Salt stress is a major abiotic factor that limits wheat production worldwide. However, this threat is increasing significantly because soil salinity affects approximately 20% of the irrigated agricultural land globally, leading to significant yield losses by impairing plant growth and photosynthetic efficiency. This study aimed to identify single-nucleotide polymorphisms (SNPs) associated with salt tolerance in wheat core collections during the heading stage under saline stress conditions. Chlorophyll content, a physiological indicator of salt tolerance at heading, and soil electrical conductivity (EC) were measured in 609 accessions and a Salt Tolerance Index (STI) was subsequently constructed. Genome-wide association studies (GWAS) were performed using a 35 K SNP chip to identify significant marker-trait associations. Three models (MLM, FarmCPU, and BLINK) were employed for the GWAS, with FarmCPU and BLINK demonstrating superior power over the MLM in controlling false positives. GWAS results revealed four significant SNPs (AX-94929101, AX- 94615611, AX-94510535, and AX-94411611) located on chromosomes 3D, 5D, and 7D. AX-94510535 exhibited significant phenotypic differences based on SNP genotype, suggesting its potential as a marker for STI. Furthermore, the identified candidate genes, TraesCS3D02G218100, TraesCS5D02G059500, and TraesCS5D02G175000, were implicated in biological processes such as DNA replication, cell death, and photosynthesis.
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Deep learning has gained considerable interest in agricultural breeding research. While advances in sequencing technologies have made genotypic data collection easier in genomic breeding, phenotypic data collection remains labor intensive and time consuming. Furthermore, as traditional phenotypic data collection relies heavily on manual processes, the results may vary based on the researcher’s skill and criteria. Thus, automated phenotypic data collection is essential for addressing these challenges. In this study, we aimed to develop a deep learning model using the YOLOv8 framework to measure the lengths of hypocotyls and roots in sprout vegetables such as mung bean, cowpea, and soybean. Our model automates the measurement process, accurately identifies the hypocotyl and root using Roboflow, and subsequently measures their lengths with high precision in various legume species. This approach addresses the challenges of extensive phenotypic data collection, which is essential for genetic breeding and agricultural improvement. Our deep learning model facilitates consistent and accurate data collection in large-scale studies by controlling variables influenced by the researcher’s skills and criteria. This reduces errors and enhances data reliability and accuracy, which are crucial for successful breeding practices and agricultural research.
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Preserving and utilizing genetic diversity is crucial in crop breeding to address unpredictable situations such as climate change and evolving consumer demands. It serves as a source of new traits and alleles. Core collections are established from approximately 10-20% of conserved resources, and they are not only used for efficient management of genetic resources in seed banks but also applied in crop improvement programs and new gene discovery. These core collections demonstrate diversity based on the geographic origin of genetic resources and provide information on genetic similarity among resource types and collection regions. Recent advances in high-throughput genotyping has enabled high-resolution association mapping, allowing for the precise discovery of new genes and QTLs. The wheat genetic diversity and population structure of core collections are important in determining appropriate GWAS statistical methods for detecting these novel genes and QTLs. To maximize their utility, collecting detailed phenotypic data is crucial. This will expand their application in gene discovery, marker development, and more. In this study, we provided reviews for wheat core collection in the world to face the digital breeding era, where precise gene detection and manipulation are possible. The accumulation of genetic diversity, and phenotypic and genotypic information by core collections will contribute to breeding cycle acceleration and trait selection optimization.
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Sybeans have been grown by plant breeding for decades. As soybeans have very limited genetic variation, it is difficult for soybean breeders to find new genetic resources for abiotic stressors. Recently, soybeans have been exposed to flooding stress from intensive summer rainfall owing to climate change.
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Globally, wheat
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Wheat (
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Wheat transformation was first initiated in 1992, and several studies were conducted to increase its efficiency; however, a very low probability of less than 0.3% was achieved. In 2011, the EU Commission announced a new plant breeding technology that modifies the DNA of seeds and plant cells to develop new varieties with desired characteristics. With the commercialization of the CRISPR/Cas9 technology, a site-directed nuclease technology, the possibility of its application in agriculture has increased with the rapid development of the technology. Recently, genome editing studies have been conducted in wheat, and they have been used for the functional analysis of genes related to various agricultural traits. The wheat full-length genome information was released in the form of a draft sequence in 2018, belatedly in comparison to other crops owing to allohexaploidy and a large genome (17 Gb) size. The recent pre-harvest sprouting resistance wheat breeding material developed in Japan suggests that it is possible to rapidly develop breeding materials through precision breeding technology. Finally, it is necessary to systematically achieve the goal of optimizing agricultural traits of crops through precise breeding technology to increase the breeding accuracy of allohexaploid wheat and rapid genetic fixation using the reduction in generation technology.
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Drought stress during the seedling stage has a disastrous effect on the growth of maize. The purpose of this study was to assess the developed expression markers that are related to drought stress in maize. For the selection of expressed genes by drought stress, co-expression analysis was carried out using published microarray data of drought stress in maize (
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Anther culture is useful and significant tool for producing haploid or doubled haploid (DH) plants in crop breeding system. Androgenesis is the way of inducing haploid and DH plants from anther (immature pollen) or microspore culture.
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