Flaxseed/linseed is an important oilseed crop having applications in the food, nutraceutical, and paint industry. Seed weight is one of the most crucial determinants of seed yield in linseed. Here, quantitative trait nucleotides (QTNs) associated with thousand-seed weight (TSW) have been identified using multi-locus genome-wide association study (ML-GWAS). Field evaluation was carried out in five environments in multi-year-location trials. SNP genotyping information of the AM panel of 131 accessions comprising 68,925 SNPs was employed for ML-GWAS. From the six ML-GWAS methods employed, five methods helped identify a total of 84 unique significant QTNs for TSW. QTNs identified in ≥ 2 methods/environments were designated as stable QTNs. Accordingly, 30 stable QTNs have been identified for TSW accounting up to 38.65% trait variation. Alleles with positive effect on trait were analyzed for 12 strong QTNs with ≥ 10.00%, which showed significant association of specific alleles with higher trait value in three or more environments. A total of 23 candidate genes have been identified for TSW, which included , , , , , , , , and . expression analysis of candidate genes was performed to validate their possible role in different stages of seed development process. The results from this study provide significant insight and elevate our understanding on genetic architecture of TSW trait in linseed.
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http://dx.doi.org/10.3389/fpls.2023.1166728 | DOI Listing |
Data Brief
December 2024
Plant Breeding Division, Bangladesh Rice Research Institute, Gazipur 1701, Bangladesh.
This dataset provides an in-depth analysis of rice yield and grain quality attributes in four successive four years across 27 diverse environments in Bangladesh. The analysis emphasizes assessing the performance of studied genotypes (GEN), environments (ENV), and their interrelations (GEI). The research aim is to detect a stable and adaptive rice cultivar that not only displays high yield, and better grain quality but also has molecular data to know favorable alleles and biotic and abiotic stress-related traits.
View Article and Find Full Text PDFInt J Mol Sci
November 2024
Suihua Branch of Heilongjiang Academy of Agricultural Sciences, Suihua 152000, China.
Lodging is one of the major problems in rice production. However, few genes that can explain the culm strength within the temperate subspecies have been identified. In this study, we identified , which encodes receptor-like cytoplasmic kinase and plays critical roles in culm strength.
View Article and Find Full Text PDFFront Nutr
September 2024
Sustainable Seed Systems Lab, Department of Crop and Soil Science, Washington States University, Pullman, WA, United States.
Climate change increases stressors that will challenge the resiliency of global agricultural production. Just three crops, wheat, maize, and rice, are estimated to sustain 50% of the caloric demand of the world population, meaning that significant loss of any of these crops would threaten global food security. However, increasing cropping system diversity can create a more resilient food system.
View Article and Find Full Text PDFFront Genet
August 2024
Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
Introduction: Turnip rape is recognized as an oilseed crop contributing to environmentally sustainable agriculture via integration into crop rotation systems. Despite its various advantages, the crop's cultivation has declined globally due to a relatively low productivity, giving way to other crops. The use of genomic tools could enhance the breeding process and accelerate genetic gains.
View Article and Find Full Text PDFPlants (Basel)
September 2024
Mathematical Biology and Bioinformatics Lab, PhysMech Institute, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia.
The chickpea plays a significant role in global agriculture and occupies an increasing share in the human diet. The main aim of the research was to develop a model for the prediction of two chickpea productivity traits in the available dataset. Genomic data for accessions were encoded in Artificial Image Objects, and a model for the thousand-seed weight (TSW) and number of seeds per plant (SNpP) prediction was constructed using a Convolutional Neural Network, dictionary learning and sparse coding for feature extraction, and extreme gradient boosting for regression.
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