Jun-ichi Yonemaru

Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms.

BackgroundTo create useful gene combinations in crop breeding, it is necessary to clarify the dynamics of the genome composition created by breeding practices. A large quantity of single-nucleotide polymorphism (SNP) data is required to permit discrimination of chromosome segments among modern cultivars, which are genetically related. Here, we used a high-throughput sequencer to conduct whole-genome sequencing of an elite Japanese rice cultivar, Koshihikari, which is closely related to Nipponbare, whose genome sequencing has been completed. Then we designed a high-throughput typing array based on the SNP information by comparison of the two sequences. Finally, we applied this array to analyze historical representative rice cultivars to understand the dynamics of their genome composition.ResultsThe total 5.89-Gb sequence for Koshihikari, equivalent to 15.7× the entire rice genome, was mapped using the Pseudomolecules 4.0 database for Nipponbare. The resultant Koshihikari genome sequence corresponded to 80.1% of the Nipponbare sequence and led to the identification of 67 051 SNPs. A high-throughput typing array consisting of 1917 SNP sites distributed throughout the genome was designed to genotype 151 representative Japanese cultivars that have been grown during the past 150 years. We could identify the ancestral origin of the pedigree haplotypes in 60.9% of the Koshihikari genome and 18 consensus haplotype blocks which are inherited from traditional landraces to current improved varieties. Moreover, it was predicted that modern breeding practices have generally decreased genetic diversityConclusionsDetection of genome-wide SNPs by both high-throughput sequencer and typing array made it possible to evaluate genomic composition of genetically related rice varieties. With the aid of their pedigree information, we clarified the dynamics of chromosome recombination during the historical rice breeding process. We also found several genomic regions decreasing genetic diversity which might be caused by a recent human selection in rice breeding. The definition of pedigree haplotypes by means of genome-wide SNPs will facilitate next-generation breeding of rice and other crops.

Towards the Understanding of Complex Traits in Rice: Substantially or Superficially?

Completion of the genome analysis followed by extensive comprehensive studies on a variety of genes and gene families of rice (Oryza sativa) resulted in rapid accumulation of information concerning the presence of many complex traits that are governed by a number of genes of distinct functions in this most important crop cultivated worldwide. The genetic and molecular biological dissection of many important rice phenotypes has contributed to our understanding of the complex nature of the genetic control with respect to these phenotypes. However, in spite of the considerable advances made in the field, details of genetic control remain largely unsolved, thereby hampering our exploitation of this useful information in the breeding of new rice cultivars. To further strengthen the field application of the genome science data of rice obtained so far, we need to develop more powerful genomics-assisted methods for rice breeding based on information derived from various quantitative trait loci (QTL) and related analyses. In this review, we describe recent progresses and outcomes in rice QTL analyses, problems associated with the application of the technology to rice breeding and their implications for the genetic study of other crops along with future perspectives of the relevant fields.

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.

Q-TARO: QTL Annotation Rice Online Database

Over the past two decades, genetic dissection of complex phenotypes of economic and biological interest has revealed the chromosomal locations of many quantitative trait loci (QTLs) in rice and their contributions to phenotypic variation. Mapping resolution has varied considerably among QTL studies owing to differences in population size and number of DNA markers used. Additionally, the same QTLs have often been reported with different locus designations. This situation has made it difficult to determine allelic relationships among QTLs and to compare their positions. To facilitate reliable comparisons of rice QTLs, we extracted QTL information from published research papers and constructed a database of 1,051 representative QTLs, which we classified into 21 trait categories. This database (QTL Annotation Rice Online database; Q-TARO, http://qtaro.abr.affrc.go.jp/) consists of two web interfaces. One interface is a table containing information on the mapping of each QTL and its genetic parameters. The other interface is a genome viewer for viewing genomic locations of the QTLs. Q-TARO clearly displays the co-localization of QTLs and distribution of QTL clusters on the rice genome.

OGRO: The Overview of functionally characterized Genes in Rice online database

BackgroundThe high-quality sequence information and rich bioinformatics tools available for rice have contributed to remarkable advances in functional genomics. To facilitate the application of gene function information to the study of natural variation in rice, we comprehensively searched for articles related to rice functional genomics and extracted information on functionally characterized genes.ResultsAs of 31 March 2012, 702 functionally characterized genes were annotated. This number represents about 1.6% of the predicted loci in the Rice Annotation Project Database. The compiled gene information is organized to facilitate direct comparisons with quantitative trait locus (QTL) information in the Q-TARO database. Comparison of genomic locations between functionally characterized genes and the QTLs revealed that QTL clusters were often co-localized with high-density gene regions, and that the genes associated with the QTLs in these clusters were different genes, suggesting that these QTL clusters are likely to be explained by tightly linked but distinct genes. Information on the functionally characterized genes compiled during this study is now available in the O verview of Functionally Characterized G enes in R ice O nline database (OGRO) on the Q-TARO website (http://qtaro.abr.affrc.go.jp/ogro). The database has two interfaces: a table containing gene information, and a genome viewer that allows users to compare the locations of QTLs and functionally characterized genes.ConclusionsOGRO on Q-TARO will facilitate a candidate-gene approach to identifying the genes responsible for QTLs. Because the QTL descriptions in Q-TARO contain information on agronomic traits, such comparisons will also facilitate the annotation of functionally characterized genes in terms of their effects on traits important for rice breeding. The increasing amount of information on rice gene function being generated from mutant panels and other types of studies will make the OGRO database even more valuable in the future.

Development of Genome-wide Simple Sequence Repeat Markers Using Whole-genome Shotgun Sequences of Sorghum (Sorghum bicolor (L.) Moench)

Simple sequence repeat (SSR) markers with a high degree of polymorphism contribute to the molecular dissection of agriculturally important traits in sorghum (Sorghum bicolor (L.) Moench). We designed 5599 non-redundant SSR markers, including regions flanking the SSRs, in whole-genome shotgun sequences of sorghum line ATx623. (AT/TA)n repeats constituted 26.1% of all SSRs, followed by (AG/TC)n at 20.5%, (AC/TG)n at 13.7% and (CG/GC)n at 11.8%. The chromosomal locations of 5012 SSR markers were determined by comparing the locations identified by means of electronic PCR with the predicted positions of 34 008 gene loci. Most SSR markers had a similar distribution to the gene loci. Among 970 markers validated by fragment analysis, 67.8% (658 of 970) markers successfully provided PCR amplification in sorghum line BTx623, with a mean polymorphism rate of 45.1% (297 of 658) for all SSR loci in combinations of 11 sorghum lines and one sudangrass (Sorghum sudanense (Piper) Stapf) line. The product of 5012 and 0.678 suggests that ∼3400 SSR markers could be used to detect SSR polymorphisms and that more than 1500 (45.1% of 3400) markers could reveal SSR polymorphisms in combinations of Sorghum lines.

PCR-based landmark unique gene (PLUG) markers effectively assign homoeologous wheat genes to A, B and D genomes

BackgroundEST-PCR markers normally represent specific products from target genes, and are therefore effective tools for genetic analysis. However, because wheat is an allohexaploid plant, PCR products derived from homoeologous genes are often simultaneously amplified. Such products may be easier to differentiate if they include intron sequences, which are more polymorphic than exon sequences. However, genomic sequence data for wheat are limited; therefore it is difficult to predict the location of introns. By using the similarities in gene structures between rice and wheat, we developed a system called PLUG (PCR-based Landmark Unique Gene) to design primers so that PCR products include intron sequences. We then investigated whether products amplified using such primers could serve as markers able to distinguish multiple products derived from homoeologous genes.ResultsThe PLUG system consists of the following steps: (1) Single-copy rice genes (Landmark Unique Gene loci; LUGs) exhibiting high degrees of homology to wheat UniGene sequences are extracted; (2) Alignment analysis is carried out using the LUGs and wheat UniGene sequences to predict exon-exon junctions, and LUGs which can be used to design wheat primers flanking introns (TaEST-LUGs) are extracted; and (3) Primers are designed in an interactive manner. From a total of 4,312 TaEST-LUGs, 24 loci were randomly selected and used to design primers. With all of these primer sets, we obtained specific, intron-containing products from the target genes. These markers were assigned to chromosomes using wheat nullisomic-tetrasomic lines. By PCR-RFLP analysis using agarose gel electrophoresis, 19 of the 24 markers were located on at least one chromosome.ConclusionIn the development of wheat EST-PCR markers capable of efficiently sorting products derived from homoeologous genes, it is important to design primers able to amplify products that include intron sequences with insertion/deletion polymorphisms. Using the PLUG system, wheat EST sequences that can be used for marker development are selected based on comparative genomics with rice, and then primer sets flanking intron sequences are prepared in an interactive, semi-automatic manner. Hence, the PLUG system is an effective tool for large-scale marker development.

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.

Genome-Wide Haplotype Changes Produced by Artificial Selection during Modern Rice Breeding in Japan

During the last 90 years, the breeding of rice has delivered cultivars with improved agronomic and economic characteristics. Crossing of different lines and successive artificial selection of progeny based on their phenotypes have changed the chromosomal constitution of the ancestors of modern rice; however, the nature of these changes is unclear. The recent accumulation of data for genome-wide single-nucleotide polymorphisms (SNPs) in rice has allowed us to investigate the change in haplotype structure and composition. To assess the impact of these changes during modern breeding, we studied 177 Japanese rice accessions, which were categorized into three groups: landraces, improved cultivars developed from 1931 to 1974 (the early breeding phase), and improved cultivars developed from 1975 to 2005 (the late breeding phase). Phylogenetic tree and structure analysis indicated genetic differentiation between non-irrigated (upland) and irrigated (lowland) rice groups as well as genetic structuring within the irrigated rice group that corresponded to the existence of three subgroups. Pedigree analysis revealed that a limited number of landraces and cultivars was used for breeding at the beginning of the period of systematic breeding and that 11 landraces accounted for 70% of the ancestors of the modern improved cultivars. The values for linkage disequilibrium estimated from SNP alleles and the haplotype diversity determined from consecutive alleles in five-SNP windows indicated that haplotype blocks became less diverse over time as a result of the breeding process. A decrease in haplotype diversity, caused by a reduced number of polymorphisms in the haplotype blocks, was observed in several chromosomal regions. However, our results also indicate that new haplotype polymorphisms have been generated across the genome during the breeding process. These findings will facilitate our understanding of the association between particular haplotypes and desirable phenotypes in modern Japanese rice cultivars.

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

BackgroundHigh-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.ResultsAn 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.ConclusionsIntrogression 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.