Fruit weight and soluble solid content (SSC) are two important quantitative traits in peach which are of importance to breeders. However, performing early prediction using a single marker is challenging as the traits are controlled by multiple minor genes. Genomic selection, a novel genome-wide tool, has been applied in fruit crops and can potentially enhance the breeding efficiency of these quantitative traits. However, its effects in peach and influencing factors require further investigation.

Establish a whole-genome selection technology system for peach single fruit weight and SSC, and laid a methodological foundation for the establishment of efficient molecular breeding technology system for peach.

The objectives of this study were to assess the accuracy of prediction of peach fruit weight and SSC in natural and hybrid populations using genomic selection. Here, a training population of 520 individuals was selected. Using genotypic data for 48 398 single nucleotide polymorphisms (SNPs) obtained from the resequencing results of the above training population, a total of 11 genome-wide prediction models were built to select the optimum model for fruit weight and SSC. Subsequently, the genomic breeding values of a small natural population of 56 individuals and 29 hybrid populations comprising a total of 1 145 seedlings were calculated.

The average sequencing data of each variety of the three groups was 1.95-3.52 Gb, and the sequencing depth was 5.29-10.79×. The sequencing data of the training natural population was aligned with the reference genome, and a total of 5 065 726 SNPs were obtained. After removing the SNPs with a high missing rate (>20%) and minor allele frequency of <0.05, a total of 48 398 SNPs on the genome were randomly selected for constructing whole-genome selection models for the training population. The models with the highest prediction accuracy for fruit weight and SSC were BayesA and randomforest, respectively. Using the above two models, it was found that the goodness of fit between the predicted breeding values and observed phenotype of fruit weight was 0.4767-0.6141, which was higher than that of SSC (0.3220-0.4329) in the natural populations. In hybrid populations, the prediction accuracy of fruit weight was 0.2319-0.4870, which was also higher than that of SSC (0.0200-0.2793). The results also showed that the prediction model constructed by training natural populations was more accurate in predicting natural populations than hybrid populations. Taking fruit weight as an example, it was also found that only 17.78% of the seedlings needed to be retained by genomic selection when targeting large fruit. Genomic selection was significantly more efficient than single and double marker selection. Furthermore, the effects of population dispersion, heritability and population structure on prediction accuracy are also discussed. The results indicated that prediction accuracy may vary and be affected by a combination of several factors.

In this study, a suitable genomic selection model for peach fruit weight and SSC was screened, and it was confirmed that the prediction efficiency of genomic selection was significantly higher than that of single marker selection. The results indicated the potential of genomic prediction in accelerating breeding progress of these two quantitative traits in peach.

To establish a background selection system in peach, the seedlings contained aphid-resistance locus and high female parent recovery rate were screened from an F₂ population crossed by Xiang Pi You Tao peach (big fruit and susceptible to aphid) and Zhou Xing Shan Tao peach (small fruit and resistant to aphid).

Firstly, three methods were used to select background markers, including the high polymorphic single nucleotide polymorphism (SNP) obtained from the previous study (Pre-work SNP), SNP randomly selected in the whole genome (Random SNP), and functional SNP affecting the start and stop codon (Functional SNP). The number of final SNP selected by the above methods were 775. Then, using these SNPs, the parents recovery rate for all 121 individuals of the F₂ population were calculated, respectively. The repeatability of the selection methods was evaluated by comparing whether the top 10 seedlings with different selection markers were coincident or not. After completing the evaluation of aphid resistance, single fruit weight, and soluble solids content of F₂ population, 10 seedlings with extreme phenotypes for the single fruit weight and soluble solids content were selected, respectively. And the superiority and inferiority of different selection methods were estimated by comparing the significance of the differences in Xiang Pi You Tao recovery rates between the two types of phenotypes. Finally, the SNPs in the aphid-resistant location area were used as the foreground markers to screen the elite seedlings with high maternal genetic background and aphid resistance.

The background recovery rates of the F₂ seedlings which calculated by the three methods were 36.34%-71.99%, 31.75%-74.92%, and 4.51%-66.53%, respectively. Among the top 10 seedlings with high Xiang Pi You Tao recovery rates screened by the three background markers, Pre-work SNP and Random SNP had two duplicate single plants, and so do Pre-work SNP and Functional SNP, and there were 6 repetitive single plants in Random SNP and Functional SNP. This result indicated that the repeatability between the Random SNP and Functional SNP was the highest among all comparisons. When single fruit weight was selected as the breeding target, among the extreme phenotypic monocots, the three background markers, such as Pre-work SNP, Random SNP, and Functional SNP, had a significant Xiang Pi You Tao background recovery rate of 0.069, 0.26, and 0.092, respectively, which meant high relativity was found between the background recovery rate calculated by Pre-work SNP and their fruit weight, followed by Functional SNP, and Random SNP difference was not significant. When soluble solids content was selected as the target, the Xiang Pi You Tao background recovery rates among extreme phenotypic monocots were significant at 0.77, 0.65 and 0.31, respectively, and the differences among the three background markers were not significant. Finally, two individuals with high recovery rate of Xiang Pi You Tao peach were screened, including N20 and N36. Among them, N20 comprised the aphid-resistant markers, and this individual showed aphid resistance with an average fruit weight of 34.42 g and soluble solids content of 16.1%, which was considered to be the superior single strain of this population.

In this study population, Pre-work SNP showed a stronger correlation between single fruit weight and Xiang Pi You Tao background recovery rates than Functional SNP and Random SNP, confirming the superiority of this background marker selection method, and the superior performance of N20 plants, which selected with this background marker in the target traits also supported this result. This study provided an idea of background selection and a method to judge the superiority and inferiority of different background markers in the study population, which could effectively improve the efficiency of resistance breeding in fruit crops.