Jaboticaba is a typical tropical plant that blossoms and bears fruit on the tree trunks and branches. The fruits resemble grapes in appearance and texture and are also known as “treegrapes”. Currently, research on the genomics of jaboticaba is lacking. In this study, we constructed an integrated, telomere-to-telomere (T2T) gap-free reference genome and two nearly complete haploid genomes, thereby providing a high-quality genomic resource. Furthermore, we unveiled the evolutionary history of several species within the Myrtaceae family, highlighting significant expansions in metabolic pathways such as the citric acid cycle, glycolysis/gluconeogenesis, and phenylpropanoid biosynthesis throughout their evolutionary process. Transcriptome analysis of jaboticaba fruits of different colors revealed that the development of fruit skin color in jaboticaba is associated with the phenylpropanoid and flavonoid biosynthesis pathways, with the flavanone 3-hydroxylase (F3H) gene potentially regulating fruit skin color. Additionally, by constructing the regulatory pathway of the citric acid cycle, we found that low citric acid content is correlated with high expression levels of genes such as thiamin diphosphate (ThDP) and low expression of phosphoenolpyruvate carboxykinase (PEPCK), indicating that PEPCK positively regulates citric acid content. These T2T genomic resources will accelerate jaboticaba pepper genetic improvement and help to understand jaboticaba genome evolution.

Genomic selection is an effective method for accelerating the enhancement of plant agronomic traits. Currently, genotype acquisition mainly depends on resequencing and chip technology, and the cost and efficiency are still the key factors restricting the wide application of genomic selection breeding. We explore the merits of Hyper-seq population sequencing technology in genomic selection breeding. Seven genomic selection models were constructed using 417 rice germplasm resources, and each model showed high prediction accuracy for amylose content (0.8316–0.8360) and gel consistency (0.7075–0.7235). We also constructed GBLUP models to explore how the marker number and population size affected prediction accuracy. With increased marker number and population size, prediction accuracy first increased significantly, then leveled off. Finally, through genome-wide association studies, SNPs were selected from five different significance levels for prediction accuracy studies. The results indicated that using markers that are significantly associated with traits greatly enhances the accuracy of prediction.

Sugarcane (Saccharum spp. Hybrids), serving as a vital sugar and energy crop, holds immense development potential on a global scale. In the process of sugarcane breeding and variety improvement, single-stalk weight stands as a crucial selection criterion. By cultivating sugarcane varieties with heavier single stalks, robust growth, high yields, and superior quality, the planting efficiency and market competitiveness of sugarcane can be further enhanced. Single-stalk weight was determined by measuring individual stalks three times in the field, calculating the average value as the phenotypic expression. The distribution of single-stalk weights in the orthogonal and reciprocal populations revealed coefficients of variation of 19.3% and 17.7%, respectively, with the reciprocal population showing greater genetic stability. After rigorous filtering of Hyper_seq_FD sequencing data from 409 sugarcane samples, we identified 31,204 high-quality single-nucleotide polymorphisms (SNPs) evenly distributed across all 32 chromosomes, providing a comprehensive representation of the sugarcane genome. In this study, we evaluated the predictive performance of various genomic selection (GS) methods for single-stalk weight in the 299 orthogonal population, with the male parent being GZ_73-204 and the female parent being GZ_P72-1210, and in the 108 reciprocal population, with the male parent being GZ_P72-1210 and the female parent being GZ_73-204. Initially, we compared the performance of five prediction approaches, including genomic best linear unbiased prediction (GBLUP), single-step genomic best linear unbiased prediction (SSBLUP), Bayes A, machine learning (ML), and deep learning (DL) approaches. The results showed that the GBLUP model had the highest prediction accuracy, at 0.35, while the deep learning model had the lowest accuracy, at 0.20. To improve prediction accuracy, we assigned different scores to various regions of the sugarcane genome based on gene annotation information, thereby giving different weights to SNPs located in these regions. Additionally, we incorporated inbred and outbred populations as fixed effects into the model. The optimized SSBLUP model achieved a prediction accuracy of 0.44, which was a 17% improvement over the original SSBLUP model and a 9% increase compared to the originally optimal GBLUP model. The research results indicate that it is crucial to fully consider genomic structural regions, population structure characteristics, and fixed effects in GS predictions.