Osamu Ideta

Physicochemical properties of amylose-free starch from transgenic sweet potato

A transgenic amylose-free sweet potato has been obtained by introduction of granule-bound starch synthase I (GBSSI) cDNA of sweet potato in sense orientation (Plant Cell Reports (2001)). In this study, starches from 6 transgenic sweet potatoes produced by introduction of GBSSI cDNA, including the amylose-free transformant, were analyzed for their physicochemical properties, granule-size distribution, enzymatic digestibility, amylopectin structure, gelatinization properties and pasting properties. We observed little difference in granule size distribution between amylose-free starch and control starch. Amylose-free starch was more susceptible to glucoamylase digestion than control starch. The amylopectin of the amylose-free transformant was found to have a slightly lower content of short chains. Amylose-free starch showed higher gelatinization temperature and gelatinization enthalpy and lower setback in comparison with control starch. Thus, it was found that the starch from the amylose-free transgenic sweet potato possessed unique physicochemical properties.

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

Key messageOur simulation results clarify the areas of applicability of nine prediction methods and suggest the factors that affect their accuracy at predicting empirical traits.AbstractWhole-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.

Population structure in Japanese rice population

It is essential to elucidate genetic diversity and relationships among even related individuals and populations for plant breeding and genetic analysis. Since Japanese rice breeding has improved agronomic traits such as yield and eating quality, modern Japanese rice cultivars originated from narrow genetic resource and closely related. To resolve the population structure and genetic diversity in Japanese rice population, we used a total of 706 alleles detected by 134 simple sequence repeat markers in a total of 114 cultivars composed of 94 improved varieties and 20 landraces, which are representative and important for Japanese rice breeding. The landraces exhibit greater gene diversity than improved lines, suggesting that landraces can provide additional genetic diversity for future breeding. Model-based Bayesian clustering analysis revealed six subgroups and admixture situation in the cultivars, showing good agreement with pedigree information. This method could be superior to phylogenetic method in classifying a related population. The leading Japanese rice cultivar, Koshihikari is unique due to the specific genome constitution. We defined Japanese rice diverse sets that capture the maximum number of alleles for given sample sizes. These sets are useful for a variety of genetic application in Japanese rice cultivars.

Yield and Lodging Resistance of 'Tachiayaka', a Novel Rice Cultivar with Short Panicles for Whole-Crop Silage

Abstract Abstract: ‘Tachiayaka’ is a novel cultivar of rice (Oryza sativa L.) with short panicles that is suitable for use in whole-crop silage. It was selected from bulked progeny obtained from two backcrosses of ‘Hoshiaoba’ to F1 plants of ‘Chugoku 146’ (‘Hoshiaoba’) × ‘Gokutansui (00kosen11)’. The unhulled rice yield of ‘Tachiayaka’ was comparable to that of ‘Tachisuzuka’, a short-panicle cultivar with high digestibility in cattle, in multi-location trials over a wide area spanning the Hokuriku to Chugoku regions in Japan. The whole-crop yield and the maturity of ‘Tachiayaka’ were comparable to those of ‘Hoshiaoba’. These findings indicated the suitability of ‘Tachiayaka’ for cultivation across a wide area of Japan. The characters associated with lodging resistance in ‘Tachiayaka’, including lodging index, were superior to those of ‘Hoshiaoba’. The high lodging resistance of ‘Tachiayaka’ will enable its cultivation in regions where lodging occurs frequently.

Uncovering a Nuisance Influence of a Phenological Trait of Plants Using a Nonlinear Structural Equation: Application to Days to Heading and Culm Length in Asian Cultivated Rice (Oryza Sativa L.).

Phenological traits of plants, such as flowering time, are linked to growth phase transition. Thus, phenological traits often influence other traits through the modification of the duration of growth period. This influence is a nuisance in plant breeding because it hampers genetic evaluation of the influenced traits. Genetic effects on the influenced traits have two components, one that directly affects the traits and one that indirectly affects the traits via the phenological trait. These cannot be distinguished by phenotypic evaluation and ordinary linear regression models. Consequently, if a phenological trait is modified by introgression or editing of the responsible genes, the phenotypes of the influenced traits can change unexpectedly. To uncover the influence of the phenological trait and evaluate the direct genetic effects on the influenced traits, we developed a nonlinear structural equation (NSE) incorporating a nonlinear influence of the phenological trait. We applied the NSE to real data for cultivated rice (Oryza sativa L.): days to heading (DH) as a phenological trait and culm length (CL) as the influenced trait. This showed that CL of the cultivars that showed extremely early heading was shortened by the strong influence of DH. In a simulation study, it was shown that the NSE was able to infer the nonlinear influence and direct genetic effects with reasonable accuracy. However, the NSE failed to infer the linear influence in this study. When no influence was simulated, an ordinary bi-trait linear model (OLM) tended to infer the genetic effects more accurately. In such cases, however, by comparing the NSE and OLM using an information criterion, we could assess whether the nonlinear assumption of the NSE was appropriate for the data analyzed. This study demonstrates the usefulness of the NSE in revealing the phenotypic influence of phenological traits.