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Simiqueli GF, Resende RT, Takahashi EK, de Sousa JE, Grattapaglia D. Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids. FRONTIERS IN PLANT SCIENCE 2023; 14:1252504. [PMID: 37965018 PMCID: PMC10641691 DOI: 10.3389/fpls.2023.1252504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 09/29/2023] [Indexed: 11/16/2023]
Abstract
Introduction Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus. Methods Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts. Results Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees. Discussion We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.
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Affiliation(s)
| | - Rafael Tassinari Resende
- School of Agronomy, Federal University of Goiás (UFG), Goiânia, GO, Brazil
- Department of Forestry, University of Brasília (UnB), Brasília, DF, Brazil
| | | | | | - Dario Grattapaglia
- Plant Genetics Laboratory, EMBRAPA Genetic Resources and Biotechnology, Brasilia, Brazil
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Mao H, Jiang C, Tang C, Nie X, Du L, Liu Y, Cheng P, Wu Y, Liu H, Kang Z, Wang X. Wheat adaptation to environmental stresses under climate change: Molecular basis and genetic improvement. MOLECULAR PLANT 2023; 16:1564-1589. [PMID: 37671604 DOI: 10.1016/j.molp.2023.09.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 08/19/2023] [Accepted: 09/01/2023] [Indexed: 09/07/2023]
Abstract
Wheat (Triticum aestivum) is a staple food for about 40% of the world's population. As the global population has grown and living standards improved, high yield and improved nutritional quality have become the main targets for wheat breeding. However, wheat production has been compromised by global warming through the more frequent occurrence of extreme temperature events, which have increased water scarcity, aggravated soil salinization, caused plants to be more vulnerable to diseases, and directly reduced plant fertility and suppressed yield. One promising option to address these challenges is the genetic improvement of wheat for enhanced resistance to environmental stress. Several decades of progress in genomics and genetic engineering has tremendously advanced our understanding of the molecular and genetic mechanisms underlying abiotic and biotic stress responses in wheat. These advances have heralded what might be considered a "golden age" of functional genomics for the genetic improvement of wheat. Here, we summarize the current knowledge on the molecular and genetic basis of wheat resistance to abiotic and biotic stresses, including the QTLs/genes involved, their functional and regulatory mechanisms, and strategies for genetic modification of wheat for improved stress resistance. In addition, we also provide perspectives on some key challenges that need to be addressed.
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Affiliation(s)
- Hude Mao
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Cong Jiang
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Chunlei Tang
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Xiaojun Nie
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Linying Du
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Life Science, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yuling Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Peng Cheng
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Yunfeng Wu
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Huiquan Liu
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Zhensheng Kang
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China.
| | - Xiaojie Wang
- State Key Laboratory of Crop Stress Biology for Arid Areas and College of Plant Protection, Northwest A&F University, Yangling, Shaanxi 712100, China.
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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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Shamuyarira KW, Shimelis H, Figlan S, Chaplot V. Combining ability analysis of yield and biomass allocation related traits in newly developed wheat populations. Sci Rep 2023; 13:11832. [PMID: 37481645 PMCID: PMC10363107 DOI: 10.1038/s41598-023-38961-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023] Open
Abstract
Increasing biomass allocation to the root system may increase soil-organic carbon stocks and confer drought adaptation in water-limited environments. Understanding the genetic bases and inheritance of biomass allocation is fundamental for drought tolerance breeding and soil health. The objective of this study was to determine the general and specific combining ability, maternal effects and the mode of gene action controlling the major yield and biomass allocation related traits in wheat to identify good combiners for breeding and enhanced carbon sequestration. Ten selected wheat genotypes were crossed in a full diallel mating design, and 90 F2 families were generated and evaluated in the field and greenhouse under drought-stressed and non-stressed conditions. Significant differences were recorded among the tested families revealing substantial variation for plant height (PH), kernels per spike (KPS), root biomass (RB), shoot biomass (SB), total plant biomass (PB) and grain yield (GY). Additive gene effects conditioned PH, SB, PB and GY under drought, suggesting the polygenic inheritance for drought tolerance. Strong maternal and reciprocal genetic effects were recorded for RB across the testing sites under drought-stressed conditions. Line BW162 had high yield and biomass production and can be used to transfer favourable genes to its progeny. The parental line LM75 maintained the general combining ability (GCA) effects in a positive and desirable direction for SB, PB and GY. Early generation selection using PH, SB, PB and GY will improve drought tolerance by exploiting additive gene action under drought conditions. Higher RB production may be maintained by a positive selection of male and female parents to capture the significant maternal and reciprocal effects found in this study.
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Affiliation(s)
- Kwame W Shamuyarira
- African Centre for Crop Improvement, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa.
| | - Hussein Shimelis
- African Centre for Crop Improvement, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa
| | - Sandiswa Figlan
- Department of Agriculture and Animal Health, University of South Africa, Florida, South Africa
| | - Vincent Chaplot
- African Centre for Crop Improvement, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg, 3209, South Africa
- Laboratoire d'Océanographie et du Climat: Expérimentations et Approches Numériques (LOCEAN), UMR 7159, IRD/C NRS/UPMC/ MNHN, IPSL, Paris, France
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Adams J, de Vries M, van Eeuwijk F. Efficient Genomic Prediction of Yield and Dry Matter in Hybrid Potato. PLANTS (BASEL, SWITZERLAND) 2023; 12:2617. [PMID: 37514232 PMCID: PMC10385487 DOI: 10.3390/plants12142617] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/27/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023]
Abstract
There is an ongoing endeavor within the potato breeding sector to rapidly adapt potato from a clonal polyploid crop to a diploid hybrid potato crop. While hybrid breeding allows for the efficient generation and selection of parental lines, it also increases breeding program complexity and results in longer breeding cycles. Over the past two decades, genomic prediction has revolutionized hybrid crop breeding through shorter breeding cycles, lower phenotyping costs, and better population improvement, resulting in increased genetic gains for genetically complex traits. In order to accelerate the genetic gains in hybrid potato, the proper implementation of genomic prediction is a crucial milestone in the rapid improvement of this crop. The authors of this paper set out to test genomic prediction in hybrid potato using current genotyped material with two alternative models: one model that predicts the general combining ability effects (GCA) and another which predicts both the general and specific combining ability effects (GCA+SCA). Using a training set comprising 769 hybrids and 456 genotyped parental lines, we found that reasonable a prediction accuracy could be achieved for most phenotypes with both zero common parents (ρ=0.36-0.61) and one (ρ=0.50-0.68) common parent between the training and test sets. There was no benefit with the inclusion of non-additive genetic effects in the GCA+SCA model despite SCA variance contributing between 9% and 19% of the total genetic variance. Genotype-by-environment interactions, while present, did not appear to affect the prediction accuracy, though prediction errors did vary across the trial's targets. These results suggest that genomically estimated breeding values on parental lines are sufficient for hybrid yield prediction.
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Affiliation(s)
- James Adams
- Biometris, Mathematical and Statistical Methods, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
- Solynta, Dreijenlaan 2, 6703 HA Wageningen, The Netherlands
| | | | - Fred van Eeuwijk
- Biometris, Mathematical and Statistical Methods, Wageningen University and Research, 6708 PB Wageningen, The Netherlands
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Labroo MR, Endelman JB, Gemenet DC, Werner CR, Gaynor RC, Covarrubias-Pazaran GE. Clonal diploid and autopolyploid breeding strategies to harness heterosis: insights from stochastic simulation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:147. [PMID: 37291402 DOI: 10.1007/s00122-023-04377-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 05/05/2023] [Indexed: 06/10/2023]
Abstract
KEY MESSAGE Reciprocal recurrent selection sometimes increases genetic gain per unit cost in clonal diploids with heterosis due to dominance, but it typically does not benefit autopolyploids. Breeding can change the dominance as well as additive genetic value of populations, thus utilizing heterosis. A common hybrid breeding strategy is reciprocal recurrent selection (RRS), in which parents of hybrids are typically recycled within pools based on general combining ability. However, the relative performances of RRS and other breeding strategies have not been thoroughly compared. RRS can have relatively increased costs and longer cycle lengths, but these are sometimes outweighed by its ability to harness heterosis due to dominance. Here, we used stochastic simulation to compare genetic gain per unit cost of RRS, terminal crossing, recurrent selection on breeding value, and recurrent selection on cross performance considering different amounts of population heterosis due to dominance, relative cycle lengths, time horizons, estimation methods, selection intensities, and ploidy levels. In diploids with phenotypic selection at high intensity, whether RRS was the optimal breeding strategy depended on the initial population heterosis. However, in diploids with rapid-cycling genomic selection at high intensity, RRS was the optimal breeding strategy after 50 years over almost all amounts of initial population heterosis under the study assumptions. Diploid RRS required more population heterosis to outperform other strategies as its relative cycle length increased and as selection intensity and time horizon decreased. The optimal strategy depended on selection intensity, a proxy for inbreeding rate. Use of diploid fully inbred parents vs. outbred parents with RRS typically did not affect genetic gain. In autopolyploids, RRS typically did not outperform one-pool strategies regardless of the initial population heterosis.
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Affiliation(s)
- Marlee R Labroo
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jeffrey B Endelman
- Department of Horticulture, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Dorcus C Gemenet
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Christian R Werner
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Giovanny E Covarrubias-Pazaran
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Texcoco, Mexico.
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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Wang TC, Casadebaig P, Chen TW. More than 1000 genotypes are required to derive robust relationships between yield, yield stability and physiological parameters: a computational study on wheat crop. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:34. [PMID: 36897399 PMCID: PMC10006026 DOI: 10.1007/s00122-023-04264-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 10/10/2022] [Indexed: 06/18/2023]
Abstract
Using in silico experiment in crop model, we identified different physiological regulations of yield and yield stability, as well as quantify the genotype and environment numbers required for analysing yield stability convincingly. Identifying target traits for breeding stable and high-yielded cultivars simultaneously is difficult due to limited knowledge of physiological mechanisms behind yield stability. Besides, there is no consensus about the adequacy of a stability index (SI) and the minimal number of environments and genotypes required for evaluating yield stability. We studied this question using the crop model APSIM-Wheat to simulate 9100 virtual genotypes grown under 9000 environments. By analysing the simulated data, we showed that the shape of phenotype distributions affected the correlation between SI and mean yield and the genotypic superiority measure (Pi) was least affected among 11 SI. Pi was used as index to demonstrate that more than 150 environments were required to estimate yield stability of a genotype convincingly and more than 1000 genotypes were necessary to evaluate the contribution of a physiological parameter to yield stability. Network analyses suggested that a physiological parameter contributed preferentially to yield or Pi. For example, soil water absorption efficiency and potential grain filling rate explained better the variations in yield than in Pi; while light extinction coefficient and radiation use efficiency were more correlated with Pi than with yield. The high number of genotypes and environments required for studying Pi highlight the necessity and potential of in silico experiments to better understand the mechanisms behind yield stability.
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Affiliation(s)
- Tien-Cheng Wang
- Section of Intensive Plant Food Systems, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.
- Institut für Gartenbauliche Produktionssysteme, Leibniz Universität Hannover, Hannover, Germany.
| | - Pierre Casadebaig
- INRAE, UMR AGIR, Université de Toulouse, 31320, Castanet-Tolosan, France
| | - Tsu-Wei Chen
- Section of Intensive Plant Food Systems, Albrecht Daniel Thaer-Institute of Agricultural and Horticultural Sciences, Humboldt Universität zu Berlin, Berlin, Germany.
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Gohar S, Sajjad M, Zulfiqar S, Liu J, Wu J, Rahman MU. Domestication of newly evolved hexaploid wheat—A journey of wild grass to cultivated wheat. Front Genet 2022; 13:1022931. [PMID: 36263418 PMCID: PMC9574122 DOI: 10.3389/fgene.2022.1022931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 09/12/2022] [Indexed: 11/13/2022] Open
Abstract
Domestication of wheat started with the dawn of human civilization. Since then, improvement in various traits including resistance to diseases, insect pests, saline and drought stresses, grain yield, and quality were improved through selections by early farmers and then planned hybridization after the discovery of Mendel’s laws. In the 1950s, genetic variability was created using mutagens followed by the selection of superior mutants. Over the last 3 decades, research was focused on developing superior hybrids, initiating marker-assisted selection and targeted breeding, and developing genetically modified wheat to improve the grain yield, tolerance to drought, salinity, terminal heat and herbicide, and nutritive quality. Acceptability of genetically modified wheat by the end-user remained a major hurdle in releasing into the environment. Since the beginning of the 21st century, changing environmental conditions proved detrimental to achieving sustainability in wheat production particularly in developing countries. It is suggested that high-tech phenotyping assays and genomic procedures together with speed breeding procedures will be instrumental in achieving food security beyond 2050.
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Affiliation(s)
- Sasha Gohar
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan
- Department of Biotechnology, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan
| | - Muhammad Sajjad
- Department of Biosciences, COMSATS University Islamabad, Islamabad, Pakistan
| | - Sana Zulfiqar
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan
- Department of Biotechnology, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan
| | - Jiajun Liu
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an, Shandong, China
| | - Jiajie Wu
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an, Shandong, China
- *Correspondence: Jiajie Wu, ; Mehboob-ur- Rahman,
| | - Mehboob-ur- Rahman
- Plant Genomics and Molecular Breeding Laboratory, National Institute for Biotechnology and Genetic Engineering, Faisalabad, Pakistan
- Department of Biotechnology, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan
- *Correspondence: Jiajie Wu, ; Mehboob-ur- Rahman,
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Varona L, Legarra A, Toro MA, Vitezica ZG. Genomic Prediction Methods Accounting for Nonadditive Genetic Effects. Methods Mol Biol 2022; 2467:219-243. [PMID: 35451778 DOI: 10.1007/978-1-0716-2205-6_8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The use of genomic information for prediction of future phenotypes or breeding values for the candidates to selection has become a standard over the last decade. However, most procedures for genomic prediction only consider the additive (or substitution) effects associated with polymorphic markers. Nevertheless, the implementation of models that consider nonadditive genetic variation may be interesting because they (1) may increase the ability of prediction, (2) can be used to define mate allocation procedures in plant and animal breeding schemes, and (3) can be used to benefit from nonadditive genetic variation in crossbreeding or purebred breeding schemes. This study reviews the available methods for incorporating nonadditive effects into genomic prediction procedures and their potential applications in predicting future phenotypic performance, mate allocation, and crossbred and purebred selection. Finally, a brief outline of some future research lines is also proposed.
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Affiliation(s)
- Luis Varona
- Departamento de Anatomía, Embriología y Genética Animal, Universidad de Zaragoza, Zaragoza, Spain.
- Instituto Agroalimentario de Aragón (IA2), Zaragoza, Spain.
| | | | - Miguel A Toro
- Dpto. Producción Agraria, ETS Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
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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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Exploitation of Drought Tolerance-Related Genes for Crop Improvement. Int J Mol Sci 2021; 22:ijms221910265. [PMID: 34638606 PMCID: PMC8508643 DOI: 10.3390/ijms221910265] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 09/15/2021] [Accepted: 09/15/2021] [Indexed: 12/03/2022] Open
Abstract
Drought has become a major threat to food security, because it affects crop growth and development. Drought tolerance is an important quantitative trait, which is regulated by hundreds of genes in crop plants. In recent decades, scientists have made considerable progress to uncover the genetic and molecular mechanisms of drought tolerance, especially in model plants. This review summarizes the evaluation criteria for drought tolerance, methods for gene mining, characterization of genes related to drought tolerance, and explores the approaches to enhance crop drought tolerance. Collectively, this review illustrates the application prospect of these genes in improving the drought tolerance breeding of crop plants.
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Labroo MR, Ali J, Aslam MU, de Asis EJ, Dela Paz MA, Sevilla MA, Lipka AE, Studer AJ, Rutkoski JE. Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice ( Oryza sativa L.). Front Genet 2021; 12:692870. [PMID: 34276796 PMCID: PMC8278103 DOI: 10.3389/fgene.2021.692870] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 06/09/2021] [Indexed: 11/16/2022] Open
Abstract
Hybrid rice varieties can outyield the best inbred varieties by 15 – 30% with appropriate management. However, hybrid rice requires more inputs and management than inbred rice to realize a yield advantage in high-yielding environments. The development of stress-tolerant hybrid rice with lowered input requirements could increase hybrid rice yield relative to production costs. We used genomic prediction to evaluate the combining abilities of 564 stress-tolerant lines used to develop Green Super Rice with 13 male sterile lines of the International Rice Research Institute for yield-related traits. We also evaluated the performance of their F1 hybrids. We identified male sterile lines with good combining ability as well as F1 hybrids with potential further use in product development. For yield per plant, accuracies of genomic predictions of hybrid genetic values ranged from 0.490 to 0.822 in cross-validation if neither parent or up to both parents were included in the training set, and both general and specific combining abilities were modeled. The accuracy of phenotypic selection for hybrid yield per plant was 0.682. The accuracy of genomic predictions of male GCA for yield per plant was 0.241, while the accuracy of phenotypic selection was 0.562. At the observed accuracies, genomic prediction of hybrid genetic value could allow improved identification of high-performing single crosses. In a reciprocal recurrent genomic selection program with an accelerated breeding cycle, observed male GCA genomic prediction accuracies would lead to similar rates of genetic gain as phenotypic selection. It is likely that prediction accuracies of male GCA could be improved further by targeted expansion of the training set. Additionally, we tested the correlation of parental genetic distance with mid-parent heterosis in the phenotyped hybrids. We found the average mid-parent heterosis for yield per plant to be consistent with existing literature values at 32.0%. In the overall population of study, parental genetic distance was significantly negatively correlated with mid-parent heterosis for yield per plant (r = −0.131) and potential yield (r = −0.092), but within female families the correlations were non-significant and near zero. As such, positive parental genetic distance was not reliably associated with positive mid-parent heterosis.
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Affiliation(s)
- Marlee R Labroo
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jauhar Ali
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Philippines
| | - M Umair Aslam
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Philippines
| | - Erik Jon de Asis
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Philippines
| | - Madonna A Dela Paz
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Philippines
| | - M Anna Sevilla
- Rice Breeding Platform, International Rice Research Institute, Los Baños, Philippines
| | - Alexander E Lipka
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Anthony J Studer
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Jessica E Rutkoski
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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14
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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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15
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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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16
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Adhikari A, Basnet BR, Crossa J, Dreisigacker S, Camarillo F, Bhati PK, Jarquin D, Manes Y, Ibrahim AMH. Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment. Front Genet 2020; 11:586687. [PMID: 33363570 PMCID: PMC7755068 DOI: 10.3389/fgene.2020.586687] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 10/13/2020] [Indexed: 11/13/2022] Open
Abstract
Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017–2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from −0.03 to 0.74 for AC and −0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.
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Affiliation(s)
- Anil Adhikari
- Texas A&M University, College Station, TX, United States.,Department of Horticulture, University of Wisconsin, Madison, WI, United States
| | - Bhoja Raj Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Fatima Camarillo
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
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17
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Selva C, Riboni M, Baumann U, Würschum T, Whitford R, Tucker MR. Hybrid breeding in wheat: how shaping floral biology can offer new perspectives. FUNCTIONAL PLANT BIOLOGY : FPB 2020; 47:675-694. [PMID: 32534601 DOI: 10.1071/fp19372] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/21/2020] [Indexed: 06/11/2023]
Abstract
Hybrid breeding in wheat (Triticum aestivum L.) has the potential to deliver major yield increases. This is a requisite to guarantee food security for increasing population demands and to counterbalance the effects of extreme environmental conditions. Successful hybrid breeding in wheat relies on forced outcrossing while preventing self-pollination. To achieve this, research has been directed towards identifying and improving fertility control systems. To maximise cross-pollination and seed set, however, fertility control systems need to be complemented by breeding phenotypically distinct male and female lines. This review summarises existing and novel male sterility systems for wheat hybridisation. We also consider the genetic resources that can be used to alter wheat's floral development and spike morphology, with a focus on the genetic variation already available. Exploiting these resources can lead to enhanced outcrossing, a key requirement in the progress towards hybrid wheat breeding.
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Affiliation(s)
- Caterina Selva
- School of Agriculture Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia
| | - Matteo Riboni
- School of Agriculture Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia
| | - Ute Baumann
- School of Agriculture Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia
| | - Tobias Würschum
- State Plant Breeding Institute, University of Hohenheim, 70593 Stuttgart, Germany
| | - Ryan Whitford
- School of Agriculture Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia; and Corresponding authors. ;
| | - Matthew R Tucker
- School of Agriculture Food and Wine, University of Adelaide, Waite Campus, Urrbrae, SA 5064, Australia; and Corresponding authors. ;
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18
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In silico simulation of future hybrid performance to evaluate heterotic pool formation in a self-pollinating crop. Sci Rep 2020; 10:4037. [PMID: 32132627 PMCID: PMC7055256 DOI: 10.1038/s41598-020-61031-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 02/19/2020] [Indexed: 12/05/2022] Open
Abstract
Hybrid vigour has the potential to substantially increase the yield of self-pollinating crops such as wheat and rice, but future hybrid performance may depend on the initial strategy to form heterotic pools. We used in silico stochastic simulation of future hybrid performance in a self-pollinating crop to evaluate three strategies of forming heterotic pools in the founder population. The model included either 500, 2000 or 8000 quantitative trait nucleotides (QTN) across 10 chromosomes that contributed to a quantitative trait with population mean 100 and variance 10. The average degree of dominance at each QTN was either 0.2, 0.4 or 0.8 with variance 0.2. Three strategies for splitting the founder population into two heterotic pools were compared: (i) random split; (ii) split based on genetic distance according to principal component analysis of SNP genotypes; and (iii) optimized split based on F1 hybrid performance in a diallel cross among the founders. Future hybrid performance was stochastically simulated over 30 cycles of reciprocal recurrent selection based on true genetic values for additive and dominance effects. The three strategies of forming heterotic pools produced similar future hybrid performance, and superior future hybrids to a control population selected on inbred line performance when the number of quantitative trait nucleotides was ≥2000 and/or the average degree of dominance was ≥0.4.
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19
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Santantonio N, Atanda SA, Beyene Y, Varshney RK, Olsen M, Jones E, Roorkiwal M, Gowda M, Bharadwaj C, Gaur PM, Zhang X, Dreher K, Ayala-Hernández C, Crossa J, Pérez-Rodríguez P, Rathore A, Gao SY, McCouch S, Robbins KR. Strategies for Effective Use of Genomic Information in Crop Breeding Programs Serving Africa and South Asia. FRONTIERS IN PLANT SCIENCE 2020; 11:353. [PMID: 32292411 PMCID: PMC7119190 DOI: 10.3389/fpls.2020.00353] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Accepted: 03/10/2020] [Indexed: 05/20/2023]
Abstract
Much of the world's population growth will occur in regions where food insecurity is prevalent, with large increases in food demand projected in regions of Africa and South Asia. While improving food security in these regions will require a multi-faceted approach, improved performance of crop varieties in these regions will play a critical role. Current rates of genetic gain in breeding programs serving Africa and South Asia fall below rates achieved in other regions of the world. Given resource constraints, increased genetic gain in these regions cannot be achieved by simply expanding the size of breeding programs. New approaches to breeding are required. The Genomic Open-source Breeding informatics initiative (GOBii) and Excellence in Breeding Platform (EiB) are working with public sector breeding programs to build capacity, develop breeding strategies, and build breeding informatics capabilities to enable routine use of new technologies that can improve the efficiency of breeding programs and increase genetic gains. Simulations evaluating breeding strategies indicate cost-effective implementations of genomic selection (GS) are feasible using relatively small training sets, and proof-of-concept implementations have been validated in the International Maize and Wheat Improvement Center (CIMMYT) maize breeding program. Progress on GOBii, EiB, and implementation of GS in CIMMYT and International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) breeding programs are discussed, as well as strategies for routine implementation of GS in breeding programs serving Africa and South Asia.
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Affiliation(s)
- Nicholas Santantonio
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Sikiru Adeniyi Atanda
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- West Africa Center for Crop Improvement (WACCI), University of Ghana, Accra, Ghana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Elizabeth Jones
- Genomic Open-Source Breeding Informatics Initiative (GOBii) Project, Institute of Biotechnology, Cornell University, Ithaca, NY, United States
| | - Manish Roorkiwal
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Chellapilla Bharadwaj
- Division of Genetics, Indian Agriculture Research Institute (ICAR), New Delhi, India
| | - Pooran M. Gaur
- Research Program - Asia, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kate Dreher
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Abhishek Rathore
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - Star Yanxin Gao
- Genomic Open-Source Breeding Informatics Initiative (GOBii) Project, Institute of Biotechnology, Cornell University, Ithaca, NY, United States
| | - Susan McCouch
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Kelly R. Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
- *Correspondence: Kelly R. Robbins,
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20
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Adhikari A, Basnet BR, Crossa J, Dreisigacker S, Camarillo F, Bhati PK, Jarquin D, Manes Y, Ibrahim AMH. Genome-Wide Association Mapping and Genomic Prediction of Anther Extrusion in CIMMYT Hybrid Wheat Breeding Program via Modeling Pedigree, Genomic Relationship, and Interaction With the Environment. Front Genet 2020. [PMID: 33363570 DOI: 10.3389/fgene.2020.586687.\] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2023] Open
Abstract
Anther extrusion (AE) is the most important male floral trait for hybrid wheat seed production. AE is a complex quantitative trait that is difficult to phenotype reliably in field experiments not only due to high genotype-by-environment effects but also due to the short expression window in the field condition. In this study, we conducted a genome-wide association scan (GWAS) and explored the possibility of applying genomic prediction (GP) for AE in the CIMMYT hybrid wheat breeding program. An elite set of male lines (n = 603) were phenotype for anther count (AC) and anther visual score (VS) across three field experiments in 2017-2019 and genotyped with the 20K Infinitum is elect SNP array. GWAS produced five marker trait associations with small effects. For GP, the main effects of lines (L), environment (E), genomic (G) and pedigree relationships (A), and their interaction effects with environments were used to develop seven statistical models of incremental complexity. The base model used only L and E, whereas the most complex model included L, E, G, A, and G × E and A × E. These models were evaluated in three cross-validation scenarios (CV0, CV1, and CV2). In cross-validation CV0, data from two environments were used to predict an untested environment; in random cross-validation CV1, the test set was never evaluated in any environment; and in CV2, the genotypes in the test set were evaluated in only a subset of environments. The prediction accuracies ranged from -0.03 to 0.74 for AC and -0.01 to 0.54 for VS across different models and CV schemes. For both traits, the highest prediction accuracies with low variance were observed in CV2, and inclusion of the interaction effects increased prediction accuracy for AC only. In CV0, the prediction accuracy was 0.73 and 0.45 for AC and VS, respectively, indicating the high reliability of across environment prediction. Genomic prediction appears to be a very reliable tool for AE in hybrid wheat breeding. Moreover, high prediction accuracy in CV0 demonstrates the possibility of implementing genomic selection across breeding cycles in related germplasm, aiding the rapid breeding cycle.
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Affiliation(s)
- Anil Adhikari
- Texas A&M University, College Station, TX, United States
- Department of Horticulture, University of Wisconsin, Madison, WI, United States
| | - Bhoja Raj Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Fatima Camarillo
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
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21
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Gupta PK, Balyan HS, Gahlaut V, Saripalli G, Pal B, Basnet BR, Joshi AK. Hybrid wheat: past, present and future. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:2463-2483. [PMID: 31321476 DOI: 10.1007/s00122-019-03397-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 07/11/2019] [Indexed: 05/02/2023]
Abstract
The review outlines past failures, present status and future prospects of hybrid wheat, and includes information on CMS/CHA/transgenic approaches for male sterility, heterotic groups and cost-effective hybrid seed production. Hybrid varieties give increased yield and improved grain quality in both cross- and self-pollinated crops. However, hybrid varieties in self-pollinated crops (particularly cereals) have not been very successful, except for hybrid rice in China. In case of hybrid wheat, despite the earlier failures, renewed efforts in recent years have been made and hybrid varieties with desirable attributes have been produced and marketed in some European countries. This review builds upon previous reviews, with a new outlook and improved knowledge base, not covered in earlier reviews. New technologies have been described, which include the Hordeum chilense-based CMS-fertility restorer system, chromosomal XYZ-4E-ms system and the following transgenic technologies: (1) conditional male sterility involving use of tapetum-specific expression of a gene that converts a pro-toxin into a phytotoxin causing male sterility; (2) barnase-barstar SeedLink system of Bayer CropScience; (3) split-barnase system that obviates the need of a barstar-based male restorer line; and (4) seed production technology of DuPont-Pioneer that makes use of transgenes in production of male-sterile lines, but gives hybrid seed with no transgenes. This review also includes a brief account of studies for discovery of heterotic QTL, genomic prediction of hybrid vigour and the development of heterotic groups/patterns and their importance in hybrid wheat production. The problem of high cost of hybrid seed due to required high seed rate in wheat relative to hybrid rice has also been addressed. The review concludes with a brief account of the current efforts and future possibilities in making hybrid wheat a commercial success.
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Affiliation(s)
- Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250001, India.
| | - Harindra Singh Balyan
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250001, India
| | - Vijay Gahlaut
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250001, India
- Department of Plant Molecular Biology, University of Delhi, South Campus, New Delhi, 110021, India
| | - Gautam Saripalli
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250001, India
| | - Bijendra Pal
- Bioseed Research India Pvt. Ltd., Hyderabad, 500 033, India
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22
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Godwin ID, Rutkoski J, Varshney RK, Hickey LT. Technological perspectives for plant breeding. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:555-557. [PMID: 30888430 DOI: 10.1007/s00122-019-03321-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 03/05/2019] [Indexed: 05/20/2023]
Affiliation(s)
- Ian D Godwin
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Jessica Rutkoski
- Rice Breeding Platform, International Rice Research Institute, 4031, Los Banos, Laguna, Philippines
| | - Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India
| | - Lee T Hickey
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, 4072, Australia
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