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Roscher-Ehrig L, Weber SE, Abbadi A, Malenica M, Abel S, Hemker R, Snowdon RJ, Wittkop B, Stahl A. Phenomic Selection for Hybrid Rapeseed Breeding. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0215. [PMID: 39049840 PMCID: PMC11268845 DOI: 10.34133/plantphenomics.0215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 06/19/2024] [Indexed: 07/27/2024]
Abstract
Phenomic selection is a recent approach suggested as a low-cost, high-throughput alternative to genomic selection. Instead of using genetic markers, it employs spectral data to predict complex traits using equivalent statistical models. Phenomic selection has been shown to outperform genomic selection when using spectral data that was obtained within the same generation as the traits that were predicted. However, for hybrid breeding, the key question is whether spectral data from parental genotypes can be used to effectively predict traits in the hybrid generation. Here, we aimed to evaluate the potential of phenomic selection for hybrid rapeseed breeding. We performed predictions for various traits in a structured population of 410 test hybrids, grown in multiple environments, using near-infrared spectroscopy data obtained from harvested seeds of both the hybrids and their parental lines with different linear and nonlinear models. We found that phenomic selection within the hybrid generation outperformed genomic selection for seed yield and plant height, even when spectral data was collected at single locations, while being less affected by population structure. Furthermore, we demonstrate that phenomic prediction across generations is feasible, and selecting hybrids based on spectral data obtained from parental genotypes is competitive with genomic selection. We conclude that phenomic selection is a promising approach for rapeseed breeding that can be easily implemented without any additional costs or efforts as near-infrared spectroscopy is routinely assessed in rapeseed breeding.
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Affiliation(s)
| | - Sven E. Weber
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | | | | | | | | | - Rod J. Snowdon
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Benjamin Wittkop
- Department of Plant Breeding,
Justus Liebig University, Giessen, Germany
| | - Andreas Stahl
- Julius Kuehn Institute (JKI), Federal Research Centre for Cultivated Plants,
Institute for Resistance Research and Stress Tolerance, Quedlinburg, Germany
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2
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Awan MJA, Farooq MA, Naqvi RZ, Karamat U, Bukhari SAR, Waqas MAB, Mahmood MA, Buzdar MI, Rasheed A, Amin I, Saeed NA, Mansoor S. Deciphering the differential expression patterns of yield-related negative regulators in hexaploid wheat cultivars and hybrids at different growth stages. Mol Biol Rep 2024; 51:537. [PMID: 38642174 DOI: 10.1007/s11033-024-09454-0] [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: 12/12/2023] [Accepted: 03/18/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Hexaploid bread wheat underwent a series of polyploidization events through interspecific hybridizations that conferred adaptive plasticity and resulted in duplication and neofunctionalization of major agronomic genes. The genetic architecture of polyploid wheat not only confers adaptive plasticity but also offers huge genetic diversity. However, the contribution of different gene copies (homeologs) encoded from different subgenomes (A, B, D) at different growth stages remained unexplored. METHODS In this study, hybrid of elite cultivars of wheat were developed via reciprocal crosses (cytoplasm swapping) and phenotypically evaluated. We assessed differential expression profiles of yield-related negative regulators in these cultivars and their F1 hybrids and identified various cis-regulatory signatures by employing bioinformatics tools. Furthermore, the preferential expression patterns of the syntenic triads encoded from A, B, and D subgenomes were assessed to decipher their functional redundancy at six different growth stages. RESULTS Hybrid progenies showed better heterosis such as up to 17% increase in the average number of grains and up to 50% increase in average thousand grains weight as compared to mid-parents. Based on the expression profiling, our results indicated significant dynamic transcriptional expression patterns, portraying the different homeolog-dominance at the same stage in the different cultivars and their hybrids. Albeit belonging to same syntenic triads, a dynamic trend was observed in the regulatory signatures of these genes that might be influencing their expression profiles. CONCLUSION These findings can substantially contribute and provide insights for the selective introduction of better cultivars into traditional and hybrid breeding programs which can be harnessed for the improvement of future wheat.
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Affiliation(s)
- Muhammad Jawad Akbar Awan
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Muhammad Awais Farooq
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding, China
| | - Rubab Zahra Naqvi
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Umer Karamat
- State Key Laboratory of North China Crop Improvement and Regulation, Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding, China
- Guangdong Key Laboratory for New Technology Research of Vegetables, Vegetable Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
| | - Sayyad Ali Raza Bukhari
- School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia
- Department of Biotechnology, University of Sargodha, Sargodha, Pakistan
| | - Muhammad Abu Bakar Waqas
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Muhammad Arslan Mahmood
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Muhammad Ismail Buzdar
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Awais Rasheed
- Department of Plant Sciences, Quaid-i-Azam University, Islamabad, 45320, Pakistan
- Institute of Crop Science, Chinese Academy of Agricultural Sciences (CAAS) & CIMMYT-China office, 12 Zhongguancun South Street, Beijing, 100081, China
| | - Imran Amin
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Nasir A Saeed
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan
| | - Shahid Mansoor
- Agricultural Biotechnology Division, National Institute for Biotechnology and Genetic Engineering (NIBGE), Constituent College of Pakistan Institute of Engineering and Applied Sciences, Jhang Road, Faisalabad, Pakistan.
- International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.
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3
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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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4
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Huang Y, Maurer A, Giehl RFH, Zhao S, Golan G, Thirulogachandar V, Li G, Zhao Y, Trautewig C, Himmelbach A, Börner A, Jayakodi M, Stein N, Mascher M, Pillen K, Schnurbusch T. Dynamic Phytomeric Growth Contributes to Local Adaptation in Barley. Mol Biol Evol 2024; 41:msae011. [PMID: 38243866 PMCID: PMC10837018 DOI: 10.1093/molbev/msae011] [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: 06/30/2023] [Revised: 11/03/2023] [Accepted: 01/10/2024] [Indexed: 01/22/2024] Open
Abstract
Vascular plants have segmented body axes with iterative nodes and internodes. Appropriate node initiation and internode elongation are fundamental to plant fitness and crop yield; however, how these events are spatiotemporally coordinated remains elusive. We show that in barley (Hordeum vulgare L.), selections during domestication have extended the apical meristematic phase to promote node initiation, but constrained subsequent internode elongation. In both vegetative and reproductive phases, internode elongation displays a dynamic proximal-distal gradient, and among subpopulations of domesticated barleys worldwide, node initiation and proximal internode elongation are associated with latitudinal and longitudinal gradients, respectively. Genetic and functional analyses suggest that, in addition to their converging roles in node initiation, flowering-time genes have been repurposed to specify the timing and duration of internode elongation. Our study provides an integrated view of barley node initiation and internode elongation and suggests that plant architecture should be recognized as a collection of dynamic phytomeric units in the context of crop adaptive evolution.
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Affiliation(s)
- Yongyu Huang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Andreas Maurer
- Faculty of Natural Sciences III, Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, 06120 Halle, Germany
| | - Ricardo F H Giehl
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Shuangshuang Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Guy Golan
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | | | - Guoliang Li
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Corinna Trautewig
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Axel Himmelbach
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Andreas Börner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Murukarthick Jayakodi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
- Center for Integrated Breeding Research (CiBreed), Georg-August-University, Göttingen, Germany
| | - Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Klaus Pillen
- Faculty of Natural Sciences III, Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, 06120 Halle, Germany
| | - Thorsten Schnurbusch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, Germany
- Faculty of Natural Sciences III, Martin Luther University Halle-Wittenberg, Institute of Agricultural and Nutritional Sciences, 06120 Halle, Germany
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5
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Krenzer D, Frisch M, Beckmann K, Kox T, Flachenecker C, Abbadi A, Snowdon R, Herzog E. Simulation-based establishment of base pools for a hybrid breeding program in winter rapeseed. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:16. [PMID: 38189816 PMCID: PMC10774156 DOI: 10.1007/s00122-023-04519-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 12/07/2023] [Indexed: 01/09/2024]
Abstract
KEY MESSAGE Simulation planned pre-breeding can increase the efficiency of starting a hybrid breeding program. Starting a hybrid breeding program commonly comprises a grouping of the initial germplasm in two pools and subsequent selection on general combining ability. Investigations on pre-breeding steps before starting the selection on general combining ability are not available. Our goals were (1) to use computer simulations on the basis of DNA markers and testcross data to plan crosses that separate genetically two initial germplasm pools of rapeseed, (2) to carry out the planned crosses, and (3) to verify experimentally the pool separation as well as the increase in testcross performance. We designed a crossing program consisting of four cycles of recombination. In each cycle, the experimentally generated material was used to plan the subsequent crossing cycle with computer simulations. After finishing the crossing program, the initially overlapping pools were clearly separated in principal coordinate plots. Doubled haploid lines derived from the material of crossing cycles 1 and 2 showed an increase in relative testcross performance for yield of about 5% per cycle. We conclude that simulation-designed pre-breeding crossing schemes, that were carried out before the general combining ability-based selection of a newly started hybrid breeding program, can save time and resources, and in addition conserve more of the initial genetic variation than a direct start of a hybrid breeding program with general combining ability-based selection.
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Affiliation(s)
- Daniel Krenzer
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | - Matthias Frisch
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany
| | | | - Tobias Kox
- NPZ Innovation GmbH, Hohenlieth-Hof, Holtsee, Germany
| | | | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth-Hof, Holtsee, Germany
| | - Rod Snowdon
- Department of Plant Breeding, Justus Liebig University, Giessen, Germany
| | - Eva Herzog
- Department of Biometry and Population Genetics, Justus Liebig University, Giessen, Germany.
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6
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Paril J, Reif J, Fournier-Level A, Pourkheirandish M. Heterosis in crop improvement. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024; 117:23-32. [PMID: 37971883 DOI: 10.1111/tpj.16488] [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: 06/15/2023] [Revised: 09/15/2023] [Accepted: 09/20/2023] [Indexed: 11/19/2023]
Abstract
Heterosis, also known as hybrid vigor, is the phenomenon wherein a progeny exhibits superior traits relative to one or both parents. In terms of crop breeding, this usually refers to the yield advantage of F1 hybrids over both inbred parents. The development of high-yielding hybrid cultivars across a wider range of crops is key to meeting future food demands. However, conventional hybrid breeding strategies are proving to be exceptionally challenging to apply commercially in many self-pollinating crops, particularly wheat and barley. Currently in these crops, the relative performance advantage of hybrids over inbred line cultivars does not outweigh the cost of hybrid seed production. Here, we review the genetic basis of heterosis, discuss the challenges in hybrid breeding, and propose a strategy to recruit multiple heterosis-associated genes to develop lines with improved agronomic characteristics. This strategy leverages modern genetic engineering tools to synthesize supergenes by fusing multiple heterotic alleles across multiple heterosis-associated loci. We outline a plan to assess the feasibility of this approach to improve line performance using barley (Hordeum vulgare) as the model self-pollinating crop species, and a few heterosis-associated genes. The proposed method can be applied to all crops for which heterotic gene combinations can be identified.
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Affiliation(s)
- Jefferson Paril
- Faculty of Science, University of Melbourne, Parkville, Melbourne, Victoria, Australia
- AgriBio, Centre for AgriBioscience, Agriculture Victoria Research, Bundoora, Victoria, Australia
| | - Jochen Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
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7
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Sharma N, Raman H, Wheeler D, Kalenahalli Y, Sharma R. Data-driven approaches to improve water-use efficiency and drought resistance in crop plants. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 336:111852. [PMID: 37659733 DOI: 10.1016/j.plantsci.2023.111852] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 08/23/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023]
Abstract
With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.
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Affiliation(s)
- Niharika Sharma
- NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia.
| | - Harsh Raman
- NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia
| | - David Wheeler
- NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia
| | - Yogendra Kalenahalli
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, Telangana 502324, India
| | - Rita Sharma
- Department of Biological Sciences, BITS Pilani, Pilani Campus, Rajasthan 333031, India
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8
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Würschum T, Zhu X, Zhao Y, Jiang Y, Reif JC, Maurer HP. Maximization through optimization? On the relationship between hybrid performance and parental genetic distance. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:186. [PMID: 37572118 PMCID: PMC10423127 DOI: 10.1007/s00122-023-04436-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/31/2023] [Indexed: 08/14/2023]
Abstract
Heterosis is the improved performance of hybrids compared with their parental components and is widely exploited in agriculture. According to quantitative genetic theory, genetic distance between parents at heterotic quantitative trait loci is required for heterosis, but how heterosis varies with genetic distance has remained elusive, despite intensive research on the topic. Experimental studies have often found a positive association between heterosis and genetic distance that, however, varied in strength. Most importantly, it has remained unclear whether heterosis increases continuously with genetic distance or whether there is an optimum genetic distance after which heterosis declines again. Here, we revisit the relationship between heterosis and genetic distance and provide perspectives on how to maximize heterosis and hybrid performance in breeding, as well as the consequences for the design of heterotic groups and the utilization of more exotic material and genetic resources.
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Affiliation(s)
- Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Xintian Zhu
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466, Stadt Seeland, Germany
| | - Yong Jiang
- 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
| | - Hans Peter Maurer
- State Plant Breeding Institute, University of Hohenheim, 70599, Stuttgart, Germany
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9
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Melchinger AE, Fernando R, Stricker C, Schön CC, Auinger HJ. Genomic prediction in hybrid breeding: I. Optimizing the training set design. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:176. [PMID: 37532821 PMCID: PMC10397156 DOI: 10.1007/s00122-023-04413-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 06/23/2023] [Indexed: 08/04/2023]
Abstract
Training sets produced by maximizing the number of parent lines, each involved in one cross, had the highest prediction accuracy for H0 hybrids, but lowest for H1 and H2 hybrids. Genomic prediction holds great promise for hybrid breeding but optimum composition of the training set (TS) as determined by the number of parents (nTS) and crosses per parent (c) has received little attention. Our objective was to examine prediction accuracy ([Formula: see text]) of GCA for lines used as parents of the TS (I1 lines) or not (I0 lines), and H0, H1 and H2 hybrids, comprising crosses of type I0 × I0, I1 × I0 and I1 × I1, respectively, as function of nTS and c. In the theory, we developed estimates for [Formula: see text] of GBLUPs for hybrids: (i)[Formula: see text] based on the expected prediction accuracy, and (ii) [Formula: see text] based on [Formula: see text] of GBLUPs of GCA and SCA effects. In the simulation part, hybrid populations were generated using molecular data from two experimental maize data sets. Additive and dominance effects of QTL borrowed from literature were used to simulate six scenarios of traits differing in the proportion (τSCA = 1%, 6%, 22%) of SCA variance in σG2 and heritability (h2 = 0.4, 0.8). Values of [Formula: see text] and [Formula: see text] closely agreed with [Formula: see text] for hybrids. For given size NTS = nTS × c of TS, [Formula: see text] of H0 hybrids and GCA of I0 lines was highest for c = 1. Conversely, for GCA of I1 lines and H1 and H2 hybrids, c = 1 yielded lowest [Formula: see text] with concordant results across all scenarios for both data sets. In view of these opposite trends, the optimum choice of c for maximizing selection response across all types of hybrids depends on the size and resources of the breeding program.
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Affiliation(s)
- Albrecht E Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Rohan Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Christian Stricker
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Hans-Jürgen Auinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
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10
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Chen SP, Tung CW, Wang PH, Liao CT. A statistical package for evaluation of hybrid performance in plant breeding via genomic selection. Sci Rep 2023; 13:12204. [PMID: 37500737 PMCID: PMC10374541 DOI: 10.1038/s41598-023-39434-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 07/25/2023] [Indexed: 07/29/2023] Open
Abstract
Hybrid breeding employs heterosis, which could potentially improve the yield and quality of a crop. Genomic selection (GS) is a promising approach for the selection of quantitative traits in plant breeding. The main objectives of this study are to (i) propose a GS-based approach to identify potential parental lines and superior hybrid combinations from a breeding population, which is composed of hybrids produced by a half diallel mating design; (ii) develop a software package for users to carry out the proposed approach. An R package, designated EHPGS, was generated to facilitate the employment of the genomic best linear unbiased model considering additive plus dominance marker effects for the hybrid performance evaluation. The R package contains a Bayesian statistical algorithm for calculating genomic estimated breeding value (GEBVs), GEBV-based specific combining ability, general combining ability, mid-parent heterosis, and better-parent heterosis. Three datasets that have been published in literature, including pumpkin (Cucurbita maxima), maize (Zea mays), and wheat (Triticum aestivum L.), were reanalyzed to illustrate the use of EHPGS.
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Affiliation(s)
- Szu-Ping Chen
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Chih-Wei Tung
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Pei-Hsien Wang
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
| | - Chen-Tuo Liao
- Department of Agronomy, National Taiwan University, Taipei, Taiwan.
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11
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Heilmann PG, Frisch M, Abbadi A, Kox T, Herzog E. Stacked ensembles on basis of parentage information can predict hybrid performance with an accuracy comparable to marker-based GBLUP. FRONTIERS IN PLANT SCIENCE 2023; 14:1178902. [PMID: 37546247 PMCID: PMC10401275 DOI: 10.3389/fpls.2023.1178902] [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: 03/03/2023] [Accepted: 06/26/2023] [Indexed: 08/08/2023]
Abstract
Testcross factorials in newly established hybrid breeding programs are often highly unbalanced, incomplete, and characterized by predominance of special combining ability (SCA) over general combining ability (GCA). This results in a low efficiency of GCA-based selection. Machine learning algorithms might improve prediction of hybrid performance in such testcross factorials, as they have been successfully applied to find complex underlying patterns in sparse data. Our objective was to compare the prediction accuracy of machine learning algorithms to that of GCA-based prediction and genomic best linear unbiased prediction (GBLUP) in six unbalanced incomplete factorials from hybrid breeding programs of rapeseed, wheat, and corn. We investigated a range of machine learning algorithms with three different types of predictor variables: (a) information on parentage of hybrids, (b) in addition hybrid performance of crosses of the parental lines with other crossing partners, and (c) genotypic marker data. In two highly incomplete and unbalanced factorials from rapeseed, in which the SCA variance contributed considerably to the genetic variance, stacked ensembles of gradient boosting machines based on parentage information outperformed GCA prediction. The stacked ensembles increased prediction accuracy from 0.39 to 0.45, and from 0.48 to 0.54 compared to GCA prediction. The prediction accuracy reached by stacked ensembles without marker data reached values comparable to those of GBLUP that requires marker data. We conclude that hybrid prediction with stacked ensembles of gradient boosting machines based on parentage information is a promising approach that is worth further investigations with other data sets in which SCA variance is high.
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Affiliation(s)
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | | | | | - Eva Herzog
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
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12
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Qin X, Li X, Xiao J, Wu Q, Li Y, Li C, Jiang D, Tang T, Nan W, Liang Y, Zhang H. Transcriptomic and Physiological Analyses of Two Rice Restorer Lines under Different Nitrogen Supplies Provide Novel Insights into Hybrid Rice Breeding. PLANTS (BASEL, SWITZERLAND) 2023; 12:2276. [PMID: 37375901 DOI: 10.3390/plants12122276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/29/2023]
Abstract
Improving plant nitrogen-use efficiency (NUE) has great significance for various crops, particularly in hybrid breeding. Reducing nitrogen inputs is key to achieving sustainable rice production and mitigating environmental problems. In this study, we analyzed the transcriptomic and physiological changes in two indica restorer lines (Nanhui511 [NH511] and Minghui23 [MH23]) under high nitrogen (HN) and low nitrogen (LN) conditions. Compared to MH23, NH511 was more sensitive to different nitrogen supplies and exhibited higher nitrogen uptake and NUE under HN conditions by increasing lateral root and tiller numbers in the seedling and maturation stages, respectively. NH511 also exhibited a lower survival rate than MH23 when planted in a chlorate-containing hydroponic solution, indicating its HN uptake ability under different nitrogen-supply conditions. Transcriptomic analysis showed that NH511 has 2456 differentially expressed genes, whereas MH23 had only 266. Furthermore, these genes related to nitrogen utilization showed differential expression in NH511 under HN conditions, while the opposite was observed in MH23. Our findings revealed that NH511 could be regarded as elite rice and used for breeding high-NUE restorer lines by regulating and integrating nitrogen-utilization genes, which provides novel insights for the cultivation of high-NUE hybrid rice.
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Affiliation(s)
- Xiaojian Qin
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing 401331, China
| | - Xiaowei Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Juan Xiao
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Qian Wu
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Yuntong Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Cuiping Li
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Dan Jiang
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Tingting Tang
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Wenbin Nan
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing 401331, China
| | - Yongshu Liang
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing 401331, China
| | - Hanma Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing 401331, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing 401331, China
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13
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Qin X, Li X, Li C, Li Y, Wu Q, Wen H, Jiang D, Tang T, Nan W, Liang Y, Zhang H. Genome-wide identification of nitrate-responsive microRNAs by small RNA sequencing in the rice restorer cultivar Nanhui 511. FRONTIERS IN PLANT SCIENCE 2023; 14:1198809. [PMID: 37332718 PMCID: PMC10272429 DOI: 10.3389/fpls.2023.1198809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 05/18/2023] [Indexed: 06/20/2023]
Abstract
Rice productivity relies heavily on nitrogen fertilization, and improving nitrogen use efficiency (NUE) is important for hybrid rice breeding. Reducing nitrogen inputs is the key to achieving sustainable rice production and reducing environmental problems. Here, we analyzed the genome-wide transcriptomic changes in microRNAs (miRNAs) in the indica rice restorer cultivar Nanhui 511 (NH511) under high (HN) and low nitrogen (LN) conditions. The results showed that NH511 is sensitive to nitrogen supplies and HN conditions promoted the growth its lateral roots at the seedling stage. Furthermore, we identified 483 known miRNAs and 128 novel miRNAs by small RNA sequencing in response to nitrogen in NH511. We also detected 100 differentially expressed genes (DEGs), including 75 upregulated and 25 downregulated DEGs, under HN conditions. Among these DEGs, 43 miRNAs that exhibited a 2-fold change in their expression were identified in response to HN conditions, including 28 upregulated and 15 downregulated genes. Additionally, some differentially expressed miRNAs were further validated by qPCR analysis, which showed that miR443, miR1861b, and miR166k-3p were upregulated, whereas miR395v and miR444b.1 were downregulated under HN conditions. Moreover, the degradomes of possible target genes for miR166k-3p and miR444b.1 and expression variations were analyzed by qPCR at different time points under HN conditions. Our findings revealed comprehensive expression profiles of miRNAs responsive to HN treatments in an indica rice restorer cultivar, which advances our understanding of the regulation of nitrogen signaling mediated by miRNAs and provides novel data for high-NUE hybrid rice cultivation.
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Affiliation(s)
- Xiaojian Qin
- College of Life Sciences, Chongqing Normal University, Chongqing, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing, China
| | - Xiaowei Li
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Cuiping Li
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Yuntong Li
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Qian Wu
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Huan Wen
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Dan Jiang
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Tingting Tang
- College of Life Sciences, Chongqing Normal University, Chongqing, China
| | - Wenbin Nan
- College of Life Sciences, Chongqing Normal University, Chongqing, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing, China
| | - Yongshu Liang
- College of Life Sciences, Chongqing Normal University, Chongqing, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing, China
| | - Hanma Zhang
- College of Life Sciences, Chongqing Normal University, Chongqing, China
- Key Laboratory of Molecular Biology of Plants Environmental Adaptations, Chongqing Normal University, Chongqing, China
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14
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Xie J, Wang W, Yang T, Zhang Q, Zhang Z, Zhu X, Li N, Zhi L, Ma X, Zhang S, Liu Y, Wang X, Li F, Zhao Y, Jia X, Zhou J, Jiang N, Li G, Liu M, Liu S, Li L, Zeng A, Du M, Zhang Z, Li J, Zhang Z, Li Z, Zhang H. Large-scale genomic and transcriptomic profiles of rice hybrids reveal a core mechanism underlying heterosis. Genome Biol 2022; 23:264. [PMID: 36550554 PMCID: PMC9773586 DOI: 10.1186/s13059-022-02822-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Heterosis is widely used in agriculture. However, its molecular mechanisms are still unclear in plants. Here, we develop, sequence, and record the phenotypes of 418 hybrids from crosses between two testers and 265 rice varieties from a mini-core collection. RESULTS Phenotypic analysis shows that heterosis is dependent on genetic backgrounds and environments. By genome-wide association study of 418 hybrids and their parents, we find that nonadditive QTLs are the main genetic contributors to heterosis. We show that nonadditive QTLs are more sensitive to the genetic background and environment than additive ones. Further simulations and experimental analysis support a novel mechanism, homo-insufficiency under insufficient background (HoIIB), underlying heterosis. We propose heterosis in most cases is not due to heterozygote advantage but homozygote disadvantage under the insufficient genetic background. CONCLUSION The HoIIB model elucidates that genetic background insufficiency is the intrinsic mechanism of background dependence, and also the core mechanism of nonadditive effects and heterosis. This model can explain most known hypotheses and phenomena about heterosis, and thus provides a novel theory for hybrid rice breeding in future.
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Affiliation(s)
- Jianyin Xie
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Weiping Wang
- grid.496830.00000 0004 7648 0514State Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Changsha, 410125 China
| | - Tao Yang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Quan Zhang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Zhifang Zhang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Xiaoyang Zhu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Ni Li
- grid.496830.00000 0004 7648 0514State Key Laboratory of Hybrid Rice, Hunan Hybrid Rice Research Center, Changsha, 410125 China
| | - Linran Zhi
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Xiaoqian Ma
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Shuyang Zhang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Yan Liu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Xueqiang Wang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Fengmei Li
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China ,grid.428986.90000 0001 0373 6302Sanya Nanfan Research Institute of Hainan University, Sanya, 572024 China
| | - Yan Zhao
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Xuewei Jia
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Jieyu Zhou
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Ningjia Jiang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China ,Sanya Institute of China Agricultural University, Sanya, 572024 China
| | - Gangling Li
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Miaosong Liu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Shijin Liu
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Lin Li
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - An Zeng
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China ,grid.428986.90000 0001 0373 6302Sanya Nanfan Research Institute of Hainan University, Sanya, 572024 China ,Sanya Institute of China Agricultural University, Sanya, 572024 China
| | - Mengke Du
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China ,grid.428986.90000 0001 0373 6302Sanya Nanfan Research Institute of Hainan University, Sanya, 572024 China
| | - Zhanying Zhang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Jinjie Li
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Ziding Zhang
- grid.22935.3f0000 0004 0530 8290State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, 100193 China
| | - Zichao Li
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China ,Sanya Institute of China Agricultural University, Sanya, 572024 China ,grid.22935.3f0000 0004 0530 8290State Key Laboratory for Agrobiotechnology, China Agricultural University, Beijing, 100193 China
| | - Hongliang Zhang
- grid.22935.3f0000 0004 0530 8290Key Laboratory of Crop Heterosis and Utilization, the Ministry of Education / Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China ,grid.428986.90000 0001 0373 6302Sanya Nanfan Research Institute of Hainan University, Sanya, 572024 China ,Sanya Institute of China Agricultural University, Sanya, 572024 China
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15
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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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16
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Zhang M, Liu YH, Wang Y, Sze SH, Scheuring CF, Qi X, Ekinci O, Pekar J, Murray SC, Zhang HB. Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2022; 324:111424. [PMID: 35995113 DOI: 10.1016/j.plantsci.2022.111424] [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: 06/14/2022] [Revised: 08/07/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Accurate prediction of hybrid offspring complex trait phenotype from parents is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report genome-wide identification of genes enabling accurate prediction of hybrid offspring complex traits from parents using maize grain yield as the target trait. We identified 181 ZmF1GY genes enabling prediction of maize (Zea mays L.) F1 hybrid grain yield from parents and tested their utility and efficiency for predicting F1 hybrid grain yields from parents using their expressions, genic SNPs, and number of favorable alleles (NFAs), respectively. The ZmF1GY genes predicted hybrid grain yields from parents at an accuracy of 0.86, presented by correlation coefficient between predicted and observed phenotypes, within an environment, 0.74 across environments, and 0.64 across populations, outperforming genomic prediction by 27-406%, 23%, and 40%, respectively. Furthermore, we identified nine of the ZmF1GY genes containing SNPs or InDels in parents that increased or decreased hybrid grain yields by 14-46%. When the NFAs of these nine ZmF1GY genes were used for hybrid grain yield prediction from parents, they predicted hybrid grain yields at an accuracy of 0.79, outperforming genomic prediction by 21% that was based on up to tens of thousands of genome-wide SNPs. These results demonstrate the feasibility of developing a gene toolkit for a species enabling gene-based breeding across environments and populations that is much more powerful and efficient than current breeding, thereby helping secure the world's food production. The methodology is applicable to all crops, livestock, and humans.
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Affiliation(s)
- Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yinglei Wang
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
| | - Chantel F Scheuring
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Xiaoli Qi
- College of Life Science, Jiamusi University, Jiamusi, Heilongjiang 154007, China.
| | - Ozge Ekinci
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Jacob Pekar
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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17
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Hackauf B, Siekmann D, Fromme FJ. Improving Yield and Yield Stability in Winter Rye by Hybrid Breeding. PLANTS (BASEL, SWITZERLAND) 2022; 11:2666. [PMID: 36235531 PMCID: PMC9571156 DOI: 10.3390/plants11192666] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 09/27/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Rye is the only cross-pollinating small-grain cereal. The unique reproduction biology results in an exceptional complexity concerning genetic improvement of rye by breeding. Rye is a close relative of wheat and has a strong adaptation potential that refers to its mating system, making this overlooked cereal readily adjustable to a changing environment. Rye breeding addresses the emerging challenges of food security associated with climate change. The systematic identification, management, and use of its valuable natural diversity became a feasible option in outbreeding rye only following the establishment of hybrid breeding late in the 20th century. In this article, we review the most recent technological advances to improve yield and yield stability in winter rye. Based on recently released reference genome sequences, SMART breeding approaches are described to counterbalance undesired linkage drag effects of major restorer genes on grain yield. We present the development of gibberellin-sensitive semidwarf hybrids as a novel plant breeding innovation based on an approach that is different from current methods of increasing productivity in rye and wheat. Breeding of new rye cultivars with improved performance and resilience is indispensable for a renaissance of this healthy minor cereal as a homogeneous commodity with cultural relevance in Europe that allows for comparatively smooth but substantial complementation of wheat with rye-based diets, supporting the necessary restoration of the balance between human action and nature.
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Affiliation(s)
- Bernd Hackauf
- Julius Kühn Institute, Institute for Breeding Research on Agricultural Crops, Rudolf-Schick-Platz 3a, 18190 Sanitz, Germany
| | - Dörthe Siekmann
- Hybro Saatzucht GmbH & Co. KG, Langlinger Straße 3, 29565 Wriedel, Germany
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18
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Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat Genet 2022; 54:1544-1552. [DOI: 10.1038/s41588-022-01189-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 08/18/2022] [Indexed: 11/06/2022]
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19
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Sang Z, Wang H, Yang Y, Zhang Z, Liu X, Li Z, Xu Y. Epistasis Activation Contributes Substantially to Heterosis in Temperate by Tropical Maize Hybrids. FRONTIERS IN PLANT SCIENCE 2022; 13:921608. [PMID: 35898210 PMCID: PMC9313604 DOI: 10.3389/fpls.2022.921608] [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: 04/16/2022] [Accepted: 06/16/2022] [Indexed: 06/15/2023]
Abstract
Epistasis strongly affects the performance of superior maize hybrids. In this study, a multiple-hybrid population, consisting of three hybrid maize sets with varied interparental divergence, was generated by crossing 28 temperate and 23 tropical inbred lines with diverse genetic backgrounds. We obtained 1,154 tested hybrids. Among these tested hybrids, heterosis increased steadily as the heterotic genetic distance increased. Mid-parent heterosis was significantly higher in the temperate by tropical hybrids than in the temperate by temperate hybrids. Genome-wide prediction and association mapping was performed for grain weight per plant (GWPP) and days to silking (DTS) using 20K high-quality SNPs, showing that epistatic effects played a more prominent role than dominance effects in temperate by tropical maize hybrids. A total of 33 and 420 epistatic QTL were identified for GWPP and DTS, respectively, in the temperate by tropical hybrids. Protein-protein interaction network and gene-set enrichment analyses showed that epistatic genes were involved in protein interactions, which play an important role in photosynthesis, biological transcription pathways, and protein synthesis. We showed that the interaction of many minor-effect genes in the hybrids could activate the transcription activators of epistatic genes, resulting in a cascade of amplified yield heterosis. The multiple-hybrid population design enhanced our understanding of heterosis in maize, providing an insight into the acceleration of hybrid maize breeding by activating epistatic effects.
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Affiliation(s)
- Zhiqin Sang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Hui Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan, China
- National Engineering Research Center of Wheat and Maize, Shandong Technology Innovation Center of Wheat, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Yuxin Yang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhanqin Zhang
- Xinjiang Academy of Agricultural and Reclamation Science, Shihezi, China
| | - Xiaogang Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Zhiwei Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- International Maize and Wheat Improvement Center, Texcoco, Mexico
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Hussain B, Akpınar BA, Alaux M, Algharib AM, Sehgal D, Ali Z, Aradottir GI, Batley J, Bellec A, Bentley AR, Cagirici HB, Cattivelli L, Choulet F, Cockram J, Desiderio F, Devaux P, Dogramaci M, Dorado G, Dreisigacker S, Edwards D, El-Hassouni K, Eversole K, Fahima T, Figueroa M, Gálvez S, Gill KS, Govta L, Gul A, Hensel G, Hernandez P, Crespo-Herrera LA, Ibrahim A, Kilian B, Korzun V, Krugman T, Li Y, Liu S, Mahmoud AF, Morgounov A, Muslu T, Naseer F, Ordon F, Paux E, Perovic D, Reddy GVP, Reif JC, Reynolds M, Roychowdhury R, Rudd J, Sen TZ, Sukumaran S, Ozdemir BS, Tiwari VK, Ullah N, Unver T, Yazar S, Appels R, Budak H. Capturing Wheat Phenotypes at the Genome Level. FRONTIERS IN PLANT SCIENCE 2022; 13:851079. [PMID: 35860541 PMCID: PMC9289626 DOI: 10.3389/fpls.2022.851079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 05/19/2022] [Indexed: 06/15/2023]
Abstract
Recent technological advances in next-generation sequencing (NGS) technologies have dramatically reduced the cost of DNA sequencing, allowing species with large and complex genomes to be sequenced. Although bread wheat (Triticum aestivum L.) is one of the world's most important food crops, efficient exploitation of molecular marker-assisted breeding approaches has lagged behind that achieved in other crop species, due to its large polyploid genome. However, an international public-private effort spanning 9 years reported over 65% draft genome of bread wheat in 2014, and finally, after more than a decade culminated in the release of a gold-standard, fully annotated reference wheat-genome assembly in 2018. Shortly thereafter, in 2020, the genome of assemblies of additional 15 global wheat accessions was released. As a result, wheat has now entered into the pan-genomic era, where basic resources can be efficiently exploited. Wheat genotyping with a few hundred markers has been replaced by genotyping arrays, capable of characterizing hundreds of wheat lines, using thousands of markers, providing fast, relatively inexpensive, and reliable data for exploitation in wheat breeding. These advances have opened up new opportunities for marker-assisted selection (MAS) and genomic selection (GS) in wheat. Herein, we review the advances and perspectives in wheat genetics and genomics, with a focus on key traits, including grain yield, yield-related traits, end-use quality, and resistance to biotic and abiotic stresses. We also focus on reported candidate genes cloned and linked to traits of interest. Furthermore, we report on the improvement in the aforementioned quantitative traits, through the use of (i) clustered regularly interspaced short-palindromic repeats/CRISPR-associated protein 9 (CRISPR/Cas9)-mediated gene-editing and (ii) positional cloning methods, and of genomic selection. Finally, we examine the utilization of genomics for the next-generation wheat breeding, providing a practical example of using in silico bioinformatics tools that are based on the wheat reference-genome sequence.
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Affiliation(s)
- Babar Hussain
- Department of Biological Sciences, Middle East Technical University, Ankara, Turkey
- Department of Biotechnology, Faculty of Life Sciences, University of Central Punjab, Lahore, Pakistan
| | | | - Michael Alaux
- Université Paris-Saclay, INRAE, URGI, Versailles, France
| | - Ahmed M. Algharib
- Department of Environment and Bio-Agriculture, Faculty of Agriculture, Al-Azhar University, Cairo, Egypt
| | - Deepmala Sehgal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Zulfiqar Ali
- Institute of Plant Breeding and Biotechnology, MNS University of Agriculture, Multan, Pakistan
| | - Gudbjorg I. Aradottir
- Department of Pathology, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Jacqueline Batley
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA, Australia
| | - Arnaud Bellec
- French Plant Genomic Resource Center, INRAE-CNRGV, Castanet Tolosan, France
| | - Alison R. Bentley
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Halise B. Cagirici
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | - Luigi Cattivelli
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Fred Choulet
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - James Cockram
- The John Bingham Laboratory, The National Institute of Agricultural Botany, Cambridge, United Kingdom
| | - Francesca Desiderio
- Council for Agricultural Research and Economics-Research Centre for Genomics and Bioinformatics, Fiorenzuola d’Arda, Italy
| | - Pierre Devaux
- Research & Innovation, Florimond Desprez Group, Cappelle-en-Pévèle, France
| | - Munevver Dogramaci
- USDA, Agricultural Research Service, Edward T. Schafer Agricultural Research Center, Fargo, ND, United States
| | - Gabriel Dorado
- Department of Bioquímica y Biología Molecular, Campus Rabanales C6-1-E17, Campus de Excelencia Internacional Agroalimentario (ceiA3), Universidad de Córdoba, Córdoba, Spain
| | | | - David Edwards
- University of Western Australia, Perth, WA, Australia
| | - Khaoula El-Hassouni
- State Plant Breeding Institute, The University of Hohenheim, Stuttgart, Germany
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium (IWGSC), Bethesda, MD, United States
| | - Tzion Fahima
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Melania Figueroa
- Commonwealth Scientific and Industrial Research Organization, Agriculture and Food, Canberra, ACT, Australia
| | - Sergio Gálvez
- Department of Languages and Computer Science, ETSI Informática, Campus de Teatinos, Universidad de Málaga, Andalucía Tech, Málaga, Spain
| | - Kulvinder S. Gill
- Department of Crop Science, Washington State University, Pullman, WA, United States
| | - Liubov Govta
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Alvina Gul
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Goetz Hensel
- Center of Plant Genome Engineering, Heinrich-Heine-Universität, Düsseldorf, Germany
- Division of Molecular Biology, Centre of Region Haná for Biotechnological and Agriculture Research, Czech Advanced Technology and Research Institute, Palacký University, Olomouc, Czechia
| | - Pilar Hernandez
- Institute for Sustainable Agriculture (IAS-CSIC), Consejo Superior de Investigaciones Científicas (CSIC), Córdoba, Spain
| | | | - Amir Ibrahim
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | | | | | - Tamar Krugman
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Yinghui Li
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Shuyu Liu
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Amer F. Mahmoud
- Department of Plant Pathology, Faculty of Agriculture, Assiut University, Assiut, Egypt
| | - Alexey Morgounov
- Food and Agriculture Organization of the United Nations, Riyadh, Saudi Arabia
| | - Tugdem Muslu
- Molecular Biology, Genetics and Bioengineering, Sabanci University, Istanbul, Turkey
| | - Faiza Naseer
- Atta-ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Etienne Paux
- French National Research Institute for Agriculture, Food and the Environment, INRAE, GDEC, Clermont-Ferrand, France
| | - Dragan Perovic
- Institute for Resistance Research and Stress Tolerance, Julius Kühn Institute, Quedlinburg, Germany
| | - Gadi V. P. Reddy
- USDA-Agricultural Research Service, Southern Insect Management Research Unit, Stoneville, MS, United States
| | - Jochen Christoph Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Germany
| | - Matthew Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Rajib Roychowdhury
- Institute of Evolution and Department of Environmental and Evolutionary Biology, University of Haifa, Haifa, Israel
| | - Jackie Rudd
- Crop and Soil Science, Texas A&M University, College Station, TX, United States
| | - Taner Z. Sen
- Crop Improvement and Genetics Research, USDA, Agricultural Research Service, Albany, CA, United States
| | | | | | | | - Naimat Ullah
- Institute of Biological Sciences (IBS), Gomal University, D. I. Khan, Pakistan
| | - Turgay Unver
- Ficus Biotechnology, Ostim Teknopark, Ankara, Turkey
| | - Selami Yazar
- General Directorate of Research, Ministry of Agriculture, Ankara, Turkey
| | | | - Hikmet Budak
- Montana BioAgriculture, Inc., Missoula, MT, United States
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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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Hao M, Zhang L, Huang L, Ning S, Yuan Z, Jiang B, Yan Z, Wu B, Zheng Y, Liu D. 渗入杂交与小麦杂种优势. CHINESE SCIENCE BULLETIN-CHINESE 2022. [DOI: 10.1360/tb-2022-0349] [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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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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Shen G, Hu W, Wang X, Zhou X, Han Z, Sherif A, Ayaad M, Xing Y. Assembly of yield heterosis of an elite rice hybrid is promising by manipulating dominant quantitative trait loci. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2022; 64:688-701. [PMID: 34995015 DOI: 10.1111/jipb.13220] [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: 09/23/2021] [Accepted: 01/04/2022] [Indexed: 05/27/2023]
Abstract
In the past, rice hybrids with strong heterosis have been obtained empirically, by developing and testing thousands of combinations. Here, we aimed to determine whether heterosis of an elite hybrid could be achieved by manipulating major quantitative trait loci. We used 202 chromosome segment substitution lines from the elite hybrid Shanyou 63 to evaluate single segment heterosis (SSH) of yield per plant and identify heterotic loci. All nine detected heterotic loci acted in a dominant fashion, and no SSH exhibited overdominance. Functional alleles of key yield-related genes Ghd7, Ghd7.1, Hd1, and GS3 were dispersed in both parents. No functional alleles of three investigated genes were expressed at higher levels in the hybrids than in the more desirable parents. A hybrid pyramiding eight heterotic loci in the female parent Zhenshan 97 background had a comparable yield to Shanyou 63 and much higher yield than Zhenshan 97. Five hybrids pyramiding eight or nine heterotic loci in the combined parental genome background showed similar yield performance to that of Shanyou 63. These results suggest that dominance underlying functional complementation is an important contributor to yield heterosis and that heterosis assembly might be successfully promised by manipulating several major dominant heterotic loci.
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Affiliation(s)
- Guojing Shen
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Wei Hu
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Xianmeng Wang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Xiangchun Zhou
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Zhongming Han
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
| | - Ahmed Sherif
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Mohammed Ayaad
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Plant Research Department, Nuclear Research Center, Atomic Energy Authority, Abo-Zaabal, 13759, Egypt
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
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Raffo MA, Sarup P, Guo X, Liu H, Andersen JR, Orabi J, Jahoor A, Jensen J. Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:965-978. [PMID: 34973112 PMCID: PMC8942904 DOI: 10.1007/s00122-021-04009-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 11/29/2021] [Indexed: 05/31/2023]
Abstract
Including additive and additive-by-additive epistasis in a NOIA parametrization did not yield orthogonal partitioning of genetic variances, nevertheless, it improved predictive ability in a leave-one-out cross-validation for wheat grain yield. Additive-by-additive epistasis is the principal non-additive genetic effect in inbred wheat lines and is potentially useful for developing cultivars based on total genetic merit; nevertheless, its practical benefits have been highly debated. In this article, we aimed to (i) evaluate the performance of models including additive and additive-by-additive epistatic effects for variance components (VC) estimation of grain yield in a wheat-breeding population, and (ii) to investigate whether including additive-by-additive epistasis in genomic prediction enhance wheat grain yield predictive ability (PA). In total, 2060 sixth-generation (F6) lines from Nordic Seed A/S breeding company were phenotyped in 21 year-location combinations in Denmark, and genotyped using a 15 K-Illumina-BeadChip. Three models were used to estimate VC and heritability at plot level: (i) "I-model" (baseline), (ii) "I + GA-model", extending I-model with an additive genomic effect, and (iii) "I + GA + GAA-model", extending I + GA-model with an additive-by-additive genomic effects. The I + GA-model and I + GA + GAA-model were based on the Natural and Orthogonal Interactions Approach (NOIA) parametrization. The I + GA + GAA-model failed to achieve orthogonal partition of genetic variances, as revealed by a change in estimated additive variance of I + GA-model when epistasis was included in the I + GA + GAA-model. The PA was studied using leave-one-line-out and leave-one-breeding-cycle-out cross-validations. The I + GA + GAA-model increased PA significantly (16.5%) compared to the I + GA-model in leave-one-line-out cross-validation. However, the improvement due to including epistasis was not observed in leave-one-breeding-cycle-out cross-validation. We conclude that epistatic models can be useful to enhance predictions of total genetic merit. However, even though we used the NOIA parameterization, the variance partition into orthogonal genetic effects was not possible.
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Affiliation(s)
- Miguel Angel Raffo
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark.
| | - Pernille Sarup
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
- Nordic Seed A/S, Galten, Denmark
| | - Xiangyu Guo
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | - Huiming Liu
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
| | | | | | - Ahmed Jahoor
- Nordic Seed A/S, Galten, Denmark
- Department of Plant Breeding, The Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Just Jensen
- Center for Quantitative Genetics and Genomics, Aarhus University, Tjele, Denmark
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Zhu X, Maurer HP, Jenz M, Hahn V, Ruckelshausen A, Leiser WL, Würschum T. The performance of phenomic selection depends on the genetic architecture of the target trait. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:653-665. [PMID: 34807268 PMCID: PMC8866387 DOI: 10.1007/s00122-021-03997-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 11/08/2021] [Indexed: 06/13/2023]
Abstract
The phenomic predictive ability depends on the genetic architecture of the target trait, being high for complex traits and low for traits with major QTL. Genomic selection is a powerful tool to assist breeding of complex traits, but a limitation is the costs required for genotyping. Recently, phenomic selection has been suggested, which uses spectral data instead of molecular markers as predictors. It was shown to be competitive with genomic prediction, as it achieved predictive abilities as high or even higher than its genomic counterpart. The objective of this study was to evaluate the performance of phenomic prediction for triticale and the dependency of the predictive ability on the genetic architecture of the target trait. We found that for traits with a complex genetic architecture, like grain yield, phenomic prediction with NIRS data as predictors achieved high predictive abilities and performed better than genomic prediction. By contrast, for mono- or oligogenic traits, for example, yellow rust, marker-based approaches achieved high predictive abilities, while those of phenomic prediction were very low. Compared with molecular markers, the predictive ability obtained using NIRS data was more robust to varying degrees of genetic relatedness between the training and prediction set. Moreover, for grain yield, smaller training sets were required to achieve a similar predictive ability for phenomic prediction than for genomic prediction. In addition, our results illustrate the potential of using field-based spectral data for phenomic prediction. Overall, our result confirmed phenomic prediction as an efficient approach to improve the selection gain for complex traits in plant breeding.
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Affiliation(s)
- Xintian Zhu
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Hans Peter Maurer
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Mario Jenz
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
- Hochschule Osnabrück, Sedanstr. 26, 49076, Osnabrück, Germany
| | - Volker Hahn
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | | | - Willmar L Leiser
- State Plant Breeding Institute, University of Hohenheim, 70593, Stuttgart, Germany
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany.
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Hussain I, Ali S, Liu W, Awais M, Li J, Liao Y, Zhu M, Fu C, Liu D, Wang F. Identification of Heterotic Groups and Patterns Based on Genotypic and Phenotypic Characteristics Among Rice Accessions of Diverse Origins. Front Genet 2022; 13:811124. [PMID: 35154278 PMCID: PMC8832281 DOI: 10.3389/fgene.2022.811124] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 01/03/2022] [Indexed: 11/13/2022] Open
Abstract
Identification of the right parental combinations to maximize heterosis is the major goal of hybrid breeding, which could be achieved through identification of heterotic groups. The main objective of this study was to identify promising heterotic groups for future rice breeding programs. A collection of 359 rice genotypes of diverse origins of China and abroad, composed of inbreds, maintainers, restorers, and temperature-sensitive genic male sterile (TGMS) lines were genotyped using 10K SNP chips. The SNP data set was subjected to genomic analyses for estimation of genetic divergence and diversity. Significant variations were observed in the germplasm with the identification of six different genetic groups. These lines were assigned to the genetic groups independent of their origin. Taking an account of commercially used heterotic groups present in each cluster, three cytoplasmic male sterile (CMS) lines and 14 inbred and restorer lines with moderate to high genetic distances selected from five heterotic patterns were crossed and obtained 42 F1 hybrids. A total of 14 hybrids were found with significant maximum mid- and better-parent heterosis, namely, TaifengA × Guang122, TaifengA × Wushansimiao, and TaifengA × Minghui63 for earliness; Guang8A × Huazhan for dwarf stature; and Guang8A × Huanghuzhan-1, TaifengA × Yuexiangzhan, Guang8A × Minhui3301, TianfengA × Guang122, Guang8A × Yahui2115, TianfengA × Huanghuazhan, TianfengA × Minghui63, TianfengA × Minhui3301, TaifengA × Gui99, and Guang8A × Yuenongsimiao for yield and yield-related traits. Mid-parent and better-parent heterotic F1 hybrids were in positive correlation with the genetic distances as that manifested by commercially used heterotic groups, encouraging the use of genotypic data for identification of heterotic groups. Our study provides an informative strategy for the development of early maturing, lodging resistant and high-yielding commercial hybrids and cultivars in future heterosis breeding programs.
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Affiliation(s)
- Izhar Hussain
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
- Department of Plant Breeding and Genetics, The University of Haripur, Haripur, Pakistan
| | - Sajid Ali
- Department of Agriculture, Hazara University Mansehra, Mansehra, Pakistan
| | - Wuge Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
| | - Muhammad Awais
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Plant Protection, Northwest A&F University, Yangling, China
| | - Jinhua Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
| | - Yilong Liao
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
| | - Manshan Zhu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
| | - Chongyun Fu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
| | - Dilin Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
- *Correspondence: Dilin Liu, ; Feng Wang,
| | - Feng Wang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, China
- Guangdong Provincial Key Laboratory of New Technology in Rice Breeding, Guangzhou, China
- Guangdong Rice Engineering Laboratory, Guangzhou, China
- *Correspondence: Dilin Liu, ; Feng Wang,
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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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31
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021. [PMID: 34643760 DOI: 10.25739/8p1e-0931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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32
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Hu H, Campbell MT, Yeats TH, Zheng X, Runcie DE, Covarrubias-Pazaran G, Broeckling C, Yao L, Caffe-Treml M, Gutiérrez LA, Smith KP, Tanaka J, Hoekenga OA, Sorrells ME, Gore MA, Jannink JL. Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:4043-4054. [PMID: 34643760 PMCID: PMC8580906 DOI: 10.1007/s00122-021-03946-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/05/2021] [Indexed: 05/26/2023]
Abstract
Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.
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Affiliation(s)
- Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Trevor H Yeats
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Xuying Zheng
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Daniel E Runcie
- Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA
| | - Giovanny Covarrubias-Pazaran
- International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México
| | - Corey Broeckling
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Linxing Yao
- Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA
| | - Lucı A Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - James Tanaka
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Owen A Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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33
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Saxena RK, Jiang Y, Khan AW, Zhao Y, Kumar Singh V, Bohra A, Sonappa M, Rathore A, Kumar CVS, Saxena K, Reif J, Varshney RK. Characterization of heterosis and genomic prediction-based establishment of heterotic patterns for developing better hybrids in pigeonpea. THE PLANT GENOME 2021; 14:e20125. [PMID: 34337867 DOI: 10.1002/tpg2.20125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 05/29/2021] [Indexed: 06/13/2023]
Abstract
Whole-genome resequencing (WGRS) of 396 lines, consisting of 104 hybrid parental lines and 292 germplasm lines, were used to study the molecular basis of mid-parent heterosis (MPH) and to identify complementary heterotic patterns in pigeonpea [Cajanus cajan (L.) Millsp.] hybrids. The lines and hybrids were assessed for yield and yield-related traits in multiple environments. Our analysis showed positive MPH values in 78.6% of hybrids, confirming the potential of hybrid breeding in pigeonpea. By using genome-wide prediction and association mapping approaches, we identified 129 single nucleotide polymorphisms and 52 copy number variations with significant heterotic effects and also established a high-yielding heterotic pattern in pigeonpea. In summary, our study highlights the role of WGRS data in the study and use of heterosis in crops where hybrid breeding is expected to boost selection gain in order to ensure global food security.
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Affiliation(s)
- Rachit K Saxena
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Yong Jiang
- Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr. 3, D-06466, Stadt Seeland, Germany
| | - Aamir W Khan
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Yusheng Zhao
- Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr. 3, D-06466, Stadt Seeland, Germany
| | - Vikas Kumar Singh
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - Abhishek Bohra
- Indian Council of Agricultural Research - Indian Institute of Pulses Research, Kanpur, 208024, India
| | - Muniswamy Sonappa
- Zonal Agricultural Research Station, Univ. of Agricultural Sciences - Raichur, Gulbarga, Karnataka, 585101, India
| | - Abhishek Rathore
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
| | - C V Sameer Kumar
- Professor Jayashankar Telangana State Agricultural Univ., Rajendranagar, Hyderabad, Telangana, 500030, India
| | | | - Jochen Reif
- Leibniz-Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstr. 3, D-06466, Stadt Seeland, Germany
| | - Rajeev K Varshney
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Telangana, 502324, India
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch Univ., Murdoch, WA, 6150, Australia
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34
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Lell M, Reif J, Zhao Y. Optimizing the setup of multienvironmental hybrid wheat yield trials for boosting the selection capability. THE PLANT GENOME 2021; 14:e20150. [PMID: 34541826 DOI: 10.1002/tpg2.20150] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 07/22/2021] [Indexed: 06/13/2023]
Abstract
The accuracy of genomic prediction increases with increasing heritability, and thus the challenge of optimizing the design of multienvironment yield trials under a limited budget arises. With this in mind, we aimed to find the best of several options to sparsely distribute a fixed number of plots across different environments to increase the accuracy of hybrid performance prediction. We used a comprehensive published genomic and phenotypic data set of 1,604 winter wheat (Triticum aestivum L.) hybrids and compared several commonly used biometric models for phenotypic data analysis in a resampling study to identify the one that most accurately estimated the hybrid performance in different imbalanced trials. Our results showed that when using information about genotypic relationships, genotypic values were more strongly associated with the reference values than when this information was ignored. In addition, a balanced environmental sampling resulted in an adequate characterization of each environment and increased the accuracy for estimating the hybrid performance. One promising design involved dividing the genotypes into equally sized subgroups that were tested in a subset of environments, with the constraint that the subgroups overlapped with respect to the environments. This scenario appears to be particularly appropriate, as it provided both high accuracies in the estimates of genotypic values and had low variability resulting from the data sample used. Thus, we were able to clearly demonstrate the utility for optimizing the design of multienvironment hybrid wheat yield trials in times of genomic selection.
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Affiliation(s)
- Moritz Lell
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, D-06466, Germany
| | - Jochen Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, D-06466, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, D-06466, Germany
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Hao D, Bai J, Du J, Wu X, Thomsen B, Gao H, Su G, Wang X. Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle. Metabolites 2021; 11:metabo11110753. [PMID: 34822411 PMCID: PMC8621036 DOI: 10.3390/metabo11110753] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/27/2021] [Accepted: 10/28/2021] [Indexed: 12/23/2022] Open
Abstract
Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.
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Affiliation(s)
- Dan Hao
- Beijing Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Beijing 100193, China; (D.H.); (J.B.); (J.D.); (X.W.)
- Shijiazhuang Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Shijiazhuang 052463, China
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark;
| | - Jiangsong Bai
- Beijing Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Beijing 100193, China; (D.H.); (J.B.); (J.D.); (X.W.)
- Shijiazhuang Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Shijiazhuang 052463, China
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Jianyong Du
- Beijing Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Beijing 100193, China; (D.H.); (J.B.); (J.D.); (X.W.)
- Shijiazhuang Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Shijiazhuang 052463, China
- College of Veterinary Medicine, China Agricultural University, Beijing 100193, China
| | - Xiaoping Wu
- Beijing Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Beijing 100193, China; (D.H.); (J.B.); (J.D.); (X.W.)
- Shijiazhuang Zhongnongtongchuang (ZNTC) Biotechnology Co., Ltd., Shijiazhuang 052463, China
| | - Bo Thomsen
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark;
| | - Hongding Gao
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark; (H.G.); (G.S.)
| | - Guosheng Su
- Center for Quantitative Genetics and Genomics, Aarhus University, 8830 Tjele, Denmark; (H.G.); (G.S.)
| | - Xiao Wang
- Konge Larsen ApS, 2800 Kongens Lyngby, Denmark
- Correspondence:
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Dan Z, Chen Y, Li H, Zeng Y, Xu W, Zhao W, He R, Huang W. The metabolomic landscape of rice heterosis highlights pathway biomarkers for predicting complex phenotypes. PLANT PHYSIOLOGY 2021; 187:1011-1025. [PMID: 34608951 PMCID: PMC8491067 DOI: 10.1093/plphys/kiab273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 05/27/2021] [Indexed: 06/13/2023]
Abstract
Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.
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Affiliation(s)
- Zhiwu Dan
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Yunping Chen
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Hui Li
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Yafei Zeng
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Wuwu Xu
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Weibo Zhao
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
| | - Ruifeng He
- Institute of Biological Chemistry, Washington State University, Pullman, Washington 99164-6414, USA
| | - Wenchao Huang
- State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China
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Rembe M, Reif JC, Ebmeyer E, Thorwarth P, Korzun V, Schacht J, Boeven PHG, Varenne P, Kazman E, Philipp N, Kollers S, Pfeiffer N, Longin CFH, Hartwig N, Gils M, Zhao Y. Reciprocal Recurrent Genomic Selection Is Impacted by Genotype-by-Environment Interactions. FRONTIERS IN PLANT SCIENCE 2021; 12:703419. [PMID: 34630453 PMCID: PMC8498042 DOI: 10.3389/fpls.2021.703419] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 08/02/2021] [Indexed: 06/13/2023]
Abstract
Reciprocal recurrent genomic selection is a breeding strategy aimed at improving the hybrid performance of two base populations. It promises to significantly advance hybrid breeding in wheat. Against this backdrop, the main objective of this study was to empirically investigate the potential and limitations of reciprocal recurrent genomic selection. Genome-wide predictive equations were developed using genomic and phenotypic data from a comprehensive population of 1,604 single crosses between 120 female and 15 male wheat lines. Twenty superior female lines were selected for initiation of the reciprocal recurrent genomic selection program. Focusing on the female pool, one cycle was performed with genomic selection steps at the F2 (60 out of 629 plants) and the F5 stage (49 out of 382 plants). Selection gain for grain yield was evaluated at six locations. Analyses of the phenotypic data showed pronounced genotype-by-environment interactions with two environments that formed an outgroup compared to the environments used for the genome-wide prediction equations. Removing these two environments for further analysis resulted in a selection gain of 1.0 dt ha-1 compared to the hybrids of the original 20 parental lines. This underscores the potential of reciprocal recurrent genomic selection to promote hybrid wheat breeding, but also highlights the need to develop robust genome-wide predictive equations.
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Affiliation(s)
- Maximilian Rembe
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
| | | | | | | | - Viktor Korzun
- KWS SAAT SE & Co. KGaA, Einbeck, Germany
- Federal State Budgetary Institution of Science Federal Research Center “Kazan Scientific Center of Russian Academy of Sciences”, Kazan, Russia
| | - Johannes Schacht
- Limagrain Europe, Ferme de l'Etang – BP3−77390, Verneuil-l'Ètang, France
| | | | - Pierrick Varenne
- Limagrain Europe, Ferme de l'Etang – BP3−77390, Verneuil-l'Ètang, France
| | | | | | | | | | | | | | - Mario Gils
- Nordsaat Saatzucht GmbH, Langenstein, Germany
| | - Yusheng Zhao
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Seeland, Germany
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Gimenez K, Blanc P, Argillier O, Pierre JB, Le Gouis J, Paux E. Dissecting Bread Wheat Heterosis through the Integration of Agronomic and Physiological Traits. BIOLOGY 2021; 10:biology10090907. [PMID: 34571784 PMCID: PMC8465846 DOI: 10.3390/biology10090907] [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: 08/15/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/04/2022]
Abstract
Simple Summary To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Similar to what has been observed in maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis, i.e., the fact that a progeny surpasses the performances of its parents, is still limited and poorly understood. To better characterize this phenomenon, we developed and phenotyped for physiological and agronomic traits 91 hybrids and their nineteen female and sixteen male parents. We showed that hybrids had a longer grain filling phase that led to bigger grains and an increased thousand kernel weight. This resulted in a better yield for 86% of hybrids compared to the average yield of their parents. In addition, hybrids appeared to be less affected by the negative correlation between protein content and yield compared to pure lines. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids that can help to meet agriculture’s challenges. Abstract To meet the challenge of feeding almost 10 billion people by 2050, wheat yield has to double by 2050. However, over the past 20 years, yield increase has slowed down and even stagnated in the main producing countries. Following the example of maize, hybrids have been suggested as a solution to overcome yield stagnation in wheat. However, wheat heterosis is still limited and poorly understood. Gaining a better understanding of hybrid vigor holds the key to breed for better varieties. To this aim, we have developed and phenotyped for physiological and agronomic traits an incomplete factorial design consisting of 91 hybrids and their nineteen female and sixteen male parents. Monitoring the plant development with normalized difference vegetation index revealed that 89% of the hybrids including the five higher yielding hybrids had a longer grain filling phase with a delayed senescence that results in larger grain size. This average increase of 7.7% in thousand kernel weight translated to a positive mid-parent heterosis for grain yield for 86% of hybrids. In addition, hybrids displayed a positive grain protein deviation leading to a +4.7% heterosis in protein yield. These results shed light on the physiological bases underlying yield heterosis in wheat, paving new ways to breed for better wheat hybrids.
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Affiliation(s)
- Kevin Gimenez
- Université Clermont Auvergne, INRAE, Genetics, Diversity and Ecophysiology of Cereals, 63000 Clermont-Ferrand, France; (K.G.); (J.L.G.)
- Syngenta France SAS, 28000 Chartres, France; (P.B.); (O.A.); (J.-B.P.)
| | - Pierre Blanc
- Syngenta France SAS, 28000 Chartres, France; (P.B.); (O.A.); (J.-B.P.)
| | - Odile Argillier
- Syngenta France SAS, 28000 Chartres, France; (P.B.); (O.A.); (J.-B.P.)
| | | | - Jacques Le Gouis
- Université Clermont Auvergne, INRAE, Genetics, Diversity and Ecophysiology of Cereals, 63000 Clermont-Ferrand, France; (K.G.); (J.L.G.)
| | - Etienne Paux
- Université Clermont Auvergne, INRAE, Genetics, Diversity and Ecophysiology of Cereals, 63000 Clermont-Ferrand, France; (K.G.); (J.L.G.)
- Correspondence:
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Affiliation(s)
- Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany. .,German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
| | - Murukarthick Jayakodi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany. .,Center for Integrated Breeding Research (CiBreed), Georg-August-University Göttingen, Göttingen, Germany.
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40
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Chen J, Xue M, Liu H, Fernie AR, Chen W. Exploring the genic resources underlying metabolites through mGWAS and mQTL in wheat: From large-scale gene identification and pathway elucidation to crop improvement. PLANT COMMUNICATIONS 2021; 2:100216. [PMID: 34327326 PMCID: PMC8299079 DOI: 10.1016/j.xplc.2021.100216] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/04/2021] [Accepted: 06/28/2021] [Indexed: 05/23/2023]
Abstract
Common wheat (Triticum aestivum L.) is a leading cereal crop, but has lagged behind with respect to the interpretation of the molecular mechanisms of phenotypes compared with other major cereal crops such as rice and maize. The recently available genome sequence of wheat affords the pre-requisite information for efficiently exploiting the potential molecular resources for decoding the genetic architecture of complex traits and identifying valuable breeding targets. Meanwhile, the successful application of metabolomics as an emergent large-scale profiling methodology in several species has demonstrated this approach to be accessible for reaching the above goals. One such productive avenue is combining metabolomics approaches with genetic designs. However, this trial is not as widespread as that for sequencing technologies, especially when the acquisition, understanding, and application of metabolic approaches in wheat populations remain more difficult and even arguably underutilized. In this review, we briefly introduce the techniques used in the acquisition of metabolomics data and their utility in large-scale identification of functional candidate genes. Considerable progress has been made in delivering improved varieties, suggesting that the inclusion of information concerning these metabolites and genes and metabolic pathways enables a more explicit understanding of phenotypic traits and, as such, this procedure could serve as an -omics-informed roadmap for executing similar improvement strategies in wheat and other species.
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Affiliation(s)
- Jie Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Mingyun Xue
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hongbo Liu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
| | - Alisdair R. Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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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: 154] [Impact Index Per Article: 51.3] [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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González-Diéguez D, Legarra A, Charcosset A, Moreau L, Lehermeier C, Teyssèdre S, Vitezica ZG. Genomic prediction of hybrid crops allows disentangling dominance and epistasis. Genetics 2021; 218:iyab026. [PMID: 33864072 PMCID: PMC8128411 DOI: 10.1093/genetics/iyab026] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/06/2021] [Indexed: 12/28/2022] Open
Abstract
We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted "residual genetic" line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme-e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.
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Affiliation(s)
| | - Andrés Legarra
- INRAE, INP, UMR 1388 GenPhySE, F-31326 Castanet-Tolosan, France
| | - Alain Charcosset
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
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Zhang J, Liu F, Reif JC, Jiang Y. On the use of GBLUP and its extension for GWAS with additive and epistatic effects. G3-GENES GENOMES GENETICS 2021; 11:6237487. [PMID: 33871030 PMCID: PMC8495923 DOI: 10.1093/g3journal/jkab122] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/04/2021] [Indexed: 11/29/2022]
Abstract
Genomic best linear unbiased prediction (GBLUP) is the most widely used model for genome-wide predictions. Interestingly, it is also possible to perform genome-wide association studies (GWAS) based on GBLUP. Although the estimated marker effects in GBLUP are shrunken and the conventional test based on such effects has low power, it was observed that a modified test statistic can be produced and the result of test was identical to a standard GWAS model. Later, a mathematical proof was given for the special case that there is no fixed covariate in GBLUP. Since then, the new approach has been called “GWAS by GBLUP”. Nevertheless, covariates such as environmental and subpopulation effects are very common in GBLUP. Thus, it is necessary to confirm the equivalence in the general case. Recently, the concept was generalized to GWAS for epistatic effects and the new approach was termed rapid epistatic mixed-model association analysis (REMMA) because it greatly improved the computational efficiency. However, the relationship between REMMA and the standard GWAS model has not been investigated. In this study, we first provided a general mathematical proof of the equivalence between “GWAS by GBLUP” and the standard GWAS model for additive effects. Then, we compared REMMA with the standard GWAS model for epistatic effects by a theoretical investigation and by empirical data analyses. We hypothesized that the similarity of the two models is influenced by the relative contribution of additive and epistatic effects to the phenotypic variance, which was verified by empirical and simulation studies.
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Affiliation(s)
- Jie Zhang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466 Stadt Seeland, Germany
| | - Fang Liu
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466 Stadt Seeland, Germany
| | - Jochen C Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466 Stadt Seeland, Germany
| | - Yong Jiang
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, 06466 Stadt Seeland, Germany
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Knoch D, Werner CR, Meyer RC, Riewe D, Abbadi A, Lücke S, Snowdon RJ, Altmann T. Multi-omics-based prediction of hybrid performance in canola. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:1147-1165. [PMID: 33523261 PMCID: PMC7973648 DOI: 10.1007/s00122-020-03759-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/19/2020] [Indexed: 05/05/2023]
Abstract
Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.
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Affiliation(s)
- Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - Christian R. Werner
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
| | - Rhonda C. Meyer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - David Riewe
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
- Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, 14195 Berlin, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363 Holtsee, Germany
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Sophie Lücke
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
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Hem IG, Selle ML, Gorjanc G, Fuglstad GA, Riebler A. Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge. Genetics 2021. [PMID: 33789346 DOI: 10.1101/2020.04.01.019497] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023] Open
Abstract
We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.
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Affiliation(s)
- Ingeborg Gullikstad Hem
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Maria Lie Selle
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Edinburgh
| | - Geir-Arne Fuglstad
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Andrea Riebler
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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Hem IG, Selle ML, Gorjanc G, Fuglstad GA, Riebler A. Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge. Genetics 2021; 217:iyab002. [PMID: 33789346 PMCID: PMC8045730 DOI: 10.1093/genetics/iyab002] [Citation(s) in RCA: 3] [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: 11/06/2020] [Accepted: 12/20/2020] [Indexed: 12/19/2022] Open
Abstract
We propose a novel Bayesian approach that robustifies genomic modeling by leveraging expert knowledge (EK) through prior distributions. The central component is the hierarchical decomposition of phenotypic variation into additive and nonadditive genetic variation, which leads to an intuitive model parameterization that can be visualized as a tree. The edges of the tree represent ratios of variances, for example broad-sense heritability, which are quantities for which EK is natural to exist. Penalized complexity priors are defined for all edges of the tree in a bottom-up procedure that respects the model structure and incorporates EK through all levels. We investigate models with different sources of variation and compare the performance of different priors implementing varying amounts of EK in the context of plant breeding. A simulation study shows that the proposed priors implementing EK improve the robustness of genomic modeling and the selection of the genetically best individuals in a breeding program. We observe this improvement in both variety selection on genetic values and parent selection on additive values; the variety selection benefited the most. In a real case study, EK increases phenotype prediction accuracy for cases in which the standard maximum likelihood approach did not find optimal estimates for the variance components. Finally, we discuss the importance of EK priors for genomic modeling and breeding, and point to future research areas of easy-to-use and parsimonious priors in genomic modeling.
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Affiliation(s)
- Ingeborg Gullikstad Hem
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Maria Lie Selle
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG, Edinburgh
| | - Geir-Arne Fuglstad
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Andrea Riebler
- Department of Mathematical Sciences, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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Labroo MR, Studer AJ, Rutkoski JE. Heterosis and Hybrid Crop Breeding: A Multidisciplinary Review. Front Genet 2021; 12:643761. [PMID: 33719351 PMCID: PMC7943638 DOI: 10.3389/fgene.2021.643761] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 02/08/2021] [Indexed: 11/24/2022] Open
Abstract
Although hybrid crop varieties are among the most popular agricultural innovations, the rationale for hybrid crop breeding is sometimes misunderstood. Hybrid breeding is slower and more resource-intensive than inbred breeding, but it allows systematic improvement of a population by recurrent selection and exploitation of heterosis simultaneously. Inbred parental lines can identically reproduce both themselves and their F1 progeny indefinitely, whereas outbred lines cannot, so uniform outbred lines must be bred indirectly through their inbred parents to harness heterosis. Heterosis is an expected consequence of whole-genome non-additive effects at the population level over evolutionary time. Understanding heterosis from the perspective of molecular genetic mechanisms alone may be elusive, because heterosis is likely an emergent property of populations. Hybrid breeding is a process of recurrent population improvement to maximize hybrid performance. Hybrid breeding is not maximization of heterosis per se, nor testing random combinations of individuals to find an exceptional hybrid, nor using heterosis in place of population improvement. Though there are methods to harness heterosis other than hybrid breeding, such as use of open-pollinated varieties or clonal propagation, they are not currently suitable for all crops or production environments. The use of genomic selection can decrease cycle time and costs in hybrid breeding, particularly by rapidly establishing heterotic pools, reducing testcrossing, and limiting the loss of genetic variance. Open questions in optimal use of genomic selection in hybrid crop breeding programs remain, such as how to choose founders of heterotic pools, the importance of dominance effects in genomic prediction, the necessary frequency of updating the training set with phenotypic information, and how to maintain genetic variance and prevent fixation of deleterious alleles.
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Affiliation(s)
| | | | - Jessica E. Rutkoski
- Department of Crop Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, United States
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Kadam DC, Rodriguez OR, Lorenz AJ. Optimization of training sets for genomic prediction of early-stage single crosses in maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:687-699. [PMID: 33398385 DOI: 10.1007/s00122-020-03722-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 11/03/2020] [Indexed: 06/12/2023]
Abstract
Training population optimization algorithms are useful for efficiently training genomic prediction models for single-cross performance, especially if the population is extended beyond only realized crosses to all possible single crosses. Genomic prediction of single-cross performance could allow effective evaluation of all possible single crosses between all inbreds developed in a hybrid breeding program. The objectives of the present study were to investigate the effect of different levels of relatedness on genomic predictive ability of single crosses, evaluate the usefulness of deterministic formula to forecast prediction accuracy in advance, and determine the potential for TRS optimization based on prediction error variance (PEVmean) and coefficient of determination (CDmean) criteria. We used 481 single crosses made by crossing 89 random recombinant inbred lines (RILs) belonging to the Iowa stiff stalk synthetic group with 103 random RILs belonging to the non-stiff stalk synthetic heterotic group. As expected, predictive ability was enhanced by ensuring close relationships between TRSs and target sets, even when TRS sizes were smaller. We found that designing a TRS based on PEVmean or CDmean criteria is useful for increasing the efficiency of genomic prediction of maize single crosses. We went further and extended the sampling space from that of all observed single crosses to all possible single crosses, providing a much larger genetic space within which to design a training population. Using all possible single crosses increased the advantage of the PEVmean and CDmean methods based on expected prediction accuracy. This finding suggests that it may be worthwhile using an optimization algorithm to select a training population from all possible single crosses to maximize efficiency in training accurate models for hybrid genomic prediction.
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Affiliation(s)
- Dnyaneshwar C Kadam
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Oscar R Rodriguez
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, 68583, USA
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA.
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50
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Li D, Zhou Z, Lu X, Jiang Y, Li G, Li J, Wang H, Chen S, Li X, Würschum T, Reif JC, Xu S, Li M, Liu W. Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize. FRONTIERS IN PLANT SCIENCE 2021; 12:774478. [PMID: 34917109 PMCID: PMC8670227 DOI: 10.3389/fpls.2021.774478] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 11/01/2021] [Indexed: 05/14/2023]
Abstract
Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.
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Affiliation(s)
- Dongdong Li
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Zhiqiang Zhou
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Xiaohuan Lu
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yong Jiang
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland, Germany
| | - Guoliang Li
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Junhui Li
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Haoying Wang
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Shaojiang Chen
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
| | - Xinhai Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, Stuttgart, Germany
| | - Jochen C. Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Stadt Seeland, Germany
| | - Shizhong Xu
- Department of Botany and Plant Sciences, University of California, Riverside, Riverside, CA, United States
- *Correspondence: Wenxin Liu,
| | - Mingshun Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing, China
- Mingshun Li,
| | - Wenxin Liu
- Key Laboratory of Crop Heterosis and Utilization, The Ministry of Education/Key Laboratory of Crop Genetic Improvement, Beijing Municipality/National Maize Improvement Center/College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
- Shizhong Xu,
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