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The aus (Oryza sativa L.) varietal group comprises of aus, boro, ashina and rayada seasonal and/or field ecotypes, and exhibits unique stress tolerance traits, making it valuable for rice breeding. Despite its importance, the agro-morphological diversity and genetic control of yield traits in aus rice remain poorly understood. To address this knowledge gap, we investigated the genetic structure of 181 aus accessions using 399,115 SNP markers and evaluated them for 11 morpho-agronomic traits. Through genome-wide association studies (GWAS), we aimed to identify key loci controlling yield and plant architectural traits.Our population genetic analysis unveiled six subpopulations with strong geographical patterns. Subpopulation-specific differences were observed in most phenotypic traits. Principal component analysis (PCA) of agronomic traits showed that principal component 1 (PC1) was primarily associated with panicle traits, plant height, and heading date, while PC2 and PC3 were linked to primary grain yield traits. GWAS using PC1 identified OsSAC1 on Chromosome 7 as a significant gene influencing multiple agronomic traits. PC2-based GWAS highlighted the importance of OsGLT1 and OsPUP4/ Big Grain 3 in determining grain yield. Haplotype analysis of these genes in the 3,000 Rice Genome Panel revealed distinct genetic variations in aus rice.In summary, this study offers valuable insights into the genetic structure and phenotypic diversity of aus rice accessions. We have identified significant loci associated with essential agronomic traits, with GLT1, PUP4, and SAC1 genes emerging as key players in yield determination.
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http://dx.doi.org/10.1186/s12284-024-00700-4 | DOI Listing |
PLoS One
September 2025
Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, Arkansas, United States of America.
As a common experimental technique, qPCR (Quantitative Real-time Polymerase Chain Reaction) is widely used to measure levels of nucleic acids, e.g., microRNAs and messenger RNA.
View Article and Find Full Text PDFPLoS One
July 2025
Department of Agricultural Economics, Gazipur Agricultural University, Gazipur, Bangladesh.
Bangladesh has three distinct rice-growing seasons: Aus, Aman, and Boro, each with its distinct climatic state. Climatic factors interacting with non-climatic factors impact seasonal rice yield. However, research hasn't yet examined how climatic and non-climatic factors (CNCFs) affect the yield of rice production during the Boro season (YBR).
View Article and Find Full Text PDFEnviron Pollut
July 2025
Faculty of Engineering and Applied Sciences, Cranfield University, Cranfield, Bedord, MK43 0AL, UK. Electronic address:
Arsenic (As) contamination of rice remains a major human health issue in Asia. Most research has been on irrigated rice. However much of the projected increase in global rice demand over coming decades must be met by rainfed lowland systems, for which As relations are poorly understood.
View Article and Find Full Text PDFComput Biol Chem
December 2025
School of Biological Sciences, University of Aberdeen, UK. Electronic address:
Bakanae, a disease of rice caused by Fusarium spp. affects different growth stages of the plant and can result in heavy losses in crop yields. The objectives of this study were to identify quantitative trait loci (QTLs), and subsequently candidate genes, with a role in bakanae disease resistance.
View Article and Find Full Text PDFGenes (Basel)
May 2025
Faculty of Agronomy and Life Science, Kunming University, Kunming 650201, China.
Introduction: Rice, a cornerstone of global food security, faces escalating demands for enhanced yield and disease resistance. We collected 300 high-quality genomes, representing both cultivated (, , and ) and wild species (, , and ).
Methods: Leveraging HMMER, NLR-Annotator, and OrthoFinder, we systematically identified 148,077 leucine-rich repeat (LRR) and 143,459 nucleotide-binding leucine-rich repeat (NLR) genes, with LRR receptor-like kinases (LRR-RLKs) dominating immune receptor proportions, followed by coiled-coil domain containing (CNL)-type NLRs and LRR receptor-like proteins (LRR-RLPs).