G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction
Genotype-to-phenotype (G2P) prediction has become a mainstream paradigm to facilitate genomic selection (GS)-assisted breeding in the seed industry. Many methods have been introduced for building GS models, but their prediction precision may vary depending on species and specific traits. Therefore,...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1207139/full |
| _version_ | 1797754503968063488 |
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| author | Qian Wang Qian Wang Shan Jiang Shan Jiang Tong Li Tong Li Zhixu Qiu Zhixu Qiu Jun Yan Jun Yan Ran Fu Ran Fu Chuang Ma Chuang Ma Xiangfeng Wang Xiangfeng Wang Shuqin Jiang Shuqin Jiang Qian Cheng Qian Cheng |
| author_facet | Qian Wang Qian Wang Shan Jiang Shan Jiang Tong Li Tong Li Zhixu Qiu Zhixu Qiu Jun Yan Jun Yan Ran Fu Ran Fu Chuang Ma Chuang Ma Xiangfeng Wang Xiangfeng Wang Shuqin Jiang Shuqin Jiang Qian Cheng Qian Cheng |
| author_sort | Qian Wang |
| collection | DOAJ |
| description | Genotype-to-phenotype (G2P) prediction has become a mainstream paradigm to facilitate genomic selection (GS)-assisted breeding in the seed industry. Many methods have been introduced for building GS models, but their prediction precision may vary depending on species and specific traits. Therefore, evaluation of multiple models and selection of the appropriate one is crucial to effective GS analysis. Here, we present the G2P container developed for the Singularity platform, which not only contains a library of 16 state-of-the-art GS models and 13 evaluation metrics. G2P works as an integrative environment offering comprehensive, unbiased evaluation analyses of the 16 GS models, which may be run in parallel on high-performance computing clusters. Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. This functionality should further improve the precision of G2P prediction. Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. This functionality is quite useful in practice, as it reduces the cost of phenotyping when constructing training population. The G2P container and source codes are freely accessible at https://g2p-env.github.io/. |
| first_indexed | 2024-03-12T17:33:24Z |
| format | Article |
| id | doaj.art-2e019816d5134a43ba696e34486faa0a |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-12T17:33:24Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-2e019816d5134a43ba696e34486faa0a2023-08-04T14:15:56ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-08-011410.3389/fpls.2023.12071391207139G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype predictionQian Wang0Qian Wang1Shan Jiang2Shan Jiang3Tong Li4Tong Li5Zhixu Qiu6Zhixu Qiu7Jun Yan8Jun Yan9Ran Fu10Ran Fu11Chuang Ma12Chuang Ma13Xiangfeng Wang14Xiangfeng Wang15Shuqin Jiang16Shuqin Jiang17Qian Cheng18Qian Cheng19Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaKey Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, Yangling, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaKey Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi, ChinaState Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, Yangling, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaFrontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, ChinaNational Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, ChinaGenotype-to-phenotype (G2P) prediction has become a mainstream paradigm to facilitate genomic selection (GS)-assisted breeding in the seed industry. Many methods have been introduced for building GS models, but their prediction precision may vary depending on species and specific traits. Therefore, evaluation of multiple models and selection of the appropriate one is crucial to effective GS analysis. Here, we present the G2P container developed for the Singularity platform, which not only contains a library of 16 state-of-the-art GS models and 13 evaluation metrics. G2P works as an integrative environment offering comprehensive, unbiased evaluation analyses of the 16 GS models, which may be run in parallel on high-performance computing clusters. Based on the evaluation outcome, G2P performs auto-ensemble algorithms that not only can automatically select the most precise models but also can integrate prediction results from multiple models. This functionality should further improve the precision of G2P prediction. Another noteworthy function is the refinement design of the training set, in which G2P optimizes the training set based on the genetic diversity analysis of a studied population. Although the training samples in the optimized set are fewer than in the original set, the prediction precision is almost equivalent to that obtained when using the whole set. This functionality is quite useful in practice, as it reduces the cost of phenotyping when constructing training population. The G2P container and source codes are freely accessible at https://g2p-env.github.io/.https://www.frontiersin.org/articles/10.3389/fpls.2023.1207139/fullgenomic selectiongenotype-to-phenotype predictionsingularity containercrop breedingmulti-model integration |
| spellingShingle | Qian Wang Qian Wang Shan Jiang Shan Jiang Tong Li Tong Li Zhixu Qiu Zhixu Qiu Jun Yan Jun Yan Ran Fu Ran Fu Chuang Ma Chuang Ma Xiangfeng Wang Xiangfeng Wang Shuqin Jiang Shuqin Jiang Qian Cheng Qian Cheng G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction Frontiers in Plant Science genomic selection genotype-to-phenotype prediction singularity container crop breeding multi-model integration |
| title | G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction |
| title_full | G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction |
| title_fullStr | G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction |
| title_full_unstemmed | G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction |
| title_short | G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction |
| title_sort | g2p provides an integrative environment for multi model genomic selection analysis to improve genotype to phenotype prediction |
| topic | genomic selection genotype-to-phenotype prediction singularity container crop breeding multi-model integration |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1207139/full |
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