Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean.

KEY MESSAGE: We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data.

Novel Soybean Variety Lacking Raffinose Synthase 2 Activity.

Variation in the raffinose family oligosaccharide (RFO) content in soybean is advantageous for livestock farming and health science. In this study, a soybean variety (GmJMC172) with a significantly low stachyose content in its seeds was identified in the NARO Genebank core collection. The results of the single-nucleotide polymorphism (SNP) analysis suggested that this phenomenon was related to a single-base deletion, inducing a frameshift mutation in raffinose synthase 2 (RS2), rather than the polymorphisms in the RS3, RS4, and stachyose synthase (STS) sequences. Differences in the enzymatic properties between the native RS2 and truncated RS2 were examined by using a three-dimensional model predicted using Alphafold2. In addition to revealing the missing active pocket in truncated RS2, the modeled structure explained the catalytic role of W331* and suggested a sufficient space to bind both sucrose and raffinose in the ligand-binding pocket. The soybean line, with seeds available from the NARO Genebank, could serve as breeding materials for manipulating the RFO content.

Random regression for modeling soybean plant response to irrigation changes using time-series multispectral data.

Plant response to drought is an important yield-related trait under abiotic stress, but the method for measuring and modeling plant responses in a time series has not been fully established. The objective of this study was to develop a method to measure and model plant response to irrigation changes using time-series multispectral (MS) data. We evaluated 178 soybean (Glycine max (L.) Merr.) accessions under three irrigation treatments at the Arid Land Research Center, Tottori University, Japan in 2019, 2020 and 2021. The irrigation treatments included W5: watering for 5 d followed by no watering 5 d, W10: watering for 10 d followed by no watering 10 d, D10: no watering for 10 d followed by watering 10 d, and D: no watering. To capture the plant responses to irrigation changes, time-series MS data were collected by unmanned aerial vehicle during the irrigation/non-irrigation switch of each irrigation treatment. We built a random regression model (RRM) for each of combination of treatment by year using the time-series MS data. To test the accuracy of the information captured by RRM, we evaluated the coefficient of variation (CV) of fresh shoot weight of all accessions under a total of nine different drought conditions as an indicator of plant's stability under drought stresses. 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 2020 and 2021,the mean prediction accuracies of MTRRM models built in the changing irrigation treatments (r = 0.44 and 0.49, respectively) were higher than that in the continuous drought treatment (r = 0.34 and 0.44, respectively) in the same year. When the CV was predicted using the MTRRM model across 2020 and 2021 in the changing irrigation treatment, the mean prediction accuracy (r = 0.46) was 42% higher than that of the simple genomic prediction model (r =0.32). The results suggest that this RRM method using the time-series MS data can effectively capture the genetic variation of plant response to drought.

Triterpenoids in aerenchymatous phellem contribute to internal root aeration and waterlogging adaptability in soybeans.

Soybeans (Glycine max) develop newly differentiated aerenchymatous phellem (AP) in response to waterlogging stress. AP is formed in the hypocotyl and root, thus contributing to internal aeration and adaptation to waterlogging for several legumes. Extensive accumulation of triterpenoids - lupeol and betulinic acid - has been identified in AP. However, their physiological roles in plants remain unclarified. Lupeol is converted from 2,3-oxidosqualene by lupeol synthase (LUS) and oxidized to betulinic acid. Notably, soybeans have two LUS genes (GmLUS1 and GmLUS2). Functional analysis was performed to reveal the biological and physiological functions of triterpenoids in AP using lus mutants. The AP cells of lus1 mutant lacked triterpenoid accumulation and epicuticular wax. Lupeol and betulinic acid were the major components of epicuticular wax and contributed to tissue hydrophobicity and oxygen transport to the roots. Tissue porosity in AP was lower in the lus1 mutant than in the wild-type, which resulted in reduced oxygen transport to the roots via AP. This reduction in oxygen transport resulted in shallow root systems under waterlogged conditions. Triterpenoid accumulation in AP contributes to effective internal aeration and root development for adaptation to waterlogging, suggesting the significance of triterpenoids in improving waterlogging tolerance.

Improved Field-Based Soybean Seed Counting and Localization with Feature Level Considered.

Developing automated soybean seed counting tools will help automate yield prediction before harvesting and improving selection efficiency in breeding programs. An integrated approach for counting and localization is ideal for subsequent analysis. The traditional method of object counting is labor-intensive and error-prone and has low localization accuracy. To quantify soybean seed directly rather than sequentially, we propose a P2PNet-Soy method. Several strategies were considered to adjust the architecture and subsequent postprocessing to maximize model performance in seed counting and localization. First, unsupervised clustering was applied to merge closely located overcounts. Second, low-level features were included with high-level features to provide more information. Third, atrous convolution with different kernel sizes was applied to low- and high-level features to extract scale-invariant features to factor in soybean size variation. Fourth, channel and spatial attention effectively separated the foreground and background for easier soybean seed counting and localization. At last, the input image was added to these extracted features to improve model performance. Using 24 soybean accessions as experimental materials, we trained the model on field images of individual soybean plants obtained from one side and tested them on images obtained from the opposite side, with all the above strategies. The superiority of the proposed P2PNet-Soy in soybean seed counting and localization over the original P2PNet was confirmed by a reduction in the value of the mean absolute error, from 105.55 to 12.94. Furthermore, the trained model worked effectively on images obtained directly from the field without background interference.

Apoplast-Localized ß-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 ß-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 root apoplastic fraction of ichg mutants, the isoflavone glycoside contents were significantly increased, while isoflavone aglycone contents were decreased, indicating that ICHG hydrolyzes isoflavone glycosides into aglycones in the root apoplast. 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 wild-types (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 for isoflavone-mediated plant-microbe interactions.

Time-series multispectral imaging in soybean for improving biomass and genomic prediction accuracy.

Multispectral (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 aboveground biomass (AGB) in soybean [Glycine max (L.) Merr.]. 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 vegetative to early reproductive stage. To predict the genotypic values of AGB, VIs at the different growth stages were used as secondary traits in a multitrait genomic prediction. The prediction accuracy of the genotypic values of AGB from MS and genomic data largely outperformed that of the genomic data alone before the flowering stage (90% of accessions did not flower), suggesting that it would be possible to determine cross-combinations based on the predicted genotypic values of AGB. We compared the prediction accuracy of a model using the five VIs and a model using only one VI to predict the phenotypic values of AGB and found that the difference in prediction accuracy decreased over time at all irrigation levels except for the most severe drought. The difference in the most severe drought was not as small as that in the other treatments. Only the prediction accuracy of a model using the five VIs in the most severe droughts gradually increased over time. Therefore, the optimal timing for MS imaging may depend on the irrigation levels.

Analysis of Physiological Variations and Genetic Architecture for Photosynthetic Capacity of Japanese Soybean Germplasm.

The culmination of conventional yield improving parameters has widened the margin between food demand and crop yield, leaving the potential yield productivity to be bridged by the manipulation of photosynthetic processes in plants. Efficient strategies to assess photosynthetic capacity in crops need to be developed to identify suitable targets that have the potential to improve photosynthetic efficiencies. Here, we assessed the photosynthetic capacity of the Japanese soybean mini core collection (GmJMC) using a newly developed high-throughput photosynthesis measurement system "MIC-100" to analyze physiological mechanisms and genetic architecture underpinning photosynthesis. K-means clustering of light-saturated photosynthesis (Asat ) classified GmJMC accessions into four distinct clusters with Cluster2 comprised of highly photosynthesizing accessions. Genome-wide association analysis based on the variation of Asat revealed a significant association with a single nucleotide polymorphism (SNP) on chromosome 17. Among the candidate genes related to photosynthesis in the genomic region, variation in expression of a gene encoding G protein alpha subunit 1 (GPA1) showed a strong correlation (r = 0.72, p < 0.01) with that of Asat . Among GmJMC accessions, GmJMC47 was characterized by the highest Asat , stomatal conductance (gs ), stomatal density (SDensity ), electron transfer rate (ETR), and light use efficiency of photosystem II (Fv'/Fm') and the lowest non-photochemical quenching [NPQ(t)], indicating that GmJMC47 has greater CO2 supply and efficient light-harvesting systems. These results provide strong evidence that exploration of plant germplasm is a useful strategy to unlock the potential of resource use efficiencies for photosynthesis.

Genomic Prediction of Green Fraction Dynamics in Soybean Using Unmanned Aerial Vehicles Observations.

With the widespread use of high-throughput phenotyping systems, growth process data are expected to become more easily available. By applying genomic prediction to growth data, it will be possible to predict the growth of untested genotypes. Predicting the growth process will be useful for crop breeding, as variability in the growth process has a significant impact on the management of plant cultivation. However, the integration of growth modeling and genomic prediction has yet to be studied in depth. In this study, we implemented new prediction models to propose a novel growth prediction scheme. Phenotype data of 198 soybean germplasm genotypes were acquired for 3 years in experimental fields in Tottori, Japan. The longitudinal changes in the green fractions were measured using UAV remote sensing. Then, a dynamic model was fitted to the green fraction to extract the dynamic characteristics of the green fraction as five parameters. Using the estimated growth parameters, we developed models for genomic prediction of the growth process and tested whether the inclusion of the dynamic model contributed to better prediction of growth. Our proposed models consist of two steps: first, predicting the parameters of the dynamics model with genomic prediction, and then substituting the predicted values for the parameters of the dynamics model. By evaluating the heritability of the growth parameters, the dynamic model was able to effectively extract genetic diversity in the growth characteristics of the green fraction. In addition, the proposed prediction model showed higher prediction accuracy than conventional genomic prediction models, especially when the future growth of the test population is a prediction target given the observed values in the first half of growth as training data. This indicates that our model was able to successfully combine information from the early growth period with phenotypic data from the training population for prediction. This prediction method could be applied to selection at an early growth stage in crop breeding, and could reduce the cost and time of field trials.

Mapping QTLs Controlling Soybean Rust Disease Resistance in Chiang Mai 5, an Induced Mutant Cultivar.

Soybean rust (SBR) caused by the fungus Phakopsora pachyrhizi is an important folia disease of soybean (Glycine max). In this study, we identified QTLs controlling SBR in Chiang Mai 5 (CM5), an SBR-resistant cultivar developed by induced mutation breeding. A recombinant inbred line (RIL) population of 108 lines developed from a cross between Sukhothai 2 (SKT2, a susceptible cultivar) and CM5 was evaluated for SBR resistance under field conditions in Thailand. QTL analysis for the resistance in the RIL population identified a single QTL, qSBR18.1, for resistance. qSBR18.1 was mapped to a 212-kb region on chromosome 18 between simple sequence repeat markers Satt288 and sc21_3420 and accounted for 21.31-35.09% depending on the traits evaluated for resistance. The qSBR18.1 interval overlapped with genomic regions containing resistance to P. pachyrhizi 4 (Rpp4), a locus for SBR resistance. Three tightly linked genes, Glyma.18G226250, Glyma.18G226300, and Glyma.18G226500, each encoding leucine-rich repeat-containing protein, were identified as candidate genes for SBR resistance at the qSRB18.1. The qSBR18.1 would be useful for breeding of SBR resistance.

Effects of irrigation on root growth and development of soybean: A 3-year sandy field experiment.

Increasing the water use efficiency of crops is an important agricultural goal closely related to the root system -the primary plant organ for water and nutrient acquisition. In an attempt to evaluate the response of root growth and development of soybean to water supply levels, 200 genotypes were grown in a sandy field for 3 years under irrigated and non-irrigated conditions, and 14 root traits together with shoot fresh weight and plant height were investigated. Three-way ANOVA revealed a significant effect of treatments and years on growth of plants, accounting for more than 80% of the total variability. The response of roots to irrigation was consistent over the years as most root traits were improved by irrigation. However, the actual values varied between years because the growth of plants was largely affected by the field microclimatic conditions (i.e., temperature, sunshine duration, and precipitation). Therefore, the best linear unbiased prediction values for each trait were calculated using the original data. Principal component analysis showed that most traits contributed to principal component (PC) 1, whereas average diameter, the ratio of thin and medium thickness root length to total root length contributed to PC2. Subsequently, we focused on selecting genotypes that exhibited significant improvements in root traits under irrigation than under non-irrigated conditions using the increment (I-index) and relative increment (RI-index) indices calculated for all traits. Finally, we screened for genotypes with high stability and root growth over the 3 years using the multi-trait selection index (MTSI).Six genotypes namely, GmJMC130, GmWMC178, GmJMC092, GmJMC068, GmWMC075, and GmJMC081 from the top 10% of genotypes scoring MTSI less than the selection threshold of 7.04 and 4.11 under irrigated and non-irrigated conditions, respectively, were selected. The selected genotypes have great potential for breeding cultivars with improved water usage abilities, meeting the goal of water-saving agriculture.

Genomic prediction modeling of soybean biomass using UAV-based remote sensing and longitudinal model parameters.

The application of remote sensing in plant breeding can provide rich information about the growth processes of plants, which leads to better understanding concerning crop yield. It has been shown that traits measured by remote sensing were also beneficial for genomic prediction (GP) because the inclusion of remote sensing data in multitrait models improved prediction accuracies of target traits. However, the present multitrait GP model cannot incorporate high-dimensional remote sensing data due to the difficulty in the estimation of a covariance matrix among the traits, which leads to failure in improving its prediction accuracy. In this study, we focused on growth models to express growth patterns using remote sensing data with a few parameters and investigated whether a multitrait GP model using these parameters could derive better prediction accuracy of soybean [Glycine max (L.) Merr.] biomass. A total of 198 genotypes of soybean germplasm were cultivated in experimental fields, and longitudinal changes of their canopy height and area were measured continuously via remote sensing with an unmanned aerial vehicle. Growth parameters were estimated by applying simple growth models and incorporated into the GP of biomass. By evaluating heritability and correlation, we showed that the estimated growth parameters appropriately represented the observed growth curves. Also, the use of these growth parameters in the multitrait GP model contributed to successful biomass prediction. We conclude that the growth models could describe the genetic variation of soybean growth curves based on several growth parameters. These dimension-reduction growth models will be indispensable for extracting useful information from remote sensing data and using this data in GP and plant breeding.

Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing.

Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.

Whole-genome sequence diversity and association analysis of 198 soybean accessions in mini-core collections.

We performed whole-genome Illumina resequencing of 198 accessions to examine the genetic diversity and facilitate the use of soybean genetic resources and identified 10 million single nucleotide polymorphisms and 2.8 million small indels. Furthermore, PacBio resequencing of 10 accessions was performed, and a total of 2,033 structure variants were identified. Genetic diversity and structure analysis congregated the 198 accessions into three subgroups (Primitive, World, and Japan) and showed the possibility of a long and relatively isolated history of cultivated soybean in Japan. Additionally, the skewed regional distribution of variants in the genome, such as higher structural variations on the R gene clusters in the Japan group, suggested the possibility of selective sweeps during domestication or breeding. A genome-wide association study identified both known and novel causal variants on the genes controlling the flowering period. Novel candidate causal variants were also found on genes related to the seed coat colour by aligning together with Illumina and PacBio reads. The genomic sequences and variants obtained in this study have immense potential to provide information for soybean breeding and genetic studies that may uncover novel alleles or genes involved in agronomically important traits.

Functional Divergence of G and Its Homologous Genes for Green Pigmentation in Soybean Seeds.

The degradation of chlorophyll in mature soybean seeds is closely related to the development of their yellow color. In this study, we examined G, its homologue G-like (GL), and their mutant alleles and investigated the relationship between these genes and chlorophyll accumulation in the seed coats of mature seeds. Transient expression of G and GL proteins fused with green fluorescent protein revealed that both were localized in plastids. Overexpression of G resulted in the accumulation of chlorophyll in the seed coats and cotyledons of mature seeds, indicating that high expression levels of G result in chlorophyll accumulation that exceeds its metabolism in the seeds of yellow soybean. Analysis of near isogenic lines at the G locus demonstrated a significant difference in the chlorophyll content of the seed coats and cotyledons of mature seeds when G and mutant g alleles were expressed in the d1d2 stay-green genetic background, indicating that the G protein might repress the SGR-independent degradation of chlorophyll. We examined the distribution of mutant alleles at the G and GL loci among cultivated and wild soybean germplasm. The g allele was widely distributed in cultivated soybean germplasm, except for green seed coat soybean lines, all of which contained the G allele. The gl alleles were much fewer in number than the g alleles and were mainly distributed in the genetic resources of cultivated soybean from Japan. None of the landraces and breeding lines investigated in this study were observed to contain both the g and gl alleles. Therefore, in conclusion, the mutation of the G locus alone is essential for establishing yellow soybeans, which are major current soybean breeding lines.

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 × 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 × 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 × E interactions in six traits including yield, flowering time, and protein content and when these factors were involved in the interactions. 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 × E interactions. Our results demonstrate the capability of data-driven approaches to bring novel insights on the G × E interactions observed in fields.

Identification and Characterization of an Early Leaf Senescence Gene ELS1 in Soybean.

Early leaf senescence phenotype in soybean could be helpful to shorten the maturation period and prevent green stem disorder. From a high-density mutation library, we identified two early leaf senescence soybean mutant lines, els1-1 (early leaf senescence 1) and els1-2. The chlorophyll contents of both els1-1 and els1-2 were low in pre-senescent leaves. They degraded rapidly in senescent leaves, revealing that ELS1 is involved in chlorophyll biosynthesis during leaf development and chlorophyll degradation during leaf senescence. The causal mutations in els1 were identified by next-generation sequencing-based bulked segregant analysis. ELS1 encodes the ortholog of the Arabidopsis CaaX-like protease BCM1, which is localized in chloroplasts. Soybean ELS1 was highly expressed in green tissue, especially in mature leaves. The accumulation of photosystem I core proteins and light-harvesting proteins in els1 was low even in pre-senescent leaves, and their degradation was accelerated during leaf senescence. These results suggest that soybean ELS1 is involved in both chlorophyll synthesis and degradation, consistent with the findings in Arabidopsis BCM1. The gene els1, characterized by early leaf senescence and subsequent early maturation, does not affect the flowering time. Hence, the early leaf senescence trait regulated by els1 helps shorten the harvesting period because of early maturation characteristics. The els1-1 allele with weakly impaired function of ELS1 has only a small effect on agricultural traits and could contribute to practical breeding.

Characterization of the genomic sequence data around common cutworm resistance genes in soybean (Glycine max) using short- and long-read sequencing methods.

The common cutworm (CCW, Spodopteraab litura Fabricius) is one of the pests that most severely infect soybean (Glycine max L. Merr.). In a previous report, quantitative trait loci (QTL) analysis of CCW resistance using a recombinant inbred line derived from a cross between a susceptible cultivar 'Fukuyutaka' and a resistant cultivar 'Himeshirazu', identified two antixenosis resistance QTLs, CCW-1 and CCW-2. To reveal sequence variation between the aforementioned two cultivars, whole genome resequencing was performed using Illumina HiSeq2000 (75,632,747 and 91,540,849 reads). The generated datasets can be used for fine mapping and gene isolation of CCW-1 and CCW-2 as well as for revealing more detailed genetic differences between 'Fukuyutaka' and 'Himeshirazu' .

Molecular Basis Underlying Common Cutworm Resistance of the Primitive Soybean Landrace Peking.

The common cutworm (CCW; Spodoptera litura) is one of the major insect pests of soybean in Asia and Oceania. Although quantitative trail loci related to CCW resistance have been introduced into leading soybean cultivars, these do not exhibit sufficient resistance against CCW. Thus, understanding the genetic and metabolic resistance mechanisms of CCW as well as integrating other new resistance genes are required. In this study, we focused on a primitive soybean landrace, Peking, which has retained resistances to various pests. We found a resistance to CCW in Peking by the detached-leaf feeding assay, and subsequently determined the genetic and metabolic basis of the resistance mechanism using chromosome segment substitution lines (CSSLs) of Peking. Several characteristic metabolites for Peking were identified by the metabolomic approach using liquid chromatography/mass spectrometry combined with a principle component analysis. The structure of seven metabolites were determined by nuclear magnetic resonance (NMR) analysis. The genomic segments of Peking on chromosome 06 (Chr06) and Chr20 had a clear association with these metabolites. Moreover, a line possessing a Peking genomic segment on Chr20 inhibited growth of the CCW. The genetic factors and the metabolites on Chr20 in Peking will be useful for understanding mechanisms underlying CCW resistance and breeding resistant soybean cultivars.

Same Locus for Non-shattering Seed Pod in Two Independently Domesticated Legumes, Vigna angularis and Vigna unguiculata.

Loss of pod shattering is one of the most important domestication-related traits in legume crops. The non-shattering phenotypes have been achieved either by disturbed formation of abscission layer between the valves, or by loss of helical tension in sclerenchyma of endocarp, that split open the pods to disperse the seeds. During domestication, azuki bean (Vigna angularis) and yard-long bean (Vigna unguiculata cv-gr. Sesquipedalis) have reduced or lost the sclerenchyma and thus the shattering behavior of seed pods. Here we performed fine-mapping with backcrossed populations and narrowed the candidate genomic region down to 4 kbp in azuki bean and 13 kbp in yard-long bean. Among the genes located in these regions, we found MYB26 genes encoded truncated proteins in azuki bean, yard-long bean, and even cowpea. As such, our findings indicate that independent domestication on the two legumes has selected the same locus for the same traits. We also argue that MYB26 could be a target gene for improving shattering phenotype in other legumes, such as soybean.