Biology subjects

Kaga, A.

Publications and source records attributed to Kaga, A..

12 recordsLinked to original sources

bioRxiv2026

Metabolome genome-wide association study reveals hierarchical and epistatic genetic control of flavonoid metabolism in soybean

Metabolic phenotypes are often governed by complex genetic architectures involving both additive and non-additive effects. However, the extent to which epistatic interactions contribute to the pathway-level regulation of plant metabolism remains unclear. In this study, we investigated the genetic architecture of flavonoid-related metabolites using metabolomic and genomic data from 200 soybean accessions cultivated under multiple environmental conditions. Broad-sense heritability estimates revealed that many metabolites were under strong genetic control, particularly flavonoid-related metabolites. Principal component analysis-based metabolome-wide genome-wide association studies identified four major loci associated with flavonoid metabolic variation, including a locus corresponding to flavonoid 3'-hydroxylase. Conditional analyses based on multilocus genetic backgrounds demonstrated that the effects of downstream loci were highly dependent on upstream genotypes. In particular, single-nucleotide polymorphism effects were frequently detectable only in specific allelic backgrounds defined by the major flavonoid 3'-hydroxylase locus, consistent with strong epistatic interactions among loci. Bayesian network analyses further supported a hierarchical genetic structure consistent with upstream regulation of downstream loci across the flavonoid biosynthetic pathway. These results demonstrate that highly heritable metabolic phenotypes can be controlled by a few loci exhibiting both additive and context-dependent non-additive effects. Our findings provide evidence that pathway-level metabolic diversity in soybean is generated through hierarchical and epistatic genetic control involving a limited set of key loci.

plant biology↗

bioRxiv2025

Integration of Proxy Intermediate Omics traits into a Nonlinear Two-Step model for accurate phenotypic prediction

Intermediate omics traits, which mediate the effects of genetic variation on phenotypic traits, are increasingly recognised as valuable components of genetic evaluation. In particular, rhizosphere microbiota play a crucial role in plant health and productivity; however, their complex interactions with host genetics remain challenging to model. Although two-step modeling frameworks have been proposed to integrate intermediate omics traits into phenotype prediction, existing approaches do not incorporate nonlinear relationships between different omics layers. To address this, we have proposed a two-step phenotype prediction framework that integrates genomic, rhizosphere microbiome, and metabolome (meta-metabolome) data, while explicitly capturing omicsomics nonlinearities. The first step is to predict meta-metabolome traits from genetic and microbial features, thus effectively isolating them from the environmental noise. In this process, intermediate "proxy" omics traits are generated as general biological information to provide robust models. The second step utilises this "proxy" to enhance the accuracy of the phenotype prediction. We compared the linear model (Best Linear Unbiased Prediction, BLUP) and the nonlinear model (Random Forest, RF) at each step, as demonstrated through simulations and empirical analysis of a multi-omics soybean dataset in which nonlinear modeling captures intricate omics interactions. Notably, our approach enables phenotype prediction without requiring the original meta-metabolome data used in model training, thereby reducing reliance on costly omics measurements. This framework integrates intermediate omics traits into genomic prediction to improve prediction accuracy and provide solutions for deeper insights into plant-microbiome interactions.

bioinformatics↗

bioRxiv2024

High-throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

High throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean (Glycine max) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole genome sequences under two irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of five conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.

plant biology↗

bioRxiv2024

Cross Potential Selection: A Proposal for Optimizing Crossing Combinations in Recurrent Selection Based on the Ability of Future Inbred Lines

In plant breeding programs, rapid production of novel varieties is highly desirable. Genomic selection allows the selection of superior individuals based on genomic estimated breeding values. However, it is worth noting that superior individuals may not always be superior parents. The choice of the crossing pair significantly influences the genotypic value of the resulting progeny. This study introduced a new strategy for selecting crossing pairs, termed Cross Potential Selection (CPS), designed to expedite the production of novel varieties. The CPS assesses the potential of each crossing pair to generate a novel variety. It considers the segregation of each crossing pair and computes the expected genotypic values of the topperforming individuals, assuming that the progeny distribution of genotypic values follows a normal distribution. We simulated a 10-year breeding program to compare CPS with three other selection strategies. CPS consistently demonstrated the highest genetic improvements among the four strategies in early cycles. In particular, during the middle cycles of the breeding program, CPS exhibited the highest genetic improvement of 73% of the 300 independent breeding simulations. In a long-term breeding scheme, some progeny distributions of genotypic values may deviate from normal distribution, affecting the efficiency of CPS. Nevertheless, compared with the other three strategies, CPS achieved significant short-term genetic improvements. In conclusion, CPS holds substantial promise for enhancing the efficiency of plant breeding programs. Article SummaryThis study introduces a novel plant breeding strategy termed Cross Potential Selection (CPS), which was designed to expedite the production of novel varieties. The CPS evaluates the potential of each crossing pair for the target generation. Through comparative breeding simulations, CPS demonstrated superior performance over the three alternative breeding strategies, particularly in the early cycles. These findings suggest that CPS holds significant promise for enhancing plant breeding efficiency.

genetics↗

bioRxiv2023

Evaluating drought tolerance stability in soybean by theresponse of irrigation change captured from time-seriesmultispectral data

This study investigated a method to evaluate the drought tolerance stability of a genotype in a single environmental trial by capturing the plant response to irrigation changes. Genotypes that exhibit stable phenotypes under various drought stress conditions are required for stable crop production. However, considerable time and money are required to evaluate the environmental stability of a genotype through multiple environmental trials. As an index of drought tolerance stability, we calculated the coefficient of variation (CV) of shoot fresh weight of 178 soybean (Glycine max (L.) Merr.) accessions in a total of nine types of drought treatments, including changing irrigation treatments (every five or ten days) over 3-year trials. To capture the plant responses to irrigation changes, time-series multispectral (MS) data were collected, including the timings of the irrigation/non-irrigation switch in the changing irrigation treatments. We built a random regression model (RRM) for each of the nine treatments using the time-series MS data. We built a genomic prediction model (MTRRM model) using the genetic random regression coefficients of RRM as secondary traits and evaluated the accuracy of each model for predicting CV. In two out of the three years, the prediction accuracy of MTRRM models built in the changing irrigation treatment was higher than that in the continuous drought treatment in the same year. When the CV was predicted using the MTRRM model across years in the changing irrigation treatment, the prediction accuracy was 61% higher than that of the simple genomic prediction model. These results suggest that drought tolerance stability can be evaluated in a single environmental trial, which may reduce the time and cost of selecting genotypes with high drought tolerance stability.

genetics↗

bioRxiv2022

Apoplast-localized β-Glucosidase Elevates Isoflavone Accumulation in the Soybean Rhizosphere

Plant specialized metabolites (PSMs) are often stored as glycosides within cells and released from the roots with some chemical modifications. While isoflavones are known to function as symbiotic signals with rhizobia and to modulate the soybean rhizosphere microbiome, the underlying mechanisms of root-to-soil delivery are poorly understood. In addition to transporter-mediated secretion, the hydrolysis of isoflavone glycosides in the apoplast by an isoflavone conjugate-hydrolyzing {beta}-glucosidase (ICHG) has been proposed but not yet verified. To clarify the role of ICHG in isoflavone supply to the rhizosphere, we have isolated two independent mutants defective in ICHG activity from a soybean high-density mutant library. In the ichg mutants, the isoflavone contents and composition in the root apoplast and root exudate significantly changed. When grown in a field, the lack of ICHG activity considerably reduced isoflavone aglycone contents in roots and the rhizosphere soil, although the transcriptomes showed no distinct differences between the ichg mutants and WTs. Despite the change in isoflavone contents and composition of the root and rhizosphere of the mutants, root and rhizosphere bacterial communities were not distinctive from those of the WTs. Root bacterial communities and nodulation capacities of the ichg mutants did not differ from the WTs under nitrogen-deficient conditions, either. Taken together, these results indicate that ICHG elevates the accumulation of isoflavones in the soybean rhizosphere but is not essential in isoflavone-mediated plant-microbe interactions.

plant biology↗

bioRxiv2021

A method for identifying environmental stimuli and genes responsible for genotype-by-environment interactions from a large-scale multi-environment data set

It has not been fully understood in real fields what environment stimuli cause the genotype-by-environment (G x E) interactions, when they occur, and what genes react to them. Large-scale multi-environment data sets are attractive data sources for these purposes because they potentially experienced various environmental conditions. Here we developed a data-driven approach termed Environmental Covariate Search Affecting Genetic Correlations (ECGC) to identify environmental stimuli and genes responsible for the G x E interactions from large-scale multi-environment data sets. ECGC was applied to a soybean (Glycine max) data set that consisted of 25,158 records collected at 52 environments. ECGC illustrated what meteorological factors shaped the G x E interactions in six traits including yield, flowering time, and protein content and when they were involved. For example, it illustrated the relevance of precipitation around sowing dates and hours of sunshine just before maturity to the interactions observed for yield. Moreover, genome-wide association mapping on the sensitivities to the identified stimuli discovered candidate and known genes responsible for the G x E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G x E interactions observed in fields. Key messageThe proposed method is able to identify environmental stimuli and genes responsible for the G x E interactions observed in multi-environmental trials. The method is based on similarity search between genetic correlation and environmental stimuli among environments.

plant biology↗

bioRxiv2021

Time-series Multi-spectral Imaging in Soybean for Improving Biomass and Genomic Prediction Accuracy

Multi-spectral (MS) imaging enables the measurement of characteristics important for increasing the prediction accuracy of genotypic and phenotypic values for yield-related traits. In this study, we evaluated the potential application of temporal MS imaging for the prediction of above-ground biomass (AGB) and determined which developmental stages should be used for accurate prediction in soybean. Field experiments with 198 accessions of soybean were conducted with four different irrigation levels. Five vegetation indices (VIs) were calculated using MS images from soybean canopies from early to late growth stages. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multi-trait genomic prediction. The accuracy of the prediction model increased starting at an early stage of growth (31 days after sowing). To predict phenotypic values of AGB, we employed multi-kernel genomic prediction. Consequently, the prediction accuracy of phenotypic values reached a maximum at a relatively early growth stage (38 days after sowing). Hence, the optimal timing for MS imaging may depend on the irrigation levels.

plant biology↗

bioRxiv2019

Genetic factor for twisting legume pods identified by fine-mapping of shattering-related traits in azuki bean and yard-long bean

Legumes have evolved a unique manner of seed dispersal in that the seed pods explosively split open with helical tension generated by sclerenchyma on the endocarp. During domestication, azuki bean (Vigna angularis) and yard-long bean (Vigna unguiculata cv-gr. Sesquipedalis) have reduced or lost the sclerenchyma and lost the shattering behavior of seed pods. Here we performed fine-mapping with back-crossed populations and narrowed the candidate genomic region down to 4 kbp in azuki bean and 13 kbp in yard-long bean. Among genes located in these regions, we found MYB26 genes encoded truncated proteins in both the domesticated species. We also found in azuki bean and other legumes that MYB26 is duplicated and only the duplicated copy is expressed in seed pods. Interestingly, in Arabidopsis MYB26 is single copy and is specifically expressed in anther to initiate secondary wall thickening that is required for anther dehiscence. These facts indicated that, in legumes, MYB26 has been duplicated and acquired a new role in development of pod sclerenchyma. However, pod shattering is unfavorable phenotype for harvesting and thus has been selected against by human.

genetics↗

bioRxiv2019

Easy MPE: Extraction of quality microplot images for UAV-based high-throughput field phenotyping

Microplot extraction (MPE) is a necessary image-processing step in unmanned aerial vehicle (UAV)-based research on breeding fields. At present, it is manually using ArcGIS, QGIS or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semi-automatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1). Binary segmentation, (2). Microplot extraction, (3). Production of *.shp files to enable further file manipulation, and (4). Projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: The average IOU ({+/-}SD) of all trials was 91% ({+/-}3).

bioengineering↗

bioRxiv2018

Miniaturization technologies for cost effective AmpliSeq library preparation for next generation sequencing

PurposeAmpliSeq technology, the target enrichment method for next-generation sequencing (NGS), enables quick and easy detection of the genomic \"hot spot\" region frequently mutated in species. Even though the cost of NGS has decreased, library preparation cost accounts for a more significant proportion of the total cost. If AmpliSeq library can be prepared at a lower cost, large-scale precision oncology can be more easily carried out. Furthermore, this technology can be widely applied not only to medical research, but also to polymorphism detection in biology. This study aimed to reduce the cost of AmpliSeq library preparation by adopting miniaturization technology.\n\nMethodsWe used approximately 10 ng of genomic DNA for ultra-multiplex PCR of 384, 768, 1152, 1920, and 3072 amplicons. Multiplex PCR was performed in a total volume of 1.6, 2.0, and 2.4 L, using a nano-liter liquid handler, for library preparation.\n\nResultsThe success rate of library construction decreased with decreasing total multiplex PCR reaction volume. Using 1.6-, 2.0-, and 2.4-L reactions, the success rates of ultra-multiplex PCR were 25%, 95%, and 100%, respectively. We could stably create libraries of the correct amplicon size, with an amplicon number of approximately 1500 or less. As a result of NGS, uniformity of PCR amplification and read length of quality-checked libraries were hardly affected by the number of amplicons.\n\nConclusionHere, we show that the minimum volume for a stable reaction was 2.4 L and the maximum number of amplicons obtained was approximately 1500. The protocol saved 86.8% in reagent usage and reduced handling time by 85% compared to that required by the manual protocol. Therefore, miniaturization technologies could reduce the cost of AmpliSeq library preparation through minimization of reagents.

genomics↗

bioRxiv2018

QTL Analysis of Domestication Syndrome in Zombi Pea (Vigna vexillata), an Underutilized Legume Crop

Zombi pea (Vigna vexillata (L.) A. Rich) is an underutilized crop belonging to the genus Vigna. Two domesticated forms of zombi pea are cultivated as crop plants; seed and tuber forms. The cultivated seed form is present in Africa, while the cultivated tuber form is present in a very limited part of Asia. Genetics of domestication have been investigated in most of cultivated Vigna crops by means of quantitative trait locus (QTL) mapping. In this study, we investigated genetics of domestication in zombi pea by QTL analysis using an F2 population of 139 plants derived from a cross between cultivated tuber form of V. vexillata (JP235863) and wild V. vexillata (AusTRCF66514). A linkage map with 11 linkage groups was constructed from this F2 population using 145 SSR, 117 RAD-seq and 2 morphological markers. Many highly segregation distorted markers were found on LGs 5, 6, 7, 8, 10 and 11. Most of the distorted markers were clustered together and all the markers on LG8 were highly distorted markers. Comparing this V. vexillata linkage map with a previous linkage map of V. vexillata and linkage maps of other four Vigna species demonstrated several macro translocations in V. vexillata. QTL analysis for 22 domestication-related traits was investigated by inclusive composite interval mapping in which 37 QTLs were identified for 18 traits; no QTL was detected for 4 traits. Number of QTLs detected in each trait ranged from 1 to 5 with an average of only 2.3. Tuber traits were controlled by five QTLs with similar effect locating on different linkage groups. Large-effect QTLs (PVE > 20%) were on LG4 (pod length), LG5 (leaf size and seed thickness), and LG7 (for seed-related traits). Comparison of domestication-related QTLs of the zombi pea with those of cowpea (Vigna unguiculata), azuki bean (Vigna angularis), mungbean (Vigna radiata) and rice bean (Vigna umbellata) revealed that there was conservation of some QTLs for seed size, pod size and leaf size between zombi pea and cowpea and that QTLs associated with seed size (weight, length, width and thickness) in each species were clustered on same linkage.

genomics↗