Dataset Information

Genetic basis of drought tolerance during seed germination in barley.

ABSTRACT: Drought is one of the harshest abiotic stresses hindering seed germination, plant growth, and crop productivity. A high rate and uniformity of germination under stressful conditions are vital for crop establishment and growth; thus, for productivity. A better understanding of the genetic architecture of seed germination under drought stress is a prerequisite for further increasing yield potential. Barley is considered one of the most abiotic stresses-tolerant cereals. Elucidating the drought tolerance of barley during seed germination would indeed pave the way towards improving the performance of all cereals. However, we still know relatively little about the genetic control of drought tolerance during the seed germination phase. In our study, 218 worldwide spring barley accessions were subjected to PEG-induced drought during seed germination. Induced drought stress "20% PEG" significantly reduced the seed germination parameters and seedling related traits. A genome-wide association scan (GWAS) was used to identify genomic regions associated with our trait of interest. In total, 338 single nucleotide polymorphisms (SNPs) were found to be associated with several traits distributed across seven barley chromosomes, of which 26 genomic regions were associated with candidate genes. The current study found some of the quantitative trait loci (QTL) that have previously been reported to be linked to seed germination-related traits under drought conditions, as well as some new associations. Noteworthy, the identified QTL colocalized with a number of genes (within interval ±0.5 Mbp) that are exclusively distributed on chromosomes 1H, 2H, and 5H. The annotation of these genes in barley shows their roles in drought tolerance through encoding different transcription factors. The function of the identified genes during seed germination was also confirmed by the annotation of their counterparts in Arabidopsis. The current analyses show the power of the GWAS both for identifying putative candidate genes and for improving plant adaptive traits in barley.

SUBMITTER: Thabet SG

PROVIDER: S-EPMC6214555 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Genetic basis of drought tolerance during seed germination in barley.

Thabet Samar G SG Moursi Yasser S YS Karam Mohamed A MA Graner Andreas A Alqudah Ahmad M AM

PloS one 20181102 11

Drought is one of the harshest abiotic stresses hindering seed germination, plant growth, and crop productivity. A high rate and uniformity of germination under stressful conditions are vital for crop establishment and growth; thus, for productivity. A better understanding of the genetic architecture of seed germination under drought stress is a prerequisite for further increasing yield potential. Barley is considered one of the most abiotic stresses-tolerant cereals. Elucidating the drought tol ...[more]

PMID: 30388157

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Project description:IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.

Dataset Information

Genetic basis of drought tolerance during seed germination in barley.

Publications

Genetic basis of drought tolerance during seed germination in barley.

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