Development of 12 sets of chromosome segment substitution lines that enhance allele mining in Asian cultivated rice.

Many agronomic traits that are important in rice breeding are controlled by multiple genes. The extensive time and effort devoted so far to identifying and selecting such genes are still not enough to target multiple agronomic traits in practical breeding in Japan because of a lack of suitable plant materials in which to efficiently detect and validate beneficial alleles from diverse genetic resources. To facilitate the comprehensive analysis of genetic variation in agronomic traits among Asian cultivated rice, we developed 12 sets of chromosome segment substitution lines (CSSLs) with the japonica background, 11 of them in the same genetic background, using donors representing the genetic diversity of Asian cultivated rice. Using these materials, we overviewed the chromosomal locations of 1079 putative QTLs for seven agronomic traits and their allelic distribution in Asian cultivated rice through multiple linear regression analysis. The CSSLs will allow the effects of putative QTLs in the highly homogeneous japonica background to be validated.

Investigation of the Genetic Diversity of a Rice Core Collection of Japanese Landraces using Whole-Genome Sequencing.

The Rice Core Collection of Japanese Landraces (JRC) consisting of 50 accessions was developed by the genebank at the National Agriculture and Food Research Organization (NARO) in 2008. As a Japanese landrace core collection, the JRC has been used for many research projects, including screening for different phenotypes and allele mining for target genes. To understand the genetic diversity of Japanese Landraces, we performed whole-genome resequencing of these 50 accessions and obtained a total of 2,145,095 single nucleotide polymorphism (SNPs) and 317,832 insertion-deletions (indels) by mapping against the Oryza sativa ssp. japonica Nipponbare genome. A JRC phylogenetic tree based on 1,394 representative SNPs showed that JRC accessions were divided into two major groups and one small group. We used the multiple genome browser, TASUKE+, to examine the haplotypes of flowering genes and detected new mutations in these genes. Finally, we performed genome-wide association studies (GWAS) for agronomical traits using the JRC and another core collection, the World Rice Core Collection (WRC), comprising 69 accessions also provided by the NARO genebank. In leaf blade width, a strong peak close to NAL1, a key gene for the regulation of leaf width, and, in heading date, a peak near HESO1 involved in flowering regulation were observed in GWAS using the JRC. They were also detected in GWAS using the combined JRC + WRC. Thus, JRC and JRC + WRC are suitable populations for GWAS of particular traits.

Endosperm enzyme activity is responsible for texture and eating quality of cooked rice grains in Japanese cultivars.

Eating quality of cooked rice grains is an important determinant of its market price and consumer acceptance. To comprehensively assess the variation of eating-quality traits in 152 Japanese rice cultivars, we evaluated activities of eight endosperm enzymes related to degradation of starch and cell-wall polysaccharides. Endosperm enzyme activities showed a wide range of variations and were lower in recently developed cultivars than in landraces and old improved cultivars. Activities of most endosperm enzymes correlated significantly with the eating-quality score and surface texture of cooked rice grains. Principal component analysis revealed that rice cultivars with high eating-quality scores had high stickiness of the grain surface and low levels of endosperm enzyme activities. These results suggest that endosperm enzyme activities control texture and eating quality of cooked rice grains in Japanese rice cultivars.

Genetic variation in rice (Oryza sativa L.) germplasm from northern Laos.

We assessed genetic variation in rice germplasm in northern Laos and Vientiane province from polymorphism data of SSR markers. We classified 314 accessions into three clusters; Ia (corresponding to the lowland Japonica Group), Ib (upland Japonica Group) and II (Indica Group). The accessions of cluster Ib grew mainly in mountainous fields, and those of cluster II grew commonly in basins and along rivers. The few accessions of cluster Ia grew in only three provinces: Houaphanh, Xiangkhouang and Vientiane. Lowland cultivars in cluster II were predominant in Vientiane. Variations in heading date under short-day conditions in 2014 and long-day conditions in 2015 indicate that many accessions were sensitive to the photoperiod on account of complex genetic mechanisms underlying both photoperiod sensitivity and basic vegetative growth. A total of 219 among whole accessions were classified into 6 groups: E1-3 and L1-3. E2 and E3 were dominant in clusters Ib and II; E1 and L1-3 were minor groups. These results demonstrate characteristic distributions of the Indica and Japonica Group's germplasms in northern Laos and their genetic variation in heading date.

The Birth of a Black Rice Gene and Its Local Spread by Introgression.

The origin and spread of novel agronomic traits during crop domestication are complex events in plant evolution. Wild rice (Oryza rufipogon) has red grains due to the accumulation of proanthocyanidins, whereas most cultivated rice (Oryza sativa) varieties have white grains induced by a defective allele in the Rc basic helix-loop-helix (bHLH) gene. Although the events surrounding the origin and spread of black rice traits remain unknown, varieties with black grains due to anthocyanin accumulation are distributed in various locations throughout Asia. Here, we show that the black grain trait originated from ectopic expression of the Kala4 bHLH gene due to rearrangement in the promoter region. Both the Rc and Kala4 genes activate upstream flavonol biosynthesis genes, such as chalcone synthase and dihydroflavonol-4-reductase, and downstream genes, such as leucoanthocyanidin reductase and leucoanthocyanidin dioxygenase, to produce the respective specific pigments. Genome analysis of 21 black rice varieties as well as red- and white-grained landraces demonstrated that black rice arose in tropical japonica and its subsequent spread to the indica subspecies can be attributed to the causal alleles of Kala4. The relatively small size of genomic fragments of tropical japonica origin in some indica varieties indicates that refined introgression must have occurred by natural crossbreeding in the course of evolution of the black trait in rice.

Genetic dissection of grain traits in Yamadanishiki, an excellent sake-brewing rice cultivar.

KEY MESSAGE: The grain traits of Yamadanishiki, an excellent sake-brewing rice cultivar in Japan, are governed by multiple QTLs, namely, a total of 42 QTLs including six major QTLs. Japanese rice wine (sake) is produced using brewing rice (Oryza sativa L.) that carries traits desirable for sake-brewing, such as a larger grain size and higher white-core expression rate (WCE) compared to cooking rice cultivars. However, the genetic basis for these traits in brewing rice cultivars is still unclear. We performed analyses of quantitative trait locus (QTL) of grain and days to heading over 3 years on populations derived from crosses between Koshihikari, a cooking rice, and Yamadanishiki, an excellent sake-brewing rice. A total of 42 QTLs were detected for the grain traits, and the Yamadanishiki alleles at 16 QTLs contributed to larger grain size. Two major QTLs essential for regulating both 100-grain weight (GWt) and grain width (GWh) were harbored in the same regions on chromosomes 5 and 10. An interaction was noted between the environment and the QTL associated with WCE on chromosome 6, which was detected in two of 3 years. In addition, two QTLs for WCE on chromosomes 3 and 10 overlapped with the QTLs for GWt and GWh, suggesting that QTLs associated with grain size also play an important role in the formation of white-core. Despite differences in the rate of grain growth in both Koshihikari and Yamadanishiki across 2 years, the WCE in Yamadanishiki remained consistent, thus demonstrating that the formation of white-core does not depend on grain filling speed. These data can be informative for programs involved in breeding better cooking and brewing rice cultivars.

Metabolome-genome-wide association study dissects genetic architecture for generating natural variation in rice secondary metabolism.

Plants produce structurally diverse secondary (specialized) metabolites to increase their fitness for survival under adverse environments. Several bioactive compounds for new drugs have been identified through screening of plant extracts. In this study, genome-wide association studies (GWAS) were conducted to investigate the genetic architecture behind the natural variation of rice secondary metabolites. GWAS using the metabolome data of 175 rice accessions successfully identified 323 associations among 143 single nucleotide polymorphisms (SNPs) and 89 metabolites. The data analysis highlighted that levels of many metabolites are tightly associated with a small number of strong quantitative trait loci (QTLs). The tight association may be a mechanism generating strains with distinct metabolic composition through the crossing of two different strains. The results indicate that one plant species produces more diverse phytochemicals than previously expected, and plants still contain many useful compounds for human applications.

Hd18, Encoding Histone Acetylase Related to Arabidopsis FLOWERING LOCUS D, is Involved in the Control of Flowering Time in Rice.

Flowering time is one of the most important agronomic traits in rice (Oryza sativa L.), because it defines harvest seasons and cultivation areas, and affects yields. We used a map-based strategy to clone Heading date 18 (Hd18). The difference in flowering time between the Japanese rice cultivars Koshihikari and Hayamasari was due to a single nucleotide polymorphism within the Hd18 gene, which encodes an amine oxidase domain-containing protein and is homologous to Arabidopsis FLOWERING LOCUS D (FLD). The Hayamasari Hd18 allele and knockdown of Hd18 gene expression delayed the flowering time of rice plants regardless of the day-length condition. Structural modeling of the Hd18 protein suggested that the non-synonymous substitution changed protein stability and function due to differences in interdomain hydrogen bond formation. Compared with those in Koshihikari, the expression levels of the flowering-time genes Early heading date 1 (Ehd1), Heading date 3a (Hd3a) and Rice flowering locus T1 (RFT1) were lower in a near-isogenic line with the Hayamasari Hd18 allele in a Koshihikari genetic background. We revealed that Hd18 acts as an accelerator in the rice flowering pathway under both short- and long-day conditions by elevating transcription levels of Ehd1 Gene expression analysis also suggested the involvement of MADS-box genes such as OsMADS50, OsMADS51 and OsMADS56 in the Hd18-associated regulation of Ehd1 These results suggest that, like FLD, its rice homolog accelerates flowering time but is involved in rice flowering pathways that differ from the autonomous pathways in Arabidopsis.

Multi-task Gaussian process for imputing missing data in multi-trait and multi-environment trials.

KEY MESSAGE: A method based on a multi-task Gaussian process using self-measuring similarity gave increased accuracy for imputing missing phenotypic data in multi-trait and multi-environment trials. Multi-environmental trial (MET) data often encounter the problem of missing data. Accurate imputation of missing data makes subsequent analysis more effective and the results easier to understand. Moreover, accurate imputation may help to reduce the cost of phenotyping for thinned-out lines tested in METs. METs are generally performed for multiple traits that are correlated to each other. Correlation among traits can be useful information for imputation, but single-trait-based methods cannot utilize information shared by traits that are correlated. In this paper, we propose imputation methods based on a multi-task Gaussian process (MTGP) using self-measuring similarity kernels reflecting relationships among traits, genotypes, and environments. This framework allows us to use genetic correlation among multi-trait multi-environment data and also to combine MET data and marker genotype data. We compared the accuracy of three MTGP methods and iterative regularized PCA using rice MET data. Two scenarios for the generation of missing data at various missing rates were considered. The MTGP performed a better imputation accuracy than regularized PCA, especially at high missing rates. Under the 'uniform' scenario, in which missing data arise randomly, inclusion of marker genotype data in the imputation increased the imputation accuracy at high missing rates. Under the 'fiber' scenario, in which missing data arise in all traits for some combinations between genotypes and environments, the inclusion of marker genotype data decreased the imputation accuracy for most traits while increasing the accuracy in a few traits remarkably. The proposed methods will be useful for solving the missing data problem in MET data.

Variation in cooking and eating quality traits in Japanese rice germplasm accessions.

The eating quality of cooked rice is important and determines its market price and consumer acceptance. To comprehensively describe the variation of eating quality in 183 rice germplasm accessions, we evaluated 33 eating-quality traits including amylose and protein contents, pasting properties of rice flour, and texture of cooked rice grains. All eating-quality traits varied widely in the germplasm accessions. Principal-components analysis (PCA) revealed that allelic differences in the Wx gene explained the largest proportion of phenotypic variation of the eating-quality traits. In 146 accessions of non-glutinous temperate japonica rice, PCA revealed that protein content and surface texture of the cooked rice grains significantly explained phenotypic variations of the eating-quality traits. An allelic difference based on simple sequence repeats, which was located near a quantitative trait locus (QTL) on the short arm of chromosome 3, was associated with differences in the eating quality of non-glutinous temperate japonica rice. These results suggest that eating quality is controlled by genetic factors, including the Wx gene and the QTL on chromosome 3, in Japanese rice accessions. These genetic factors have been consciously selected for eating quality during rice breeding programs in Japan.

Genetic architecture of variation in heading date among Asian rice accessions.

BACKGROUND: Heading date, a crucial factor determining regional and seasonal adaptation in rice (Oryza sativa L.), has been a major selection target in breeding programs. Although considerable progress has been made in our understanding of the molecular regulation of heading date in rice during last two decades, the previously isolated genes and identified quantitative trait loci (QTLs) cannot fully explain the natural variation for heading date in diverse rice accessions. RESULTS: To genetically dissect naturally occurring variation in rice heading date, we collected QTLs in advanced-backcross populations derived from multiple crosses of the japonica rice accession Koshihikari (as a common parental line) with 11 diverse rice accessions (5 indica, 3 aus, and 3 japonica) that originate from various regions of Asia. QTL analyses of over 14,000 backcrossed individuals revealed 255 QTLs distributed widely across the rice genome. Among the detected QTLs, 128 QTLs corresponded to genomic positions of heading date genes identified by previous studies, such as Hd1, Hd6, Hd3a, Ghd7, DTH8, and RFT1. The other 127 QTLs were detected in different chromosomal regions than heading date genes. CONCLUSIONS: Our results indicate that advanced-backcross progeny allowed us to detect and confirm QTLs with relatively small additive effects, and the natural variation in rice heading date could result from combinations of large- and small-effect QTLs. We also found differences in the genetic architecture of heading date (flowering time) among maize, Arabidopsis, and rice.

Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.).

KEY MESSAGE: Our simulation results clarify the areas of applicability of nine prediction methods and suggest the factors that affect their accuracy at predicting empirical traits. Whole-genome prediction is used to predict genetic value from genome-wide markers. The choice of method is important for successful prediction. We compared nine methods using empirical data for eight phenological and morphological traits of Asian rice cultivars (Oryza sativa L.) and data simulated from real marker genotype data. The methods were genomic BLUP (GBLUP), reproducing kernel Hilbert spaces regression (RKHS), Lasso, elastic net, random forest (RForest), Bayesian lasso (Blasso), extended Bayesian lasso (EBlasso), weighted Bayesian shrinkage regression (wBSR), and the average of all methods (Ave). The objectives were to evaluate the predictive ability of these methods in a cultivar population, to characterize them by exploring the area of applicability of each method using simulation, and to investigate the causes of their different accuracies for empirical traits. GBLUP was the most accurate for one trait, RKHS and Ave for two, and RForest for three traits. In the simulation, Blasso, EBlasso, and Ave showed stable performance across the simulated scenarios, whereas the other methods, except wBSR, had specific areas of applicability; wBSR performed poorly in most scenarios. For each method, the accuracy ranking for the empirical traits was largely consistent with that in one of the simulated scenarios, suggesting that the simulation conditions reflected the factors that affected the method accuracy for the empirical results. This study will be useful for genomic prediction not only in Asian rice, but also in populations from other crops with relatively small training sets and strong linkage disequilibrium structures.

Detection of QTLs for white-back and basal-white grains caused by high temperature during ripening period in japonica rice.

There is increasing evidence that global warming affects the development of rice. High temperatures during ripening increase the ratio of undesirable chalky grains followed by deteriorating grain appearance quality. In order to detect quantitative trait loci (QTLs) controlling the occurrence of white-back and basal-white chalky grains of brown rice, QTL analysis was performed using recombinant inbred lines derived from a cross between two strains, 'Tsukushiroman' (sensitive to heat stress) and 'Chikushi 52' (tolerant of heat stress). The F7 and F8 lines were exposed to heat stress during the ripening period in two locations, Fukuoka and Kagoshima, in Japan. QTLs for white-back grains and basal-white grains were detected on chromosomes 1, 3, and 8, and those for basal-white grains were detected on chromosomes 2, 3, and 12. QTLs on chromosome 8 for white-back grains were shared in the plants grown in both locations. Near-isogenic lines (NILs), which harbored a segment from 'Chikushi 52' on chromosome 8 with the genetic background of 'Tsukushiroman', showed relatively lower ratios of white-back grains than 'Tsukushiroman'. Therefore, insertion of the 'Chikushi 52' genomic region of the QTL on chromosome 8 can improve the quality of rice when it is grown under heat stress conditions.

Hd16, a gene for casein kinase I, is involved in the control of rice flowering time by modulating the day-length response.

The alteration of photoperiod sensitivity has let breeders diversify flowering time in Oryza sativa (rice) and develop cultivars adjusted to a range of growing season periods. Map-based cloning revealed that the rice flowering-time quantitative trait locus (QTL) Heading date 16 (Hd16) encodes a casein kinase-I protein. One non-synonymous substitution in Hd16 resulted in decreased photoperiod sensitivity in rice, and this substitution occurred naturally in an old rice cultivar. By using near-isogenic lines with functional or deficient alleles of several rice flowering-time genes, we observed significant digenetic interactions between Hd16 and four other flowering-time genes (Ghd7, Hd1, DTH8 and Hd2). In a near-isogenic line with the weak-photoperiod-sensitivity allele of Hd16, transcription levels of Ehd1, Hd3a, and RFT1 increased under long-day conditions, and transcription levels of Hd3a and RFT1 decreased under short-day conditions. Expression analysis under continuous light and dark conditions showed that Hd16 was not likely to be associated with circadian clock regulation. Biochemical characterization indicated that the functional Hd16 recombinant protein specifically phosphorylated Ghd7. These results demonstrate that Hd16 acts as an inhibitor in the rice flowering pathway by enhancing the photoperiod response as a result of the phosphorylation of Ghd7.

Genomic regions involved in yield potential detected by genome-wide association analysis in Japanese high-yielding rice cultivars.

BACKGROUND: High-yielding cultivars of rice (Oryza sativa L.) have been developed in Japan from crosses between overseas indica and domestic japonica cultivars. Recently, next-generation sequencing technology and high-throughput genotyping systems have shown many single-nucleotide polymorphisms (SNPs) that are proving useful for detailed analysis of genome composition. These SNPs can be used in genome-wide association studies to detect candidate genome regions associated with economically important traits. In this study, we used a custom SNP set to identify introgressed chromosomal regions in a set of high-yielding Japanese rice cultivars, and we performed an association study to identify genome regions associated with yield. RESULTS: An informative set of 1152 SNPs was established by screening 14 high-yielding or primary ancestral cultivars for 5760 validated SNPs. Analysis of the population structure of high-yielding cultivars showed three genome types: japonica-type, indica-type and a mixture of the two. SNP allele frequencies showed several regions derived predominantly from one of the two parental genome types. Distinct regions skewed for the presence of parental alleles were observed on chromosomes 1, 2, 7, 8, 11 and 12 (indica) and on chromosomes 1, 2 and 6 (japonica). A possible relationship between these introgressed regions and six yield traits (blast susceptibility, heading date, length of unhusked seeds, number of panicles, surface area of unhusked seeds and 1000-grain weight) was detected in eight genome regions dominated by alleles of one parental origin. Two of these regions were near Ghd7, a heading date locus, and Pi-ta, a blast resistance locus. The allele types (i.e., japonica or indica) of significant SNPs coincided with those previously reported for candidate genes Ghd7 and Pi-ta. CONCLUSIONS: Introgression breeding is an established strategy for the accumulation of QTLs and genes controlling high yield. Our custom SNP set is an effective tool for the identification of introgressed genome regions from a particular genetic background. This study demonstrates that changes in genome structure occurred during artificial selection for high yield, and provides information on several genomic regions associated with yield performance.

HapRice, an SNP haplotype database and a web tool for rice.

Genome-wide single nucleotide polymorphism (SNP) analysis is a promising tool to examine the genetic diversity of rice populations and genetic traits of scientific and economic importance. Next-generation sequencing technology has accelerated the re-sequencing of diverse rice varieties and the discovery of genome-wide SNPs. Notably, validation of these SNPs by a high-throughput genotyping system, such as an SNP array, could provide a manageable and highly accurate SNP set. To enhance the potential utility of genome-wide SNPs for geneticists and breeders, analysis tools need to be developed. Here, we constructed an SNP haplotype database, which allows visualization of the allele frequency of all SNPs in the genome browser. We calculated the allele frequencies of 3,334 SNPs in 76 accessions from the world rice collection and 3,252 SNPs in 177 Japanese rice accessions; all these SNPs have been validated in our previous studies. The SNP haplotypes were defined by the allele frequency in each cultivar group (aus, indica, tropical japonica and temperate japonica) for the world rice accessions, and in non-irrigated and three irrigated groups (three variety registration periods) for Japanese rice accessions. We also developed web tools for finding polymorphic SNPs between any two rice accessions and for the primer design to develop cleaved amplified polymorphic sequence markers at any SNP. The 'HapRice' database and the web tools can be accessed at http://qtaro.abr.affrc.go.jp/index.html. In addition, we established a core SNP set consisting of 768 SNPs uniformly distributed in the rice genome; this set is of a practically appropriate size for use in rice genetic analysis.

Molecular distinction in genetic regulation of nonphotochemical quenching in rice.

Nonphotochemical quenching (NPQ) regulates energy conversion in photosystem II and protects plants from photoinhibition. Here we analyze NPQ capacity in a number of rice cultivars. NPQ was strongly induced under medium and high light intensities in rice leaves. Japonica cultivars generally showed higher NPQ capacities than Indica cultivars when we measured a rice core collection. We mapped NPQ regulator and identified a locus (qNPQ1-2) that seems to be responsible for the difference in NPQ capacity between Indica and Japonica. One of the two rice PsbS homologues (OsPsbS1) was found within the qNPQ1-2 region. PsbS protein was not accumulated in the leaf blade of the mutant harboring transferred DNA insertion in OsPsbS1. NPQ capacity increased as OsPsbS1 expression increased in a series of transgenic lines ectopically expressing OsPsbS1 in an Indica cultivar. Indica cultivars lack a 2.7-kb region at the point 0.4 kb upstream of the OsPsbS1 gene, suggesting evolutionary discrimination of this gene.

Artificial selection for a green revolution gene during japonica rice domestication.

The semidwarf phenotype has been extensively selected during modern crop breeding as an agronomically important trait. Introduction of the semidwarf gene, semi-dwarf1 (sd1), which encodes a gibberellin biosynthesis enzyme, made significant contributions to the "green revolution" in rice (Oryza sativa L.). Here we report that SD1 was involved not only in modern breeding including the green revolution, but also in early steps of rice domestication. We identified two SNPs in O. sativa subspecies (ssp.) japonica SD1 as functional nucleotide polymorphisms (FNPs) responsible for shorter culm length and low gibberellin biosynthetic activity. Genetic diversity analysis among O. sativa ssp. japonica and indica, along with their wild ancestor O. rufipogon Griff, revealed that these FNPs clearly differentiate the japonica landrace and O. rufipogon. We also found a dramatic reduction in nucleotide diversity around SD1 only in the japonica landrace, not in the indica landrace or O. rufipogon. These findings indicate that SD1 has been subjected to artificial selection in rice evolution and that the FNPs participated in japonica domestication, suggesting that ancient humans already used the green revolution gene.

Dissection of genotype-phenotype associations in rice grains using metabolome quantitative trait loci analysis.

A comprehensive and large-scale metabolome quantitative trait loci (mQTL) analysis was performed to investigate the genetic backgrounds associated with metabolic phenotypes in rice grains. The metabolome dataset consisted of 759 metabolite signals obtained from the grains of 85 lines of rice (Oryza sativa, Sasanishiki × Habataki back-crossed inbred lines). Metabolome analysis was performed using four mass spectrometry pipelines to enhance detection of different classes of metabolites. This mQTL analysis of a wide range of metabolites highlighted an uneven distribution of 802 mQTLs on the rice genome, as well as different modes of metabolic trait (m-trait) control among various types of metabolites. The levels of most metabolites within rice grains were highly sensitive to environmental factors, but only weakly associated with mQTLs. Coordinated control was observed for several groups of metabolites, such as amino acids linked to the mQTL hotspot on chromosome 3. For flavonoids, m-trait variation among the experimental lines was tightly governed by genetic factors that alter the glycosylation of flavones. Many loci affecting levels of metabolites were detected by QTL analysis, and plausible gene candidates were evaluated by in silico analysis. Several mQTLs profoundly influenced metabolite levels, providing insight into the control of rice metabolism. The genomic region and genes potentially responsible for the biosynthesis of apigenin-6,8-di-C-α-l-arabinoside are presented as an example of a critical mQTL identified by the analysis.

SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis.

Seed shape and size are among the most important agronomic traits because they affect yield and market price. To obtain accurate seed size data, a large number of measurements are needed because there is little difference in size among seeds from one plant. To promote genetic analysis and selection for seed shape in plant breeding, efficient, reliable, high-throughput seed phenotyping methods are required. We developed SmartGrain software for high-throughput measurement of seed shape. This software uses a new image analysis method to reduce the time taken in the preparation of seeds and in image capture. Outlines of seeds are automatically recognized from digital images, and several shape parameters, such as seed length, width, area, and perimeter length, are calculated. To validate the software, we performed a quantitative trait locus (QTL) analysis for rice (Oryza sativa) seed shape using backcrossed inbred lines derived from a cross between japonica cultivars Koshihikari and Nipponbare, which showed small differences in seed shape. SmartGrain removed areas of awns and pedicels automatically, and several QTLs were detected for six shape parameters. The allelic effect of a QTL for seed length detected on chromosome 11 was confirmed in advanced backcross progeny; the cv Nipponbare allele increased seed length and, thus, seed weight. High-throughput measurement with SmartGrain reduced sampling error and made it possible to distinguish between lines with small differences in seed shape. SmartGrain could accurately recognize seed not only of rice but also of several other species, including Arabidopsis (Arabidopsis thaliana). The software is free to researchers.