Peach (Prunus persica), a significant economic fruit tree in the Rosaceae family, is extensively cultivated in temperate and subtropical regions due to its abundant genetic diversity, robust adaptability, and high nutritional value. Originating from China over 4000 years ago, peaches were introduced to Persia through the Silk Road during the Han Dynasty and gradually spread to India, Greece, Rome, Egypt, Europe, and America. Currently grown in more than 80 countries worldwide, the expansion of peach cultivation in Egypt is mainly due to the development and utilization of peach varieties with low chilling requirements. These varieties exhibit unique phenotypic characteristics such as early maturity, reduced need for winter cold temperatures, low water requirements, and high economic value. In this study, a systematic analysis was conducted on the genetic characteristics and kinship relationships of peaches with low chilling requirements in Egypt. We conducted a comprehensive evolutionary and Identity-by-Descent (IBD) analysis on over 300 peach core germplasm resources, including Egyptian cultivars with low chilling requirements, to investigate their origin and genetic characteristics. The evolutionary analysis revealed that ‘Bitter almond’ is closely related to China’s wild relative species Prunus tangutica Batal, while ‘Early grand’ shares one branch with Chinese ornamental peach cultivars, and ‘Nemaguard’ clusters with some ancient local varieties from China. The IBD analysis also indicated similar genetic backgrounds, suggesting a plausible origin from China. Similarly, the analysis suggested that ‘Swelling’ may have originated from the Czech Republic while ‘Met ghamr’ has connections to South Africa. ‘Desert red’, ‘Early swelling’, and ‘Florida prince’ are likely derived from Brazil. These findings provide valuable insights into the genetic characteristics of Egyptian peach cultivars. They offer a significant foundation for investigating the origin and spread of cultivated peaches worldwide and serve as a valuable genetic resource for breeding low chilling requirement cultivars, which is of considerable significance for the advancement of peach cultivation in Egypt.

Background/Objectives: Genomic prediction is a powerful approach that predicts phenotypic traits from genotypic information, enabling the acceleration of trait improvement in plant breeding. Traditional genomic prediction methods have primarily relied on linear mixed models, such as Genomic Best Linear Unbiased Prediction (GBLUP), and conventional machine learning methods like Support Vector Regression (SVR). Traditional methods are limited in handling high-dimensional data and nonlinear relationships. Thus, deep learning methods have also been applied to genomic prediction in recent years. Methods: We proposed iADEP, Integrated Additive, Dominant, and Epistatic Prediction model based on deep learning. Specifically, single nucleotide polymorphism (SNP) data integrating latent genetic interactions and genome-wide association study results as biological prior knowledge are fused to an SNP embedding block, which is then input to a local encoder. The local encoder is fused with an omic-data-incorporated global decoder through a multi-head attention mechanism, followed by multilayer perceptrons. Results: Firstly, we demonstrated through experiments on four datasets that iADEP outperforms existing methods in genotype-to-phenotype prediction. Secondly, we validated the effectiveness of SNP embedding through ablation experiments. Third, we provided an available module for combining other omics data in iADEP and propose a novel method for fusing them. Fourthly, we explored the impact of feature selection on iADEP performance and conclude that utilizing the full set of SNPs generally provides optimal results. Finally, by altering the partition of training and testing sets, we investigated the differences between transductive learning and inductive learning. Conclusions: iADEP provides a new approach for AI breeding, a promising method that integrates biological prior knowledge and enables combination with other omics data.

Small RNAs (sRNAs) control a wide range of development and physiological pathways in plants. To address the response of sRNA biogenesis to drought stress, we identified sRNA biogenesis genes, including 11 encoding argonautes (AGO), 8 encoding Dicer-like proteins (DCL), and 9 encoding RNA-dependent RNA polymerases (RDR) in the peach genome. Notably, the largest numbers of sRNA biogenesis genes are located to chromosome 1. The PAZ, PIWI, and MID domains were identified in PpAGOs, while the ribonuclease IIIa and IIIb domains were characterized in PpDCLs. The RDRP domain was recognized in PpRDRs. Orthologous similarity and collinearity analyses between Arabidopsis and peach revealed 5, 1, and 2 collinear blocks in AGOs, DCLs, and RDRs, respectively. Moreover, 41, 40, and 42 cis-acting elements were located in the promoters of PpAGOs, PpDCLs, and PpRDRs, respectively, with the majority related to drought stress response. Analysis of RNA sequencing (RNA-seq) data revealed that sRNA biogenesis genes were involved in drought stress response in different tissues. Furthermore, the expression of candidate genes was verified in two peach cultivars, Beijing 2-7 (BJ2-7) and Sinai (SN), which are tested as drought-tolerant and sensitive cultivars, respectively, based on the physiological and biochemical analyses, which revealed that the Chinese peach cultivar ‘BJ2-7’ exhibits greater drought resistance compared to the Egyptian peach cultivar ‘SN’. Interestingly, the expression of PpAGO2b, PpDCL2b, PpDCL4, and PpRDR4 genes was induced in ‘BJ2-7’ but inhibited in ‘SN’ under drought stress. Overall, this study provides insight into the roles of sRNA biogenesis genes in response to drought stress in peach.

To address the response of small RNAs (sRNAs) biogenesis to drought stress, we identified sRNAs biogenesis genes, including 11 encoding argonauts (AGO), 8 encoding dicer-like proteins (DCL), and 9 encoding RNA-dependent RNA polymerases (RDR) in the peach genome. Notably, the largest numbers of sRNAs biogenesis genes are located to chromosome 1. The PAZ, PIWI, and MID domains were identified in PpAGOs, while the ribonuclease IIIa and IIIb domains were characterized in PpDCLs. The RDRP domain was recognized in PpRDRs. Orthologous similarity and collinearity analyses between Arabidopsis and peach revealed 5, 1, and 2 collinear blocks in AGOs, DCLs, and RDRs, respectively. Moreover, 41, 40, and 42 cis-acting elements were located in the promoters of PpAGOs, PpDCLs, and PpRDRs, respectively, with the majority related to drought stress response. Analysis of RNA sequencing (RNA-seq) data revealed that sRNAs biogenesis genes were involved in drought stress response in different tissues. Furthermore, the expression of candidate genes was verified in two peach cultivars, Beijing 2-7 (BJ2-7) and Sinai (SN), which are drought tolerance and sensitive, respectively. Interestingly, the expression of PpAGO2b, PpDCL2b, PpDCL4, and PpRDR4 genes was induced in ‘BJ2-7’ but inhibited in ‘SN’ under drought stress. Overall, this study provides insight into the roles of sRNA biogenesis genes in response to drought stress in peach.