment with applying genomic selection in conjunction with GWAS to a rice breeding program at the International Rice Research Institute in the Philippines and show that genomic selection can result in more accurate predictions of breeding line performance than pedigree data alone and that GWAS results can inform the results of GS. Our results suggest that GS could be an effective tool for increasing the efficiency of rice breeding.

Rc is a domestication-related gene required for red pericarp in rice (Oryza sativa). The red grain color is ubiquitous among the wild ancestors of O. sativa, in which it is closely associated with seed shattering and dormancy. Rc encodes a basic helix-loop-helix (bHLH) protein that was fine-mapped to an 18.5-kb region on rice chromosome 7 using a cross between Oryza rufipogon (red pericarp) and O. sativa cv Jefferson (white pericarp). Sequencing of the alleles from both mapping parents as well as from two independent genetic stocks of Rc revealed that the dominant red allele differed from the recessive white allele by a 14-bp deletion within exon 6 that knocked out the bHLH domain of the protein. A premature stop codon was identified in the second mutant stock that had a light red pericarp. RT-PCR experiments confirmed that the Rc gene was expressed in both red- and white-grained rice but that a shortened transcript was present in white varieties. Phylogenetic analysis, supported by comparative mapping in rice and maize (Zea mays), showed that Rc, a positive regulator of proanthocyanidin, is orthologous with INTENSIFIER1, a negative regulator of anthocyanin production in maize, and is not in the same clade as rice bHLH anthocyanin regulators.