This study proposes a genomic-assisted breeding framework for sweet maize using a low-density SNP chip. After genotyping 95 inbred lines (68 retained with 8,484 SNPs), we assessed genetic diversity and population structure, revealing four distinct clusters. Using this information, 25 representative lines were crossed in incomplete diallel crossing designs to generate 102 F₁ hybrids for phenotyping. An exploratory genome-wide association study (GWAS) on this F₁ population identified 15 candidate loci for agronomic traits. Trait-specific positive correlations were observed between parental genetic distance and hybrid performance. This integrated strategy supported the release of four elite cultivars. The GWAS findings are preliminary and require independent validation due to the modest population size. Nevertheless, this work demonstrates that integrating low-density SNP data for diversity analysis and heterotic grouping effectively guides parental selection and accelerates cultivar development.

We evaluated resistance of 87 sweetpotato cultivars to 6 races (SP1, SP2, SP3, SP4, SP6-1, and SP6-2) of the southern root-knot nematode (SRKN) using a 5-point scale based on the number of egg masses (NEM) formed in laboratory tests. NEM values for all 6 races showed highly skewed distributions. Significant differences in resistance were observed among the 6 races, with resistance to SP1, SP2, and SP3 generally higher than or equal to that observed for SP4, SP6-1, and SP6-2. Laboratory resistance evaluations for the 6 races were significantly correlated with field test results from Miyakonojo, Yawara, Katori, and Hamaoka. No significant differences in laboratory resistance were observed among cultivars grouped by their 3 major primary usages, nor between resistance evaluations for 5 of the 6 races (SP2 being the exception) and production area ranks. However, cultivars registered after 1970 exhibited higher or equal resistance compared to those registered before 1970 and landrace cultivars, suggesting that although higher SRKN resistance in a cultivar did not correlate with an expansion of its production area, breeding for SRKN resistance across various usages intensified from the 1960s. These results provide valuable insights for breeding SRKN-resistant sweetpotato cultivars.

The adzuki bean cultivar ‘Erimo-shozu’ has been grown in Hokkaido for around 40 years for its high yield and tolerance to cold. It is susceptible to brown stem rot (BSR), a soilborne disease that reduces yields, but processors value its quality, and new BSR-resistant cultivars have not been able to replace it. We bred the BSR-resistant cultivar ‘Erimo 167’ through repeated backcrossing using DNA markers. ‘Erimo 167’ has the same agronomic traits and quality as ‘Erimo-shozu’ but is resistant to BSR. Genome-wide SNP polymorphism analysis confirmed that the genomes of ‘Erimo 167’ and ‘Erimo-shozu’ were almost identical except for part of chromosome 3 where the BSR-resistance gene is located.

Genome-wide association studies (GWAS) are widely used to identify genetic loci underlying various agronomic traits. Conventional Manhattan plots provide an effective two-dimensional (2D) summary of an individual GWAS result. However, recent advances in high-throughput phenotyping have led to study designs that generate multiple GWAS outputs across time points, traits, or experimental conditions. In such settings, biological insight increasingly depends on comparative interpretation of multiple association maps, yet panel-based arrangements of 2D plots fragment related information and impede recognition of shared or dynamic genetic signals. Here, I present 3D-Manhattan, an interactive visualization framework that integrates multiple GWAS results within a unified three-dimensional (3D) coordinate sys‍tem. By extending the conventional Manhattan plot with an additional axis representing time, trait, or condition, 3D-‍Manhattan enables simultaneous, axis-aligned comparison of association landscapes while preserving genomic coordinates and statistical values. The tool is implemented as a stand-alone, browser-based application using WebGL-based rendering and supports smooth interaction without server-side computation. The framework provides flexible visualization controls, region highlighting, and variant-level correspondence across datasets, facilitating exploratory analysis of stable and context-dependent genetic associations. Collectively, 3D-Manhattan provides an alternative approach for visualizing multi-dimensional GWAS results and offers a powerful platform for visualizing general a series of genome-wide datasets.

The transition from mitotic proliferation to meiotic division is a defining developmental event in reproduction. In land plants, meiotic entry occurs within complex multicellular tissues and must be precisely coordinated with developmental cues. Recent studies have revealed that meiotic initiation is not a simple cell-cycle switch but is genetically and mechanistically separable from germ-founder cell specification, leading to the emerging concept that meiotic entry is governed by a hierarchical licensing system operating prior to irreversible commitment to meiosis. This review summarizes current knowledge of the developmental, metabolic, redox, and post-transcriptional mechanisms that establish meiotic competence in plants, and discuss how environmental and metabolic inputs, such as temperature and nitrogen availability, are linked to intracellular permissiveness through these mechanisms, ultimately converging on a meiotic commitment point that marks the transition to meiotic mode. Finally, it is proposed that meiotic entry in multicellular land plants represents a developmentally programmed engagement of intrinsic stress-response pathways evolution­arily originated in unicellular organisms. A deeper understanding of these mechanisms will provide a foundation for fu‍ture studies improving reproductive stability and crop fertility under global climate change.

To efficiently develop soybean (Glycine max) cultivars resistant to root lodging, which occurs in the late growth stages and causes significant yield losses, it is essential to understand the root traits that contribute to this resistance. However, the root zones most responsible for plant support remain unidentified, leaving uncertainty about which should be prioritized in root trait evaluation. This study aimed to identify root distributions associated with pushing resistance, which is closely related to root lodging, through vertical and horizontal root pruning combined with analyses of root distribution during the full pod to full seed stages. The results suggest that root length (≥0.5 mm diameter) within the top 15 cm of the soil and within 20 cm horizontally from the plant, especially directly beneath the plant, plays a crucial role in maintaining pushing resistance. The root zone identified in this study may provide a useful target for the de‍tailed evaluation of root traits in field-grown soybean during the late growth stages, and approaches such as shov‍elomics and the basket method could facilitate such evaluations in future breeding programs.

Rice is a staple food for almost half of the world’s population. During domestication from wild rice, the selection of plants with reduced seed shattering enabled efficient harvesting. Further selection for increased grain number improved yield potential. Breeding high-yielding cultivars remains necessary to support the growing world population. Despite success in increasing yield potential, improvements in micronutrient content in rice grains have lagged. Zinc (Zn) is an essential micronutrient for crop productivity and human health. Previously, we reported that reduced seed-setting rate was associated with increased grain Zn concentration, suggesting a trade-off between grain number and Zn concentration. Based on these findings, we investigated the grain Zn concentration in high-yielding rice conferred by Gn1a, a gene involved in the regulation of grain number per panicle. Lower grain Zn concentration was observed in high-yielding plants. However, reducing the seed-setting rate by artificially trimming spikelets at the flowering stage restored grain Zn concentrations in high-yielding plants. Other mineral element concentrations were less affected under high-yielding plants. Our results showed clear trade-off between grain number and Zn concentration, emphasizing the importance of enhancing grain Zn concentrations in breeding programs of high-yielding rice.

Red radish (Raphanus sativus L.) pigments are attractive natural colorants, because they resist heat and light and give ‍foods an attractive red hue. The major limitation of using red radish colorants in food is their distinctly undesir­able ‍flavor resulting from the degradation of glucoraphasatin, which is a glucosinolate predominantly contained in radishes. In the present study, we developed a novel red radish cultivar, ‘Saint Rouge’, for odorless food coloring. ‘Saint Rouge’ was derived from ‘AD-1’, a red radish cultivar used for colorant and bred by introducing a gene conferring gluco‍raphasatin-less trait using marker-assisted selection. ‘Saint Rouge’ offers a ca. 1.2–1.9-fold greater index for the total color amount than that of the original ‘AD-1’. Meanwhile, the color tone of the extract was quite similar to that of ‍‘AD-1’. ‘Saint Rouge’ did not contain glucoraphasatin. Therefore, the amount of methyl mercaptan, dimethyl disulfide and dimethyl trisulfide, substances responsible for off-flavors and the sulfur-like odor of radish pigment extracts, was drastically lower in ‘Saint Rouge’ than in ‘AD-1’. These results demonstrate that ‘Saint Rouge’ is a breakthrough red radish cultivar for odorless food colorants.

Accurate extraction of organ contours from images remains a major bottleneck in quantitative analyses of crop morphology. Here, I present MEGAcontour, a Python-based graphical user interface that streamlines contour extraction and normalized elliptic Fourier descriptor (nEFD) analysis, and demonstrate its utility through genomic prediction of eggplant (Solanum melongena L.) fruit contours. To enable robust extraction under challenging imaging conditions, MEGAcontour integrates the salient object detection model U²-Net. I further fine-tuned U²-Net for eggplant fruit segmentation to improve specificity, particularly by excluding attached fruit branches from extracted contours. PCA of nEFDs indicated that the dominant axis of variation corresponded to fruit elongation. Using DNA markers, I performed leave-one-accession-out genomic prediction for selected nEFD coefficients, reconstructed contours from the predicted coefficients, and evaluated agreement with observed contours using intersection over union (IoU). IoU of reconstructed contours ranged from 0.34 to 0.99 depending on accession. These results provide a practical workflow for contour extraction and support the feasibility of genomic prediction for eggplant fruit shape in breeding programs.