Development of microsatellite markers and characterization of simple sequence length polymorphism (SSLP) in rice (Oryza sativa L.)

Microsatellite markers containing simple sequence repeats (SSR) are a valuable tool for genetic analysis. Our objective is to augment the existing RFLP map of rice with simple sequence length polymorphisms (SSLP). In this study, we describe 20 new microsatellite markers that have been assigned to positions along the rice chromosomes, characterized for their allelic diversity in cultivated and wild rice, and tested for amplification in distantly related species. Our results indicate that the genomic distribution of microsatellites in rice appears to be random, with no obvious bias for, or clustering in particular regions, that mapping results are identical in intersubspecific and interspecific populations, and that amplification in wild relatives ofOryza sativa is reliable in species most closely related to cultivated rice but becomes less successful as the genetic distance increases. Sequence analysis of SSLP alleles in three relatedindica varieties demonstrated the clustering of complex arrays of SSR motifs in a single 300-bp region with independent variation in each. Two microsatellite markers amplified multiple loci that were mapped onto independent rice chromosomes, suggesting the presence of duplicated regions within the rice genome. The availability of increasing numbers of mapped SSLP markers can be expected to increase the power and resolution of genome analysis in rice.     

Saturated Molecular Map of the Rice Genome Based on an Interspecific Backcross Population

A molecular map has been constructed for the rice genome comprised of 726 markers (mainly restriction fragment length polymorphisms; RFLPs). The mapping population was derived from a backcross between cultivated rice, Oryza sativa, and its wild African relative, Oryza longistaminata. The very high level of polymorphism between these species, combined with the use of polymerase chain reaction-amplified cDNA libraries, contributed to mapping efficiency. A subset of the probes used in this study was previously used to construct an RFLP map derived from an inter subspecific cross, providing a basis for comparison of the two maps and of the relative mapping efficiencies in the two crosses. In addition to the previously described PstI genomic rice library, three cDNA libraries from rice (Oryza), oat (Avena) and barley (Hordeum) were used in this mapping project. Levels of polymorphism detected by each and the frequency of identifying heterologous sequences for use in rice mapping are discussed. Though strong reproductive barriers isolate O. sativa from O. longistaminata, the percentage of markers showing distorted segregation in this backcross population was not significantly different than that observed in an intraspecific F(2) population previously used for mapping. The map contains 1491 cM with an average interval size of 4.0 cM on the framework map, and 2.0 cM overall. A total of 238 markers from the previously described PstI genomic rice library, 250 markers from a cDNA library of rice (Oryza), 112 cDNA markers from oat (Avena), and 20 cDNA markers from a barley (Hordeum) library, two genomic clones from maize (Zea), 11 microsatellite markers, three telomere markers, eleven isozymes, 26 cloned genes, six RAPD, and 47 mutant phenotypes were used in this mapping project. Applications of a molecular map for plant improvement are discussed.     

Molecular-marker-facilitated investigation on the ability to stimulate N2 fixation in the rhizosphere by irrigated rice plants

An F₂ population, consisting of 231 individuals derived from a cross between rice cultivars with a similar growing duration, Palawan and IR42, was utilized to investigate the genetic nature of rice varietal ability to stimulate N₂ fixation in the rice rhizosphere. To assess rhizospheric N₂ fixation, an isotope-enriched ¹⁵N dilution technique was employed, using ¹⁵N-stabilized soil in pots. IR42, an indica variety, had 23% higher N derived from fixation (Ndfa) than Palawan, a javanica genotype. Normal segregation of atom% ¹⁵N excess was obtained in the F₂ population, with an average of 0.218 with 8% of plants below IR42 (0.188) and 10% of plants above Palawan (0.248). One-hundred-and-four RFLP markers mapped on 12 chromosomes were tested for linkage to the putative QTLs. Significant (P<0.01) associations between markers and segregation of atom% ¹⁵N excess were observed for seven marker loci located on chromosomes 1, 3, 6 and 11. Four QTLs defined by the detected marker loci were identified by interval-mapping analysis. Additive gene action was found to be predominant, but for at least one locus, dominance and partial dominance effects were observed. Significant (P<0.01) epistatic effects were also identified. Individual marker loci detected between 8 and 16% of the total phenotypic variation. All four putative QTLs showed recessive gene action, and no phenotypic effects associated with heterozygosity of marker loci were observed. The results of this study suggest that rice genetic factors can be identified which affect levels of atom% ¹⁵N excess in the soil by interacting with diazotrophs in the rice rhizosphere.     

Molecular-genetic maps for group 1 chromosomes of Triticeae species and their relation to chromosomes in rice and oat

Group 1 chromosomes of the Triticeae tribe have been studied extensively because many important genes have been assigned to them. In this paper, chromosome 1 linkage maps of Triticum aestivum, T. tauschii, and T. monococcum are compared with existing barley and rye maps to develop a consensus map for Triticeae species and thus facilitate the mapping of agronomic genes in this tribe. The consensus map that was developed consists of 14 agronomically important genes, 17 DNA markers that were derived from known-function clones, and 76 DNA markers derived from anonymous clones. There are 12 inconsistencies in the order of markers among seven wheat, four barley, and two rye maps. A comparison of the Triticeae group 1 chromosome consensus map with linkage maps of homoeologous chromosomes in rice indicates that the linkage maps for the long arm and the proximal portion of the short arm of group 1 chromosomes are conserved among these species. Similarly, gene order is conserved between Triticeae chromosome 1 and its homoeologous chromosome in oat. The location of the centromere in rice and oat chromosomes is estimated from its position in homoeologous group 1 chromosomes of Triticeae.     

Can biochemical traits bridge the gap between genomics and plant performance? A study in rice under drought

The possibility of introducing metabolic/biochemical phenotyping to complement genomics-based predictions in breeding pipelines has been considered for years. Here we examine to what extent and under what environmental conditions metabolic/biochemical traits can effectively contribute to understanding and predicting plant performance. In this study, multivariable statistical models based on flag leaf central metabolism and oxidative stress status were used to predict grain yield (GY) performance for 271 indica rice (Oryza sativa) accessions grown in the field under well-watered and reproductive stage drought conditions. The resulting models displayed significantly higher predictability than multivariable models based on genomic data for the prediction of GY under drought (Q² = 0.54–0.56 versus 0.35) and for stress-induced GY loss (Q² = 0.59–0.64 versus 0.03–0.06). Models based on the combined datasets showed predictabilities similar to metabolic/biochemical-based models alone. In contrast to genetic markers, models with enzyme activities and metabolite values also quantitatively integrated the effect of physiological differences such as plant height on GY. The models highlighted antioxidant enzymes of the ascorbate–glutathione cycle and a lipid oxidation stress marker as important predictors of rice GY stability under drought at the reproductive stage, and these stress-related variables were more predictive than leaf central metabolites. These findings provide evidence that metabolic/biochemical traits can integrate dynamic cellular and physiological responses to the environment and can help bridge the gap between the genome and the phenome of crops as predictors of GY performance under drought.

Biochemical traits outperform the explanatory power of genetic markers when used as variables in models for predicting yield performance in rice under drought stress.     

Gramene: a resource for comparative grass genomics

Gramene (http://www.gramene.org) is a comparative genome mapping database for grasses and a community resource for rice. Rice, in addition to being an economically important crop, is also a model monocot for understanding other agronomically important grass genomes. Gramene replaces the existing AceDB database ‘RiceGenes’ with a relational database based on Oracle. Gramene provides curated and integrative information about maps, sequence, genes, genetic markers, mutants, QTLs, controlled vocabularies and publications. Its aims are to use the rice genetic, physical and sequence maps as fundamental organizing units, to provide a common denominator for moving from one crop grass to another and is to serve as a portal for interconnecting with other web-based crop grass resources. This paper describes the initial steps we have taken towards realizing these goals.     

Genetic diversity and its relationship to hybrid performance and heterosis in rice as revealed by PCR-based markers

Ten elite inbred lines (four japonica, six indica), chosen from those widely used in the hybrid rice breeding program at Human Hybrid Rice Research Center in China, were crossed to produce all possible hybrids excluding reciprocals. The 45 F₁ hybrids along with the ten parents were evaluated for eight traits of agronomic importance, including yield potential, in a replicated field trial. The ten parents were analyzed with 100 arbitrary decamer oligonucleotide primers and 22 microsatellite (simple sequence repeats, SSRs) primer sets via polymerase chain reaction (PCR). Out of the 100 random primers used, 74 were informative and amplified 202 non-redundant bands (variants) with a mean of 2.73 bands per polymorphic primer. All 22 microsatellite primer sets representing 23 loci in the rice genome showed polymorphisms among the ten parents and revealed 90 alleles with an average of 3.91 per SSR locus. Cluster analysis based on Nei's genetic distance calculated from the 291 (202 RAPDs, 89 SSRs) non-redundant variants separated the ten parental lines into two major groups that corresponds to indica and japonica subspecies, which is consistent with the pedigree information. Strong heterosis was observed in hybrids for most of the traits examined. For the 43 diallel crosses (excluding 2 crosses not heading), yield potential, its components (including panicles per plant, spikelets per panicle and 1000-grain weight) and their heterosis in F₁ hybrids showed a significant positive correlation with genetic distance. When separate analyses were performed for the three subsets, yield potential and its heterosis showed significant positive correlations with genetic distance for the 15 indica x indica crosses and the 6 japonica x japonica crosses; however, yield potential and its heterosis were not correlated with genetic distance for the 22 indica x japonica crosses. Results indicated that genetic distance measures based on RAPDs and SSRs may be useful for predicting yield potential and heterosis of intra-subspecific hybrids, but not inter-subspecies hybrids.     

Evidence for divergence of response in Indica,Japonica, and wild rice to high CO2 × temperature interaction

High CO₂ and high temperature have an antagonistic interaction effect on rice yield potential and present a unique challenge to adapting rice to projected future climates. Understanding how the differences in response to these two abiotic variables are partitioned across rice germplasm accessions may be key to identifying potentially useful sources of resilient alleles for adapting rice to climate change. In this study, we evaluated eleven globally diverse rice accessions under controlled conditions at two carbon dioxide concentrations (400 and 600 ppm) and four temperature environments (29 °C day/21 °C night; 29 °C day/21 °C night with additional heat stress at anthesis; 34 °C day/26 °C night; and 34 °C day/26 °C night with additional heat stress at anthesis) for a suite of traits including five yield components, five growth characteristics, one phenological trait, and four photosynthesis‐related measurements. Multivariate analyses of mean trait data from these eight treatments divide our rice panel into two primary groups consistent with the genetic classification of INDICA/INDICA‐like and JAPONICA populations. Overall, we find that the productivity of plants grown under elevated [CO₂] was more sensitive (negative response) to high temperature stress compared with that of plants grown under ambient [CO₂] across this diversity panel. We report differential response to CO₂ × temperature interaction for INDICA/INDICA‐like and JAPONICA rice accessions and find preliminary evidence for the beneficial introduction of exotic alleles into cultivated rice genomic background. Overall, these results support the idea of using wild or currently unadapted gene pools in rice to enhance breeding efforts to secure future climate change adaptation.     

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement

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.