Khang Dan 18 (KD18) is an Oryza sativa L. subsp. indica rice cultivar widely cultivated in northern Vietnam and used as an experimental and breeding background in Vietnamese rice research. Although KD18 has previously been represented in low-depth population resequencing datasets, a contiguous and annotated cultivar-specific genome has not been available. Here, we report a chromosome-scale genome assembly of KD18 generated using Oxford Nanopore long-read and Illumina short-read sequencing. The 395.3-Mb assembly comprises 12 chromosome-scale pseudomolecules containing approximately 95% of the assembled sequence and 99.6% of the predicted protein-coding genes. The assembly showed 97.2% BUSCO completeness, an average Merqury quality value of 46 and a long terminal repeat assembly index of 13.21. A total of 56,546 protein-coding genes representing 71,237 transcripts were predicted, with 99% BUSCO and 98.68% OMArk completeness. These statistics are similar to those of other high-quality genome assemblies that were recently published for different Asian rice cultivars, therefore providing a cultivar-specific genomic resource for research involving KD18 and KD18-derived materials.
T. Q. Nguyen, K. Do, T. M. Vu et al.· bioRxiv· 0 citations
Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.
Ha Duc Chu, T. Q. Nguyen, Loc Van Nguyen et al.· Genes· 1 citation
