Genomic selection of eight fruit traits in pear
Genomic selection (GS) has the potential to improve selection efficiency and shorten the breeding cycle in fruit tree breeding. In this study, we evaluated the effect of prediction methods, marker density and the training population (TP) size on pear GS for improving its performance and reducing cos...
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2024-03-01
|
| Series: | Horticultural Plant Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468014123000699 |
| _version_ | 1797271309292404736 |
|---|---|
| author | Manyi Sun Mingyue Zhang Satish Kumar Mengfan Qin Yueyuan Liu Runze Wang Kaijie Qi Shaoling Zhang Wenjing Chang Jiaming Li Jun Wu |
| author_facet | Manyi Sun Mingyue Zhang Satish Kumar Mengfan Qin Yueyuan Liu Runze Wang Kaijie Qi Shaoling Zhang Wenjing Chang Jiaming Li Jun Wu |
| author_sort | Manyi Sun |
| collection | DOAJ |
| description | Genomic selection (GS) has the potential to improve selection efficiency and shorten the breeding cycle in fruit tree breeding. In this study, we evaluated the effect of prediction methods, marker density and the training population (TP) size on pear GS for improving its performance and reducing cost. We evaluated GS under two scenarios: (1) five-fold cross-validation in an interspecific pear family; (2) independent validation. Based on the cross-validation scheme, the prediction accuracy (PA) of eight fruit traits varied between 0.33 (fruit core vertical diameter) and 0.65 (stone cell content). Except for single fruit weight, a slightly better prediction accuracy (PA) was observed for the five parametrical methods compared with the two non-parametrical methods. In our TP of 310 individuals, 2 000 single nucleotide polymorphism (SNP) markers were sufficient to make reasonably accurate predictions. PAs for different traits increased by 18.21%–46.98% when the TP size increased from 50 to 100, but the increment was smaller (−4.13%–33.91%) when the TP size increased from 200 to 250. For independent validation, the PAs ranged from 0.11 to 0.45 using rrBLUP method. In summary, our results showed that the TP size and SNP numbers had a greater impact on the PA than prediction methods. Furthermore, relatedness among the training and validation sets, and the complexity of traits should be considered when designing a TP to predict the test panel. |
| first_indexed | 2024-03-07T14:00:52Z |
| format | Article |
| id | doaj.art-359308a17c8a4c6da0fae833191e75ce |
| institution | Directory Open Access Journal |
| issn | 2468-0141 |
| language | English |
| last_indexed | 2024-03-07T14:00:52Z |
| publishDate | 2024-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Horticultural Plant Journal |
| spelling | doaj.art-359308a17c8a4c6da0fae833191e75ce2024-03-07T05:28:47ZengKeAi Communications Co., Ltd.Horticultural Plant Journal2468-01412024-03-01102318326Genomic selection of eight fruit traits in pearManyi Sun0Mingyue Zhang1Satish Kumar2Mengfan Qin3Yueyuan Liu4Runze Wang5Kaijie Qi6Shaoling Zhang7Wenjing Chang8Jiaming Li9Jun Wu10College of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaState Key Laboratory of Crop Biology, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, Shandong 271018, ChinaThe New Zealand Institute for Plant and Food Research Limited, Private Bag 1401, Havelock North 4157, New ZealandCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, ChinaCollege of Horticulture, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing, Jiangsu 210095, China; Zhongshan Biological Breeding Laboratory, Nanjing, Jiangsu 210014, China; Corresponding author.Genomic selection (GS) has the potential to improve selection efficiency and shorten the breeding cycle in fruit tree breeding. In this study, we evaluated the effect of prediction methods, marker density and the training population (TP) size on pear GS for improving its performance and reducing cost. We evaluated GS under two scenarios: (1) five-fold cross-validation in an interspecific pear family; (2) independent validation. Based on the cross-validation scheme, the prediction accuracy (PA) of eight fruit traits varied between 0.33 (fruit core vertical diameter) and 0.65 (stone cell content). Except for single fruit weight, a slightly better prediction accuracy (PA) was observed for the five parametrical methods compared with the two non-parametrical methods. In our TP of 310 individuals, 2 000 single nucleotide polymorphism (SNP) markers were sufficient to make reasonably accurate predictions. PAs for different traits increased by 18.21%–46.98% when the TP size increased from 50 to 100, but the increment was smaller (−4.13%–33.91%) when the TP size increased from 200 to 250. For independent validation, the PAs ranged from 0.11 to 0.45 using rrBLUP method. In summary, our results showed that the TP size and SNP numbers had a greater impact on the PA than prediction methods. Furthermore, relatedness among the training and validation sets, and the complexity of traits should be considered when designing a TP to predict the test panel.http://www.sciencedirect.com/science/article/pii/S2468014123000699PearPyrusPrediction methodTP sizeSNP marker number |
| spellingShingle | Manyi Sun Mingyue Zhang Satish Kumar Mengfan Qin Yueyuan Liu Runze Wang Kaijie Qi Shaoling Zhang Wenjing Chang Jiaming Li Jun Wu Genomic selection of eight fruit traits in pear Horticultural Plant Journal Pear Pyrus Prediction method TP size SNP marker number |
| title | Genomic selection of eight fruit traits in pear |
| title_full | Genomic selection of eight fruit traits in pear |
| title_fullStr | Genomic selection of eight fruit traits in pear |
| title_full_unstemmed | Genomic selection of eight fruit traits in pear |
| title_short | Genomic selection of eight fruit traits in pear |
| title_sort | genomic selection of eight fruit traits in pear |
| topic | Pear Pyrus Prediction method TP size SNP marker number |
| url | http://www.sciencedirect.com/science/article/pii/S2468014123000699 |
| work_keys_str_mv | AT manyisun genomicselectionofeightfruittraitsinpear AT mingyuezhang genomicselectionofeightfruittraitsinpear AT satishkumar genomicselectionofeightfruittraitsinpear AT mengfanqin genomicselectionofeightfruittraitsinpear AT yueyuanliu genomicselectionofeightfruittraitsinpear AT runzewang genomicselectionofeightfruittraitsinpear AT kaijieqi genomicselectionofeightfruittraitsinpear AT shaolingzhang genomicselectionofeightfruittraitsinpear AT wenjingchang genomicselectionofeightfruittraitsinpear AT jiamingli genomicselectionofeightfruittraitsinpear AT junwu genomicselectionofeightfruittraitsinpear |