Yosuke Yoshioka

Gene pyramiding enhances durable blast disease resistance in rice

Effective control of blast, a devastating fungal disease of rice, would increase and stabilize worldwide food production. Resistance mediated by quantitative trait loci (QTLs), which usually have smaller individual effects than R-genes but confer broad-spectrum or non-race-specific resistance, is a promising alternative to less durable race-specific resistance for crop improvement, yet evidence that validates the impact of QTL combinations (pyramids) on the durability of plant disease resistance has been lacking. Here, we developed near-isogenic experimental lines representing all possible combinations of four QTL alleles from a durably resistant cultivar. These lines enabled us to evaluate the QTLs singly and in combination in a homogeneous genetic background. We present evidence that pyramiding QTL alleles, each controlling a different response to M. oryzae, confers strong, non-race-specific, environmentally stable resistance to blast disease. Our results suggest that this robust defence system provides durable resistance, thus avoiding an evolutionary “arms race” between a crop and its pathogen.

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among Oryza sativa L. germplasms

One way to use a crop germplasm collection directly to map QTLs without using line-crossing experiments is the whole genome association mapping. A major problem with association mapping is the presence of population structure, which can lead to both false positives and failure to detect genuine associations (i.e., false negatives). Particularly in highly selfing species such as Asian cultivated rice, high levels of population structure are expected and therefore the efficiency of association mapping remains almost unknown. Here, we propose an approach that combines a Bayesian method for mapping multiple QTLs with a regression method that directly incorporates estimates of population structure. That is, the effects due to both multiple QTLs and population structure were included in our statistical model. We evaluated the efficiency of our approach in simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our model could suppress both false-positive and false-negative rates and the error of estimation of genetic effects over single QTL models, indicating that our model has statistically desirable attributes over single QTL models. As real traits, we analyzed the size and shape of milled rice grains and found significant markers that may be linked to QTLs reported previously. Association mapping should have good prospects in highly selfing species such as rice if proper methods are adopted. Our approach will be useful for the whole genome association mapping of various selfing crop species.

On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods

Fully automated yield estimation of intact fruits prior to harvesting provides various benefits to farmers. Until now, several studies have been conducted to estimate fruit yield using image-processing technologies. However, most of these techniques require thresholds for features such as color, shape and size. In addition, their performance strongly depends on the thresholds used, although optimal thresholds tend to vary with images. Furthermore, most of these techniques have attempted to detect only mature and immature fruits, although the number of young fruits is more important for the prediction of long-term fluctuations in yield. In this study, we aimed to develop a method to accurately detect individual intact tomato fruits including mature, immature and young fruits on a plant using a conventional RGB digital camera in conjunction with machine learning approaches. The developed method did not require an adjustment of threshold values for fruit detection from each image because image segmentation was conducted based on classification models generated in accordance with the color, shape, texture and size of the images. The results of fruit detection in the test images showed that the developed method achieved a recall of 0.80, while the precision was 0.88. The recall values of mature, immature and young fruits were 1.00, 0.80 and 0.78, respectively.

Quantitative evaluation of the petal shape variation in Primula sieboldii caused by breeding process in the last 300 years

Primula sieboldii (E. Morren) has been a popular garden plant at least since the Edo period, about 300 years ago. We compared petal form between cultivars and wild populations in order to characterise the changes that have occurred during domestication. The comparison was made using EF-PCA analysis, which describes overall petal shape mathematically by transforming petal contour coordinates into elliptic Fourier descriptors; it subsequently summarises these descriptors by principal component analysis (PCA). Rearing cultivars in a common-garden experiment identified the PCs with a substantial genetic element. A clear heritable component was detected for the PCs characterising symmetrical variation in flower shape, but not the asymmetrical variation. Wild populations of this species have become endangered owing to habitat destruction by human activity, and many lowland floodplain habitats have been lost. Variation within the remaining wild populations was significantly lower than in the cultivars for PC1 (aspect ratio), PC3 (curvature of proximal and distal parts) and petal area; but not for PC2 (depth of head notch) and PC4 (position of the centre of gravity). The shifts in petal form from the wild populations to the cultivars parallel those seen in other crop-types following domestication, including an increase in size and diversity of forms: cultivars have shallower head notches, more fan-shaped petals and larger petals than do wild P. sieboldii.

Quantitative evaluation of flower colour pattern by image analysis and principal component analysis of Primula sieboldii E. Morren

In Primula sieboldii E. Morren, many cultivars have been bred with the purpose of obtaining various petal colour patterns. Colour pattern is an important breeding target in this species, and the availability of an objective and quantitative evaluation method is of vital importance for both genetic analysis and variety registration. Our objective was to establish a new quantitative evaluation method of P. sieboldii flower colour patterns in regions of interest (ROIs) by principal component analysis (PCA). We first set a ROI in each petal as a region that represented the petal colour pattern and defined the maximum square on each petal as the ROI. We then converted each ROI image to a 10 × 10 pixel mosaic image and defined a total of 300 variables (the colour values for red, green and blue components of the 100 pixels) per mosaic image. Finally, we summarized the information on the 300 variables by PCA, and redrew the mosaic images to correspond to some typical principal component scores to determine the effect of each principal component on colour pattern. By this method, we detected five different features of petal colour pattern, four of which were revealed to be mainly genetically controlled. Thus, we successfully established a procedure for evaluating petal colour patterns in P. sieboldii cultivars. This new procedure can be used as a basis for an objective and efficient variety registration system.

Identification of quantitative trait loci for downy mildew resistance in cucumber (Cucumis sativus L.)

Downy mildew, caused by Pseudoperonospora cubensis (Berk. & Curt.) Rostov, is one of the most economically important foliar diseases in cucumber (Cucumis sativus L.). Cucumber line CS-PMR1, derived from self-pollination of USDA Plant Introduction 197088, has a high level of resistance to downy mildew and is considered to be promising breeding material. In this study, we performed quantitative trait locus (QTL) analysis for downy mildew resistance using 111 recombinant inbred lines (RILs) derived from a cross between CS-PMR1 and the old Japanese cultivar Santou, which exhibits moderate resistance. The resistance of the RILs and their parents was evaluated by diverse methods using different plant organs (cotyledons, true leaves), stages (seedlings and adult plants), and evaluation criteria (lesion expansion and extent of sporulation). The high resistance of CS-PMR1 was associated with many QTLs with relatively small effects, whereas the moderate resistance of Santou was associated with one major QTL and possibly two others with relatively small effects. In all assays, the major QTL at which the Santou allele was associated with increased resistance had the largest effect. This QTL allele from Santou and several of the most effective QTL alleles identified in CS-PMR1 should be highest priority for selection to efficiently breed new cultivars that carry adequate levels of downy mildew resistance.

Effect of floral morphology on pollination in Brassica rapa L

SummaryFlower structure, especially the anther–stigma separation (ASS), is well known to affect pollination efficiency, and thus to potentially increase or decrease seed production in crops. Therefore, investigating the relationship between flower characteristics and pollination ability is crucial to a full understanding of mechanisms to improve F1 seed production in Brassica rapa. We used image analysis to measure three flower characteristics: short stamen height (SSH); long stamen height (LSH); and pistil height (PH) in seven cultivars. We calculated the ratio of PH to LSH as an index of anther–stigma separation (ASS). We investigated the number of pollen grains (NPG) deposited on the stigma and the seed-set percentage (SSP) under open-pollination and self-pollination conditions (with and without insects, respectively). Nested ANOVA indicated significant differences between the seven cultivars in the floral characteristics except for PH. Moreover, much larger variation was observed in NPG and SSP than in floral characteristics. Although stepwise multiple regression analysis indicated that plants with relatively high PHs produced more seed under self-pollination, the number of seeds that resulted from self-pollination did not affect seed production because of incompatibility. Therefore, the effect of the spatial position of pistils on the F1 seed production was low. Possibly other factors such as the total pollen production and visiting times of pollinators were important factors in the low yields observed in some cultivars

Genetic Combining Ability of Petal Shape in Garden Pansy ( Viola × wittrockiana Gams) based on Image Analysis

Flower appearance is an important target for improvement in garden pansy (Viola × wittrockiana Gams). Flowers of this species consist of three petal types: inferior, lateral, and superior. By means of principal-components (PC) analysis of elliptic Fourier descriptors (EFDs), we estimated the general and specific combining abilities (GCA and SCA) of the floral characteristics of F1 progeny derived from diallel crosses of four inbred lines. The greatest variation in petal shape was explained by the aspect ratio (the first PC) in each petal type. The second or third PC of each petal type was associated with the curvatures of the various parts of the petal. The highly significant GCA effects indicate the importance of additive genetic variance in the transmission of parental petal characteristics to the progeny. The fact that the SCA mean squares were not significant for aspect ratio and petal area indicates that these characteristics of a single-cross progeny can be sufficiently predicted on the basis of GCA. Significant SCA effects were observed in the curvatures of the distal and proximal parts of lateral and superior petals. Correlation analyses indicated several associations between the shape elements of lateral and superior petals, suggesting that genes for these shape elements may be associated, linked, or pleiotropic in the parents used in this study. We successfully demonstrated the use of EFD–PCA to evaluate the petal shape of garden pansy, and of analyzing combining ability and correlations based on the PC scores of EFDs.

Effect of temperature on symptom expression and viral spread of Melon yellow spot virus in resistant cucumber accessions

Melon yellow spot virus (MYSV), a member of the genus Tospovirus, is a devastating thrips-transmitted virus of cucurbits in Japan. Recently, we reported that cucumber accessions originating from South Asia, in particular Southeast Asia, had moderate resistance to MYSV. Here, we investigated the effect of three temperatures (20°C, 25°C, and 30°C) on symptom expression and viral spread of MYSV in plants of resistant cucumber accessions. No systemic infection developed in resistant cucumber plants after inoculation with melon isolate MYSV-S at low temperature (20°C); viral spread of MYSV-S and cucumber isolate MYSV-FuCu05P in inoculated cotyledons was suppressed. In contrast, higher incubation temperatures (25°C and 30°C) facilitated viral spread in inoculated cotyledons and systemic infection of MYSV-S. These data suggest that the resistance to MYSV of resistant cucumber accessions is temperature dependent.

Strawberry cultivar identification and quality evaluation on the basis of multiple fruit appearance features

Display Omitted We developed an image analysis system to evaluate the appearances of strawberries.The developed system could simultaneously evaluate multiple appearances in detail.The system could sort/classify strawberries based on the multiple appearances.Combining the multiple appearances improved the accuracy of cultivar identification.Our system provides a new method for phenotyping may contribute to breeding programs. The appearances of agricultural products are important indices for evaluating the quality of commodities and the characteristics of different varieties. In general, the appearances are evaluated by experts based on visual observations. However, the concern regarding this method is that it lacks objectivity, and it is not quantifiable because it depends greatly on an empirical knowledge. In addition, agricultural products have multiple appearance features; therefore, several of them need to be analyzed simultaneously for correct evaluation of the appearance. In this study, we developed a new image analysis system that can simultaneously evaluate multiple appearance characteristics such as the color, shape and size, of agricultural products in detail. To evaluate the effectiveness of this system, we conducted quality evaluations and cultivar identification on the basis of cluster analysis, multidimensional scaling and discriminant analysis of the appearance characteristics. The results of the cluster analysis revealed that strawberries could be classified on the basis of their appearance characteristics. Furthermore, we were able to visualize the small differences in the appearance of the fruit based on multiple characteristics on a two-dimensional surface by performing multidimensional scaling. The results demonstrate that our system is effective for qualitative evaluations of the appearance of strawberries. The results of the discriminant analysis revealed that the accuracy of strawberry cultivar classification using 14 cultivars was <42%, when only single feature was used. However, the rate increased to 68% after combining the three features. These results indicate that our system exploits the advantage of analyzing multiple appearance characteristics.