51
|
Xu Y, Liu X, Fu J, Wang H, Wang J, Huang C, Prasanna BM, Olsen MS, Wang G, Zhang A. Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants. PLANT COMMUNICATIONS 2020; 1:100005. [PMID: 33404534 PMCID: PMC7747995 DOI: 10.1016/j.xplc.2019.100005] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies, the rate of genetic gain needs to be accelerated to meet humanity's demand for agricultural products. In this regard, genomic selection (GS) has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects. Livestock scientists pioneered GS application largely due to livestock's significantly higher individual values and the greater reduction in generation interval that can be achieved in GS. Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction. Moreover, it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain. In addition, establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small- and medium-sized enterprises and agricultural research systems in developing countries. New strategies centered on GS for enhancing genetic gain need to be developed.
Collapse
Affiliation(s)
- Yunbi Xu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
- CIMMYT-China Tropical Maize Research Center, Foshan University, Foshan 528231, China
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Xiaogang Liu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Junjie Fu
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Hongwu Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jiankang Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Changling Huang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Boddupalli M. Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Michael S. Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Guoying Wang
- Institute of Crop Science/CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Aimin Zhang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| |
Collapse
|
52
|
Guo R, Dhliwayo T, Mageto EK, Palacios-Rojas N, Lee M, Yu D, Ruan Y, Zhang A, San Vicente F, Olsen M, Crossa J, Prasanna BM, Zhang L, Zhang X. Genomic Prediction of Kernel Zinc Concentration in Multiple Maize Populations Using Genotyping-by-Sequencing and Repeat Amplification Sequencing Markers. FRONTIERS IN PLANT SCIENCE 2020; 11:534. [PMID: 32457778 PMCID: PMC7225839 DOI: 10.3389/fpls.2020.00534] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 04/08/2020] [Indexed: 05/20/2023]
Abstract
Enriching of kernel zinc (Zn) concentration in maize is one of the most effective ways to solve the problem of Zn deficiency in low and middle income countries where maize is the major staple food, and 17% of the global population is affected with Zn deficiency. Genomic selection (GS) has shown to be an effective approach to accelerate genetic gains in plant breeding. In the present study, an association-mapping panel and two maize double-haploid (DH) populations, both genotyped with genotyping-by-sequencing (GBS) and repeat amplification sequencing (rAmpSeq) markers, were used to estimate the genomic prediction accuracy of kernel Zn concentration in maize. Results showed that the prediction accuracy of two DH populations was higher than that of the association mapping population using the same set of markers. The prediction accuracy estimated with the GBS markers was significantly higher than that estimated with the rAmpSeq markers in the same population. The maximum prediction accuracy with minimum standard error was observed when half of the genotypes were included in the training set and 3,000 and 500 markers were used for prediction in the association mapping panel and the DH populations, respectively. Appropriate levels of minor allele frequency and missing rate should be considered and selected to achieve good prediction accuracy and reduce the computation burden by balancing the number of markers and marker quality. Training set development with broad phenotypic variation is possible to improve prediction accuracy. The transferability of the GS models across populations was assessed, the prediction accuracies in a few pairwise populations were above or close to 0.20, which indicates the prediction accuracies across years and populations have to be assessed in a larger breeding dataset with closer relationship between the training and prediction sets in further studies. GS outperformed MAS (marker-assisted-selection) on predicting the kernel Zn concentration in maize, the decision of a breeding strategy to implement GS individually or to implement MAS and GS stepwise for improving kernel Zn concentration in maize requires further research. Results of this study provide valuable information for understanding how to implement GS for improving kernel Zn concentration in maize.
Collapse
Affiliation(s)
- Rui Guo
- College of Agronomy, Shenyang Agricultural University, Shenyang, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Edna K. Mageto
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | | | - Michael Lee
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Diansi Yu
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Yanye Ruan
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China
| | - Ao Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Lijun Zhang
- College of Biosciences and Biotechnology, Shenyang Agricultural University, Shenyang, China
- *Correspondence: Lijun Zhang,
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Xuecai Zhang,
| |
Collapse
|
53
|
Liu X, Wang H, Hu X, Li K, Liu Z, Wu Y, Huang C. Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize. FRONTIERS IN PLANT SCIENCE 2019; 10:1129. [PMID: 31620155 PMCID: PMC6759780 DOI: 10.3389/fpls.2019.01129] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Accepted: 08/15/2019] [Indexed: 05/20/2023]
Abstract
Genomic selection (GS), a tool developed for molecular breeding, is used by plant breeders to improve breeding efficacy by shortening the breeding cycle and to facilitate the selection of candidate lines for creating hybrids without phenotyping in various environments. Association and linkage mapping have been widely used to explore and detect candidate genes in order to understand the genetic mechanisms of quantitative traits. In the current study, phenotypic and genotypic data from three experimental populations, including data on six agronomic traits (e.g., plant height, ear height, ear length, ear diameter, grain yield per plant, and hundred-kernel weight), were used to evaluate the effect of trait-relevant markers (TRMs) on prediction accuracy estimation. Integrating information from mapping into a statistical model can efficiently improve prediction performance compared with using stochastically selected markers to perform GS. The prediction accuracy can reach plateau when a total of 500-1,000 TRMs are utilized in GS. The prediction accuracy can be significantly enhanced by including nonadditive effects and TRMs in the GS model when genotypic data with high proportions of heterozygous alleles and complex agronomic traits with high proportion of nonadditive variancein phenotypic variance are used to perform GS. In addition, taking information on population structure into account can slightly improve prediction performance when the genetic relationship between the training and testing sets is influenced by population stratification due to different allele frequencies. In conclusion, GS is a useful approach for prescreening candidate lines, and the empirical evidence provided by the current study for TRMs and nonadditive effects can inform plant breeding and in turn contribute to the improvement of selection efficiency in practical GS-assisted breeding programs.
Collapse
|