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Wang Q, Jiang S, Li T, Qiu Z, Yan J, Fu R, Ma C, Wang X, Jiang S, Cheng Q. G2P Provides an Integrative Environment for Multi-model genomic selection analysis to improve genotype-to-phenotype prediction. FRONTIERS IN PLANT SCIENCE 2023; 14:1207139. [PMID: 37600179 PMCID: PMC10437076 DOI: 10.3389/fpls.2023.1207139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 07/21/2023] [Indexed: 08/22/2023]
Abstract
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/.
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Affiliation(s)
- Qian Wang
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Shan Jiang
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Tong Li
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Zhixu Qiu
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi, China
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, Yangling, China
| | - Jun Yan
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Ran Fu
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Chuang Ma
- Key Laboratory of Biology and Genetics Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture, Northwest A&F University, Yangling, Shaanxi, China
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, Yangling, China
| | - Xiangfeng Wang
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Shuqin Jiang
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
| | - Qian Cheng
- Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, China
- National Maize Improvement Center of China, College of Agriculture and Biotechnology, China Agricultural University, Beijing, China
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Gui S, Wei W, Jiang C, Luo J, Chen L, Wu S, Li W, Wang Y, Li S, Yang N, Li Q, Fernie AR, Yan J. A pan-Zea genome map for enhancing maize improvement. Genome Biol 2022; 23:178. [PMID: 35999561 PMCID: PMC9396798 DOI: 10.1186/s13059-022-02742-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 07/27/2022] [Indexed: 12/22/2022] Open
Abstract
Background Maize (Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking. Results Here, we construct an approximately 6.71-Gb pan-Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits. Conclusions Our results highlight the underexplored role of the pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02742-7.
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Affiliation(s)
- Songtao Gui
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenjie Wei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chenglin Jiang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lu Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shenshen Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenqiang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuebin Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shuyan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Qing Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Alisdair R Fernie
- Department of Molecular Physiology, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, Germany
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China. .,Hubei Hongshan Laboratory, Wuhan, 430070, China.
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Cheng Q, Jiang S, Xu F, Wang Q, Xiao Y, Zhang R, Zhao J, Yan J, Ma C, Wang X. Genome optimization via virtual simulation to accelerate maize hybrid breeding. Brief Bioinform 2021; 23:6407728. [PMID: 34676389 DOI: 10.1093/bib/bbab447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/24/2021] [Accepted: 09/28/2021] [Indexed: 11/13/2022] Open
Abstract
The employment of doubled-haploid (DH) technology in maize has vastly accelerated the efficiency of developing inbred lines. The selection of superior lines has to rely on genotypes with genomic selection (GS) model, rather than phenotypes due to the high expense of field phenotyping. In this work, we implemented 'genome optimization via virtual simulation (GOVS)' using the genotype and phenotype data of 1404 maize lines and their F1 progeny. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' or 'advantageous alleles' in a genetic pool. Such a virtually optimized genome, although can never be developed in reality, may help plot the optimal route to direct breeding decisions. GOVS assists in the selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. The assumption is that the more fragments of optimal genotypes a line contributes to the assembly, the higher the likelihood of the line favored in the F1 phenotype, e.g. grain yield. Compared to traditional GS method, GOVS-assisted selection may avoid using an arbitrary threshold for the predicted F1 yield to assist selection. Additionally, the selected lines contributed complementary sets of advantageous alleles to the virtual genome. This feature facilitates plotting the optimal route for DH production, whereby the fewest lines and F1 combinations are needed to pyramid a maximum number of advantageous alleles in the new DH lines. In summary, incorporation of DH production, GS and genome optimization will ultimately improve genomically designed breeding in maize. Short abstract: Doubled-haploid (DH) technology has been widely applied in maize breeding industry, as it greatly shortens the period of developing homozygous inbred lines via bypassing several rounds of self-crossing. The current challenge is how to efficiently screen the large volume of inbred lines based on genotypes. We present the toolbox of genome optimization via virtual simulation (GOVS), which complements the traditional genomic selection model. GOVS simulates a virtual genome encompassing the most abundant 'optimal genotypes' in a breeding population, and then assists in selection of superior lines based on the genomic fragments that a line contributes to the simulated genome. Availability of GOVS (https://govs-pack.github.io/) to the public may ultimately facilitate genomically designed breeding in maize.
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Affiliation(s)
- Qian Cheng
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China
| | - Shuqing Jiang
- National Maize Improvement Center of China Agricultural University, Beijing, China
| | - Feng Xu
- National Maize Improvement Center of China Agricultural University, Beijing, China
| | - Qian Wang
- National Maize Improvement Center of China Agricultural University, Beijing, China
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and Technology at Huazhong Agricultural University, Wuhan, China
| | - Ruyang Zhang
- Maize Research Center at Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiuran Zhao
- Maize Research Center at Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, College of Plant Sciences and Technology at Huazhong Agricultural University, Wuhan, China
| | - Chuang Ma
- State Key Laboratory of Crop Stress Biology for Arid Areas, Center of Bioinformatics, College of Life Sciences, Northwest A&F University, Shaanxi, China
| | - Xiangfeng Wang
- Sanya Institute of China Agricultural University, Hainan, China
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Fernie AR, Alseekh S, Liu J, Yan J. Using precision phenotyping to inform de novo domestication. PLANT PHYSIOLOGY 2021; 186:1397-1411. [PMID: 33848336 PMCID: PMC8260140 DOI: 10.1093/plphys/kiab160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 03/22/2021] [Indexed: 05/09/2023]
Abstract
An update on the use of precision phenotyping to assess the potential of lesser cultivated species as candidates for de novo domestication or similar development for future agriculture.
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Affiliation(s)
- Alisdair R Fernie
- Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Centre of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Saleh Alseekh
- Max Planck Institute for Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Centre of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Jie Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070 Wuhan, Hubei, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, 430070 Wuhan, Hubei, China
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