Scientia Agricultura Sinica ›› 2023, Vol. 56 ›› Issue (5): 951-963.doi: 10.3864/j.issn.0578-1752.2023.05.011

• HORTICULTURE • Previous Articles     Next Articles

Genomic Selection for Fruit Weight and Soluble Solid Contents in Peach

CAO Ke(), CHEN ChangWen, YANG XuanWen, BIE HangLing, WANG LiRong()   

  1. Zhengzhou Fruit Research Institute, Chinese Academy of Agricultural Sciences, Zhengzhou 450009
  • Received:2022-04-28 Accepted:2022-09-09 Online:2023-03-01 Published:2023-03-13

Abstract:

【Background】 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.【Objective】 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.【Method】 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.【Result】 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.【Conclusion】 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.

Key words: peach, fruit weight, soluble solid contents, genomic selection, early prediction

Table 1

The resequencing results of samples using in the study"

群体类型
Population
群体大小
Population size
平均测序reads数
Average clean reads
平均测序数据量
Average clean bases (Gb)
平均测序深度
Mean depth (×)
平均覆盖度
Coverage rate (%)
训练自然群体 Training population 520 21267611 2.96 7.76 79.57
预测自然群体 Predicted nature population 56 14603168 1.95 5.29 78.22
预测杂交群体 Predicted cross population 1145 23493089 3.52 10.79 89.58

Table 2

The distribution of SNP for genomic selection study in peach genome"

Chr.1 Chr.2 Chr.3 Chr.4 Chr.5 Chr.6 Chr.7 Chr.8
染色体长度 Chromosome size (Mb) 47.85 30.40 27.37 25.84 18.50 30.77 22.39 22.57
SNP数目(个) SNP number 8610 7133 5979 7095 3239 6294 5006 5042
SNP密度(个/MB) SNP density (per Mb) 179.93 234.59 218.47 274.54 175.11 204.57 223.60 223.35

Fig. 1

The distribution of selected SNPs in peach genome"

Fig. 2

The phenotypic distribution of two quantitative traits in training population"

Fig. 3

The phenotypic distribution of two quantitative traits in two prediction populations The values in the table indicate the correlation between phenotypes evaluated in different years"

Fig. 4

The prediction accuracy of two quantitative traits using different prediction models"

Fig. 5

The correlation analysis between estimated breeding values and observed values of two quantitative traits in predicted nature population"

Fig. 6

The correlation analysis between estimated breeding values and observed values of two quantitative traits in predicted cross population"

Fig. 7

The comparison of selection efficiency of fruit weight using genomic selection and single marker"

Fig. 8

The haplotypes which made up by two association markers (Chr6: 2 281 398 bp and Chr6: 3 296 344 bp) of fruit weight and their phenotypes Different lowercase letters indicate significant differences between treatments (P<0.05)"

Table 3

The correlation analysis between estimated breeding values and observed values in 2020 of fruit weight in different cross populations"

群体名称
Population name
群体编号
Population number
群体大小Population size 相关性 Correlation index 相关性阈值
Threshold of correlation index (P<0.05)
Honey Blaze×黄07-8东-9 Honey Blaze×Huang 07-8 Dong-9 1 30 0.14 0.355
NJC83×C18 2 30 0.03 0.355
Spring Prince×五月金 Spring Prince×Wu Yue Jin 3 30 0.20 0.355
春雪×黄07-8东-9 Spring Snow×Huang 07-8 Dong-9 4 27 -0.03 0.374
红不软×如皋油桃 Hong Bu Ruan×Rugao Zi You Tao 5 30 -0.15 0.355
华玉×中油桃16号 Hua Yu×Zhong You Tao 16# 6 30 0.03 0.355
锦花×黄07-10西-48 Jin Hua×Huang 07-10 Xi-48 7 30 -0.14 0.355
南二区西26-10×中油桃16号 Nan Er Qu Xi 26-10×Zhong You Tao 16# 8 30 0.26 0.355
晴朗×中油蟠7号 Fairlane×Zhong You Pan 7 # 9 29 0.22 0.361
晴朗×07-8东-9 Fairlane×07-8 Dong-9 10 99 0.20 0.197
瑞光39号×08-9-106 Rui Guang 39#×08-9-106 11 97 -0.04 0.199
晚黄金×黄07-1-36 Wan Huang Jin×Huang 07-1-36 12 30 0.22 0.355
万州酸桃×石育白桃 Wanzhou Suan Tao×Shi Yu Bai Tao 13 30 -0.04 0.355
温07-2-30×中油墨玉 Wen07-2-30×Zhong You Mo Yu 14 80 -0.12 0.220
温08-7-58×01-9-11 Wen08-7-58×01-9-11 15 30 -0.11 0.355
霞脆×中桃红玉 Xia Cui×Zhong Tao Hong Yu 16 28 0.15 0.367
橡皮桃×Sweet Dream Xiang Pi Tao×Sweet Dream 17 30 0.25 0.355
有明白桃×中油桃16号 Yumyeong×Zhong You Tao 16# 18 30 0.18 0.355
郑油紫红桃×07区-7-6 Zheng You Zi Hong Tao×07 Qu-7-6 19 30 -0.10 0.355
中桃红玉×黄07-8东-9 Zhong Tao Hong Yu×Huang 07-8 Dong-9 20 90 0.12 0.207

Fig. 9

The principal component analysis of 20 cross populations"

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