Plant metabolites are important to world food security in terms of maintaining sustainable yield and providing food with enriched phytonutrients. Here we report comprehensive profiling of 840 metabolites and a further metabolic genome-wide association study based on ∼6.4 million SNPs obtained from 529 diverse accessions of Oryza sativa. We identified hundreds of common variants influencing numerous secondary metabolites with large effects at high resolution. We observed substantial heterogeneity in the natural variation of metabolites and their underlying genetic architectures among different subspecies of rice. Data mining identified 36 candidate genes modulating levels of metabolites that are of potential physiological and nutritional importance. As a proof of concept, we functionally identified or annotated five candidate genes influencing metabolic traits. Our study provides insights into the genetic and biochemical bases of rice metabolome variation and can be used as a powerful complementary tool to classical phenotypic trait mapping for rice improvement.

Even as the study of plant genomics rapidly develops through the use of high-throughput sequencing techniques, traditional plant phenotyping lags far behind. Here we develop a high-throughput rice phenotyping facility (HRPF) to monitor 13 traditional agronomic traits and 2 newly defined traits during the rice growth period. Using genome-wide association studies (GWAS) of the 15 traits, we identify 141 associated loci, 25 of which contain known genes such as the Green Revolution semi-dwarf gene, SD1. Based on a performance evaluation of the HRPF and GWAS results, we demonstrate that high-throughput phenotyping has the potential to replace traditional phenotyping techniques and can provide valuable gene identification information. The combination of the multifunctional phenotyping tools HRPF and GWAS provides deep insights into the genetic architecture of important traits.

Summary Growth and development of a plant are controlled by programmed expression of suits of genes at the appropriate time, tissue and abundance. Although genomic resources have been developed rapidly in recent years in rice, a model plant for cereal genome research, data of gene expression profiling are still insufficient to relate the developmental processes to transcriptomes, leaving a large gap between the genome sequence and phenotype. In this study, we generated genome‐wide expression data by hybridizing 190 Affymetrix GeneChip Rice Genome Arrays with RNA from 39 tissues collected throughout the life cycle of the rice plant from two varieties, Zhenshan 97 and Minghui 63. Analyses of the global transcriptomes revealed many interesting features of dynamic patterns of gene expression across the tissues and stages. In total, 38 793 probe sets were detected as expressed and 69% of the expressed transcripts showed significantly variable expression levels among tissues/organs. We found that similarity of transcriptomes among organs corresponded well to their developmental relatedness. About 5.2% of the expressed transcripts showed tissue‐specific expression in one or both varieties and 22.7% of the transcripts exhibited constitutive expression including 19 genes with high and stable expression in all the tissues. This dataset provided a versatile resource for plant genomic research, which can be used for associating the transcriptomes to the developmental processes, understanding the regulatory network of these processes, tracing the expression profile of individual genes and identifying reference genes for quantitative expression analyses.

Grain chalkiness is a highly undesirable quality trait in the marketing and consumption of rice grain. However, the molecular basis of this trait is poorly understood. Here we show that a major quantitative trait locus (QTL), Chalk5, influences grain chalkiness, which also affects head rice yield and many other quality traits. Chalk5 encodes a vacuolar H(+)-translocating pyrophosphatase (V-PPase) with inorganic pyrophosphate (PPi) hydrolysis and H(+)-translocation activity. Elevated expression of Chalk5 increases the chalkiness of the endosperm, putatively by disturbing the pH homeostasis of the endomembrane trafficking system in developing seeds, which affects the biogenesis of protein bodies and is coupled with a great increase in small vesicle-like structures, thus forming air spaces among endosperm storage substances and resulting in chalky grain. Our results indicate that two consensus nucleotide polymorphisms in the Chalk5 promoter in rice varieties might partly account for the differences in Chalk5 mRNA levels that contribute to natural variation in grain chalkiness.

Bar-coded multiplexed sequencing approaches based on newgeneration sequencing technologies provide capacity to sequence a mapping population in a single sequencing run. However, such approaches usually generate low-coverage and error-prone sequences for each line in a population. Thus, it is a significant challenge to genotype individual lines in a population for linkage map construction based on low-coverage sequences without the availability of high-quality genotype data of the parental lines. In this paper, we report a method for constructing ultrahigh-density linkage maps composed of high-quality single-nucleotide polymorphisms (SNPs) based on low-coverage sequences of recombinant inbred lines. First, all potential SNPs were identified to obtain drafts of parental genotypes using a maximum parsimonious inference of recombination, making maximum use of SNP information found in the entire population. Second, high-quality SNPs were identified by filtering out low-quality ones by permutations involving resampling of windows of SNPs followed by Bayesian inference. Third, lines in the mapping population were genotyped using the high-quality SNPs assisted by a hidden Markov model. With 0.05× genome sequence per line, an ultrahigh-density linkage map composed of bins of high-quality SNPs using 238 recombinant inbred lines derived from a cross between two rice varieties was constructed. Using this map, a quantitative trait locus for grain width (GW5) was localized to its presumed genomic region in a bin of 200 kb, confirming the accuracy and quality of the map. This method is generally applicable in genetic map construction with low-coverage sequence data.genomics | maximum parsimony of recombination | Bayesian inference | hidden Markov model | rice G enetic maps provide the bases for a wide range of genetic and genomic studies and are pivotal for mapping and identifying genes associated with phenotypic performance, referred to as traits. The resolution of a genetic linkage map depends on the number of recombination events in the mapping population and the density of molecular markers. The number of recombination events depends on how the population is created, whereas the density of the markers can be improved continually with advances in molecular techniques. Traditional molecular markers that have been widely used in genotyping assays of populations, although laborious and time-consuming, have limitations in throughput and can provide information only for low-density maps, and thus are of low efficiency.Oligonucleotide microarrays, composed of millions of probes based on genome sequences, can capture large numbers of sequence variations between different samples by comparative genomic hybridization, which can be used for high-throughput marker discovery and genotyping (1-3). However, restrictions in microarray design and the number of probes on the microarrays limit the applications of this technology. In addition, it is cost-prohibitive for genotyping especially if the mapping population is large.New sequencing t...