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LaPorte MF, Suwarno WB, Hannok P, Koide A, Bradbury P, Crossa J, Palacios-Rojas N, Diepenbrock CH. Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel. G3 (BETHESDA, MD.) 2024; 14:jkae044. [PMID: 38427914 PMCID: PMC11075567 DOI: 10.1093/g3journal/jkae044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 02/13/2024] [Accepted: 02/22/2024] [Indexed: 03/03/2024]
Abstract
Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)-an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.
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Affiliation(s)
- Mary-Francis LaPorte
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Willy Bayuardi Suwarno
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor 16680, Indonesia
| | - Pattama Hannok
- Division of Agronomy, Faculty of Agricultural Production, Maejo University, Chiang Mai 50200, Thailand
- Plant Breeding and Plant Genetics Program, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Akiyoshi Koide
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616, USA
| | - Peter Bradbury
- United States Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, NY 14853, USA
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera Mexico-Veracruz, Texcoco 56130, Mexico
| | - Natalia Palacios-Rojas
- International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera Mexico-Veracruz, Texcoco 56130, Mexico
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Song J, Liu Y, Guo R, Pacheco A, Muñoz-Zavala C, Song W, Wang H, Cao S, Hu G, Zheng H, Dhliwayo T, San Vicente F, Prasanna BM, Wang C, Zhang X. Exploiting genomic tools for genetic dissection and improving the resistance to Fusarium stalk rot in tropical maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:109. [PMID: 38649662 DOI: 10.1007/s00122-024-04597-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/07/2024] [Indexed: 04/25/2024]
Abstract
KEY MESSAGE A stable genomic region conferring FSR resistance at ~250 Mb on chromosome 1 was identified by GWAS. Genomic prediction has the potential to improve FSR resistance. Fusarium stalk rot (FSR) is a global destructive disease in maize; the efficiency of phenotypic selection for improving FSR resistance was low. Novel genomic tools of genome-wide association study (GWAS) and genomic prediction (GP) provide an opportunity for genetic dissection and improving FSR resistance. In this study, GWAS and GP analyses were performed on 562 tropical maize inbred lines consisting of two populations. In total, 15 SNPs significantly associated with FSR resistance were identified across two populations and the combinedPOP consisting of all 562 inbred lines, with the P-values ranging from 1.99 × 10-7 to 8.27 × 10-13, and the phenotypic variance explained (PVE) values ranging from 0.94 to 8.30%. The genetic effects of the 15 favorable alleles ranged from -4.29 to -14.21% of the FSR severity. One stable genomic region at ~ 250 Mb on chromosome 1 was detected across all populations, and the PVE values of the SNPs detected in this region ranged from 2.16 to 5.18%. Prediction accuracies of FSR severity estimated with the genome-wide SNPs were moderate and ranged from 0.29 to 0.51. By incorporating genotype-by-environment interaction, prediction accuracies were improved between 0.36 and 0.55 in different breeding scenarios. Considering both the genome coverage and the threshold of the P-value of SNPs to select a subset of molecular markers further improved the prediction accuracies. These findings extend the knowledge of exploiting genomic tools for genetic dissection and improving FSR resistance in tropical maize.
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Affiliation(s)
- Junqiao Song
- Henan University of Science and Technology, Luoyang, 471000, Henan, China
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Anyang Academy of Agricultural Sciences, Anyang, 455000, Henan, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Rui Guo
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, 050035, Hebei, China
| | - Angela Pacheco
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Carlos Muñoz-Zavala
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Wei Song
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang, 050035, Hebei, China
| | - Hui Wang
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Shiliang Cao
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150070, Heilongjiang, China
| | - Guanghui Hu
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
- Institute of Maize Research, Heilongjiang Academy of Agricultural Sciences, Harbin, 150070, Heilongjiang, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 200063, China
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), Village Market, P. O. Box 1041, Nairobi, 00621, Kenya
| | - Chunping Wang
- Henan University of Science and Technology, Luoyang, 471000, Henan, China.
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), 56237, Texcoco, Mexico.
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), CIMMYT-China Office, 12 Zhongguancun South Street, Beijing, 100081, China.
- Nanfan Research Institute, CAAS, Sanya, 572024, Hainan, China.
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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Chen S, Dang D, Liu Y, Ji S, Zheng H, Zhao C, Dong X, Li C, Guan Y, Zhang A, Ruan Y. Genome-wide association study presents insights into the genetic architecture of drought tolerance in maize seedlings under field water-deficit conditions. FRONTIERS IN PLANT SCIENCE 2023; 14:1165582. [PMID: 37223800 PMCID: PMC10200999 DOI: 10.3389/fpls.2023.1165582] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/24/2023] [Indexed: 05/25/2023]
Abstract
Introduction Drought stress is one of the most serious abiotic stresses leading to crop yield reduction. Due to the wide range of planting areas, the production of maize is particularly affected by global drought stress. The cultivation of drought-resistant maize varieties can achieve relatively high, stable yield in arid and semi-arid zones and in the erratic rainfall or occasional drought areas. Therefore, to a great degree, the adverse impact of drought on maize yield can be mitigated by developing drought-resistant or -tolerant varieties. However, the efficacy of traditional breeding solely relying on phenotypic selection is not adequate for the need of maize drought-resistant varieties. Revealing the genetic basis enables to guide the genetic improvement of maize drought tolerance. Methods We utilized a maize association panel of 379 inbred lines with tropical, subtropical and temperate backgrounds to analyze the genetic structure of maize drought tolerance at seedling stage. We obtained the high quality 7837 SNPs from DArT's and 91,003 SNPs from GBS, and a resultant combination of 97,862 SNPs of GBS with DArT's. The maize population presented the lower her-itabilities of the seedling emergence rate (ER), seedling plant height (SPH) and grain yield (GY) under field drought conditions. Results GWAS analysis by MLM and BLINK models with the phenotypic data and 97862 SNPs revealed 15 variants that were significantly independent related to drought-resistant traits at the seedling stage above the threshold of P < 1.02 × 10-5. We found 15 candidate genes for drought resistance at the seedling stage that may involve in (1) metabolism (Zm00001d012176, Zm00001d012101, Zm00001d009488); (2) programmed cell death (Zm00001d053952); (3) transcriptional regulation (Zm00001d037771, Zm00001d053859, Zm00001d031861, Zm00001d038930, Zm00001d049400, Zm00001d045128 and Zm00001d043036); (4) autophagy (Zm00001d028417); and (5) cell growth and development (Zm00001d017495). The most of them in B73 maize line were shown to change the expression pattern in response to drought stress. These results provide useful information for understanding the genetic basis of drought stress tolerance of maize at seedling stage.
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Affiliation(s)
- Shan Chen
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Dongdong Dang
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shang-hai Academy of Agricultural Sciences, Shanghai, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Yubo Liu
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shang-hai Academy of Agricultural Sciences, Shanghai, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Shuwen Ji
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shang-hai Academy of Agricultural Sciences, Shanghai, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Chenghao Zhao
- Dandong Academy of Agricultural Sciences, Fengcheng, Liaoning, China
| | - Xiaomei Dong
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Cong Li
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Yuan Guan
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shang-hai Academy of Agricultural Sciences, Shanghai, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ao Zhang
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Yanye Ruan
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
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Sun B, Guo R, Liu Z, Shi X, Yang Q, Shi J, Zhang M, Yang C, Zhao S, Zhang J, He J, Zhang J, Su J, Song Q, Yan L. Genetic variation and marker-trait association affect the genomic selection prediction accuracy of soybean protein and oil content. FRONTIERS IN PLANT SCIENCE 2022; 13:1064623. [PMID: 36582644 PMCID: PMC9793221 DOI: 10.3389/fpls.2022.1064623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 11/10/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION Genomic selection (GS) is a potential breeding approach for soybean improvement. METHODS In this study, GS was performed on soybean protein and oil content using the Ridge Regression Best Linear Unbiased Predictor (RR-BLUP) based on 1,007 soybean accessions. The SoySNP50K SNP dataset of the accessions was obtained from the USDA-ARS, Beltsville, MD lab, and the protein and oil content of the accessions were obtained from GRIN. RESULTS Our results showed that the prediction accuracy of oil content was higher than that of protein content. When the training population size was 100, the prediction accuracies for protein content and oil content were 0.60 and 0.79, respectively. The prediction accuracy increased with the size of the training population. Training populations with similar phenotype or with close genetic relationships to the prediction population exhibited better prediction accuracy. A greatest prediction accuracy for both protein and oil content was observed when approximately 3,000 markers with -log10(P) greater than 1 were included. DISCUSSION This information will help improve GS efficiency and facilitate the application of GS.
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Affiliation(s)
- Bo Sun
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
- College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Rui Guo
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Zhi Liu
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Xiaolei Shi
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Qing Yang
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Jiayao Shi
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Mengchen Zhang
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Chunyan Yang
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Shugang Zhao
- College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Jie Zhang
- College of Life Sciences, Hebei Agricultural University, Baoding, China
| | - Jianhan He
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing, China
- Key Laboratory for Biology and Genetic Improvement of Soybean, (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing, China
| | - Jianhui Su
- Agricultural-Regionalization Workstation of Shijiazhuang’s Gaocheng District, Shijiazhuang, China
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Agricultural Research Service, United States Department of Agriculture, Beltsville, MD, United States
| | - Long Yan
- Institute of Cereal and Oil Crops, Hebei Academy of Agricultural and Forestry Sciences, Shijiazhuang Branch Center of National Center for Soybean Improvement, The Key Laboratory of Crop Genetics and Breeding, Shijiazhuang, China
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Yu H, Zhao Z, Liu D, Cheng F. Integrating machine learning interpretation methods for investigating nanoparticle uptake during seed priming and its biological effects. NANOSCALE 2022; 14:15305-15315. [PMID: 36111874 DOI: 10.1039/d2nr01904c] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Seed priming by nanoparticles is an environmentally-friendly solution for alleviating malnutrition, promoting crop growth, and mitigating environmental stress. However, there is a knowledge gap regarding the nanoparticle uptake and the underlying physiological mechanism. Machine learning has great potential for understanding the biological effects of nanoparticles. However, its interpretability is a challenge for building trust and providing insights into the learned relationships. Herein, we systematically investigated how the factors influence nanoparticle uptake during seed priming by ZnO nanoparticles and its effects on seed germination. The properties of the nanoparticles, priming solution, and seeds were considered. Post hoc interpretation and model-based interpretation of machine learning were integrated into two ways to understand the mechanism of nanoparticle uptake during seed priming and its biological effects on seed germination. The results indicated that nanoparticle concentration and ionic strength influenced the shoot fresh weight mainly by controlling the nanoparticle uptake. The nanoparticle uptake had a significant slowdown when the nanoparticle concentration exceeded 50 mg L-1. Although other factors, such as zeta potential and hydrodynamic diameter, had no obvious effects on nanoparticle uptake, their biological effects cannot be ignored. This approach can promote the safer-by-design strategy of nanomaterials for sustainable agriculture.
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Affiliation(s)
- Hengjie Yu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China.
| | - Zhilin Zhao
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China.
| | - Da Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China.
| | - Fang Cheng
- College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, P.R. China.
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Muvunyi BP, Zou W, Zhan J, He S, Ye G. Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice. Front Genet 2022; 13:883853. [PMID: 35812754 PMCID: PMC9257107 DOI: 10.3389/fgene.2022.883853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/05/2022] [Indexed: 11/13/2022] Open
Abstract
Multi-trait (MT) genomic prediction models enable breeders to save phenotyping resources and increase the prediction accuracy of unobserved target traits by exploiting available information from non-target or auxiliary traits. Our study evaluated different MT models using 250 rice accessions from Asian countries genotyped and phenotyped for grain content of zinc (Zn), iron (Fe), copper (Cu), manganese (Mn), and cadmium (Cd). The predictive performance of MT models compared to a traditional single trait (ST) model was assessed by 1) applying different cross-validation strategies (CV1, CV2, and CV3) inferring varied phenotyping patterns and budgets; 2) accounting for local epistatic effects along with the main additive effect in MT models; and 3) using a selective marker panel composed of trait-associated SNPs in MT models. MT models were not statistically significantly (p < 0.05) superior to ST model under CV1, where no phenotypic information was available for the accessions in the test set. After including phenotypes from auxiliary traits in both training and test sets (MT-CV2) or simply in the test set (MT-CV3), MT models significantly (p < 0.05) outperformed ST model for all the traits. The highest increases in the predictive ability of MT models relative to ST models were 11.1% (Mn), 11.5 (Cd), 33.3% (Fe), 95.2% (Cu) and 126% (Zn). Accounting for the local epistatic effects using a haplotype-based model further improved the predictive ability of MT models by 4.6% (Cu), 3.8% (Zn), and 3.5% (Cd) relative to MT models with only additive effects. The predictive ability of the haplotype-based model was not improved after optimizing the marker panel by only considering the markers associated with the traits. This study first assessed the local epistatic effects and marker optimization strategies in the MT genomic prediction framework and then illustrated the power of the MT model in predicting trace element traits in rice for the effective use of genetic resources to improve the nutritional quality of rice grain.
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Affiliation(s)
- Blaise Pascal Muvunyi
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Wenli Zou
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Junhui Zhan
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Sang He
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- *Correspondence: Sang He, ; Guoyou Ye,
| | - Guoyou Ye
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute in Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- Rice Breeding Innovations Platform, International Rice Research Institute, Los Baños, Philippines
- *Correspondence: Sang He, ; Guoyou Ye,
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Medina-Lozano I, Díaz A. Applications of Genomic Tools in Plant Breeding: Crop Biofortification. Int J Mol Sci 2022; 23:3086. [PMID: 35328507 PMCID: PMC8950180 DOI: 10.3390/ijms23063086] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/04/2022] [Accepted: 03/10/2022] [Indexed: 12/02/2022] Open
Abstract
Crop breeding has mainly been focused on increasing productivity, either directly or by decreasing the losses caused by biotic and abiotic stresses (that is, incorporating resistance to diseases and enhancing tolerance to adverse conditions, respectively). Quite the opposite, little attention has been paid to improve the nutritional value of crops. It has not been until recently that crop biofortification has become an objective within breeding programs, through either conventional methods or genetic engineering. There are many steps along this long path, from the initial evaluation of germplasm for the content of nutrients and health-promoting compounds to the development of biofortified varieties, with the available and future genomic tools assisting scientists and breeders in reaching their objectives as well as speeding up the process. This review offers a compendium of the genomic technologies used to explore and create biodiversity, to associate the traits of interest to the genome, and to transfer the genomic regions responsible for the desirable characteristics into potential new varieties. Finally, a glimpse of future perspectives and challenges in this emerging area is offered by taking the present scenario and the slow progress of the regulatory framework as the starting point.
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Affiliation(s)
- Inés Medina-Lozano
- Departamento de Ciencia Vegetal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, Avda. Montañana 930, 50059 Zaragoza, Spain;
- Instituto Agroalimentario de Aragón—IA2, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, 50013 Zaragoza, Spain
| | - Aurora Díaz
- Departamento de Ciencia Vegetal, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, Avda. Montañana 930, 50059 Zaragoza, Spain;
- Instituto Agroalimentario de Aragón—IA2, Centro de Investigación y Tecnología Agroalimentaria de Aragón (CITA), Universidad de Zaragoza, 50013 Zaragoza, Spain
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Xu J, Wang X, Zhu H, Yu F. Maize Genotypes With Different Zinc Efficiency in Response to Low Zinc Stress and Heterogeneous Zinc Supply. FRONTIERS IN PLANT SCIENCE 2021; 12:736658. [PMID: 34691112 PMCID: PMC8531504 DOI: 10.3389/fpls.2021.736658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 08/30/2021] [Indexed: 06/13/2023]
Abstract
All over the world, a common problem in the soil is the low content of available zinc (Zn), which is unevenly distributed and difficult to move. However, information on the foraging strategies of roots in response to heterogeneous Zn supply is still very limited. Few studies have analyzed the adaptability of maize inbred lines with different Zn efficiencies to different low Zn stress time lengths in maize. This study analyzed the effects of different time lengths of low Zn stress on various related traits in different inbred lines. In addition, morphological plasticity of roots and the response of Zn-related important gene iron-regulated transporter-like proteins (ZIPs) were studied via simulating the heterogeneity of Zn nutrition in the soil. In this report, when Zn deficiency stress duration was extended (from 14 to 21 days), under Zn-deficient supply (0.5 μM), Zn efficiency (ZE) based on shoot dry weight of Wu312 displayed no significant difference, and ZE for Ye478 was increased by 92.9%. Under longer-term Zn deficiency, shoot, and root dry weights of Ye478 were 6.5 and 2.1-fold higher than those of Wu312, respectively. Uneven Zn supply strongly inhibited the development of some root traits in the -Zn region. Difference in shoot dry weights between Wu312 and Ye478 was larger in T1 (1.97 times) than in T2 (1.53 times). Under heterogeneous condition of Zn supply, both the -Zn region and the +Zn region upregulated the expressions of ZmZIP3, ZmZIP4, ZmZIP5, ZmZIP7, and ZmZIP8 in the roots of two inbred lines. These results indicate that extended time length of low-Zn stress will enlarge the difference of multiple physiological traits, especially biomass, between Zn-sensitive and Zn-tolerant inbred lines. There were significant genotypic differences of root morphology in response to heterogeneous Zn supply. Compared with split-supply with +Zn/+Zn, the difference of above-ground biomass between Zn-sensitive and Zn-tolerant inbred lines under split-supply with -Zn/+Zn was higher. Under the condition of heterogeneous Zn supply, several ZmZIP genes may play important roles in tolerance to low Zn stress, which can provide a basis for further functional characterization.
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10
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Baseggio M, Murray M, Wu D, Ziegler G, Kaczmar N, Chamness J, Hamilton JP, Buell CR, Vatamaniuk OK, Buckler ES, Smith ME, Baxter I, Tracy WF, Gore MA. Genome-wide association study suggests an independent genetic basis of zinc and cadmium concentrations in fresh sweet corn kernels. G3 (BETHESDA, MD.) 2021; 11:jkab186. [PMID: 34849806 PMCID: PMC8496296 DOI: 10.1093/g3journal/jkab186] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 05/25/2021] [Indexed: 01/05/2023]
Abstract
Despite being one of the most consumed vegetables in the United States, the elemental profile of sweet corn (Zea mays L.) is limited in its dietary contributions. To address this through genetic improvement, a genome-wide association study was conducted for the concentrations of 15 elements in fresh kernels of a sweet corn association panel. In concordance with mapping results from mature maize kernels, we detected a probable pleiotropic association of zinc and iron concentrations with nicotianamine synthase5 (nas5), which purportedly encodes an enzyme involved in synthesis of the metal chelator nicotianamine. In addition, a pervasive association signal was identified for cadmium concentration within a recombination suppressed region on chromosome 2. The likely causal gene underlying this signal was heavy metal ATPase3 (hma3), whose counterpart in rice, OsHMA3, mediates vacuolar sequestration of cadmium and zinc in roots, whereby regulating zinc homeostasis and cadmium accumulation in grains. In our association panel, hma3 associated with cadmium but not zinc accumulation in fresh kernels. This finding implies that selection for low cadmium will not affect zinc levels in fresh kernels. Although less resolved association signals were detected for boron, nickel, and calcium, all 15 elements were shown to have moderate predictive abilities via whole-genome prediction. Collectively, these results help enhance our genomics-assisted breeding efforts centered on improving the elemental profile of fresh sweet corn kernels.
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Affiliation(s)
- Matheus Baseggio
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Matthew Murray
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Gregory Ziegler
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - Nicholas Kaczmar
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - James Chamness
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Olena K Vatamaniuk
- Soil and Crop Sciences Section, Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
- US Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, NY 14853, USA
| | - Margaret E Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, MO 63132, USA
| | - William F Tracy
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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11
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Cao S, Song J, Yuan Y, Zhang A, Ren J, Liu Y, Qu J, Hu G, Zhang J, Wang C, Cao J, Olsen M, Prasanna BM, San Vicente F, Zhang X. Genomic Prediction of Resistance to Tar Spot Complex of Maize in Multiple Populations Using Genotyping-by-Sequencing SNPs. FRONTIERS IN PLANT SCIENCE 2021; 12:672525. [PMID: 34335648 PMCID: PMC8322742 DOI: 10.3389/fpls.2021.672525] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Accepted: 06/21/2021] [Indexed: 06/13/2023]
Abstract
Tar spot complex (TSC) is one of the most important foliar diseases in tropical maize. TSC resistance could be furtherly improved by implementing marker-assisted selection (MAS) and genomic selection (GS) individually, or by implementing them stepwise. Implementation of GS requires a profound understanding of factors affecting genomic prediction accuracy. In the present study, an association-mapping panel and three doubled haploid populations, genotyped with genotyping-by-sequencing, were used to estimate the effectiveness of GS for improving TSC resistance. When the training and prediction sets were independent, moderate-to-high prediction accuracies were achieved across populations by using the training sets with broader genetic diversity, or in pairwise populations having closer genetic relationships. A collection of inbred lines with broader genetic diversity could be used as a permanent training set for TSC improvement, which can be updated by adding more phenotyped lines having closer genetic relationships with the prediction set. The prediction accuracies estimated with a few significantly associated SNPs were moderate-to-high, and continuously increased as more significantly associated SNPs were included. It confirmed that TSC resistance could be furtherly improved by implementing GS for selecting multiple stable genomic regions simultaneously, or by implementing MAS and GS stepwise. The factors of marker density, marker quality, and heterozygosity rate of samples had minor effects on the estimation of the genomic prediction accuracy. The training set size, the genetic relationship between training and prediction sets, phenotypic and genotypic diversity of the training sets, and incorporating known trait-marker associations played more important roles in improving prediction accuracy. The result of the present study provides insight into less complex trait improvement via GS in maize.
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Affiliation(s)
- Shiliang Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Junqiao Song
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Henan University of Science and Technology, Luoyang, China
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Yibing Yuan
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Ao Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jiaojiao Ren
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Agronomy, Xinjiang Agricultural University, Urumqi, China
| | - Yubo Liu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- College of Biological Science and Technology, Shenyang Agricultural University, Shenyang, China
| | - Jingtao Qu
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Guanghui Hu
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Jianguo Zhang
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Chunping Wang
- Maize Research Institute, Anyang Academy of Agricultural Sciences, Anyang, China
| | - Jingsheng Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Mexico
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12
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Wu D, Tanaka R, Li X, Ramstein GP, Cu S, Hamilton JP, Buell CR, Stangoulis J, Rocheford T, Gore MA. High-resolution genome-wide association study pinpoints metal transporter and chelator genes involved in the genetic control of element levels in maize grain. G3-GENES GENOMES GENETICS 2021; 11:6156830. [PMID: 33677522 PMCID: PMC8759812 DOI: 10.1093/g3journal/jkab059] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 02/21/2021] [Indexed: 12/18/2022]
Abstract
Despite its importance to plant function and human health, the genetics underpinning element levels in maize grain remain largely unknown. Through a genome-wide association study in the maize Ames panel of nearly 2,000 inbred lines that was imputed with ∼7.7 million SNP markers, we investigated the genetic basis of natural variation for the concentration of 11 elements in grain. Novel associations were detected for the metal transporter genes rte2 (rotten ear2) and irt1 (iron-regulated transporter1) with boron and nickel, respectively. We also further resolved loci that were previously found to be associated with one or more of five elements (copper, iron, manganese, molybdenum, and/or zinc), with two metal chelator and five metal transporter candidate causal genes identified. The nas5 (nicotianamine synthase5) gene involved in the synthesis of nicotianamine, a metal chelator, was found associated with both zinc and iron and suggests a common genetic basis controlling the accumulation of these two metals in the grain. Furthermore, moderate predictive abilities were obtained for the 11 elemental grain phenotypes with two whole-genome prediction models: Bayesian Ridge Regression (0.33–0.51) and BayesB (0.33–0.53). Of the two models, BayesB, with its greater emphasis on large-effect loci, showed ∼4–10% higher predictive abilities for nickel, molybdenum, and copper. Altogether, our findings contribute to an improved genotype-phenotype map for grain element accumulation in maize.
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Affiliation(s)
- Di Wu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Ryokei Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Xiaowei Li
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | | | - Suong Cu
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - John P Hamilton
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - C Robin Buell
- Department of Plant Biology, Michigan State University, East Lansing, MI 48824, USA
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Bedford Park, SA 5042, Australia
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
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13
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Tolley SA, Singh A, Tuinstra MR. Heterotic Patterns of Temperate and Tropical Maize by Ear Photometry. FRONTIERS IN PLANT SCIENCE 2021; 12:616975. [PMID: 34194445 PMCID: PMC8238002 DOI: 10.3389/fpls.2021.616975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 05/17/2021] [Indexed: 06/13/2023]
Abstract
As the plant variety protection (PVP) of commercial inbred lines expire, public breeding programs gain a wealth of genetic materials that have undergone many years of intense selection; however, the value of these inbred lines is only fully realized when they have been well characterized and are used in hybrid combinations. Additionally, while yield is the primary trait by which hybrids are evaluated, new phenotyping technologies, such as ear photometry (EP), may provide an assessment of yield components that can be scaled to breeding programs. The objective of this experiment was to use EP to describe the testcross performance of inbred lines from temperate and tropical origins. We evaluated the performance of 298 public and ex-PVP inbred lines and 274 Drought Tolerant Maize for Africa (DTMA) inbred lines when crossed to Iodent (PHP02) and/or Stiff Stalk (2FACC) testers for 25 yield-related traits. Kernel weight, kernels per ear, and grain yield predicted by EP were correlated with their reference traits with r = 0.49, r = 0.88, and r = 0.75, respectively. The testcross performance of each maize inbred line was tester dependent. When lines were crossed to a tester within the heterotic group, many yield components related to ear size and kernels per ear were significantly reduced, but kernel size was rarely impacted. Thus, the effect of heterosis was more noticeable on traits that increased kernels per ear rather than kernel size. Hybrids of DTMA inbred lines crossed to PHP02 exhibited phenotypes similar to testcrosses of Stiff Stalk and Non-Stiff Stalk heterotic groups for yield due to significant increases in kernel size to compensate for a reduction in kernels per ear. Kernels per ear and ear length were correlated (r = 0.89 and r = 0.84, respectively) with and more heritable than yield, suggesting these traits could be useful for inbred selection.
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Affiliation(s)
- Seth A. Tolley
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Amritpal Singh
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
- Advanta Seeds, College Station, TX, United States
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14
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Zhu M, Tong L, Xu M, Zhong T. Genetic dissection of maize disease resistance and its applications in molecular breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:32. [PMID: 37309327 PMCID: PMC10236108 DOI: 10.1007/s11032-021-01219-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 02/25/2021] [Indexed: 06/14/2023]
Abstract
Disease resistance is essential for reliable maize production. In a long-term tug-of-war between maize and its pathogenic microbes, naturally occurring resistance genes gradually accumulate and play a key role in protecting maize from various destructive diseases. Recently, significant progress has been made in deciphering the genetic basis of disease resistance in maize. Enhancing disease resistance can now be explored at the molecular level, from marker-assisted selection to genomic selection, transgenesis technique, and genome editing. In view of the continuing accumulation of cloned resistance genes and in-depth understanding of their resistance mechanisms, coupled with rapid progress of biotechnology, it is expected that the large-scale commercial application of molecular breeding of resistant maize varieties will soon become a reality.
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Affiliation(s)
- Mang Zhu
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Lixiu Tong
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Mingliang Xu
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
| | - Tao Zhong
- State Key Laboratory of Plant Physiology and Biochemistry/College of Agronomy and Biotechnology/National Maize Improvement Center/Center for Crop Functional Genomics and Molecular Breeding, China Agricultural University, 2 West Yuanmingyuan Road, Beijing, 100193 People’s Republic of China
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15
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Ren J, Li Z, Wu P, Zhang A, Liu Y, Hu G, Cao S, Qu J, Dhliwayo T, Zheng H, Olsen M, Prasanna BM, San Vicente F, Zhang X. Genetic Dissection of Quantitative Resistance to Common Rust ( Puccinia sorghi) in Tropical Maize ( Zea mays L.) by Combined Genome-Wide Association Study, Linkage Mapping, and Genomic Prediction. FRONTIERS IN PLANT SCIENCE 2021; 12:692205. [PMID: 34276741 PMCID: PMC8284423 DOI: 10.3389/fpls.2021.692205] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 06/08/2021] [Indexed: 05/03/2023]
Abstract
Common rust is one of the major foliar diseases in maize, leading to significant grain yield losses and poor grain quality. To dissect the genetic architecture of common rust resistance, a genome-wide association study (GWAS) panel and a bi-parental doubled haploid (DH) population, DH1, were used to perform GWAS and linkage mapping analyses. The GWAS results revealed six single-nucleotide polymorphisms (SNPs) significantly associated with quantitative resistance of common rust at a very stringent threshold of P-value 3.70 × 10-6 at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04. Linkage mapping identified five quantitative trait loci (QTL) at bins 1.03, 2.06, 4.08, 7.03, and 9.00. The phenotypic variation explained (PVE) value of each QTL ranged from 5.40 to 12.45%, accounting for the total PVE value of 40.67%. Joint GWAS and linkage mapping analyses identified a stable genomic region located at bin 4.08. Five significant SNPs were only identified by GWAS, and four QTL were only detected by linkage mapping. The significantly associated SNP of S10_95231291 detected in the GWAS analysis was first reported. The linkage mapping analysis detected two new QTL on chromosomes 7 and 10. The major QTL on chromosome 7 in the region between 144,567,253 and 149,717,562 bp had the largest PVE value of 12.45%. Four candidate genes of GRMZM2G328500, GRMZM2G162250, GRMZM2G114893, and GRMZM2G138949 were identified, which played important roles in the response of stress resilience and the regulation of plant growth and development. Genomic prediction (GP) accuracies observed in the GWAS panel and DH1 population were 0.61 and 0.51, respectively. This study provided new insight into the genetic architecture of quantitative resistance of common rust. In tropical maize, common rust could be improved by pyramiding the new sources of quantitative resistance through marker-assisted selection (MAS) or genomic selection (GS), rather than the implementation of MAS for the single dominant race-specific resistance gene.
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Affiliation(s)
- Jiaojiao Ren
- College of Agronomy, Xinjiang Agricultural University, Urumqi, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zhimin Li
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Penghao Wu
- College of Agronomy, Xinjiang Agricultural University, Urumqi, China
| | - Ao Zhang
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, China
| | - Yubo Liu
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Guanghui Hu
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Shiliang Cao
- Maize Research Institute, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Jingtao Qu
- Maize Research Institute, Sichuan Agricultural University, Chengdu, China
| | - Thanda Dhliwayo
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | | | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- *Correspondence: Felix San Vicente,
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Xuecai Zhang,
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