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Zhang H, Tang Y, Yue Y, Chen Y. Advances in the evolution research and genetic breeding of peanut. Gene 2024; 916:148425. [PMID: 38575102 DOI: 10.1016/j.gene.2024.148425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 03/15/2024] [Accepted: 04/01/2024] [Indexed: 04/06/2024]
Abstract
Peanut is an important cash crop used in oil, food and feed in our country. The rapid development of sequencing technology has promoted the research on the related aspects of peanut genetic breeding. This paper reviews the research progress of peanut origin and evolution, genetic breeding, molecular markers and their applications, genomics, QTL mapping and genome selection techniques. The main problems of molecular genetic breeding in peanut research worldwide include: the narrow genetic resources of cultivated species, unstable genetic transformation and unclear molecular mechanism of important agronomic traits. Considering the severe challenges regarding the supply of edible oil, and the main problems in peanut production, the urgent research directions of peanut are put forward: The de novo domestication and the exploitation of excellent genes from wild resources to improve modern cultivars; Integration of multi-omics data to enhance the importance of big data in peanut genetics and breeding; Cloning the important genes related to peanut agronomic traits and analyzing their fine regulation mechanisms; Precision molecular design breeding and using gene editing technology to accurately improve the key traits of peanut.
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Affiliation(s)
- Hui Zhang
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China.
| | - Yueyi Tang
- Shandong Peanut Research Institute, Qingdao 266100, China
| | - Yunlai Yue
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China
| | - Yong Chen
- College of Agriculture, South China Agricultural University, Guangzhou 510642, China.
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2
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Shi TL, Jia KH, Bao YT, Nie S, Tian XC, Yan XM, Chen ZY, Li ZC, Zhao SW, Ma HY, Zhao Y, Li X, Zhang RG, Guo J, Zhao W, El-Kassaby YA, Müller N, Van de Peer Y, Wang XR, Street NR, Porth I, An X, Mao JF. High-quality genome assembly enables prediction of allele-specific gene expression in hybrid poplar. PLANT PHYSIOLOGY 2024; 195:652-670. [PMID: 38412470 PMCID: PMC11060683 DOI: 10.1093/plphys/kiae078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Revised: 01/08/2024] [Accepted: 01/09/2024] [Indexed: 02/29/2024]
Abstract
Poplar (Populus) is a well-established model system for tree genomics and molecular breeding, and hybrid poplar is widely used in forest plantations. However, distinguishing its diploid homologous chromosomes is difficult, complicating advanced functional studies on specific alleles. In this study, we applied a trio-binning design and PacBio high-fidelity long-read sequencing to obtain haplotype-phased telomere-to-telomere genome assemblies for the 2 parents of the well-studied F1 hybrid "84K" (Populus alba × Populus tremula var. glandulosa). Almost all chromosomes, including the telomeres and centromeres, were completely assembled for each haplotype subgenome apart from 2 small gaps on one chromosome. By incorporating information from these haplotype assemblies and extensive RNA-seq data, we analyzed gene expression patterns between the 2 subgenomes and alleles. Transcription bias at the subgenome level was not uncovered, but extensive-expression differences were detected between alleles. We developed machine-learning (ML) models to predict allele-specific expression (ASE) with high accuracy and identified underlying genome features most highly influencing ASE. One of our models with 15 predictor variables achieved 77% accuracy on the training set and 74% accuracy on the testing set. ML models identified gene body CHG methylation, sequence divergence, and transposon occupancy both upstream and downstream of alleles as important factors for ASE. Our haplotype-phased genome assemblies and ML strategy highlight an avenue for functional studies in Populus and provide additional tools for studying ASE and heterosis in hybrids.
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Affiliation(s)
- Tian-Le Shi
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Kai-Hua Jia
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- Key Laboratory of Crop Genetic Improvement & Ecology and Physiology, Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences, Ji’nan 250100, China
| | - Yu-Tao Bao
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shuai Nie
- Rice Research Institute, Guangdong Academy of Agricultural Sciences & Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Ministry of Agriculture and Rural Affairs & Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
| | - Xue-Chan Tian
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xue-Mei Yan
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Zhao-Yang Chen
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Zhi-Chao Li
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Shi-Wei Zhao
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Hai-Yao Ma
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Ye Zhao
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Xiang Li
- School of Agriculture, Ningxia University, Yinchuan 750021, China
| | - Ren-Gang Zhang
- Yunnan Key Laboratory for Integrative Conservation of Plant Species with Extremely Small Populations, Key Laboratory for Plant Diversity and Biogeography of East Asia, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China
| | - Jing Guo
- College of Forestry, Shandong Agricultural University, Tai’an 271000, China
| | - Wei Zhao
- Umeå Plant Science Centre, Department of Ecology and Environmental Science, Umeå University, SE-901 87 Umeå, Sweden
| | - Yousry Aly El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, Bc, V6T 1Z4, Canada
| | - Niels Müller
- Thünen-Institute of Forest Genetics, 22927 Grosshansdorf, Germany
| | - Yves Van de Peer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
- Centre for Microbial Ecology and Genomics, Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria 0028, South Africa
- College of Horticulture, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing 210095, China
| | - Xiao-Ru Wang
- Umeå Plant Science Centre, Department of Ecology and Environmental Science, Umeå University, SE-901 87 Umeå, Sweden
| | - Nathaniel Robert Street
- Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden
| | - Ilga Porth
- Départment des Sciences du Bois et de la Forêt, Faculté de Foresterie, de Géographie et Géomatique, Université Laval, Québec, QC G1V 0A6, Canada
| | - Xinmin An
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
| | - Jian-Feng Mao
- State Key Laboratory of Tree Genetics and Breeding, National Engineering Research Center of Tree Breeding and Ecological Restoration, Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China
- Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, SE-901 87 Umeå, Sweden
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Yang X, Yu S, Yan S, Wang H, Fang W, Chen Y, Ma X, Han L. Progress in Rice Breeding Based on Genomic Research. Genes (Basel) 2024; 15:564. [PMID: 38790193 PMCID: PMC11121554 DOI: 10.3390/genes15050564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/18/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024] Open
Abstract
The role of rice genomics in breeding progress is becoming increasingly important. Deeper research into the rice genome will contribute to the identification and utilization of outstanding functional genes, enriching the diversity and genetic basis of breeding materials and meeting the diverse demands for various improvements. Here, we review the significant contributions of rice genomics research to breeding progress over the last 25 years, discussing the profound impact of genomics on rice genome sequencing, functional gene exploration, and novel breeding methods, and we provide valuable insights for future research and breeding practices.
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Affiliation(s)
- Xingye Yang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (X.Y.); (S.Y.); (H.W.); (W.F.); (Y.C.)
| | - Shicong Yu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Rice Research Institute, Sichuan Agricultural University, Chengdu 611130, China;
| | - Shen Yan
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (X.Y.); (S.Y.); (H.W.); (W.F.); (Y.C.)
| | - Hao Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (X.Y.); (S.Y.); (H.W.); (W.F.); (Y.C.)
| | - Wei Fang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (X.Y.); (S.Y.); (H.W.); (W.F.); (Y.C.)
| | - Yanqing Chen
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (X.Y.); (S.Y.); (H.W.); (W.F.); (Y.C.)
| | - Xiaoding Ma
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (X.Y.); (S.Y.); (H.W.); (W.F.); (Y.C.)
| | - Longzhi Han
- National Crop Genebank, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, 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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5
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Singh G, Kaur N, Khanna R, Kaur R, Gudi S, Kaur R, Sidhu N, Vikal Y, Mangat GS. 2Gs and plant architecture: breaking grain yield ceiling through breeding approaches for next wave of revolution in rice ( Oryza sativa L.). Crit Rev Biotechnol 2024; 44:139-162. [PMID: 36176065 DOI: 10.1080/07388551.2022.2112648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 07/10/2022] [Accepted: 07/27/2022] [Indexed: 11/03/2022]
Abstract
Rice is a principal food crop for more than half of the global population. Grain number and grain weight (2Gs) are the two complex traits controlled by several quantitative trait loci (QTLs) and are considered the most critical components for yield enhancement in rice. Novel molecular biology and QTL mapping strategies can be utilized in dissecting the complex genetic architecture of these traits. Discovering the valuable genes/QTLs associated with 2Gs traits hidden in the rice genome and utilizing them in breeding programs may bring a revolution in rice production. Furthermore, the positional cloning and functional characterization of identified genes and QTLs may aid in understanding the molecular mechanisms underlying the 2Gs traits. In addition, knowledge of modern genomic tools aids the understanding of the nature of plant and panicle architecture, which enhances their photosynthetic activity. Rice researchers continue to combine important yield component traits (including 2Gs for the yield ceiling) by utilizing modern breeding tools, such as marker-assisted selection (MAS), haplotype-based breeding, and allele mining. Physical co-localization of GW7 (for grain weight) and DEP2 (for grain number) genes present on chromosome 7 revealed the possibility of simultaneous introgression of these two genes, if desirable allelic variants were found in the single donor parent. This review article will reveal the genetic nature of 2Gs traits and use this knowledge to break the yield ceiling by using different breeding and biotechnological tools, which will sustain the world's food requirements.
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Affiliation(s)
- Gurjeet Singh
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Navdeep Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Renu Khanna
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Rupinder Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Santosh Gudi
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Rajvir Kaur
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Navjot Sidhu
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | - Yogesh Vikal
- School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, India
| | - G S Mangat
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
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6
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Fritsche-Neto R, Ali J, De Asis EJ, Allahgholipour M, Labroo MR. Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 137:3. [PMID: 38085288 PMCID: PMC10716074 DOI: 10.1007/s00122-023-04508-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/18/2023] [Indexed: 12/18/2023]
Abstract
KEY MESSAGE Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.
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Affiliation(s)
- Roberto Fritsche-Neto
- International Rice Research Institute (IRRI), Los Banos, Philippines.
- H. Rouse Caffey Rice Research Station, LSU AgCenter, Rayne, USA.
| | - Jauhar Ali
- International Rice Research Institute (IRRI), Los Banos, Philippines.
| | - Erik Jon De Asis
- International Rice Research Institute (IRRI), Los Banos, Philippines
| | | | - Marlee Rose Labroo
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Lisbon, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Raza A, Tabassum J, Fakhar AZ, Sharif R, Chen H, Zhang C, Ju L, Fotopoulos V, Siddique KHM, Singh RK, Zhuang W, Varshney RK. Smart reprograming of plants against salinity stress using modern biotechnological tools. Crit Rev Biotechnol 2023; 43:1035-1062. [PMID: 35968922 DOI: 10.1080/07388551.2022.2093695] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 05/08/2022] [Indexed: 01/19/2023]
Abstract
Climate change gives rise to numerous environmental stresses, including soil salinity. Salinity/salt stress is the second biggest abiotic factor affecting agricultural productivity worldwide by damaging numerous physiological, biochemical, and molecular processes. In particular, salinity affects plant growth, development, and productivity. Salinity responses include modulation of ion homeostasis, antioxidant defense system induction, and biosynthesis of numerous phytohormones and osmoprotectants to protect plants from osmotic stress by decreasing ion toxicity and augmented reactive oxygen species scavenging. As most crop plants are sensitive to salinity, improving salt tolerance is crucial in sustaining global agricultural productivity. In response to salinity, plants trigger stress-related genes, proteins, and the accumulation of metabolites to cope with the adverse consequence of salinity. Therefore, this review presents an overview of salinity stress in crop plants. We highlight advances in modern biotechnological tools, such as omics (genomics, transcriptomics, proteomics, and metabolomics) approaches and different genome editing tools (ZFN, TALEN, and CRISPR/Cas system) for improving salinity tolerance in plants and accomplish the goal of "zero hunger," a worldwide sustainable development goal proposed by the FAO.
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Affiliation(s)
- Ali Raza
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, Oil Crops Research Institute, Center of Legume Crop Genetics and Systems Biology/College of Agriculture, Fujian Agriculture and Forestry University (FAFU), Fuzhou, China
| | - Javaria Tabassum
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Science (CAAS), Zhejiang, China
| | - Ali Zeeshan Fakhar
- National Institute for Biotechnology and Genetic Engineering (NIBGE), Faisalabad, Pakistan
| | - Rahat Sharif
- Department of Horticulture, College of Horticulture and Plant Protection, Yangzhou University, Yangzhou, China
| | - Hua Chen
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, Oil Crops Research Institute, Center of Legume Crop Genetics and Systems Biology/College of Agriculture, Fujian Agriculture and Forestry University (FAFU), Fuzhou, China
| | - Chong Zhang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, Oil Crops Research Institute, Center of Legume Crop Genetics and Systems Biology/College of Agriculture, Fujian Agriculture and Forestry University (FAFU), Fuzhou, China
| | - Luo Ju
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Chinese Academy of Agricultural Science (CAAS), Zhejiang, China
| | - Vasileios Fotopoulos
- Department of Agricultural Sciences, Biotechnology & Food Science, Cyprus University of Technology, Lemesos, Cyprus
| | - Kadambot H M Siddique
- The UWA Institute of Agriculture, The University of Western Australia, Crawley, Perth, Australia
| | - Rakesh K Singh
- Crop Diversification and Genetics, International Center for Biosaline Agriculture, Dubai, United Arab Emirates
| | - Weijian Zhuang
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, Oil Crops Research Institute, Center of Legume Crop Genetics and Systems Biology/College of Agriculture, Fujian Agriculture and Forestry University (FAFU), Fuzhou, China
| | - Rajeev K Varshney
- Key Laboratory of Ministry of Education for Genetics, Breeding and Multiple Utilization of Crops, Oil Crops Research Institute, Center of Legume Crop Genetics and Systems Biology/College of Agriculture, Fujian Agriculture and Forestry University (FAFU), Fuzhou, China
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Murdoch's Centre for Crop and Food Innovation, State Agricultural Biotechnology Centre, Murdoch University, Murdoch, Australia
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8
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Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, Seal S, Das M, Banerjee S, Chowdhury S, Adeyemi SB, Chen JT. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes (Basel) 2023; 14:1484. [PMID: 37510388 PMCID: PMC10380062 DOI: 10.3390/genes14071484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.
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Affiliation(s)
- Dwaipayan Sinha
- Department of Botany, Government General Degree College, Mohanpur 721436, India
| | - Arun Kumar Maurya
- Department of Botany, Multanimal Modi College, Modinagar, Ghaziabad 201204, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Muhammad Majeed
- Department of Botany, University of Gujrat, Punjab 50700, Pakistan
| | - Rachna Agarwal
- Applied Genomics Section, Bhabha Atomic Research Centre, Mumbai 400085, India
| | - Rashmi Mukherjee
- Research Center for Natural and Applied Sciences, Department of Botany (UG & PG), Raja Narendralal Khan Women's College, Gope Palace, Midnapur 721102, India
| | - Sharmistha Ganguly
- Department of Dravyaguna, Institute of Post Graduate Ayurvedic Education and Research, Kolkata 700009, India
| | - Robina Aziz
- Department of Botany, Government, College Women University, Sialkot 51310, Pakistan
| | - Manika Bhatia
- TERI School of Advanced Studies, New Delhi 110070, India
| | - Aqsa Majgaonkar
- Department of Botany, St. Xavier's College (Autonomous), Mumbai 400001, India
| | - Sanchita Seal
- Department of Botany, Polba Mahavidyalaya, Polba 712148, India
| | - Moumita Das
- V. Sivaram Research Foundation, Bangalore 560040, India
| | - Swastika Banerjee
- Department of Botany, Kairali College of +3 Science, Champua, Keonjhar 758041, India
| | - Shahana Chowdhury
- Department of Biotechnology, Faculty of Engineering Sciences, German University Bangladesh, TNT Road, Telipara, Chandona Chowrasta, Gazipur 1702, Bangladesh
| | - Sherif Babatunde Adeyemi
- Ethnobotany/Phytomedicine Laboratory, Department of Plant Biology, Faculty of Life Sciences, University of Ilorin, Ilorin P.M.B 1515, Nigeria
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
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9
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Gao Z, Bian J, Lu F, Jiao Y, He H. Triticeae crop genome biology: an endless frontier. FRONTIERS IN PLANT SCIENCE 2023; 14:1222681. [PMID: 37546276 PMCID: PMC10399237 DOI: 10.3389/fpls.2023.1222681] [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: 05/15/2023] [Accepted: 07/04/2023] [Indexed: 08/08/2023]
Abstract
Triticeae, the wheatgrass tribe, includes several major cereal crops and their wild relatives. Major crops within the Triticeae are wheat, barley, rye, and oat, which are important for human consumption, animal feed, and rangeland protection. Species within this tribe are known for their large genomes and complex genetic histories. Powered by recent advances in sequencing technology, researchers worldwide have made progress in elucidating the genomes of Triticeae crops. In addition to assemblies of high-quality reference genomes, pan-genome studies have just started to capture the genomic diversities of these species, shedding light on our understanding of the genetic basis of domestication and environmental adaptation of Triticeae crops. In this review, we focus on recent signs of progress in genome sequencing, pan-genome analyses, and resequencing analysis of Triticeae crops. We also propose future research avenues in Triticeae crop genomes, including identifying genome structure variations, the association of genomic regions with desired traits, mining functions of the non-coding area, introgression of high-quality genes from wild Triticeae resources, genome editing, and integration of genomic resources.
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Affiliation(s)
- Zhaoxu Gao
- State Key Laboratory of Protein and Plant Gene Research, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
| | - Jianxin Bian
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Shandong, China
| | - Fei Lu
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Yuling Jiao
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Shandong, China
- University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory for Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences, Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
- State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China
| | - Hang He
- State Key Laboratory of Protein and Plant Gene Research, School of Advanced Agriculture Sciences and School of Life Sciences, Peking University, Beijing, China
- Peking University Institute of Advanced Agricultural Sciences, Shandong Laboratory of Advanced Agricultural Sciences in Weifang, Shandong, China
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10
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He S, Liang S, Meng L, Cao L, Ye G. Sparse Phenotyping and Haplotype-Based Models for Genomic Prediction in Rice. RICE (NEW YORK, N.Y.) 2023; 16:27. [PMID: 37284992 DOI: 10.1186/s12284-023-00643-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 05/20/2023] [Indexed: 06/08/2023]
Abstract
The multi-environment genomic selection enables plant breeders to select varieties resilient to diverse environments or particularly adapted to specific environments, which holds a great potential to be used in rice breeding. To realize the multi-environment genomic selection, a robust training set with multi-environment phenotypic data is of necessity. Considering the huge potential of genomic prediction enhanced sparse phenotyping on the cost saving of multi-environment trials (MET), the establishment of a multi-environment training set could also benefit from it. Optimizing the genomic prediction methods is also crucial to enhance the multi-environment genomic selection. Using haplotype-based genomic prediction models is able to capture local epistatic effects which could be conserved and accumulated across generations much like additive effects thereby benefitting breeding. However, previous studies often used fixed length haplotypes composed by a few adjacent molecular markers disregarding the linkage disequilibrium (LD) which is of essential role in determining the haplotype length. In our study, based on three rice populations with different sizes and compositions, we investigated the usefulness and effectiveness of multi-environment training sets with varying phenotyping intensities and different haplotype-based genomic prediction models based on LD-derived haplotype blocks for two agronomic traits, i.e., days to heading (DTH) and plant height (PH). Results showed that phenotyping merely 30% records in multi-environment training set is able to provide a comparable prediction accuracy to high phenotyping intensities; the local epistatic effects are much likely existent in DTH; dividing the LD-derived haplotype blocks into small segments with two or three single nucleotide polymorphisms (SNPs) helps to maintain the predictive ability of haplotype-based models in large populations; modelling the covariances between environments improves genomic prediction accuracy. Our study provides means to improve the efficiency of multi-environment genomic selection in rice.
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Affiliation(s)
- Sang He
- Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China
| | - Shanshan Liang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, 300387, China
| | - Lijun Meng
- Kunpeng Institute of Modern Agriculture at Foshan, Foshan, 528200, China
| | - Liyong Cao
- Key Laboratory for Zhejiang Super Rice Research, China National Rice Research Institute, Hangzhou, 310006, China.
| | - Guoyou Ye
- CAAS-IRRI Joint Laboratory for Genomics-Assisted Germplasm Enhancement, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, China.
- Rice Breeding Innovations Platform, International Rice Research Institute, Metro Manila, Philippines.
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11
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Miller MJ, Song Q, Fallen B, Li Z. Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean ( Glycine max). FRONTIERS IN PLANT SCIENCE 2023; 14:1171135. [PMID: 37235007 PMCID: PMC10206060 DOI: 10.3389/fpls.2023.1171135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/17/2023] [Indexed: 05/28/2023]
Abstract
Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean's profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross's offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.
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Affiliation(s)
- Mark J. Miller
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture - Agricultural Research Service, Beltsville, MD, United States
| | - Benjamin Fallen
- Soybean and Nitrogen Fixation Research Unit, United States Department of Agriculture - Agricultural Research Service, Raleigh, NC, United States
| | - Zenglu Li
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
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12
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Maulana F, Perumal R, Serba DD, Tesso T. Genomic prediction of hybrid performance in grain sorghum ( Sorghum bicolor L.). FRONTIERS IN PLANT SCIENCE 2023; 14:1139896. [PMID: 37180401 PMCID: PMC10167770 DOI: 10.3389/fpls.2023.1139896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 03/22/2023] [Indexed: 05/16/2023]
Abstract
Genomic selection is expected to improve selection efficiency and genetic gain in breeding programs. The objective of this study was to assess the efficacy of predicting the performance of grain sorghum hybrids using genomic information of parental genotypes. One hundred and two public sorghum inbred parents were genotyped using genotyping-by-sequencing. Ninty-nine of the inbreds were crossed to three tester female parents generating a total of 204 hybrids for evaluation at two environments. The hybrids were sorted in to three sets of 77,59 and 68 and evaluated along with two commercial checks using a randomized complete block design in three replications. The sequence analysis generated 66,265 SNP markers that were used to predict the performance of 204 F1 hybrids resulted from crosses between the parents. Both additive (partial model) and additive and dominance (full model) were constructed and tested using various training population (TP) sizes and cross-validation procedures. Increasing TP size from 41 to 163 increased prediction accuracies for all traits. With the partial model, the five-fold cross validated prediction accuracies ranged from 0.03 for thousand kernel weight (TKW) to 0.58 for grain yield (GY) while it ranged from 0.06 for TKW to 0.67 for GY with the full model. The results suggest that genomic prediction could become an effective tool for predicting the performance of sorghum hybrids based on parental genotypes.
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Affiliation(s)
- Frank Maulana
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Ramasamy Perumal
- Kansas State University, Agricultural Research Center, Hays, KS, United States
| | - Desalegn D. Serba
- United States Department of Agriculture-Agricultural Research Service (USDA-ARS), U.S. Arid Land Agricultural Research Center, Maricopa, AZ, United States
| | - Tesfaye Tesso
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
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13
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Yan H, Guo H, Li T, Zhang H, Xu W, Xie J, Zhu X, Yu Y, Chen J, Zhao S, Xu J, Hu M, Jiang Y, Zhang H, Ma M, He Z. High-precision early warning system for rice cadmium accumulation risk assessment. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 859:160135. [PMID: 36375547 DOI: 10.1016/j.scitotenv.2022.160135] [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: 08/10/2022] [Revised: 10/01/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Rapid global industrialization has resulted in widespread cadmium contamination in agricultural soils and products. A considerable proportion of rice consumers are exposed to Cd levels above the provisional safe intake limit, raising widespread environmental concerns on risk management. Therefore, a generalized approach is urgently needed to enable correct evaluation and early warning of cadmium contaminants in rice products. Combining big data and computer science together, this study developed a system named "SMART Cd Early Warning", which integrated 4 modules including genotype-to-phenotype (G2P) modelling, high-throughput sequencing, G2P prediction and rice Cd contamination risk assessment, for rice cadmium accumulation early warning. This system can rapidly assess the risk of rice cadmium accumulation by genotyping leaves at seeding stage. The parameters including statistical methods, population size, training population-testing population ratio, SNP density were assessed to ensure G2P model exhibited superior performance in terms of prediction precision (up to 0.76 ± 0.003) and computing efficiency (within 2 h). In field trials of cadmium-contaminated farmlands in Wenling and Fuyang city, Zhejiang Province, "SMART Cd Early Warning" exhibited superior capability for identification risk rice varieties, suggesting a potential of "SMART Cd Early-Warning system" in OsGCd risk assessment and early warning in the age of smart.
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Affiliation(s)
- Huili Yan
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Hanyao Guo
- Hebei Normal University, Shijiazhuang 050024, China
| | - Ting Li
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hezifan Zhang
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenxiu Xu
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Jianyin Xie
- Key Lab of Crop Heterosis and Utilization of Ministry of Education, Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Xiaoyang Zhu
- Key Lab of Crop Heterosis and Utilization of Ministry of Education, Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Yijun Yu
- Zhejiang Station for Management of Arable Land Quality and Fertilizer, Hangzhou 310020, China
| | - Jian Chen
- Plant Protection, Fertilizer and Rural Energy Agency of Wenling, Wenling 317500, China
| | - Shouqing Zhao
- Plant Protection, Fertilizer and Rural Energy Agency of Wenling, Wenling 317500, China
| | - Jun Xu
- Fuyang Agricultural Technology Extension Center, Fuyang 311400, China
| | - Minjun Hu
- Fuyang Agricultural Technology Extension Center, Fuyang 311400, China
| | - Yugen Jiang
- Fuyang Agricultural Technology Extension Center, Fuyang 311400, China
| | - Hongliang Zhang
- Key Lab of Crop Heterosis and Utilization of Ministry of Education, Beijing Key Lab of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China; Sanya Institute of China Agricultural University, Sanya 572024, China
| | - Mi Ma
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Zhenyan He
- Key Laboratory of Plant Resources, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.
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14
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Yan J, Wang X. Machine learning bridges omics sciences and plant breeding. TRENDS IN PLANT SCIENCE 2023; 28:199-210. [PMID: 36153276 DOI: 10.1016/j.tplants.2022.08.018] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
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Affiliation(s)
- Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
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15
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Zhang J, Yang J, Lv Y, Zhang X, Xia C, Zhao H, Wen C. Genetic diversity analysis and variety identification using SSR and SNP markers in melon. BMC PLANT BIOLOGY 2023; 23:39. [PMID: 36650465 PMCID: PMC9847184 DOI: 10.1186/s12870-023-04056-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
Melon is an important horticultural crop with a pleasant aromatic flavor and abundance of health-promoting substances. Numerous melon varieties have been cultivated worldwide in recent years, but the high number of varieties and the high similarity between them poses a major challenge for variety evaluation, discrimination, as well as innovation in breeding. Recently, simple sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs), two robust molecular markers, have been utilized as a rapid and reliable method for variety identification. To elucidate the genetic structure and diversity of melon varieties, we screened out 136 perfect SSRs and 164 perfect SNPs from the resequencing data of 149 accessions, including the most representative lines worldwide. This study established the DNA fingerprint of 259 widely-cultivated melon varieties in China using Target-seq technology. All melon varieties were classified into five subgruops, including ssp. agrestis, ssp. melo, muskmelon and two subgroups of foreign individuals. Compared with ssp. melo, the ssp. agrestis varieties might be exposed to a high risk of genetic erosion due to their extremely narrow genetic background. Increasing the gene exchange between ssp. melo and ssp. agrestis is therefore necessary in the breeding procedure. In addition, analysis of the DNA fingerprints of the 259 melon varieties showed a good linear correlation (R2 = 0.9722) between the SSR genotyping and SNP genotyping methods in variety identification. The pedigree analysis based on the DNA fingerprint of 'Jingyu' and 'Jingmi' series melon varieties was consistent with their breeding history. Based on the SNP index analysis, ssp. agrestis had low gene exchange with ssp. melo in chromosome 4, 7, 10, 11and 12, two specific SNP loci were verified to distinguish ssp. agrestis and ssp. melon varieties. Finally, 23 SSRs and 40 SNPs were selected as the core sets of markers for application in variety identification, which could be efficiently applied to variety authentication, variety monitoring, as well as the protection of intellectual property rights in melon.
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Affiliation(s)
- Jian Zhang
- Beijing Vegetable Research Center (BVRC), Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Jingjing Yang
- Beijing Vegetable Research Center (BVRC), Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Yanling Lv
- Institute of Vegetable, Liaoning Academy of Agricultural Sciences, Shenyang, 110161, China
| | - Xiaofei Zhang
- Beijing Vegetable Research Center (BVRC), Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Changxuan Xia
- Beijing Vegetable Research Center (BVRC), Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Hong Zhao
- Beijing Vegetable Research Center (BVRC), Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China
| | - Changlong Wen
- Beijing Vegetable Research Center (BVRC), Beijing Academy of Agricultural and Forestry Sciences, National Engineering Research Center for Vegetables, Beijing, 100097, China.
- Beijing Key Laboratory of Vegetable Germplasms Improvement, Beijing, 100097, China.
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16
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Niedziela A, Bednarek PT. Population structure and genetic diversity of a germplasm for hybrid breeding in rye (Secale cereale L.) using high-density DArTseq-based silicoDArT and SNP markers. J Appl Genet 2023; 64:217-229. [PMID: 36595165 PMCID: PMC10076414 DOI: 10.1007/s13353-022-00740-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 11/25/2022] [Accepted: 12/02/2022] [Indexed: 01/04/2023]
Abstract
Investigating genetic structure and diversity is crucial for the rye hybrid breeding strategy, leading to improved plant productivity and adaptation. The present study elucidated the population structure and genetic diversity of 188 rye accessions, comprising 94 pollen fertility restoration lines (RF) and 94 cytoplasmic male-sterile (CMS) lines with Pampa sterilizing cytoplasm using SNP and silicoDArT markers from the diversity array technology (DArT)-based sequencing platform (DArTseq). Expected heterozygosity (He) and Shanon's diversity (I) indexes varied slightly between marker systems and groups of germplasms (He = 0.34, I = 0.51 for RF and CMS lines genotyped using SNPs; He = 0.31, I = 0.48, and He = 0.35, I = 0.53 for RF and CMS using silicoDArTs, respectively). ANOVA indicated moderate variation (7%) between RF and CMS breeding materials. The same parameter varied when chromosome-assigned markers were used and ranged from 5.8% for 5R to 7.4% for 4R. However, when silicoDArT markers were applied, the respective values varied from 6.4% (1R) to 8.2% (3R and 4R). The model-based (Bayesian) population structure analysis based on the total marker pool identified two major subpopulations for the studied rye germplasm. The first one (P1) encompasses 93 RF accessions, and the second one (P2) encompasses 94 CMS and one RF accession. However, a similar analysis related to markers assigned to selected chromosomes failed to put plant materials into any of the populations in the same way as the total marker pool. Furthermore, the differences in grouping depended on marker types used for analysis.
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Affiliation(s)
- Agnieszka Niedziela
- Plant Breeding and Acclimatization Institute - National Research Institute, 05-870, Błonie, Radzików, Poland
| | - Piotr Tomasz Bednarek
- Plant Breeding and Acclimatization Institute - National Research Institute, 05-870, Błonie, Radzików, Poland.
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17
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Ma J, Cao Y, Wang Y, Ding Y. Development of the maize 5.5K loci panel for genomic prediction through genotyping by target sequencing. FRONTIERS IN PLANT SCIENCE 2022; 13:972791. [PMID: 36438102 PMCID: PMC9691890 DOI: 10.3389/fpls.2022.972791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Genotyping platforms are important for genetic research and molecular breeding. In this study, a low-density genotyping platform containing 5.5K SNP markers was successfully developed in maize using genotyping by target sequencing (GBTS) technology with capture-in-solution. Two maize populations (Pop1 and Pop2) were used to validate the GBTS panel for genetic and molecular breeding studies. Pop1 comprised 942 hybrids derived from 250 inbred lines and four testers, and Pop2 contained 540 hybrids which were generated from 123 new-developed inbred lines and eight testers. The genetic analyses showed that the average polymorphic information content and genetic diversity values ranged from 0.27 to 0.38 in both populations using all filtered genotyping data. The mean missing rate was 1.23% across populations. The Structure and UPGMA tree analyses revealed similar genetic divergences (76-89%) in both populations. Genomic prediction analyses showed that the prediction accuracy of reproducing kernel Hilbert space (RKHS) was slightly lower than that of genomic best linear unbiased prediction (GBLUP) and three Bayesian methods for general combining ability of grain yield per plant and three yield-related traits in both populations, whereas RKHS with additive effects showed superior advantages over the other four methods in Pop1. In Pop1, the GBLUP and three Bayesian methods with additive-dominance model improved the prediction accuracies by 4.89-134.52% for the four traits in comparison to the additive model. In Pop2, the inclusion of dominance did not improve the accuracy in most cases. In general, low accuracies (0.33-0.43) were achieved for general combing ability of the four traits in Pop1, whereas moderate-to-high accuracies (0.52-0.65) were observed in Pop2. For hybrid performance prediction, the accuracies were moderate to high (0.51-0.75) for the four traits in both populations using the additive-dominance model. This study suggests a reliable genotyping platform that can be implemented in genomic selection-assisted breeding to accelerate maize new cultivar development and improvement.
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18
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Kim KW, Nawade B, Nam J, Chu SH, Ha J, Park YJ. Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice. FRONTIERS IN PLANT SCIENCE 2022; 13:1036177. [PMID: 36352876 PMCID: PMC9637963 DOI: 10.3389/fpls.2022.1036177] [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: 09/04/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world's population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder's interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, -log10 p-value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.
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Affiliation(s)
- Kyu-Won Kim
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Bhagwat Nawade
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Jungrye Nam
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Sang-Ho Chu
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
| | - Jungmin Ha
- Department of Plant Science, Gangneung-Wonju National University, Gangneung, South Korea
| | - Yong-Jin Park
- Center for Crop Breeding on Omics and Artificial Intelligence, Kongju National University, Yesan, South Korea
- Department of Plant Resources, College of Industrial Sciences, Kongju National University, Yesan, South Korea
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Zhang F, Zhang C, Zhao X, Zhu S, Chen K, Zhou G, Wu Z, Li M, Zheng T, Wang W, Yan Z, Fei Q, Li Z, Chen J, Xu J. Genomic Architecture of Yield Performance of an Elite Rice Hybrid Revealed by its Derived Recombinant Inbred Line and Their Backcross Hybrid Populations. RICE (NEW YORK, N.Y.) 2022; 15:49. [PMID: 36181551 PMCID: PMC9526777 DOI: 10.1186/s12284-022-00595-z] [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: 08/12/2022] [Accepted: 09/17/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Since its development and wide adoption in China, hybrid rice has reached the yield plateau for more than three decades. To understand the genetic basis of heterosis in rice and accelerate hybrid rice breeding, the yield performances of the elite rice hybrid, Quan-you-si-miao (QYSM) were genetically dissected by whole-genome sequencing, large-scale phenotyping of 1061 recombined inbred lines (RILs) and 1061 backcross F1 (BCF1) hybrids derived from QYSM's parents across three environments and gene-based analyses. RESULTS Genome-wide scanning of 13,847 segregating genes between the parents and linkage mapping based on 855 bins across the rice genome and phenotyping experiments across three environments resulted in identification of large numbers of genes, 639 main-effect QTLs (M-QTLs) and 2736 epistatic QTLs with significant additive or heterotic effects on the trait performances of the combined population consisting of RILs and BCF1 hybrids, most of which were environment-specific. The 324 M-QTLs affecting yield components included 32.7% additive QTLs, 38.0% over-dominant or dominant ones with strong and positive effects and 29.3% under-dominant or incomplete recessive ones with significant negative heterotic effects. 63.6% of 1403 genes with allelic introgression from subspecies japonica/Geng in the parents of QYSM may have contributed significantly to the enhanced yield performance of QYSM. CONCLUSIONS The parents of QYSM and related rice hybrids in China carry disproportionally more additive and under-dominant genes/QTLs affecting yield traits. Further focus in indica/Xian rice breeding should shift back to improving inbred varieties, while breaking yield ceiling of Xian hybrids can be achieved by one or combinations of the three strategies: (1) by pyramiding favorable alleles of additive genes, (2) by eliminating or minimizing under-dominant loci, and (3) by pyramiding overdominant/dominant genes polymorphic, particularly those underlying inter-subspecific heterosis.
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Affiliation(s)
- Fan Zhang
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Agronomy, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Conghe Zhang
- Winall Hi-Tech Seed Co., Ltd., Hefei, 230088, Anhui, China
| | - Xiuqin Zhao
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shuangbing Zhu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou, 518120, China
| | - Kai Chen
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou, 518120, China
| | - Guixiang Zhou
- Winall Hi-Tech Seed Co., Ltd., Hefei, 230088, Anhui, China
| | - Zhichao Wu
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Min Li
- College of Agronomy, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Tianqing Zheng
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Wensheng Wang
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- College of Agronomy, Anhui Agricultural University, Hefei, 230036, Anhui, China
| | - Zhi Yan
- Winall Hi-Tech Seed Co., Ltd., Hefei, 230088, Anhui, China
| | - Qinyong Fei
- Winall Hi-Tech Seed Co., Ltd., Hefei, 230088, Anhui, China
| | - Zhikang Li
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
- College of Agronomy, Anhui Agricultural University, Hefei, 230036, Anhui, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou, 518120, China.
| | - Jinjie Chen
- Winall Hi-Tech Seed Co., Ltd., Hefei, 230088, Anhui, China.
| | - Jianlong Xu
- Institute of Crop Sciences/National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangzhou, 518120, China.
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Anilkumar C, Sunitha NC, Devate NB, Ramesh S. Advances in integrated genomic selection for rapid genetic gain in crop improvement: a review. PLANTA 2022; 256:87. [PMID: 36149531 DOI: 10.1007/s00425-022-03996-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Accepted: 09/11/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection and its importance in crop breeding. Integration of GS with new breeding tools and developing SOP for GS to achieve maximum genetic gain with low cost and time. The success of conventional breeding approaches is not sufficient to meet the demand of a growing population for nutritious food and other plant-based products. Whereas, marker assisted selection (MAS) is not efficient in capturing all the favorable alleles responsible for economic traits in the process of crop improvement. Genomic selection (GS) developed in livestock breeding and then adapted to plant breeding promised to overcome the drawbacks of MAS and significantly improve complicated traits controlled by gene/QTL with small effects. Large-scale deployment of GS in important crops, as well as simulation studies in a variety of contexts, addressed G × E interaction effects and non-additive effects, as well as lowering breeding costs and time. The current study provides a complete overview of genomic selection, its process, and importance in modern plant breeding, along with insights into its application. GS has been implemented in the improvement of complex traits including tolerance to biotic and abiotic stresses. Furthermore, this review hypothesises that using GS in conjunction with other crop improvement platforms accelerates the breeding process to increase genetic gain. The objective of this review is to highlight the development of an appropriate GS model, the global open source network for GS, and trans-disciplinary approaches for effective accelerated crop improvement. The current study focused on the application of data science, including machine learning and deep learning tools, to enhance the accuracy of prediction models. Present study emphasizes on developing plant breeding strategies centered on GS combined with routine conventional breeding principles by developing GS-SOP to achieve enhanced genetic gain.
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Affiliation(s)
- C Anilkumar
- ICAR-National Rice Research Institute, Cuttack, India
| | - N C Sunitha
- University of Agricultural Sciences, Bangalore, India
| | | | - S Ramesh
- University of Agricultural Sciences, Bangalore, India.
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Liu H, Rao D, Guo T, Gangurde SS, Hong Y, Chen M, Huang Z, Jiang Y, Xu Z, Chen Z. Whole Genome Sequencing and Morphological Trait-Based Evaluation of UPOV Option 2 for DUS Testing in Rice. Front Genet 2022; 13:945015. [PMID: 36092943 PMCID: PMC9458885 DOI: 10.3389/fgene.2022.945015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
To evaluate the application potential of high-density SNPs in rice distinctness, uniformity, and stability (DUS) testing, we screened 37,929 SNP loci distributed on 12 rice chromosomes based on whole-genome resequencing of 122 rice accessions. These SNP loci were used to analyze the DUS testing of rice varieties based on the correlation between the molecular and phenotypic distances of varieties according to UPOV option 2. The results showed that statistical algorithms and the number of phenotypic traits and SNP loci all affected the correlation between the molecular and phenotypic distances of rice varieties. Relative to the other nine algorithms, the Jaccard similarity algorithm had the highest correlation of 0.6587. Both the number of SNPs and the number of phenotypes had a ceiling effect on the correlation between the molecular and phenotypic distances of varieties, and the ceiling effect of the number of SNP loci was more obvious. To overcome the correlation bottleneck, we used the genome-wide prediction method to predict 30 phenotypic traits and found that the prediction accuracy of some traits, such as the basal sheath anthocyanin color, glume length, and intensity of the green color of the leaf blade, was very low. In combination with group comparison analysis, we found that the key to overcoming the ceiling effect of correlation was to improve the resolution of traits with low predictive values. In addition, we also performed distinctness testing on rice varieties by using the molecular distance and phenotypic distance, and we found that there were large differences between the two methods, indicating that UPOV option 2 alone cannot replace the traditional phenotypic DUS testing. However, genotype and phenotype analysis together can increase the efficiency of DUS testing.
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Affiliation(s)
- Hong Liu
- National Engineering Research Center of Plant Space Breeding, South China Agricultural University, Guangzhou, Guangdong, China
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
| | - Dehua Rao
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
| | - Tao Guo
- National Engineering Research Center of Plant Space Breeding, South China Agricultural University, Guangzhou, Guangdong, China
| | - Sunil S. Gangurde
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
- Crop Protection and Management Research Unit, USDA-ARS, Tifton, GA, United States
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
| | - Yanbin Hong
- Guangdong Provincial Key Laboratory for Crops Genetic Improvement, Crops Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Mengqiang Chen
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zhanquan Huang
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
| | - Yuan Jiang
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
| | - Zhenjiang Xu
- College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, China
- *Correspondence: Zhenjiang Xu, ; Zhiqiang Chen,
| | - Zhiqiang Chen
- National Engineering Research Center of Plant Space Breeding, South China Agricultural University, Guangzhou, Guangdong, China
- *Correspondence: Zhenjiang Xu, ; Zhiqiang Chen,
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22
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Wang X, Wang W, Tai S, Li M, Gao Q, Hu Z, Hu W, Wu Z, Zhu X, Xie J, Li F, Zhang Z, Zhi L, Zhang F, Ma X, Yang M, Xu J, Li Y, Zhang W, Yang X, Chen Y, Zhao Y, Fu B, Zhao X, Li J, Wang M, Yue Z, Fang X, Zeng W, Yin Y, Zhang G, Xu J, Zhang H, Li Z, Li Z. Selective and comparative genome architecture of Asian cultivated rice (Oryza sativa L.) attributed to domestication and modern breeding. J Adv Res 2022; 42:1-16. [PMID: 35988902 PMCID: PMC9788959 DOI: 10.1016/j.jare.2022.08.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 07/28/2022] [Accepted: 08/07/2022] [Indexed: 12/27/2022] Open
Abstract
INTRODUCTION Rice, Oryza sativa L. (Os), is one of the oldest domesticated cereals that has also gone through extensive improvement in modern breeding. OBJECTIVES How rice was domesticated and impacted by modern breeding. METHODS We performed comprehensive analyses of genomic sequences of 504 accessions of Os and 456 accessions of O. rufipogon/O. nivara (Or). RESULTS The natural selection on Or before domestication and the natural and artificial selection during domestication together shaped the well-differentiated genomes of two subspecies, geng(j) (japonica) and xian(i) (indica), while breeding has made apparent genomic imprints between landrace and modern varieties of each subspecies, and also between primary modern and advanced modern varieties of xian(i). Selection during domestication and breeding left genome-wide selective signals covering ∼ 22.8 % and ∼ 8.6 % of the Os genome, significantly reduced within-population genomic diversity by ∼ 22 % in xian(i) and ∼ 53 % in geng(j) plus more pronounced subspecific differentiation. Only ∼ 10 % reduction in the total genomic diversity was observed between the Os and Or populations, indicating domestication did not suffer severe genetic bottleneck. CONCLUSION Our results revealed clear differentiation of the Or accessions into three large populations, two of which correspond to the well-differentiated Os subspecies, geng(j) and xian(i). Improved productivity and common changes in the same suit of adaptive traits in xian(i) and geng(j) during domestication and breeding resulted apparently from compensatory and convergent selections for different genes/alleles acting in the common KEGG terms and/or same gene families, and thus maintaining or even increasing the within population diversity and subspecific differentiation of Os, while more genes/alleles of novel function were selected during domestication than modern breeding. Our results supported the multiple independent domestication of Os in Asia and suggest the more efficient utilization of the rich diversity within Os by exploiting inter-subspecific and among population diversity in future rice improvement.
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Affiliation(s)
- Xueqiang Wang
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China,Institute of Crop Science, Plant Precision Breeding Academy, Zhejiang Provincial Key Laboratory of Crop Genetic Resources, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Wensheng Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China,The College of Agronomy, Anhui Agricultural University, Hefei, China
| | | | - Min Li
- The College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Qiang Gao
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Zhiqiang Hu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Wushu Hu
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Zhichao Wu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiaoyang Zhu
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Jianyin Xie
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Fengmei Li
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Zhifang Zhang
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Linran Zhi
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Fan Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China,The College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Xiaoqian Ma
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Ming Yang
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Jiabao Xu
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Yanhong Li
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Wenzhuo Zhang
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Xiyu Yang
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Ying Chen
- College of Information and Electrical Engineering, China Agricultural University, Beijing 100193, China
| | - Yan Zhao
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Binying Fu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xiuqin Zhao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jinjie Li
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Miao Wang
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Zhen Yue
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | | | - Wei Zeng
- The College of Agronomy, Anhui Agricultural University, Hefei, China
| | - Ye Yin
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Gengyun Zhang
- BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China,State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Jianlong Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Hongliang Zhang
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China,Corresponding authors at: Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China (Z. Li).
| | - Zichao Li
- State Key Laboratory of Agrobiotechnology / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China,Corresponding authors at: Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China (Z. Li).
| | - Zhikang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China,The College of Agronomy, Anhui Agricultural University, Hefei, China,Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China,Corresponding authors at: Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China (Z. Li).
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Lu Y, Chuan M, Wang H, Chen R, Tao T, Zhou Y, Xu Y, Li P, Yao Y, Xu C, Yang Z. Genetic and molecular factors in determining grain number per panicle of rice. FRONTIERS IN PLANT SCIENCE 2022; 13:964246. [PMID: 35991390 PMCID: PMC9386260 DOI: 10.3389/fpls.2022.964246] [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: 06/09/2022] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
It was suggested that the most effective way to improve rice grain yield is to increase the grain number per panicle (GN) through the breeding practice in recent decades. GN is a representative quantitative trait affected by multiple genetic and environmental factors. Understanding the mechanisms controlling GN has become an important research field in rice biotechnology and breeding. The regulation of rice GN is coordinately controlled by panicle architecture and branch differentiation, and many GN-associated genes showed pleiotropic effect in regulating tillering, grain size, flowering time, and other domestication-related traits. It is also revealed that GN determination is closely related to vascular development and the metabolism of some phytohormones. In this review, we summarize the recent findings in rice GN determination and discuss the genetic and molecular mechanisms of GN regulators.
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Affiliation(s)
- Yue Lu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Mingli Chuan
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Hanyao Wang
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Rujia Chen
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Tianyun Tao
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Yong Zhou
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Yang Xu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Pengcheng Li
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
| | - Youli Yao
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
| | - Chenwu Xu
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, China
| | - Zefeng Yang
- Key Laboratory of Plant Functional Genomics of the Ministry of Education, Jiangsu Key Laboratory of Crop Genomics and Molecular Breeding, College of Agriculture, Yangzhou University, Yangzhou, China
- Jiangsu Key Laboratory of Crop Genetics and Physiology, Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, China
- Joint International Research Laboratory of Agriculture and Agri-Product Safety of the Ministry of Education, Yangzhou University, Yangzhou, China
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Li J, Zhou K, Wang Z, Zhou J, Deng X. 基于隐性核雄性不育系的杂交小麦制种技术研究进展、问题与展望. CHINESE SCIENCE BULLETIN-CHINESE 2022. [DOI: 10.1360/tb-2022-0386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Hudzenko VM, Polishchuk TP, Lysenko AA, Fedorenko IV, Fedorenko MV, Khudolii LV, Ishchenko VA, Kozelets HM, Babenko AI, Tanchyk SP, Mandrovska SM. Elucidation of gene action and combining ability for productive tillering in spring barley. REGULATORY MECHANISMS IN BIOSYSTEMS 2022. [DOI: 10.15421/022225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
The purpose of the present study is to identify breeding and genetic peculiarities for productive tillering in spring barley genotypes of different origin, purposes of usage and botanical affiliation, as well as to identify effective genetic sources to further improving of the trait. There were created two complete (6 × 6) diallel crossing schemes. Into the Scheme I elite Ukrainian (MIP Tytul and Avhur) and Western European (Datcha, Quench, Gladys, and Beatrix) malting spring barley varieties were involved. Scheme II included awnless covered barley varieties Kozyr and Vitrazh bred at the Plant Production Institute named after V. Y. Yuriev of NAAS of Ukraine, naked barley varieties Condor and CDC Rattan from Canada, as well as awned feed barley variety MIP Myroslav created at MIW and malting barley variety Sebastian from Denmark. For more reliable and informative characterization of barley varieties and their progeny for productive tillering in terms of inheritance, parameters of genetic variation and general combining ability (GCA) statistical analyses of experimental data from different (2019 and 2020) growing seasons were conducted. Accordingly to the indicator of phenotypic dominance all possible modes of inheritance were detected, except for negative dominance in the Scheme I in 2020. The degree of phenotypic dominance significantly varied depending on both varieties involved in crossing schemes and conditions of the years of trials. There was overdominance in loci in both schemes in both years. The other parameters of genetic variation showed significant differences in gene action for productive tillering between crossing Schemes. In Scheme I in both years the dominance was mainly unidirectional and due to dominant effects. In the Scheme II in both years there was multidirectional dominance. In Scheme I compliance with the additive-dominant system was revealed in 2019, but in 2020 there was a strong epistasis. In Scheme II in both years non-allelic interaction was identified. In general, the mode of gene action showed a very complex gene action for productive tillering in barley and a significant role of non-genetic factors in phenotypic manifestation of the trait. Despite this, the level of heritability in the narrow sense in both Schemes pointed to the possibility of the successful selection of individuals with genetically determined increased productive tillering in the splitting generations. In Scheme I the final selection for productive tillering will be more effective in later generations, when dominant alleles become homozygous. In Scheme II it is theoretically possible to select plants with high productive tillering on both recessive and dominant basis. In both schemes the non-allelic interaction should be taken into consideration. Spring barley varieties Beatrix, Datcha, MIP Myroslav and Kozyr can be used as effective genetic sources for involvement in crossings aimed at improving the productive tillering. The results of present study contribute to further development of studies devoted to evaluation of gene action for yield-related traits in spring barley, as well as identification of new genetic sources for plant improvement.
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Jiang C, Rashid MAR, Zhang Y, Zhao Y, Pan Y. Genome wide association study on development and evolution of glutinous rice. BMC Genom Data 2022; 23:33. [PMID: 35508973 PMCID: PMC9066796 DOI: 10.1186/s12863-022-01033-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 03/02/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Glutinous rice as a special endosperm type is consumed as a staple food in East Asian countries by consumers' preference. Genetic studies on glutinous rice could be conducive to improve rice quality and understand its development and evolution. Therefor, we sought to explore more genes related to glutinous by genome wide association study and research the formation history for glutinous. RESULTS Here, genome-wide association study was performed to explore the associated loci/genes underlying glutinous rice by using 2108 rice accessions. Combining the expression patterns analysis, 127, 81, and 48 candidate genes were identified to be associated with endosperm type in whole rice panel, indica, and japonica sub-populations. There were 32 genes, including three starch synthesis-related genes Wx, SSG6, and OsSSIIa, detected simultaneously in the whole rice panel and subpopulations, playing important role in determining glutinous rice. The combined haplotype analyses revealed that the waxy haplotypes combination of three genes mainly distributed in Southeast Asia (SEA), SEA islands (SER) and East Asia islands (EAR). Through population structure and genetic differentiation, we suggest that waxy haplotypes of the three genes firstly evolved or were directly inherited from wild rice in japonica, and then introgressed into indica in SER, SEA and EAR. CONCLUSIONS The cloning and natural variation analysis of waxy-related genes are of great significance for the genetic improvement of quality breeding and comprehend the history in glutinous rice. This work provides valuable information for further gene discovery and understanding the evolution and formation for glutinous rice in SEA, SER and EAR.
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Affiliation(s)
- Conghui Jiang
- Shandong Rice Engineering Technology Research Center, Shandong Rice Research Institute, Shandong Academy of Agricultural Sciences, Jinan, 250100, China
| | - Muhammad Abdul Rehman Rashid
- Department of Bioinformatics and Biotechnology, Government College University, Faisalabad, 38000, Pakistan.,State Key Laboratory for Conservation and Utilization of Bio-Resources in Yunnan, Research Center of Perennial Rice Engineering and Technology in Yunnan, School of Agriculture, Yunnan University, Kunming, 650500, China
| | - Yanhong Zhang
- Institute of Nuclear and Biological Technologies, Xinjiang Academy of Agricultural Sciences, Urumqi, 830091, China
| | - Yan Zhao
- State Key Laboratory of Crop Biology, Shandong Key Laboratory of Crop Biology, College of Agronomy, Shandong Agricultural University, Tai'an, Shandong, 271018, PR China.
| | - Yinghua Pan
- Rice Research Institute, Guangxi Academy of Agricultural Sciences/Guangxi Key Laboratory of Rice Genetics and Breeding, Nanning, 530007, China.
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28
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Ma Y, Li D, Xu Z, Gu R, Wang P, Fu J, Wang J, Du W, Zhang H. Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize. Int J Mol Sci 2022; 23:5074. [PMID: 35563470 PMCID: PMC9102962 DOI: 10.3390/ijms23095074] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/31/2022] Open
Abstract
Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker-trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.
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Affiliation(s)
- Yuting Ma
- Agronomy College, Shenyang Agricultural University, Shenyang 110866, China;
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Dongdong Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Zhenxiang Xu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Riliang Gu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Pingxi Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Jianhua Wang
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Wanli Du
- Agronomy College, Shenyang Agricultural University, Shenyang 110866, China;
| | - Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
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29
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Wang C, Han B. Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics. MOLECULAR PLANT 2022; 15:593-619. [PMID: 35331914 DOI: 10.1016/j.molp.2022.03.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/04/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Since the completion of the rice genome sequencing project in 2005, we have entered the era of rice genomics, which is still in its ascendancy. Rice genomics studies can be classified into three stages: structural genomics, functional genomics, and quantitative genomics. Structural genomics refers primarily to genome sequencing for the construction of a complete map of rice genome sequence. This is fundamental for rice genetics and molecular biology research. Functional genomics aims to decode the functions of rice genes. Quantitative genomics is large-scale sequence- and statistics-based research to define the quantitative traits and genetic features of rice populations. Rice genomics has been a transformative influence on rice biological research and contributes significantly to rice breeding, making rice a good model plant for studying crop sciences.
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Affiliation(s)
- Changsheng Wang
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200233, China.
| | - Bin Han
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200233, China.
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30
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Yu P, Ye C, Li L, Yin H, Zhao J, Wang Y, Zhang Z, Li W, Long Y, Hu X, Xiao J, Jia G, Tian B. Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:16. [PMID: 37309463 PMCID: PMC10248665 DOI: 10.1007/s11032-022-01289-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 03/08/2022] [Indexed: 06/14/2023]
Abstract
Genomic selection is an efficient tool for breeding selection, especially for quantitative traits controlled by multiples genes with low heritability. To validate the application of genomic selection in hybrid rice breeding, the yield and grain quality traits of 404 hybrid rice breeding lines were investigated, and the same accessions were genotyped by using a 56 K SNP chip. There were wide variances among the tested accessions for all the measured traits, and most of the traits were correlated. A total of 67 significant loci were identified for the yield-related traits, and 123 significant loci were identified for the grain quality traits by GWAS. Two of these loci associated with increasing grain yield but decreasing grain quality. The GEBVs of all the yield and grain quality traits were calculated by using 15 different prediction algorithms. The plant height, panicle length, thousand grain weight, grain length and width ratio, amylose content, and alkali value have higher predictability than other traits. However, the predictive accuracy of different GS models is different for different traits. This study provided useful information for genomic selection of specific trait using proper markers and prediction models. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01289-6.
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Affiliation(s)
- Peiyi Yu
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Changrong Ye
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Le Li
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Hexing Yin
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Jian Zhao
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Yongka Wang
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Zhe Zhang
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Weiguo Li
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Yu Long
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Xueyi Hu
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Jinhua Xiao
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Gaofeng Jia
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
| | - Bingchuan Tian
- Huazhi Biotechnology Co. Ltd, Changsha, 410125 Hunan China
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31
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Sun R, Sun B, Tian Y, Su S, Zhang Y, Zhang W, Wang J, Yu P, Guo B, Li H, Li Y, Gao H, Gu Y, Yu L, Ma Y, Su E, Li Q, Hu X, Zhang Q, Guo R, Chai S, Feng L, Wang J, Hong H, Xu J, Yao X, Wen J, Liu J, Li Y, Qiu L. Dissection of the practical soybean breeding pipeline by developing ZDX1, a high-throughput functional array. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:1413-1427. [PMID: 35187586 PMCID: PMC9033737 DOI: 10.1007/s00122-022-04043-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 01/22/2022] [Indexed: 05/13/2023]
Abstract
KEY MESSAGE We developed the ZDX1 high-throughput functional soybean array for high accuracy evaluation and selection of both parents and progeny, which can greatly accelerate soybean breeding. Microarray technology facilitates rapid, accurate, and economical genotyping. Here, using resequencing data from 2214 representative soybean accessions, we developed the high-throughput functional array ZDX1, containing 158,959 SNPs, covering 90.92% of soybean genes and sites related to important traits. By application of the array, a total of 817 accessions were genotyped, including three subpopulations of candidate parental lines, parental lines and their progeny from practical breeding. The fixed SNPs were identified in progeny, indicating artificial selection during the breeding process. By identifying functional sites of target traits, novel soybean cyst nematode-resistant progeny and maturity-related novel sources were identified by allele combinations, demonstrating that functional sites provide an efficient method for the rapid screening of desirable traits or gene sources. Notably, we found that the breeding index (BI) was a good indicator for progeny selection. Superior progeny were derived from the combination of distantly related parents, with at least one parent having a higher BI. Furthermore, new combinations based on good performance were proposed for further breeding after excluding redundant and closely related parents. Genomic best linear unbiased prediction (GBLUP) analysis was the best analysis method and achieved the highest accuracy in predicting four traits when comparing SNPs in genic regions rather than whole genomic or intergenic SNPs. The prediction accuracy was improved by 32.1% by using progeny to expand the training population. Collectively, a versatile assay demonstrated that the functional ZDX1 array provided efficient information for the design and optimization of a breeding pipeline for accelerated soybean breeding.
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Affiliation(s)
- Rujian Sun
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Bincheng Sun
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Yu Tian
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Shanshan Su
- Beijing Compass Biotechnology Co, Ltd, Beijing, 102200, People's Republic of China
| | - Yong Zhang
- Keshan Branch of Heilongjiang Academy of Agricultural Sciences, Qiqihar, 161600, People's Republic of China
| | - Wanhai Zhang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Jingshun Wang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Ping Yu
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Bingfu Guo
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huihui Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yanfei Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huawei Gao
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yongzhe Gu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Lili Yu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Yansong Ma
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Erhu Su
- Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, 010000, People's Republic of China
| | - Qiang Li
- Inner Mongolia Academy of Agricultural and Animal Husbandry Sciences, Hohhot, 010000, People's Republic of China
| | - Xingguo Hu
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Qi Zhang
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Rongqi Guo
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Shen Chai
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Lei Feng
- Hulunbuir Institute of Agriculture and Animal Husbandry, Hulunbuir, 021000, People's Republic of China
| | - Jun Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Huilong Hong
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiangyuan Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Xindong Yao
- Department of Crop Sciences, University of Natural Resources and Life Sciences Vienna (BOKU), 3430, Tulln, Austria
| | - Jing Wen
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China
| | - Jiqiang Liu
- Beijing Compass Biotechnology Co, Ltd, Beijing, 102200, People's Republic of China
| | - Yinghui Li
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
| | - Lijuan Qiu
- College of Agriculture, Northeast Agricultural University, Harbin, 150030, People's Republic of China.
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South Street, Haidian District, Beijing, 100081, People's Republic of China.
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Sun S, Wang Z, Xiang S, Lv M, Zhou K, Li J, Liang P, Li M, Li R, Ling Y, He G, Zhao F. Identification, pyramid, and candidate gene of QTL for yield-related traits based on rice CSSLs in indica Xihui18 background. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:19. [PMID: 37309460 PMCID: PMC10248596 DOI: 10.1007/s11032-022-01284-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Abstract
Chromosome segment substitution line (CSSL) is important for functional analysis and design breeding of target genes. Here, a novel rice CSSL-Z431 was identified from indica restorer line Xihui18 as recipient and japonica Huhan3 as donor. Z431 contained six segments from Huhan3, with average substitution length of 2.12 Mb. Compared with Xihui18, Z431 increased panicle number per plant (PN) and displayed short-wide grains. The short-wide grain of Z431 was caused by decreased length and increased width of glume cell. Then, thirteen QTLs were identified in secondary F2 population from Xihui18/Z431. Again, eleven QTLs (qPL3, qPN3, qGPP12, qSPP12, qGL3, qGW5, qRLW2, qRLW3, qRLW5, qGWT3, qGWT5-2) were validated by six single-segment substitution lines (SSSLs, S1-S6) developed in F3. In addition, fifteen QTLs (qPN5, qGL1, qGL2, qGL5, qGW1, qGW5-1, qRLW1, qRLW5-2, qGWT1, qGWT2, qYD1, qYD2, qYD3, qYD5, qYD12) were detected by these SSSLs, while not be identified in the F2 population. Multiple panicles of Z431 were controlled by qPN3 and qPN5. OsIAGLU should be the candidate gene for qPN3. The short-wide grain of Z431 was controlled by qGL3, qGW5, etc. By DNA sequencing and qRT-PCR analysis, two best candidate genes for qGL3 and qGW5 were identified, respectively. In addition, pyramid of different QTLs in D1-D3 and T1-T2 showed independent inheritance or various epistatic effects. So, it is necessary to understand all genetic effects of target QTLs for designing breeding. Furthermore, these secondary substitution lines improved the deficiencies of Xihui18 to some extent, especially increasing yield per plant in S1, S3, S5, D1-D3, T1, and T2. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01284-x.
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Affiliation(s)
- Shuangfei Sun
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Zongbing Wang
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Siqian Xiang
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Meng Lv
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Kai Zhou
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Juan Li
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Peixuan Liang
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Miaomiao Li
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Ruxiang Li
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Yinghua Ling
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Guanghua He
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
| | - Fangming Zhao
- Rice Research Institute, Academy of Agricultural Science,, Southwest University, Chongqing, 400715 People’s Republic of China
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33
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Yang W, Guo T, Luo J, Zhang R, Zhao J, Warburton ML, Xiao Y, Yan J. Target-oriented prioritization: targeted selection strategy by integrating organismal and molecular traits through predictive analytics in breeding. Genome Biol 2022; 23:80. [PMID: 35292095 PMCID: PMC8922918 DOI: 10.1186/s13059-022-02650-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Accepted: 03/08/2022] [Indexed: 11/10/2022] Open
Abstract
Genomic prediction in crop breeding is hindered by modeling on limited phenotypic traits. We propose an integrative multi-trait breeding strategy via machine learning algorithm, target-oriented prioritization (TOP). Using a large hybrid maize population, we demonstrate that the accuracy for identifying a candidate that is phenotypically closest to an ideotype, or target variety, achieves up to 91%. The strength of TOP is enhanced when omics level traits are included. We show that TOP enables selection of inbreds or hybrids that outperform existing commercial varieties. It improves multiple traits and accurately identifies improved candidates for new varieties, which will greatly influence breeding.
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Affiliation(s)
- Wenyu Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,College of Science, Huazhong Agricultural University, Wuhan, 430070, China
| | | | - Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ruyang Zhang
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agricultural & Forestry Sciences, Beijing, 100097, China
| | - Jiuran Zhao
- Beijing Key Laboratory of Maize DNA Fingerprinting and Molecular Breeding, Beijing Academy of Agricultural & Forestry Sciences, Beijing, 100097, China
| | - Marilyn L Warburton
- United States Department of Agriculture-Agricultural Research Service, Corn Host Plant Resistance Research Unit, Box 9555, Mississippi State, MS, 39762, USA
| | - Yingjie Xiao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China. .,Hubei Hongshan Laboratory, Wuhan, 430070, China.
| | - 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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34
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Li YF, Li YH, Su SS, Reif JC, Qi ZM, Wang XB, Wang X, Tian Y, Li DL, Sun RJ, Liu ZX, Xu ZJ, Fu GH, Ji YL, Chen QS, Liu JQ, Qiu LJ. SoySNP618K array: A high-resolution single nucleotide polymorphism platform as a valuable genomic resource for soybean genetics and breeding. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2022; 64:632-648. [PMID: 34914170 DOI: 10.1111/jipb.13202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 12/05/2021] [Indexed: 05/13/2023]
Abstract
Innovations in genomics have enabled the development of low-cost, high-resolution, single nucleotide polymorphism (SNP) genotyping arrays that accelerate breeding progress and support basic research in crop science. Here, we developed and validated the SoySNP618K array (618,888 SNPs) for the important crop soybean. The SNPs were selected from whole-genome resequencing data containing 2,214 diverse soybean accessions; 29.34% of the SNPs mapped to genic regions representing 86.85% of the 56,044 annotated high-confidence genes. Identity-by-state analyses of 318 soybeans revealed 17 redundant accessions, highlighting the potential of the SoySNP618K array in supporting gene bank management. The patterns of population stratification and genomic regions enriched through domestication were highly consistent with previous findings based on resequencing data, suggesting that the ascertainment bias in the SoySNP618K array was largely compensated for. Genome-wide association mapping in combination with reported quantitative trait loci enabled fine-mapping of genes known to influence flowering time, E2 and GmPRR3b, and of a new candidate gene, GmVIP5. Moreover, genomic prediction of flowering and maturity time in 502 recombinant inbred lines was highly accurate (>0.65). Thus, the SoySNP618K array is a valuable genomic tool that can be used to address many questions in applied breeding, germplasm management, and basic crop research.
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Affiliation(s)
- Yan-Fei Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Shan-Shan Su
- Beijing Compass Biotechnology Co. Ltd, Beijing, 102206, China
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, 06466, Germany
| | - Zhao-Ming Qi
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Xiao-Bo Wang
- School of Agronomy, Anhui Agricultural University, Hefei, 230036, China
| | - Xing Wang
- Xuzhou Institute of Agricultural Sciences of Xu-huai Region of Jiangsu, Xuzhou, 221131, China
| | - Yu Tian
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - De-Lin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Department of Plant Genetics and Breeding, China Agricultural University, Beijing, 100193, China
| | - Ru-Jian Sun
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
- Hulun Buir Institution of Agricultural Sciences, Zhalantun, Inner Mongolia, 021000, China
| | - Zhang-Xiong Liu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Ze-Jun Xu
- Xuzhou Institute of Agricultural Sciences of Xu-huai Region of Jiangsu, Xuzhou, 221131, China
| | - Guang-Hui Fu
- Suzhou Academy of Agricultural Sciences, Suzhou, 234000, China
| | - Ya-Liang Ji
- Beijing Compass Biotechnology Co. Ltd, Beijing, 102206, China
| | - Qing-Shan Chen
- Key Laboratory of Soybean Biology in Chinese Ministry of Education (Key Laboratory of Soybean Biology and Breeding/Genetics of Chinese Agriculture Ministry), Northeast Agricultural University, Harbin, 150030, China
| | - Ji-Qiang Liu
- Beijing Compass Biotechnology Co. Ltd, Beijing, 102206, China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Crop Gene Resource and Germplasm Enhancement (MOA)/Key Laboratory of Soybean Biology (Beijing) (MOA), Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
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Sandhu KS, Merrick LF, Sankaran S, Zhang Z, Carter AH. Prospectus of Genomic Selection and Phenomics in Cereal, Legume and Oilseed Breeding Programs. Front Genet 2022. [PMCID: PMC8814369 DOI: 10.3389/fgene.2021.829131] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The last decade witnessed an unprecedented increase in the adoption of genomic selection (GS) and phenomics tools in plant breeding programs, especially in major cereal crops. GS has demonstrated the potential for selecting superior genotypes with high precision and accelerating the breeding cycle. Phenomics is a rapidly advancing domain to alleviate phenotyping bottlenecks and explores new large-scale phenotyping and data acquisition methods. In this review, we discuss the lesson learned from GS and phenomics in six self-pollinated crops, primarily focusing on rice, wheat, soybean, common bean, chickpea, and groundnut, and their implementation schemes are discussed after assessing their impact in the breeding programs. Here, the status of the adoption of genomics and phenomics is provided for those crops, with a complete GS overview. GS’s progress until 2020 is discussed in detail, and relevant information and links to the source codes are provided for implementing this technology into plant breeding programs, with most of the examples from wheat breeding programs. Detailed information about various phenotyping tools is provided to strengthen the field of phenomics for a plant breeder in the coming years. Finally, we highlight the benefits of merging genomic selection, phenomics, and machine and deep learning that have resulted in extraordinary results during recent years in wheat, rice, and soybean. Hence, there is a potential for adopting these technologies into crops like the common bean, chickpea, and groundnut. The adoption of phenomics and GS into different breeding programs will accelerate genetic gain that would create an impact on food security, realizing the need to feed an ever-growing population.
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Affiliation(s)
- Karansher S. Sandhu
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
- *Correspondence: Karansher S. Sandhu,
| | - Lance F. Merrick
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Sindhuja Sankaran
- Department of Biological System Engineering, Washington State University, Pullman, WA, United States
| | - Zhiwu Zhang
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
| | - Arron H. Carter
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA, United States
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Gaballah MM, Attia KA, Ghoneim AM, Khan N, EL-Ezz AF, Yang B, Xiao L, Ibrahim EI, Al-Doss AA. Assessment of Genetic Parameters and Gene Action Associated with Heterosis for Enhancing Yield Characters in Novel Hybrid Rice Parental Lines. PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11030266. [PMID: 35161248 PMCID: PMC8838428 DOI: 10.3390/plants11030266] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/21/2021] [Accepted: 12/21/2021] [Indexed: 05/03/2023]
Abstract
The technology of hybrid rice utilizing heterosis is an essential requirement for achieving food security. The current study was aimed at assessing the genetic parameters and the gene actions of 15 yield-component traits associated with heterosis, in 9 new parental lines of hybrid rice and their generated hybrids. Five cytoplasmic male sterile (CMS) lines were crossed with four restorer (R) lines using twenty generated line × tester designation hybrid combinations. The results revealed that all the traits were controlled by additive and non-additive gene actions. However, the additive variance was the main component of the total genotypic variance. Assessment of the general combining ability (GCA) detected the best combiners among the genotypes. The hybrid combinations that expressed the highest-positive specific combining ability (SCA) for grain-yield were detected. The correlation between the GCA and SCA was evaluated. The hybrid crosses with high-positive heterosis, due to having a better parent for grain yield, were detected. The principal component analysis (PCA) recorded the first four principal axis displayed Eigenvalues >1 and existing variation cumulative of 83.92% in the genotypes for yield component characteristics. Three-dimensional plots corresponding to the studied traits illustrated that the genotypes Guang8A × Giza181, Quan-9311A × Giza179, II-32A × Giza181, and II-32A × Giza179 are classified as possessing superior grain yield.
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Affiliation(s)
- Mahmoud M. Gaballah
- Rice Research and Training Center, Field Crops Research Institute, Agricultural Research Center, Kafr El-Sheikh 33717, Egypt; (M.M.G.); (A.M.G.); (A.F.E.-E.)
| | - Kotb A. Attia
- Department of Biochemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
- Correspondence:
| | - Adel M. Ghoneim
- Rice Research and Training Center, Field Crops Research Institute, Agricultural Research Center, Kafr El-Sheikh 33717, Egypt; (M.M.G.); (A.M.G.); (A.F.E.-E.)
| | - Naeem Khan
- Department of Agronomy, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA;
| | - Aziz F. EL-Ezz
- Rice Research and Training Center, Field Crops Research Institute, Agricultural Research Center, Kafr El-Sheikh 33717, Egypt; (M.M.G.); (A.M.G.); (A.F.E.-E.)
| | - Baochang Yang
- Hunan Provincial Key Laboratory of Phytohormones and Growth Development, Hunan Agricultural University, Changsha 410128, China; (B.Y.); (L.X.)
| | - Langtao Xiao
- Hunan Provincial Key Laboratory of Phytohormones and Growth Development, Hunan Agricultural University, Changsha 410128, China; (B.Y.); (L.X.)
| | - Eid I. Ibrahim
- Biotechnology Lab., Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (E.I.I.); (A.A.A.-D.)
| | - Abdullah A. Al-Doss
- Biotechnology Lab., Plant Production Department, College of Food and Agriculture Sciences, King Saud University, Riyadh 11451, Saudi Arabia; (E.I.I.); (A.A.A.-D.)
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Bartholomé J, Prakash PT, Cobb JN. Genomic Prediction: Progress and Perspectives for Rice Improvement. Methods Mol Biol 2022; 2467:569-617. [PMID: 35451791 DOI: 10.1007/978-1-0716-2205-6_21] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage "To someone with a hammer, everything looks like a nail" describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France.
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France.
- Rice Breeding Platform, International Rice Research Institute, Manila, Philippines.
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Saradadevi GP, Das D, Mangrauthia SK, Mohapatra S, Chikkaputtaiah C, Roorkiwal M, Solanki M, Sundaram RM, Chirravuri NN, Sakhare AS, Kota S, Varshney RK, Mohannath G. Genetic, Epigenetic, Genomic and Microbial Approaches to Enhance Salt Tolerance of Plants: A Comprehensive Review. BIOLOGY 2021; 10:biology10121255. [PMID: 34943170 PMCID: PMC8698797 DOI: 10.3390/biology10121255] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/23/2021] [Accepted: 11/24/2021] [Indexed: 12/17/2022]
Abstract
Simple Summary Globally, soil salinity, which refers to salt-affected soils, is increasing due to various environmental factors and human activities. Soil salinity poses one of the most serious challenges in the field of agriculture as it significantly reduces the growth and yield of crop plants, both quantitatively and qualitatively. Over the last few decades, several studies have been carried out to understand plant biology in response to soil salinity stress with a major emphasis on genetic and other hereditary components. Based on the outcome of these studies, several approaches are being followed to enhance plants’ ability to tolerate salt stress while still maintaining reasonable levels of crop yields. In this manuscript, we comprehensively list and discuss various biological approaches being followed and, based on the recent advances in the field of molecular biology, we propose some new approaches to improve salinity tolerance of crop plants. The global scientific community can make use of this information for the betterment of crop plants. This review also highlights the importance of maintaining global soil health to prevent several crop plant losses. Abstract Globally, soil salinity has been on the rise owing to various factors that are both human and environmental. The abiotic stress caused by soil salinity has become one of the most damaging abiotic stresses faced by crop plants, resulting in significant yield losses. Salt stress induces physiological and morphological modifications in plants as a result of significant changes in gene expression patterns and signal transduction cascades. In this comprehensive review, with a major focus on recent advances in the field of plant molecular biology, we discuss several approaches to enhance salinity tolerance in plants comprising various classical and advanced genetic and genetic engineering approaches, genomics and genome editing technologies, and plant growth-promoting rhizobacteria (PGPR)-based approaches. Furthermore, based on recent advances in the field of epigenetics, we propose novel approaches to create and exploit heritable genome-wide epigenetic variation in crop plants to enhance salinity tolerance. Specifically, we describe the concepts and the underlying principles of epigenetic recombinant inbred lines (epiRILs) and other epigenetic variants and methods to generate them. The proposed epigenetic approaches also have the potential to create additional genetic variation by modulating meiotic crossover frequency.
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Affiliation(s)
- Gargi Prasad Saradadevi
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Debajit Das
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Satendra K. Mangrauthia
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Sridev Mohapatra
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
| | - Channakeshavaiah Chikkaputtaiah
- Biological Sciences and Technology Division, CSIR-North East Institute of Science and Technology (CSIR-NEIST), Jorhat 785006, India; (D.D.); (C.C.)
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
| | - Manish Solanki
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Raman Meenakshi Sundaram
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Neeraja N. Chirravuri
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Akshay S. Sakhare
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
| | - Suneetha Kota
- ICAR-Indian Institute of Rice Research, Hyderabad 500030, India; (S.K.M.); (M.S.); (R.M.S.); (N.N.C.); (A.S.S.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad 502324, India;
- The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, WA 6150, Australia
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
| | - Gireesha Mohannath
- Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, Hyderabad 500078, India; (G.P.S.); (S.M.)
- Correspondence: (S.K.); (R.K.V.); (G.M.); Tel.: +91-40-245-91268 (S.K.); +91-84-556-83305 (R.K.V.); +91-40-66303697 (G.M.)
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Wang Q, Yan T, Long Z, Huang LY, Zhu Y, Xu Y, Chen X, Pak H, Li J, Wu D, Xu Y, Hua S, Jiang L. Prediction of heterosis in the recent rapeseed (Brassica napus) polyploid by pairing parental nucleotide sequences. PLoS Genet 2021; 17:e1009879. [PMID: 34735437 PMCID: PMC8608326 DOI: 10.1371/journal.pgen.1009879] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 11/22/2021] [Accepted: 10/15/2021] [Indexed: 11/19/2022] Open
Abstract
The utilization of heterosis is a successful strategy in increasing yield for many crops. However, it consumes tremendous manpower to test the combining ability of the parents in fields. Here, we applied the genomic-selection (GS) strategy and developed models that significantly increase the predictability of heterosis by introducing the concept of a regional parental genetic-similarity index (PGSI) and reducing dimension in the calculation matrix in a machine-learning approach. Overall, PGSI negatively affected grain yield and several other traits but positively influenced the thousand-seed weight of the hybrids. It was found that the C subgenome of rapeseed had a greater impact on heterosis than the A subgenome. We drew maps with overviews of quantitative-trait loci that were responsible for the heterosis (h-QTLs) of various agronomic traits. Identifications and annotations of genes underlying high impacting h-QTLs were provided. Using models that we elaborated, combining abilities between an Ogu-CMS-pool member and a potential restorer can be simulated in silico, sidestepping laborious work, such as testing crosses in fields. The achievements here provide a case of heterosis prediction in polyploid genomes with relatively large genome sizes. Oilseed rape (Brassica napus) is of significant economic interest worldwide, providing high-quality oil with excellent health-promoting properties. It represents an excellent model of a successful recent polyploid that rapidly became an important crop worldwide. The utilization of hybridization, leading to hybrid vigor, or heterosis, is a successful strategy in increasing yield and vigor for many field crops including rapeseed (Brassica napus). However, the procedure of using classical breeding methods remains slow and laborious, illustrating the need for predictive and innovative methods. Here, we have achieved a significant breakthrough by using genome selection and significantly advanced models to predict the heterosis by pairing genome-wide nucleotides of parents. We provided maps with overviews of quantitative trait loci that were responsible for the heterosis of various agronomic traits. The research used deep resequencing (>30x) data of the entire polyploidy rapeseed genome, providing a successful case for the prediction of heterosis in polyploid genomes with relatively large genome sizes. Moreover, we provided the genetic information (SNPs) of 1007 core accessions of this species in the public domain for testing combinations with high heterosis using our predicting model for rapeseed breeders all over the world.
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Affiliation(s)
- Qian Wang
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Tao Yan
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Zhengbiao Long
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Luna Yue Huang
- Department of Agricultural and Resource Economics, University of California, Berkeley, California, United States of America
| | - Yang Zhu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Ying Xu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Xiaoyang Chen
- Institute of Crop Science, Jinhua Academy of Agricultural Sciences, Jinhua, China
| | - Haksong Pak
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Jiqiang Li
- Institute of Crop Science, Zhangye Academy of Agricultural Sciences, Zhangye, China
| | - Dezhi Wu
- Institute of Crop Science, Zhejiang University, Hangzhou, China
| | - Yang Xu
- Agricultural College, Yangzhou University, Yangzhou, China
- * E-mail: (YX); (SH); (LJ)
| | - Shuijin Hua
- Institute of Crop and Nuclear Agricultural Sciences, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
- * E-mail: (YX); (SH); (LJ)
| | - Lixi Jiang
- Institute of Crop Science, Zhejiang University, Hangzhou, China
- * E-mail: (YX); (SH); (LJ)
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Feng L, Ma A, Song B, Yu S, Qi X. Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice. G3 (BETHESDA, MD.) 2021; 11:6369515. [PMID: 34515770 PMCID: PMC8527483 DOI: 10.1093/g3journal/jkab318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Accepted: 08/26/2021] [Indexed: 11/13/2022]
Abstract
Dissecting the genetic mechanisms underlying agronomic traits is of great importance for crop breeding. Agronomic traits are usually controlled by multiple quantitative trait loci (QTLs) and genetic interactions, and mapping the underlying causal genes is still labor-intensive and time-consuming. Here, we present a genetic tool for directly targeting the specific causal genes by using a single-gene resolution linkage map that was constructed from 3756 F2 rice plants via targeted sequencing technology and Tukey-Kramer multiple comparisons test. Three large- and moderate-effect QTLs, qHD6-2, qGL3-1, and qGW5-2, were successfully mapped to their specific causal genes, Hd1, GS3, and GW5, respectively. A complex genetic interaction network containing 30 QTL-QTL interactions was constructed, revealing that the alternative allele of hub QTL, qHD6-2, can hide or release the genetic contributions of the alleles at interacting loci. Moreover, arranging genetic interactions in the models lead to more accurate phenotypic predictions. These results provide a community resource and new feasible strategy for deciphering the genetic mechanisms of complex agronomic traits and accelerating crop breeding.
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Affiliation(s)
- Laibao Feng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,University of Chinese Academy of Sciences, Beijing 100049, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Aimin Ma
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Bo Song
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
| | - Sibin Yu
- National Key Laboratory of Crop Genetic Improvement, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, Hubei, China
| | - Xiaoquan Qi
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.,Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100049, China
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Cappetta E, Andolfo G, Guadagno A, Di Matteo A, Barone A, Frusciante L, Ercolano MR. Tomato genomic prediction for good performance under high-temperature and identification of loci involved in thermotolerance response. HORTICULTURE RESEARCH 2021; 8:212. [PMID: 34593775 PMCID: PMC8484564 DOI: 10.1038/s41438-021-00647-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 07/05/2021] [Accepted: 07/14/2021] [Indexed: 06/13/2023]
Abstract
Many studies showed that few degrees above tomato optimum growth temperature threshold can lead to serious loss in production. Therefore, the development of innovative strategies to obtain tomato cultivars with improved yield under high temperature conditions is a main goal both for basic genetic studies and breeding activities. In this paper, a F4 segregating population was phenotypically evaluated for quantitative and qualitative traits under heat stress conditions. Moreover, a genotyping by sequencing (GBS) approach has been employed for building up genomic selection (GS) models both for yield and soluble solid content (SCC). Several parameters, including training population size, composition and marker quality were tested to predict genotype performance under heat stress conditions. A good prediction accuracy for the two analyzed traits (0.729 for yield production and 0.715 for SCC) was obtained. The predicted models improved the genetic gain of selection in the next breeding cycles, suggesting that GS approach is a promising strategy to accelerate breeding for heat tolerance in tomato. Finally, the annotation of SNPs located in gene body regions combined with QTL analysis allowed the identification of five candidates putatively involved in high temperatures response, and the building up of a GS model based on calibrated panel of SNP markers.
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Affiliation(s)
- Elisa Cappetta
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy
- Institute of Bioscience and BioResources, National Research Council, Via Università 100, 80055, Portici, Italy
| | - Giuseppe Andolfo
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy
| | - Anna Guadagno
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy
| | - Antonio Di Matteo
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy
| | - Amalia Barone
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy
| | - Luigi Frusciante
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy
| | - Maria Raffaella Ercolano
- Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055, Portici, Naples, Italy.
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Exploitation of Drought Tolerance-Related Genes for Crop Improvement. Int J Mol Sci 2021; 22:ijms221910265. [PMID: 34638606 PMCID: PMC8508643 DOI: 10.3390/ijms221910265] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/15/2021] [Accepted: 09/15/2021] [Indexed: 12/03/2022] Open
Abstract
Drought has become a major threat to food security, because it affects crop growth and development. Drought tolerance is an important quantitative trait, which is regulated by hundreds of genes in crop plants. In recent decades, scientists have made considerable progress to uncover the genetic and molecular mechanisms of drought tolerance, especially in model plants. This review summarizes the evaluation criteria for drought tolerance, methods for gene mining, characterization of genes related to drought tolerance, and explores the approaches to enhance crop drought tolerance. Collectively, this review illustrates the application prospect of these genes in improving the drought tolerance breeding of crop plants.
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Liu C, Liu X, Han Y, Wang X, Ding Y, Meng H, Cheng Z. Genomic Prediction and the Practical Breeding of 12 Quantitative-Inherited Traits in Cucumber ( Cucumis sativus L.). FRONTIERS IN PLANT SCIENCE 2021; 12:729328. [PMID: 34504510 PMCID: PMC8421847 DOI: 10.3389/fpls.2021.729328] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from -0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.
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Affiliation(s)
- Ce Liu
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Xiaoxiao Liu
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Yike Han
- State Key Laboratory of Vegetable Germplasm Innovation, Tianjin Key Laboratory of Vegetable Breeding Enterprise, Cucumber Research Institute, Tianjin Academy of Agricultural Sciences, Tianjin, China
| | - Xi'ao Wang
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Yuanyuan Ding
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Huanwen Meng
- College of Horticulture, Northwest A&F University, Yangling, China
| | - Zhihui Cheng
- College of Horticulture, Northwest A&F University, Yangling, China
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Li D, Xu Z, Gu R, Wang P, Xu J, Du D, Fu J, Wang J, Zhang H, Wang G. Genomic Prediction across Structured Hybrid Populations and Environments in Maize. PLANTS 2021; 10:plants10061174. [PMID: 34207722 PMCID: PMC8227059 DOI: 10.3390/plants10061174] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022]
Abstract
Genomic prediction (GP) across different populations and environments should be enhanced to increase the efficiency of crop breeding. In this study, four populations were constructed and genotyped with DNA chips containing 55,000 SNPs. These populations were testcrossed to a common tester, generating four hybrid populations. Yields of the four hybrid populations were evaluated in three environments. We demonstrated by using real data that the prediction accuracies of GP across structured hybrid populations were lower than those of within-population GP. Including relatives of the validation population in the training population could increase the prediction accuracies of GP across structured hybrid populations drastically. G × E models (including main and genotype-by-environment effect) had better performance than single environment (within environment) and across environment (including only main effect) GP models in the structured hybrid population, especially in the environment where yields had higher heritability. GP by implementing G × E models in two cross-validation schemes indicated that, to increase the prediction accuracy of a new hybrid line, it would be better to field-test the hybrid line in at least one environment. Our results would be helpful for designing training population and planning field testing in hybrid breeding.
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Affiliation(s)
- Dongdong Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Zhenxiang Xu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Riliang Gu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Pingxi Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Jialiang Xu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Dengxiang Du
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China;
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
| | - Jianhua Wang
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
- Correspondence: (H.Z.); (G.W.)
| | - Guoying Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.X.); (J.F.)
- Correspondence: (H.Z.); (G.W.)
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Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. TRENDS IN PLANT SCIENCE 2021; 26:631-649. [PMID: 33893045 DOI: 10.1016/j.tplants.2021.03.010] [Citation(s) in RCA: 146] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 05/18/2023]
Abstract
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.
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Affiliation(s)
- Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crops Plant Research (IPK), Gatersleben, Germany
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Mark E Sorrells
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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Zhao Y, Thorwarth P, Jiang Y, Philipp N, Schulthess AW, Gils M, Boeven PHG, Longin CFH, Schacht J, Ebmeyer E, Korzun V, Mirdita V, Dörnte J, Avenhaus U, Horbach R, Cöster H, Holzapfel J, Ramgraber L, Kühnle S, Varenne P, Starke A, Schürmann F, Beier S, Scholz U, Liu F, Schmidt RH, Reif JC. Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat. SCIENCE ADVANCES 2021; 7:7/24/eabf9106. [PMID: 34117061 PMCID: PMC8195483 DOI: 10.1126/sciadv.abf9106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 04/28/2021] [Indexed: 05/07/2023]
Abstract
The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing ~125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.
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Affiliation(s)
- Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Patrick Thorwarth
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70593 Stuttgart, Germany
| | - Yong Jiang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Norman Philipp
- Syngenta Seeds GmbH, Kroppenstedterstr. 4, 39398 Hadmersleben, Germany
| | - Albert W Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Mario Gils
- Nordsaat Saatzucht GmbH, , Böhnshauserstr. 1, 38895 Langenstein, Germany
| | | | - C Friedrich H Longin
- State Plant Breeding Institute, University of Hohenheim, Fruwirthstr. 21, 70593 Stuttgart, Germany
| | | | - Erhard Ebmeyer
- KWS LOCHOW GmbH, Ferdinand-von-Lochow-Str. 5, 29303 Bergen, Germany
| | - Viktor Korzun
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37574 Einbeck, Germany
- Federal State Budgetary Institution of Science Federal Research Center, "Kazan Scientific Center of Russian Academy of Sciences," ul. Lobachevskogo, 2/31, Kazan, 420111 Tatarstan, Russian Federation
| | - Vilson Mirdita
- BASF Agricultural Solutions Seed GmbH, OT Gatersleben, Am Schwabeplan 8, 06466 Seeland, Germany
| | - Jost Dörnte
- Deutsche Saatveredelung AG, Leutewitz 26, 01665 Käbschütztal, Germany
| | - Ulrike Avenhaus
- W. von Borries-Eckendorf GmbH & Co. KG, Hovedisserstr. 92, 33818 Leopoldshöhe, Germany
| | - Ralf Horbach
- Saatzucht Bauer GmbH & Co. KG, Hofmarkstr.1, 93083 Niederträubling, Germany
| | | | - Josef Holzapfel
- Secobra Saatzucht GmbH, Feldkirchen 3, 85368 Moosburg, Germany
| | - Ludwig Ramgraber
- Saatzucht Josef Breun GmbH & Co. KG, Amselweg 1, 91074 Herzogenaurach, Germany
| | - Simon Kühnle
- Pflanzenzucht Oberlimpurg, Oberlimpurg 2, 74523 Schwäbisch Hall, Germany
| | - Pierrick Varenne
- Limagrain Europe, Ferme de l'Etang BP3, 77390 Verneuil l'Etang, France
| | - Anne Starke
- Limagrain GmbH, Salderstr. 4, 31226 Peine-Rosenthal, Germany
| | | | - Sebastian Beier
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Fang Liu
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Renate H Schmidt
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany.
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Zhang F, Wang C, Li M, Cui Y, Shi Y, Wu Z, Hu Z, Wang W, Xu J, Li Z. The landscape of gene-CDS-haplotype diversity in rice: Properties, population organization, footprints of domestication and breeding, and implications for genetic improvement. MOLECULAR PLANT 2021; 14:787-804. [PMID: 33578043 DOI: 10.1016/j.molp.2021.02.003] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 01/14/2021] [Accepted: 02/04/2021] [Indexed: 05/27/2023]
Abstract
Polymorphisms within gene coding regions represent the most important part of the overall genetic diversity of rice. We characterized the gene-coding sequence-haplotype (gcHap) diversity of 45 963 rice genes in 3010 rice accessions. With an average of 226 ± 390 gcHaps per gene in rice populations, rice genes could be classified into three main categories: 12 865 conserved genes, 10 254 subspecific differentiating genes, and 22 844 remaining genes. We found that 39 218 rice genes carry >255 179 major gcHaps of potential functional importance. Most (87.5%) of the detected gcHaps were specific to subspecies or populations. The inferred proto-ancestors of local landrace populations reconstructed from conserved predominant (ancient) gcHaps correlated strongly with wild rice accessions from the same geographic regions, supporting a multiorigin (domestication) model of Oryza sativa. Past breeding efforts generally increased the gcHap diversity of modern varieties and caused significant frequency shifts in predominant gcHaps of 14 266 genes due to independent selection in the two subspecies. Low frequencies of "favorable" gcHaps at most known genes related to rice yield in modern varieties suggest huge potential for rice improvement by mining and pyramiding of favorable gcHaps. The gcHap data were demonstrated to have greater power than SNPs for the detection of causal genes that affect complex traits. The rice gcHap diversity dataset generated in this study would facilitate rice basic research and improvement in the future.
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Affiliation(s)
- Fan Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; College of Agronomy, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Chunchao Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Min Li
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Yanru Cui
- College of Agronomy, Hebei Agricultural University, Baoding, Hebei, 071001, China
| | - Yingyao Shi
- College of Agronomy, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Zhichao Wu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, 518120, China
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, CA, 94720, USA
| | - Wensheng Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; College of Agronomy, Anhui Agricultural University, Hefei, Anhui, 230036, China
| | - Jianlong Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, 518120, China.
| | - Zhikang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China; College of Agronomy, Anhui Agricultural University, Hefei, Anhui, 230036, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong, 518120, China.
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Ganie SA, Wani SH, Henry R, Hensel G. Improving rice salt tolerance by precision breeding in a new era. CURRENT OPINION IN PLANT BIOLOGY 2021; 60:101996. [PMID: 33444976 DOI: 10.1016/j.pbi.2020.101996] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/16/2020] [Accepted: 12/19/2020] [Indexed: 05/03/2023]
Abstract
Rice is a premier staple food that constitutes the bulk of the daily diet of the majority of people in Asia. Agricultural productivity must be boosted to support this huge demand for rice. However, production is jeopardized by soil salinity. Advances in whole-genome sequencing, marker-assisted breeding strategies, and targeted mutagenesis have substantially improved the toolbox of today's breeders. Given that salinity has a major influence on rice at both the seedling and reproductive stages, understanding and manipulating this trait will have an enormous impact on sustainable production. This article summarizes recent developments in the understanding of the mechanisms of salt tolerance and how state-of-the-art tools such as RNA guided CRISPR endonuclease technology including targeted mutagenesis or base and prime editing can help in gene discovery and functional analysis as well as in transferring favorable alleles into elite breeding material to speed the breeding of salt-tolerant rice cultivars.
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Affiliation(s)
- Showkat Ahmad Ganie
- Department of Biotechnology, Visva-Bharati, Santiniketan 731235, West Bengal, India.
| | - Shabir Hussain Wani
- Mountain Research Centre for Field Crops, Khudwani - 192101, Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir, J&K, India
| | - Robert Henry
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, St Lucia, QLD 4072, Australia
| | - Goetz Hensel
- Centre for Plant Genome Engineering, Institute of Plant Biochemistry, Heinrich-Heine-University, Universitätsstraße 1, 40225 Düsseldorf, Germany; Department of Physiology and Cell Biology, Leibniz Institute of Plant Genetics and Crop Plant Research, Corrensstraße 3, OT Gatersleben, 06466 Seeland, Germany; Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czech Republic.
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49
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Abbas A, Yu P, Sun L, Yang Z, Chen D, Cheng S, Cao L. Exploiting Genic Male Sterility in Rice: From Molecular Dissection to Breeding Applications. FRONTIERS IN PLANT SCIENCE 2021; 12:629314. [PMID: 33763090 PMCID: PMC7982899 DOI: 10.3389/fpls.2021.629314] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
Rice (Oryza sativa L.) occupies a very salient and indispensable status among cereal crops, as its vast production is used to feed nearly half of the world's population. Male sterile plants are the fundamental breeding materials needed for specific propagation in order to meet the elevated current food demands. The development of the rice varieties with desired traits has become the ultimate need of the time. Genic male sterility is a predominant system that is vastly deployed and exploited for crop improvement. Hence, the identification of new genetic elements and the cognizance of the underlying regulatory networks affecting male sterility in rice are crucial to harness heterosis and ensure global food security. Over the years, a variety of genomics studies have uncovered numerous mechanisms regulating male sterility in rice, which provided a deeper and wider understanding on the complex molecular basis of anther and pollen development. The recent advances in genomics and the emergence of multiple biotechnological methods have revolutionized the field of rice breeding. In this review, we have briefly documented the recent evolution, exploration, and exploitation of genic male sterility to the improvement of rice crop production. Furthermore, this review describes future perspectives with focus on state-of-the-art developments in the engineering of male sterility to overcome issues associated with male sterility-mediated rice breeding to address the current challenges. Finally, we provide our perspectives on diversified studies regarding the identification and characterization of genic male sterility genes, the development of new biotechnology-based male sterility systems, and their integrated applications for hybrid rice breeding.
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Affiliation(s)
- Adil Abbas
- Key Laboratory for Zhejiang Super Rice Research and State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Ping Yu
- Key Laboratory for Zhejiang Super Rice Research and State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Lianping Sun
- Key Laboratory for Zhejiang Super Rice Research and State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Zhengfu Yang
- State Key Laboratory of Subtropical Silviculture, Zhejiang A&F University, Hangzhou, China
| | - Daibo Chen
- Key Laboratory for Zhejiang Super Rice Research and State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Shihua Cheng
- Key Laboratory for Zhejiang Super Rice Research and State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Liyong Cao
- Key Laboratory for Zhejiang Super Rice Research and State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
- Northern Center of China National Rice Research Institute, Shuangyashan, China
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50
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Krishnappa G, Savadi S, Tyagi BS, Singh SK, Mamrutha HM, Kumar S, Mishra CN, Khan H, Gangadhara K, Uday G, Singh G, Singh GP. Integrated genomic selection for rapid improvement of crops. Genomics 2021; 113:1070-1086. [PMID: 33610797 DOI: 10.1016/j.ygeno.2021.02.007] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2020] [Revised: 11/08/2020] [Accepted: 02/15/2021] [Indexed: 11/15/2022]
Abstract
An increase in the rate of crop improvement is essential for achieving sustained food production and other needs of ever-increasing population. Genomic selection (GS) is a potential breeding tool that has been successfully employed in animal breeding and is being incorporated into plant breeding. GS promises accelerated breeding cycles through a rapid selection of superior genotypes. Numerous empirical and simulation studies on GS and realized impacts on improvement in the crop yields are recently being reported. For a holistic understanding of the technology, we briefly discuss the concept of genetic gain, GS methodology, its current status, advantages of GS over other breeding methods, prediction models, and the factors controlling prediction accuracy in GS. Also, integration of speed breeding and other novel technologies viz. high throughput genotyping and phenotyping technologies for enhancing the efficiency and pace of GS, followed by its prospective applications in varietal development programs is reviewed.
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Affiliation(s)
| | | | | | | | | | - Satish Kumar
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | - Hanif Khan
- Indian Institute of Wheat and Barley Research, Karnal, India
| | | | | | - Gyanendra Singh
- Indian Institute of Wheat and Barley Research, Karnal, India
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