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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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Sanchez D, Allier A, Ben Sadoun S, Mary-Huard T, Bauland C, Palaffre C, Lagardère B, Madur D, Combes V, Melkior S, Bettinger L, Murigneux A, Moreau L, Charcosset A. Assessing the potential of genetic resource introduction into elite germplasm: a collaborative multiparental population for flint maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:19. [PMID: 38214870 PMCID: PMC10786986 DOI: 10.1007/s00122-023-04509-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/18/2023] [Indexed: 01/13/2024]
Abstract
KEY MESSAGE Implementing a collaborative pre-breeding multi-parental population efficiently identifies promising donor x elite pairs to enrich the flint maize elite germplasm. Genetic diversity is crucial for maintaining genetic gains and ensuring breeding programs' long-term success. In a closed breeding program, selection inevitably leads to a loss of genetic diversity. While managing diversity can delay this loss, introducing external sources of diversity is necessary to bring back favorable genetic variation. Genetic resources exhibit greater diversity than elite materials, but their lower performance levels hinder their use. This is the case for European flint maize, for which elite germplasm has incorporated only a limited portion of the diversity available in landraces. To enrich the diversity of this elite genetic pool, we established an original cooperative maize bridging population that involves crosses between private elite materials and diversity donors to create improved genotypes that will facilitate the incorporation of original favorable variations. Twenty donor × elite BC1S2 families were created and phenotyped for hybrid value for yield related traits. Crosses showed contrasted means and variances and therefore contrasted potential in terms of selection as measured by their usefulness criterion (UC). Average expected mean performance gain over the initial elite material was 5%. The most promising donor for each elite line was identified. Results also suggest that one more generation, i.e., 3 in total, of crossing to the elite is required to fully exploit the potential of a donor. Altogether, our results support the usefulness of incorporating genetic resources into elite flint maize. They call for further effort to create fixed diversity donors and identify those most suitable for each elite program.
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Affiliation(s)
- Dimitri Sanchez
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Antoine Allier
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Syngenta, 12 Chemin de L'Hobit, 31790, Saint-Sauveur, France
| | - Sarah Ben Sadoun
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Bernard Lagardère
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | | | | | - Alain Murigneux
- Limagrain Europe, 28 Route d'Ennezat, 63720, Chappes, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France.
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Vanavermaete D, Maenhout S, Fostier J, De Baets B. Oracle selection provides insight into how far off practice is from Utopia in plant breeding. FRONTIERS IN PLANT SCIENCE 2023; 14:1218665. [PMID: 37546253 PMCID: PMC10401442 DOI: 10.3389/fpls.2023.1218665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 06/27/2023] [Indexed: 08/08/2023]
Abstract
Since the introduction of genomic selection in plant breeding, high genetic gains have been realized in different plant breeding programs. Various methods based on genomic estimated breeding values (GEBVs) for selecting parental lines that maximize the genetic gain as well as methods for improving the predictive performance of genomic selection have been proposed. Unfortunately, it remains difficult to measure to what extent these methods really maximize long-term genetic values. In this study, we propose oracle selection, a hypothetical frame of mind that uses the ground truth to optimally select parents or optimize the training population in order to maximize the genetic gain in each breeding cycle. Clearly, oracle selection cannot be applied in a true breeding program, but allows for the assessment of existing parental selection and training population update methods and the evaluation of how far these methods are from the optimal utopian solution.
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Affiliation(s)
- David Vanavermaete
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
| | - Steven Maenhout
- Predictive Breeding, Department of Plants and Crops, Ghent University, Ghent, Belgium
| | - Jan Fostier
- IDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Ghent, Belgium
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Jacques A, Leroy G, Rognon X, Verrier E, Tixier-Boichard M, Restoux G. Reintroducing genetic diversity in populations from cryopreserved material: the case of Abondance, a French local dairy cattle breed. Genet Sel Evol 2023; 55:28. [PMID: 37076793 PMCID: PMC10114384 DOI: 10.1186/s12711-023-00801-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND Genetic diversity is a necessary condition for populations to evolve under natural adaptation, artificial selection, or both. However, genetic diversity is often threatened, in particular in domestic animal populations where artificial selection, genetic drift and inbreeding are strong. In this context, cryopreserved genetic resources are a promising option to reintroduce lost variants and to limit inbreeding. However, while the use of ancient genetic resources is more common in plant breeding, it is less documented in animals due to a longer generation interval, making it difficult to fill the gap in performance due to continuous selection. This study investigates one of the only concrete cases available in animals, for which cryopreserved semen from a bull born in 1977 in a lost lineage was introduced into the breeding scheme of a French local dairy cattle breed, the Abondance breed, more than 20 years later. RESULTS We found that this re-introduced bull was genetically distinct with respect to the current population and thus allowed part of the genetic diversity lost over time to be restored. The expected negative gap in milk production due to continuous selection was absorbed in a few years by preferential mating with elite cows. Moreover, the re-use of this bull more than two decades later did not increase the level of inbreeding, and even tended to reduce it by avoiding mating with relatives. Finally, the reintroduction of a bull from a lost lineage in the breeding scheme allowed for improved performance for reproductive abilities, a trait that was less subject to selection in the past. CONCLUSIONS The use of cryopreserved material is an efficient way to manage the genetic diversity of an animal population, by mitigating the effects of both inbreeding and strong selection. However, attention should be paid to mating of animals to limit the disadvantages associated with incorporating original genetic material, notably a discrepancy in the breeding values for selected traits or an increase in inbreeding. Therefore, careful characterization of the genetic resources available in cryobanks could help to ensure the sustainable management of populations, in particular local or small populations. These results could also be transferred to the conservation of wild threatened populations.
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Affiliation(s)
- Alicia Jacques
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Grégoire Leroy
- Food and Agriculture Organization, viale delle Terme de Caracalla, 00153, Rome, Italy
| | - Xavier Rognon
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | - Etienne Verrier
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, 78350, Jouy-en-Josas, France
| | | | - Gwendal Restoux
- INRAE, AgroParisTech, GABI, Université Paris-Saclay, 78350, Jouy-en-Josas, France.
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Physiological adaptive traits are a potential allele reservoir for maize genetic progress under challenging conditions. Nat Commun 2022; 13:3225. [PMID: 35680899 PMCID: PMC9184527 DOI: 10.1038/s41467-022-30872-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Accepted: 05/23/2022] [Indexed: 11/08/2022] Open
Abstract
Combined phenomic and genomic approaches are required to evaluate the margin of progress of breeding strategies. Here, we analyze 65 years of genetic progress in maize yield, which was similar (101 kg ha-1 year-1) across most frequent environmental scenarios in the European growing area. Yield gains were linked to physiologically simple traits (plant phenology and architecture) which indirectly affected reproductive development and light interception in all studied environments, marked by significant genomic signatures of selection. Conversely, studied physiological processes involved in stress adaptation remained phenotypically unchanged (e.g. stomatal conductance and growth sensitivity to drought) and showed no signatures of selection. By selecting for yield, breeders indirectly selected traits with stable effects on yield, but not physiological traits whose effects on yield can be positive or negative depending on environmental conditions. Because yield stability under climate change is desirable, novel breeding strategies may be needed for exploiting alleles governing physiological adaptive traits.
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Baertschi C, Cao TV, Bartholomé J, Ospina Y, Quintero C, Frouin J, Bouvet JM, Grenier C. Impact of early genomic prediction for recurrent selection in an upland rice synthetic population. G3 (BETHESDA, MD.) 2021; 11:jkab320. [PMID: 34498036 PMCID: PMC8664429 DOI: 10.1093/g3journal/jkab320] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 08/16/2021] [Indexed: 11/14/2022]
Abstract
Population breeding through recurrent selection is based on the repetition of evaluation and recombination among best-selected individuals. In this type of breeding strategy, early evaluation of selection candidates combined with genomic prediction could substantially shorten the breeding cycle length, thus increasing the rate of genetic gain. The objective of this study was to optimize early genomic prediction in an upland rice (Oryza sativa L.) synthetic population improved through recurrent selection via shuttle breeding in two sites. To this end, we used genomic prediction on 334 S0 genotypes evaluated with early generation progeny testing (S0:2 and S0:3) across two sites. Four traits were measured (plant height, days to flowering, grain yield, and grain zinc concentration) and the predictive ability was assessed for the target site. For days to flowering and plant height, which correlate well among sites (0.51-0.62), an increase of up to 0.4 in predictive ability was observed when the model was trained using the two sites. For grain zinc concentration, adding the phenotype of the predicted lines in the nontarget site to the model improved the predictive ability (0.51 with two-site and 0.31 with single-site model), whereas for grain yield the gain was less (0.42 with two-site and 0.35 with single-site calibration). Through these results, we found a good opportunity to optimize the genomic recurrent selection scheme and maximize the use of resources by performing early progeny testing in two sites for traits with best expression and/or relevance in each specific environment.
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Affiliation(s)
- Cédric Baertschi
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Tuong-Vi Cao
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Rice Breeding Platform, International Rice Research Institute, Metro Manila, Philippines
| | - Yolima Ospina
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
| | | | - Julien Frouin
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jean-Marc Bouvet
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- CIRAD, Dispositif de Recherche et d’Enseignement en Partenariat “Forêts et Biodiversité à Madagascar”, Antananarivo, Madagascar
| | - Cécile Grenier
- CIRAD, UMR AGAP Institut, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Alliance Bioversity-CIAT, Recta Palmira Cali, Colombia
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Vanavermaete D, Fostier J, Maenhout S, De Baets B. Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3845-3861. [PMID: 34387711 PMCID: PMC8580937 DOI: 10.1007/s00122-021-03932-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.
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Affiliation(s)
- David Vanavermaete
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, B-9000, Ghent, Belgium.
| | - Jan Fostier
- IDLab, Department of Information Technology, Ghent University - imec, B-9052, Ghent, Belgium
| | | | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, B-9000, Ghent, Belgium
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Sharma S, Pinson SRM, Gealy DR, Edwards JD. Genomic prediction and QTL mapping of root system architecture and above-ground agronomic traits in rice (Oryza sativa L.) with a multitrait index and Bayesian networks. G3 (BETHESDA, MD.) 2021; 11:jkab178. [PMID: 34568907 PMCID: PMC8496310 DOI: 10.1093/g3journal/jkab178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 02/17/2021] [Indexed: 11/13/2022]
Abstract
Root system architecture (RSA) is a crucial factor in resource acquisition and plant productivity. Roots are difficult to phenotype in the field, thus new tools for predicting phenotype from genotype are particularly valuable for plant breeders aiming to improve RSA. This study identifies quantitative trait loci (QTLs) for RSA and agronomic traits in a rice (Oryza sativa) recombinant inbred line (RIL) population derived from parents with contrasting RSA traits (PI312777 × Katy). The lines were phenotyped for agronomic traits in the field, and separately grown as seedlings on agar plates which were imaged to extract RSA trait measurements. QTLs were discovered from conventional linkage analysis and from a machine learning approach using a Bayesian network (BN) consisting of genome-wide SNP data and phenotypic data. The genomic prediction abilities (GPAs) of multi-QTL models and the BN analysis were compared with the several standard genomic prediction (GP) methods. We found GPAs were improved using multitrait (BN) compared to single trait GP in traits with low to moderate heritability. Two groups of individuals were selected based on GPs and a modified rank sum index (GSRI) indicating their divergence across multiple RSA traits. Selections made on GPs did result in differences between the group means for numerous RSA. The ranking accuracy across RSA traits among the individual selected RILs ranged from 0.14 for root volume to 0.59 for lateral root tips. We conclude that the multitrait GP model using BN can in some cases improve the GPA of RSA and agronomic traits, and the GSRI approach is useful to simultaneously select for a desired set of RSA traits in a segregating population.
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Affiliation(s)
- Santosh Sharma
- Dale Bumpers National Rice Research Center, United States Department of Agriculture—Agricultural Research Service, Stuttgart, AR 72160, USA
| | - Shannon R M Pinson
- Dale Bumpers National Rice Research Center, United States Department of Agriculture—Agricultural Research Service, Stuttgart, AR 72160, USA
| | - David R Gealy
- Dale Bumpers National Rice Research Center, United States Department of Agriculture—Agricultural Research Service, Stuttgart, AR 72160, USA
| | - Jeremy D Edwards
- Dale Bumpers National Rice Research Center, United States Department of Agriculture—Agricultural Research Service, Stuttgart, AR 72160, USA
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9
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Zhang H, Lu Y, Ma Y, Fu J, Wang G. Genetic and molecular control of grain yield in maize. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:18. [PMID: 37309425 PMCID: PMC10236077 DOI: 10.1007/s11032-021-01214-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/07/2021] [Indexed: 06/14/2023]
Abstract
Understanding the genetic and molecular basis of grain yield is important for maize improvement. Here, we identified 49 consensus quantitative trait loci (cQTL) controlling maize yield-related traits using QTL meta-analysis. Then, we collected yield-related traits associated SNPs detected by association mapping and identified 17 consensus significant loci. Comparing the physical positions of cQTL with those of significant SNPs revealed that 47 significant SNPs were located within 20 cQTL regions. Furthermore, intensive reviews of 31 genes regulating maize yield-related traits found that the functions of many genes were conservative in maize and other plant species. The functional conservation indicated that some of the 575 maize genes (orthologous to 247 genes controlling yield or seed traits in other plant species) might be functionally related to maize yield-related traits, especially the 49 maize orthologous genes in cQTL regions, and 41 orthologous genes close to the physical positions of significant SNPs. In the end, we prospected on the integration of the public sources for exploring the genetic and molecular mechanisms of maize yield-related traits, and on the utilization of genetic and molecular mechanisms for maize improvement. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01214-3.
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Affiliation(s)
- Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Yantian Lu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Yuting Ma
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Guoying Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
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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: 49] [Impact Index Per Article: 16.3] [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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Beche E, Gillman JD, Song Q, Nelson R, Beissinger T, Decker J, Shannon G, Scaboo AM. Genomic prediction using training population design in interspecific soybean populations. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:15. [PMID: 37309481 PMCID: PMC10236090 DOI: 10.1007/s11032-021-01203-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 01/11/2021] [Indexed: 06/14/2023]
Abstract
Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01203-6.
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Affiliation(s)
- Eduardo Beche
- Division of Plant Science, University of Missouri, Columbia, MO USA
| | | | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD USA
| | - Randall Nelson
- Department of Crop Sciences, University of Illinois, and USDA-Agricultural Research Service (retired), 1101 W. Peabody Dr., Urbana, IL 61801 USA
| | - Tim Beissinger
- Division of Plant Breeding Methodology, Department of Crop Sciences, Georg-August-Universität, Göttingen, Germany
| | - Jared Decker
- Division of Animal Science, University of Missouri, Columbia, MO USA
| | - Grover Shannon
- Division of Plant Science, University of Missouri, Columbia, MO USA
| | - Andrew M. Scaboo
- Division of Plant Science, University of Missouri, Columbia, MO USA
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He T, Li C. Harness the power of genomic selection and the potential of germplasm in crop breeding for global food security in the era with rapid climate change. ACTA ACUST UNITED AC 2020. [DOI: 10.1016/j.cj.2020.04.005] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Origin Specific Genomic Selection: A Simple Process To Optimize the Favorable Contribution of Parents to Progeny. G3-GENES GENOMES GENETICS 2020; 10:2445-2455. [PMID: 32430306 PMCID: PMC7341124 DOI: 10.1534/g3.120.401132] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Modern crop breeding is in constant demand for new genetic diversity as part of the arms race with genetic gain. The elite gene pool has limited genetic variation and breeders are trying to introduce novelty from unadapted germplasm, landraces and wild relatives. For polygenic traits, currently available approaches to introgression are not ideal, as there is a demonstrable bias against exotic alleles during selection. Here, we propose a partitioned form of genomic selection, called Origin Specific Genomic Selection (OSGS), where we identify and target selection on favorable exotic alleles. Briefly, within a population derived from a bi-parental cross, we isolate alleles originating from the elite and exotic parents, which then allows us to separate out the predicted marker effects based on the allele origins. We validated the usefulness of OSGS using two nested association mapping (NAM) datasets: barley NAM (elite-exotic) and maize NAM (elite-elite), as well as by computer simulation. Our results suggest that OSGS works well in its goal to increase the contribution of favorable exotic alleles in bi-parental crosses, and it is possible to extend the approach to broader multi-parental populations.
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Allier A, Teyssèdre S, Lehermeier C, Moreau L, Charcosset A. Optimized breeding strategies to harness genetic resources with different performance levels. BMC Genomics 2020; 21:349. [PMID: 32393177 PMCID: PMC7216646 DOI: 10.1186/s12864-020-6756-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Accepted: 04/23/2020] [Indexed: 11/10/2022] Open
Abstract
Background The narrow genetic base of elite germplasm compromises long-term genetic gain and increases the vulnerability to biotic and abiotic stresses in unpredictable environmental conditions. Therefore, an efficient strategy is required to broaden the genetic base of commercial breeding programs while not compromising short-term variety release. Optimal cross selection aims at identifying the optimal set of crosses that balances the expected genetic value and diversity. We propose to consider genomic selection and optimal cross selection to recurrently improve genetic resources (i.e. pre-breeding), to bridge the improved genetic resources with elites (i.e. bridging), and to manage introductions into the elite breeding population. Optimal cross selection is particularly adapted to jointly identify bridging, introduction and elite crosses to ensure an overall consistency of the genetic base broadening strategy. Results We compared simulated breeding programs introducing donors with different performance levels, directly or indirectly after bridging. We also evaluated the effect of the training set composition on the success of introductions. We observed that with recurrent introductions of improved donors, it is possible to maintain the genetic diversity and increase mid- and long-term performances with only limited penalty at short-term. Considering a bridging step yielded significantly higher mid- and long-term genetic gain when introducing low performing donors. The results also suggested to consider marker effects estimated with a broad training population including donor by elite and elite by elite progeny to identify bridging, introduction and elite crosses. Conclusion Results of this study provide guidelines on how to harness polygenic variation present in genetic resources to broaden elite germplasm.
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Affiliation(s)
- Antoine Allier
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France. .,RAGT2n, Statistical Genetics Unit, 12510, Druelle, France.
| | | | | | - Laurence Moreau
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Alain Charcosset
- GQE - Le Moulon, INRAE, University Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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