Early selection of grain quality traits in rice (Oryza sativa L.), is essential to improve the yield and quality of this staple crop. We analyzed four key traits—protein content, grain filling rate, height, and panicle length—in 85 Korean cultivars. Through whole- genome resequencing we identified 12,718,879 raw single nucleotide polymorphisms (SNPs); after PLINK-based quality control (bi-allelic selection, call rate≥0.90, MAF≥0.03), ~2.20 million high-quality SNPs remained for machine-learning (ML) pre-screening. To rank the features (without marker-level inference), we applied a liberal univariate PLINK case-control scan using the top and bottom 30% per trait. We also analyzed associations with a linear mixed model (GCTA v1.93.2, MLMA; fixed covariates: ecotype, PC1, PC2; random effect: GRM) to verify calibration under population structure; with n=85, no genome-wide significant hits were detected, and QQ-plots indicated adequate calibration (per-trait effective tests m≈1.54-1.57 million under stricter filters). The random forest feature importance prioritized 26, 51, 19, and 20 core SNPs for the four traits, respectively. Across the algorithms, the best models achieved mean accuracies of 81.8% (protein content), 81.0% (grain filling rate), 73.1% (height), and 94.0% (panicle length). All selected SNPs met the Fluidigm array design requirements, supporting its deployment as a compact, genotype-based panel for early selection and a practical step toward digital breeding in rice.

In this study, we constructed a reference transcriptome database for the special crops Codonopsis lanceolata (known as “deodeok” in Korean) and Ixeridium dentata (known as “sseumbagwi” in Korean). The reference transcriptome sequence based on PacBio Iso-Seq technology was rapidly analyzed using various programs on a supercomputer at the Rural Development Administration to determine homologous gene searches, domain searches, and metabolic pathway-related information. Transcriptome information was organized into a single file database (DB) with a simple user interface using Microsoft Access, enabling efficient access and management of transcriptome sequences and functional prediction information. The deodeok reference transcriptome DB included 49,677 transcriptome sequences, whereas the sseumbagwi reference transcriptome DB included 73,757 transcriptome sequences. These reference transcriptome DBs can be utilized for various genomic analyses, such as gene discovery, gene expression analysis, inter-and intraspecies comparisons, and marker development, with particular potential for use in secondary metabolite research. The reference transcriptome DBs constructed in this study are expected to contribute to enhancing the agricultural value and industrial applicability of deodeok and sseumbagwi.