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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. Mol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Fernández-González J, Haquin B, Combes E, Bernard K, Allard A, Isidro Y Sánchez J. Maximizing efficiency in sunflower breeding through historical data optimization. Plant Methods 2024; 20:42. [PMID: 38493115 PMCID: PMC10943787 DOI: 10.1186/s13007-024-01151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 01/30/2024] [Indexed: 03/18/2024]
Abstract
Genomic selection (GS) has become an increasingly popular tool in plant breeding programs, propelled by declining genotyping costs, an increase in computational power, and rediscovery of the best linear unbiased prediction methodology over the past two decades. This development has led to an accumulation of extensive historical datasets with genotypic and phenotypic information, triggering the question of how to best utilize these datasets. Here, we investigate whether all available data or a subset should be used to calibrate GS models for across-year predictions in a 7-year dataset of a commercial hybrid sunflower breeding program. We employed a multi-objective optimization approach to determine the ideal years to include in the training set (TRS). Next, for a given combination of TRS years, we further optimized the TRS size and its genetic composition. We developed the Min_GRM size optimization method which consistently found the optimal TRS size, reducing dimensionality by 20% with an approximately 1% loss in predictive ability. Additionally, the Tails_GEGVs algorithm displayed potential, outperforming the use of all data by using just 60% of it for grain yield, a high-complexity, low-heritability trait. Moreover, maximizing the genetic diversity of the TRS resulted in a consistent predictive ability across the entire range of genotypic values in the test set. Interestingly, the Tails_GEGVs algorithm, due to its ability to leverage heterogeneity, enhanced predictive performance for key hybrids with extreme genotypic values. Our study provides new insights into the optimal utilization of historical data in plant breeding programs, resulting in improved GS model predictive ability.
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Affiliation(s)
- Javier Fernández-González
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
| | | | | | | | | | - Julio Isidro Y Sánchez
- Centro de Biotecnologia y Genómica de Plantas (CBGP, UPM-INIA)-Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria (INIA), Universidad Politécnica de Madrid (UPM), Campus de Montegancedo-UPM, Pozuelo de Alarcón, Madrid, 28223, Spain.
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Lorenzi A, Bauland C, Pin S, Madur D, Combes V, Palaffre C, Guillaume C, Touzy G, Mary-Huard T, Charcosset A, Moreau L. Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles. Theor Appl Genet 2024; 137:75. [PMID: 38453705 DOI: 10.1007/s00122-024-04566-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 01/30/2024] [Indexed: 03/09/2024]
Abstract
KEY MESSAGE We validated the efficiency of genomic predictions calibrated on sparse factorial training sets to predict the next generation of hybrids and tested different strategies for updating predictions along generations. Genomic selection offers new prospects for revisiting hybrid breeding schemes by replacing extensive phenotyping of individuals with genomic predictions. Finding the ideal design for training genomic prediction models is still an open question. Previous studies have shown promising predictive abilities using sparse factorial instead of tester-based training sets to predict single-cross hybrids from the same generation. This study aims to further investigate the use of factorials and their optimization to predict line general combining abilities (GCAs) and hybrid values across breeding cycles. It relies on two breeding cycles of a maize reciprocal genomic selection scheme involving multiparental connected reciprocal populations from flint and dent complementary heterotic groups selected for silage performances. Selection based on genomic predictions trained on a factorial design resulted in a significant genetic gain for dry matter yield in the new generation. Results confirmed the efficiency of sparse factorial training sets to predict candidate line GCAs and hybrid values across breeding cycles. Compared to a previous study based on the first generation, the advantage of factorial over tester training sets appeared lower across generations. Updating factorial training sets by adding single-cross hybrids between selected lines from the previous generation or a random subset of hybrids from the new generation both improved predictive abilities. The CDmean criterion helped determine the set of single-crosses to phenotype to update the training set efficiently. Our results validated the efficiency of sparse factorial designs for calibrating hybrid genomic prediction experimentally and showed the benefit of updating it along generations.
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Affiliation(s)
- Alizarine Lorenzi
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Sophie Pin
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | | | - Gaëtan Touzy
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA Paris-Saclay, 91120, Palaiseau, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution (GQE) - Le Moulon, 91190, Gif-Sur-Yvette, France.
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Liu M, Zhang Y, Shaw RK, Zhang X, Li J, Li L, Li S, Adnan M, Jiang F, Bi Y, Yin X, Fan X. Genome-Wide Association Study and Prediction of Tassel Weight of Tropical Maize Germplasm in Multi-Parent Population. Int J Mol Sci 2024; 25:1756. [PMID: 38339032 PMCID: PMC10855296 DOI: 10.3390/ijms25031756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/20/2024] [Accepted: 01/29/2024] [Indexed: 02/12/2024] Open
Abstract
Tassel weight (TW) is a crucial agronomic trait that significantly affects pollen supply and grain yield development in maize breeding. To improve maize yield and develop new varieties, a comprehensive understanding of the genetic mechanisms underlying tassel weight is essential. In this study, tropical maize inbred lines, namely CML312, CML373, CML444, and YML46, were selected as female parents and crossed with the elite maize inbred line Ye107, which served as the common male parent, to develop a multi-parent population comprising four F8 recombinant inbred line (RIL) subpopulations. Using 6616 high-quality single nucleotide polymorphism (SNP) markers, we conducted genome-wide association analysis (GWAS) and genomic selection (GS) on 642 F8 RILs in four subpopulations across three different environments. Through GWAS, we identified 16 SNPs that were significantly associated with TW, encompassing two stable loci expressed across multiple environments. Furthermore, within the candidate regions of these SNPs, we discovered four novel candidate genes related to TW, namely Zm00001d044362, Zm00001d011048, Zm00001d011049, and Zm00001d031173 distributed on chromosomes 1, 3, and 8, which have not been previously reported. These genes are involved in processes such as signal transduction, growth and development, protein splicing, and pollen development, all of which play crucial roles in inflorescence meristem development, directly affecting TW. The co-localized SNP, S8_137379725, on chromosome 8 was situated within a 16.569 kb long terminal repeat retrotransposon (LTR-RT), located 22.819 kb upstream and 26.428 kb downstream of the candidate genes (Zm00001d011048 and Zm00001d011049). When comparing three distinct GS models, the BayesB model demonstrated the highest accuracy in predicting TW. This study establishes the theoretical foundation for future research into the genetic mechanisms underlying maize TW and the efficient breeding of high-yielding varieties with desired tassel weight through GS.
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Affiliation(s)
- Meichen Liu
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Ranjan K. Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingjie Zhang
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Jinfeng Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Linzhuo Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Shaoxiong Li
- School of Agriculture, Yunnan University, Kunming 650500, China; (M.L.); (X.Z.); (J.L.); (L.L.); (S.L.)
| | - Muhammad Adnan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingfu Yin
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (Y.Z.); (R.K.S.); (M.A.); (F.J.); (Y.B.); (X.Y.)
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Fritsche-Neto R, Ali J, De Asis EJ, Allahgholipour M, Labroo MR. Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance. Theor Appl Genet 2023; 137:3. [PMID: 38085288 PMCID: PMC10716074 DOI: 10.1007/s00122-023-04508-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 11/18/2023] [Indexed: 12/18/2023]
Abstract
KEY MESSAGE Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.
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Affiliation(s)
- Roberto Fritsche-Neto
- International Rice Research Institute (IRRI), Los Banos, Philippines.
- H. Rouse Caffey Rice Research Station, LSU AgCenter, Rayne, USA.
| | - Jauhar Ali
- International Rice Research Institute (IRRI), Los Banos, Philippines.
| | - Erik Jon De Asis
- International Rice Research Institute (IRRI), Los Banos, Philippines
| | | | - Marlee Rose Labroo
- Excellence in Breeding Platform, Consultative Group of International Agricultural Research, Lisbon, Mexico
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Sweet PK, Bernardo R. Reciprocal testcross design for genome-wide prediction of maize single-cross performance. Theor Appl Genet 2023; 136:184. [PMID: 37555961 DOI: 10.1007/s00122-023-04435-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 07/25/2023] [Indexed: 08/10/2023]
Abstract
KEY MESSAGE A reciprocal testcross design increases the relatedness among single crosses and testcrosses, thereby increasing the effectiveness of genome-wide prediction in maize. A reciprocal testcross design uses parental inbreds in an opposite heterotic group as testers in maize (Zea mays L.) inbred development. In particular, doubled haploids from the A × B cross are testcrossed with inbreds Y and Z, and doubled haploids from Y × Z are testcrossed with inbreds A and B. Our objective was to determine if a reciprocal testcross design is superior to a traditional, non-reciprocal testcross design. A total of 700 Iowa Stiff Stalk Synthetic (BSSS) doubled haploids and 231 non-BSSS doubled haploids were developed from 10 breeding populations and had data on 11,032 single nucleotide polymorphism markers. Each doubled haploid was testcrossed to one to five testers from the opposite heterotic group, and the resulting 1642 testcrosses were evaluated in multilocation yield trials in 2019. Divergent selection for yield/moisture, on the basis of genome-wide predictions according to a reciprocal testcross design, led to significant responses (in 2020) in all 10 populations for yield/moisture and moisture and in three populations for yield. Predictive ability for yield/moisture and moisture was 0.11 to 0.26 higher with a reciprocal testcross design than with a testcross design. This higher predictive ability was attributed to a stronger relatedness between the training and test populations. No significant difference in predictive ability was found for yield, for which predictive ability was lower. Differences among genetic models that included and excluded specific combining ability were small. Overall, the empirical results supported the usefulness of a reciprocal testcross design in maize breeding.
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Affiliation(s)
| | - Rex Bernardo
- Department of Agronomy and Plant Genetics, University of Minnesota, Saint Paul, USA.
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Kamweru I, Beyene Y, Bruce AY, Makumbi D, Adetimirin VO, Pérez-Rodríguez P, Toledo F, Crossa J, Prasanna BM, Gowda M. Genetic analyses of tropical maize lines under artificial infestation of fall armyworm and foliar diseases under optimum conditions. Front Plant Sci 2023; 14:1086757. [PMID: 36743507 PMCID: PMC9896009 DOI: 10.3389/fpls.2023.1086757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 01/04/2023] [Indexed: 06/18/2023]
Abstract
Development and deployment of high-yielding maize varieties with native resistance to Fall armyworm (FAW), turcicum leaf blight (TLB), and gray leaf spot (GLS) infestation is critical for addressing the food insecurity in sub-Saharan Africa. The objectives of this study were to determine the inheritance of resistance for FAW, identity hybrids which in addition to FAW resistance, also show resistance to TLB and GLS, and investigate the usefulness of models based on general combining ability (GCA) and SNP markers in predicting the performance of new untested hybrids. Half-diallel mating scheme was used to generate 105 F1 hybrids from 15 parents and another 55 F1 hybrids from 11 parents. These were evaluated in two experiments, each with commercial checks in multiple locations under FAW artificial infestation and optimum management in Kenya. Under artificial FAW infestation, significant mean squares among hybrids and hybrids x environment were observed for most traits in both experiments, including at least one of the three assessments carried out for foliar damage caused by FAW. Interaction of GCA x environment and specific combining ability (SCA) x environment interactions were significant for all traits under FAW infestation and optimal conditions. Moderate to high heritability estimates were observed for GY under both management conditions. Correlation between GY and two of the three scorings (one and three weeks after infestation) for foliar damage caused by FAW were negative (-0.27 and -0.38) and significant. Positive and significant correlation (0.84) was observed between FAW-inflicted ear damage and the percentage of rotten ears. We identified many superior-performing hybrids compared to the best commercial checks for both GY and FAW resistance associated traits. Inbred lines CML312, CML567, CML488, DTPYC9-F46-1-2-1-2, CKDHL164288, CKDHL166062, and CLRCY039 had significant and positive GCA for GY (positive) and FAW resistance-associated traits (negative). CML567 was a parent in four of the top ten hybrids under optimum and FAW conditions. Both additive and non-additive gene action were important in the inheritance of FAW resistance. Both GCA and marker-based models showed high correlation with field performance, but marker-based models exhibited considerably higher correlation. The best performing hybrids identified in this study could be used as potential single cross testers in the development of three-way FAW resistance hybrids. Overall, our results provide insights that help breeders to design effective breeding strategies to develop FAW resistant hybrids that are high yielding under FAW and optimum conditions.
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Affiliation(s)
- Isaac Kamweru
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- Pan African University, Life and Earth Sciences Institute (Including Health and Agriculture), Ibadan, Nigeria
| | - Yoseph Beyene
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Anani Y. Bruce
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Dan Makumbi
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Victor O. Adetimirin
- Department of Crop and Horticultural Sciences, University of Ibadan, Ibadan, Nigeria
| | - Paulino Pérez-Rodríguez
- Colegio de Postgraduados, Montecillo, Mexico
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Fernando Toledo
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Manje Gowda
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
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Dang D, Guan Y, Zheng H, Zhang X, Zhang A, Wang H, Ruan Y, Qin L. Genome-Wide Association Study and Genomic Prediction on Plant Architecture Traits in Sweet Corn and Waxy Corn. Plants (Basel) 2023; 12:303. [PMID: 36679015 PMCID: PMC9867343 DOI: 10.3390/plants12020303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/01/2023] [Accepted: 01/03/2023] [Indexed: 06/17/2023]
Abstract
Sweet corn and waxy corn has a better taste and higher accumulated nutritional value than regular maize, and is widely planted and popularly consumed throughout the world. Plant height (PH), ear height (EH), and tassel branch number (TBN) are key plant architecture traits, which play an important role in improving grain yield in maize. In this study, a genome-wide association study (GWAS) and genomic prediction analysis were conducted on plant architecture traits of PH, EH, and TBN in a fresh edible maize population consisting of 190 sweet corn inbred lines and 287 waxy corn inbred lines. Phenotypic data from two locations showed high heritability for all three traits, with significant differences observed between sweet corn and waxy corn for both PH and EH. The differences between the three subgroups of sweet corn were not obvious for all three traits. Population structure and PCA analysis results divided the whole population into three subgroups, i.e., sweet corn, waxy corn, and the subgroup mixed with sweet and waxy corn. Analysis of GWAS was conducted with 278,592 SNPs obtained from resequencing data; 184, 45, and 68 significantly associated SNPs were detected for PH, EH, and TBN, respectively. The phenotypic variance explained (PVE) values of these significant SNPs ranged from 3.50% to 7.0%. The results of this study lay the foundation for further understanding the genetic basis of plant architecture traits in sweet corn and waxy corn. Genomic selection (GS) is a new approach for improving quantitative traits in large plant breeding populations that uses whole-genome molecular markers. The marker number and marker quality are essential for the application of GS in maize breeding. GWAS can choose the most related markers with the traits, so it can be used to improve the predictive accuracy of GS.
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Affiliation(s)
- Dongdong Dang
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
| | - Yuan Guan
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), El Batan, Texcoco 56237, Mexico
| | - Ao Zhang
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China
| | - Hui Wang
- CIMMYT-China Specialty Maize Research Center, Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, China
| | - Yanye Ruan
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China
| | - Li Qin
- Shenyang City Key Laboratory of Maize Genomic Selection Breeding, College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang 110866, China
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DoVale JC, Carvalho HF, Sabadin F, Fritsche-Neto R. Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops. Theor Appl Genet 2022; 135:4523-4539. [PMID: 36261658 DOI: 10.1007/s00122-022-04236-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 10/09/2022] [Indexed: 06/16/2023]
Abstract
In genomic recurrent selection, the more markers, the better because they buffer the linkage disequilibrium losses caused by recombination over cycles, and consequently, provide higher responses to selection. Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, studies that focus on long selective breeding cycles generally do not consider GS models with the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models [additive, additive-dominant, general (GCA), and this plus specific combining ability (GCA + SCA)]. Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additive-dominant or GCA + SCA for reciprocal.
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Affiliation(s)
- Júlio César DoVale
- Department of Crop Science, Federal University of Ceará, Fortaleza, CE, Brazil.
| | | | - Felipe Sabadin
- Virginia Tech: Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
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10
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Rogers AR, Bian Y, Krakowsky M, Peters D, Turnbull C, Nelson P, Holland JB. Genomic prediction for the Germplasm Enhancement of Maize project. Plant Genome 2022; 15:e20267. [PMID: 36281214 DOI: 10.1002/tpg2.20267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
The Germplasm Enhancement of Maize (GEM) project was initiated in 1993 as a cooperative effort of public- and private-sector maize (Zea mays L.) breeders to enhance the genetic diversity of the U.S. maize crop. The GEM project selects progeny lines with high topcross yield potential from crosses between elite temperate lines and exotic parents. The GEM project has released hundreds of useful breeding lines based on phenotypic selection within selfing generations and multienvironment yield evaluations of GEM line topcrosses to elite adapted testers. Developing genomic selection (GS) models for the GEM project may contribute to increases in the rate of genetic gain. Here we evaluated the prediction ability of GS models trained on 6 yr of topcross evaluations from the two GEM programs in Raleigh, NC, and Ames, IA, documenting prediction abilities ranging from 0.36 to 0.75 for grain yield and from 0.78 to 0.96 for grain moisture when models were cross-validated within program and heterotic group. Predicted genetic gain from GS ranged from 0.95 to 2.58 times the gain from phenotypic selection. Prediction ability across program and heterotic group was generally poorer than within groups. Based on observed genomic relationships between GEM breeding lines and their tropical ancestors, GS for either yield or moisture would reduce recovery of exotic germplasm only slightly. Using GS models trained within program, the GEM programs should be able to more effectively deliver on its mission to broaden the genetic base of U.S. germplasm.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State Univ., Raleigh, NC, 27695, USA
| | - Yang Bian
- Bayer Crop Science, 700 Chesterfield Pkwy W, Chesterfield, MO, 63017, USA
| | - Matthew Krakowsky
- USDA-ARS Plant Science Research Unit and Dep. of Crop and Soil Sciences, North Carolina State Univ., Raleigh, NC, 27695, USA
| | - David Peters
- USDA-ARS Plant Introduction Research Unit, Iowa State Univ., Ames, IA, 50011, USA
| | - Clint Turnbull
- Bayer Crop Science, 700 Chesterfield Pkwy W, Chesterfield, MO, 63017, USA
| | - Paul Nelson
- Bayer Crop Science, 700 Chesterfield Pkwy W, Chesterfield, MO, 63017, USA
| | - James B Holland
- USDA-ARS Plant Science Research Unit and Dep. of Crop and Soil Sciences and NC Plant Sciences Initiative, North Carolina State Univ., Raleigh, NC, 27695, USA
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11
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Zhang M, Liu YH, Wang Y, Sze SH, Scheuring CF, Qi X, Ekinci O, Pekar J, Murray SC, Zhang HB. Genome-wide identification of genes enabling accurate prediction of hybrid performance from parents across environments and populations for gene-based breeding in maize. Plant Sci 2022; 324:111424. [PMID: 35995113 DOI: 10.1016/j.plantsci.2022.111424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 08/07/2022] [Accepted: 08/16/2022] [Indexed: 06/15/2023]
Abstract
Accurate prediction of hybrid offspring complex trait phenotype from parents is paramount to enhanced plant breeding, animal breeding, and human medicine. Here we report genome-wide identification of genes enabling accurate prediction of hybrid offspring complex traits from parents using maize grain yield as the target trait. We identified 181 ZmF1GY genes enabling prediction of maize (Zea mays L.) F1 hybrid grain yield from parents and tested their utility and efficiency for predicting F1 hybrid grain yields from parents using their expressions, genic SNPs, and number of favorable alleles (NFAs), respectively. The ZmF1GY genes predicted hybrid grain yields from parents at an accuracy of 0.86, presented by correlation coefficient between predicted and observed phenotypes, within an environment, 0.74 across environments, and 0.64 across populations, outperforming genomic prediction by 27-406%, 23%, and 40%, respectively. Furthermore, we identified nine of the ZmF1GY genes containing SNPs or InDels in parents that increased or decreased hybrid grain yields by 14-46%. When the NFAs of these nine ZmF1GY genes were used for hybrid grain yield prediction from parents, they predicted hybrid grain yields at an accuracy of 0.79, outperforming genomic prediction by 21% that was based on up to tens of thousands of genome-wide SNPs. These results demonstrate the feasibility of developing a gene toolkit for a species enabling gene-based breeding across environments and populations that is much more powerful and efficient than current breeding, thereby helping secure the world's food production. The methodology is applicable to all crops, livestock, and humans.
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Affiliation(s)
- Meiping Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yun-Hua Liu
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Yinglei Wang
- Department of Computer Science, Cornell University, Ithaca, NY 14853, USA.
| | - Sing-Hoi Sze
- Department of Computer Science and Engineering and Department of Biochemistry and Biophysics, Texas A&M University, College Station, TX 77843, USA.
| | - Chantel F Scheuring
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Xiaoli Qi
- College of Life Science, Jiamusi University, Jiamusi, Heilongjiang 154007, China.
| | - Ozge Ekinci
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Jacob Pekar
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
| | - Hong-Bin Zhang
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA.
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Lorenzi A, Bauland C, Mary-Huard T, Pin S, Palaffre C, Guillaume C, Lehermeier C, Charcosset A, Moreau L. Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage. Theor Appl Genet 2022; 135:3143-3160. [PMID: 35918515 DOI: 10.1007/s00122-022-04176-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Calibrating a genomic selection model on a sparse factorial design rather than on tester designs is advantageous for some traits, and equivalent for others. In maize breeding, the selection of the candidate inbred lines is based on topcross evaluations using a limited number of testers. Then, a subset of single-crosses between these selected lines is evaluated to identify the best hybrid combinations. Genomic selection enables the prediction of all possible single-crosses between candidate lines but raises the question of defining the best training set design. Previous simulation results have shown the potential of using a sparse factorial design instead of tester designs as the training set. To validate this result, a 363 hybrid factorial design was obtained by crossing 90 dent and flint inbred lines from six segregating families. Two tester designs were also obtained by crossing the same inbred lines to two testers of the opposite group. These designs were evaluated for silage in eight environments and used to predict independent performances of a 951 hybrid factorial design. At a same number of hybrids and lines, the factorial design was as efficient as the tester designs, and, for some traits, outperformed them. All available designs were used as both training and validation set to evaluate their efficiency. When the objective was to predict single-crosses between untested lines, we showed an advantage of increasing the number of lines involved in the training set, by (1) allocating each of them to a different tester for the tester design, or (2) reducing the number of hybrids per line for the factorial design. Our results confirm the potential of sparse factorial designs for genomic hybrid breeding.
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Affiliation(s)
- Alizarine Lorenzi
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Cyril Bauland
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- MIA, INRAE, AgroParisTech, Université Paris-Saclay, 75005, Paris, France
| | - Sophie Pin
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | | | | | - Alain Charcosset
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Laurence Moreau
- Génétique Quantitative et Evolution - Le Moulon, INRAE, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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Mesterhazy A, Szabó B, Szél S, Nagy Z, Berényi A, Tóth B. Novel Insights into the Inheritance of Gibberella Ear Rot (GER), Deoxynivalenol (DON) Accumulation, and DON Production. Toxins (Basel) 2022; 14:toxins14090583. [PMID: 36136521 PMCID: PMC9504231 DOI: 10.3390/toxins14090583] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 08/10/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
Gibberella ear rot (GER) is an important fungal ear pathogen of maize that causes ear rot and toxin contamination. Most previous works have only dealt with the visual symptoms, but not with the toxins of GER. As food and feed safety rankings depend on toxin contamination, including deoxynivalenol (DON), without toxins, nothing can be said about the risks involved in food and feed quality. Therefore, three susceptible, three medium-susceptible, and three medium-resistant mother lines were crossed with three testers with differing degrees of resistance and tested between 2017–2020. Two plot replicates and two fungal strains were used separately. The highest heterosis was found at the GER% with a 13% increase across 27 hybrids, including 7 hybrids showing negative heterosis (a higher hybrid performance above the parental mean), with a variance ranging between 63.5 and −55.4. For DON, the mean heterosis was negative at −35%, and only 10 of the 27 hybrids showed a positive heterosis. The mean heterosis for DON contamination, at 1% GER, was again negative (−19.6%, varying between 85% and 224%). Only 17 hybrids showed heterosis, while that of the other 17 was rated higher than the parental mean. A positive significant correlation was found only for GER% and DON; the other factors were not significant. Seven hybrids were identified with positive (2) or negative (5) heterosis for all traits, while the rest varied. For DON and GER, only 13 provided identical (positive or negative) heteroses. The majority of the hybrids appeared to diverge in the regulation of the three traits. The stability of GER and DON (variance across eight data sets) did not agree—only half of the genotypes responded similarly for the two traits. The genetic background for this trait is unknown, and there was no general agreement between traits. Thus, without toxin analyses, the evaluation of food safety is not possible. The variety in degrees of resistance to toxigenic fungi and resistance to toxin accumulation is an inevitable factor.
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John Martin JJ, Yarra R, Wei L, Cao H. Oil Palm Breeding in the Modern Era: Challenges and Opportunities. Plants 2022; 11:plants11111395. [PMID: 35684168 PMCID: PMC9183044 DOI: 10.3390/plants11111395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 05/15/2022] [Accepted: 05/19/2022] [Indexed: 11/30/2022]
Abstract
Oil palm, a cross-pollinated crop with long generation time, poses a lot of challenges in achieving sustainable oil palm with high yield and quality. The African oil palm (Elaeis guineensis Jacq.) is the most productive and versatile oil-yielding crop in the world, producing more than any other oil-yielding crop. Despite recent challenges, such as stress tolerance, superior oil quality, disease tolerance, and the need for new market niches, there is a growing need to explore and develop new varieties with high yield potential and the genetic diversity required to maintain oil palm yield stability. Breeding is an indispensable part of producing high-quality planting materials to increase oil palm yield. Biotechnological technologies have transformed conventional plant breeding approaches by introducing novel genotypes for breeding. Innovative pre-breeding and breeding approaches, such as identifying candidate genes in wild or land races using genomics tools, can pave the way for genetic improvement in oil palm. In this review, we highlighted the modern breeding tools, including genomics, marker-assisted breeding, genetic engineering, and genome editing techniques in oil palm crops, and we explored certain concerns connected to the techniques and their applications in practical breeding.
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Affiliation(s)
- Jerome Jeyakumar John Martin
- Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, China
- Hainan Key Laboratory of Tropical Oil Crops Biology, Wenchang 571339, China
| | - Rajesh Yarra
- Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, China
- Hainan Key Laboratory of Tropical Oil Crops Biology, Wenchang 571339, China
| | - Lu Wei
- Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, China
- Hainan Key Laboratory of Tropical Oil Crops Biology, Wenchang 571339, China
| | - Hongxing Cao
- Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang 571339, China
- Hainan Key Laboratory of Tropical Oil Crops Biology, Wenchang 571339, China
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Shamsabadi EE, Sabouri H, Soughi H, Sajadi SJ. Using of Molecular Markers in Prediction of Wheat (Triticum aestivum L.) Hybrid Grain Yield Based on Artificial Intelligence Methods and Multivariate Statistics. RUSS J GENET+ 2022. [DOI: 10.1134/s102279542205009x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Rio S, Charcosset A, Mary-Huard T, Moreau L, Rincent R. Building a Calibration Set for Genomic Prediction, Characteristics to Be Considered, and Optimization Approaches. Methods Mol Biol 2022; 2467:77-112. [PMID: 35451773 DOI: 10.1007/978-1-0716-2205-6_3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
The efficiency of genomic selection strongly depends on the prediction accuracy of the genetic merit of candidates. Numerous papers have shown that the composition of the calibration set is a key contributor to prediction accuracy. A poorly defined calibration set can result in low accuracies, whereas an optimized one can considerably increase accuracy compared to random sampling, for a same size. Alternatively, optimizing the calibration set can be a way of decreasing the costs of phenotyping by enabling similar levels of accuracy compared to random sampling but with fewer phenotypic units. We present here the different factors that have to be considered when designing a calibration set, and review the different criteria proposed in the literature. We classified these criteria into two groups: model-free criteria based on relatedness, and criteria derived from the linear mixed model. We introduce criteria targeting specific prediction objectives including the prediction of highly diverse panels, biparental families, or hybrids. We also review different ways of updating the calibration set, and different procedures for optimizing phenotyping experimental designs.
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Fu J, Hao Y, Li H, Reif JC, Chen S, Huang C, Wang G, Li X, Xu Y, Li L. Integration of genomic selection with doubled-haploid evaluation in hybrid breeding: From GS 1.0 to GS 4.0 and beyond. Mol Plant 2022; 15:577-580. [PMID: 35149251 DOI: 10.1016/j.molp.2022.02.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 01/27/2022] [Accepted: 02/07/2022] [Indexed: 06/14/2023]
Affiliation(s)
- Junjie Fu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yangfan Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Huihui Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Jochen C Reif
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Stadt Seeland, Germany
| | - Shaojiang Chen
- College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
| | - Changling Huang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guoying Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xinhai Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yunbi Xu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; International Maize and Wheat Improvement Center (CIMMYT), EI Batan, Texcoco 56130, Mexico
| | - Liang Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Roth M, Beugnot A, Mary-Huard T, Moreau L, Charcosset A, Fiévet JB. Improving genomic predictions with inbreeding and nonadditive effects in two admixed maize hybrid populations in single and multienvironment contexts. Genetics 2022; 220:6527635. [PMID: 35150258 PMCID: PMC8982028 DOI: 10.1093/genetics/iyac018] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 01/28/2022] [Indexed: 11/12/2022] Open
Abstract
Genetic admixture, resulting from the recombination between structural groups, is frequently encountered in breeding populations. In hybrid breeding, crossing admixed lines can generate substantial nonadditive genetic variance and contrasted levels of inbreeding which can impact trait variation. This study aimed at testing recent methodological developments for the modeling of inbreeding and nonadditive effects in order to increase prediction accuracy in admixed populations. Using two maize (Zea mays L.) populations of hybrids admixed between dent and flint heterotic groups, we compared a suite of five genomic prediction models incorporating (or not) parameters accounting for inbreeding and nonadditive effects with the natural and orthogonal interaction approach in single and multienvironment contexts. In both populations, variance decompositions showed the strong impact of inbreeding on plant yield, height, and flowering time which was supported by the superiority of prediction models incorporating this effect (+0.038 in predictive ability for mean yield). In most cases dominance variance was reduced when inbreeding was accounted for. The model including additivity, dominance, epistasis, and inbreeding effects appeared to be the most robust for prediction across traits and populations (+0.054 in predictive ability for mean yield). In a multienvironment context, we found that the inclusion of nonadditive and inbreeding effects was advantageous when predicting hybrids not yet observed in any environment. Overall, comparing variance decompositions was helpful to guide model selection for genomic prediction. Finally, we recommend the use of models including inbreeding and nonadditive parameters following the natural and orthogonal interaction approach to increase prediction accuracy in admixed populations.
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Affiliation(s)
- Morgane Roth
- Plant Breeding Research Division, Agroscope, Wädenswil, 8820 Zurich, Switzerland,Corresponding author: INRAE GAFL, 67 Allée des Chênes 84140 Montfavet, France.
| | - Aurélien Beugnot
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France,Université Paris-Saclay, INRAE, AgroParisTech, UMR MIA-Paris Paris, 75005 Paris, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
| | - Julie B Fiévet
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, UMR GQE-Le Moulon, 91190 Gif-sur-Yvette, France
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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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Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids. Methods Mol Biol 2022; 2467:543-567. [PMID: 35451790 DOI: 10.1007/978-1-0716-2205-6_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
For many plant and animal species, commercial products are hybrids between individuals from different genetic groups. For allogamous plant species such as maize, the breeding objective is to produce single-cross hybrid varieties from two inbred lines each selected in complementary groups. Efficient hybrid breeding requires methods that (1) quickly generate homozygous and homogeneous parental lines with high combining abilities, (2) efficiently choose among the large number of available parental lines the most promising ones, and (3) predict the performances of sets of non-phenotyped single-cross hybrids, or hybrids phenotyped in a limited number of environments, based on their relationship with another set of hybrids with known performances. The maize breeding community has been developing model-based prediction of hybrid performances well before the genomic era. This chapter (1) provides a reminder of the maize breeding scheme before the genomic era; (2) describes how genomic data were incorporated in the prediction models involved in different steps of genomic-based single-cross maize hybrid breeding; and (3) reviews factors affecting the accuracy of genomic prediction, approaches for optimizing GP-based single-cross maize hybrid breeding schemes, and ensuring the long-term sustainability of genomic selection.
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Affiliation(s)
| | - Arthur Bernardeli
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa-MG, Brazil
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Mahadevaiah C, Appunu C, Aitken K, Suresha GS, Vignesh P, Mahadeva Swamy HK, Valarmathi R, Hemaprabha G, Alagarasan G, Ram B. Genomic Selection in Sugarcane: Current Status and Future Prospects. Front Plant Sci 2021; 12:708233. [PMID: 34646284 PMCID: PMC8502939 DOI: 10.3389/fpls.2021.708233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/24/2021] [Indexed: 05/18/2023]
Abstract
Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12-14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.
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Affiliation(s)
| | - Chinnaswamy Appunu
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Karen Aitken
- CSIRO (Commonwealth Scientific and Industrial Research Organization), St. Lucia, QLD, Australia
| | | | - Palanisamy Vignesh
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | | | | | - Govind Hemaprabha
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Ganesh Alagarasan
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Bakshi Ram
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
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22
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Isidro y Sánchez J, Akdemir D. Training Set Optimization for Sparse Phenotyping in Genomic Selection: A Conceptual Overview. Front Plant Sci 2021; 12:715910. [PMID: 34589099 PMCID: PMC8475495 DOI: 10.3389/fpls.2021.715910] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection (GS) is becoming an essential tool in breeding programs due to its role in increasing genetic gain per unit time. The design of the training set (TRS) in GS is one of the key steps in the implementation of GS in plant and animal breeding programs mainly because (i) TRS optimization is critical for the efficiency and effectiveness of GS, (ii) breeders test genotypes in multi-year and multi-location trials to select the best-performing ones. In this framework, TRS optimization can help to decrease the number of genotypes to be tested and, therefore, reduce phenotyping cost and time, and (iii) we can obtain better prediction accuracies from optimally selected TRS than an arbitrary TRS. Here, we concentrate the efforts on reviewing the lessons learned from TRS optimization studies and their impact on crop breeding and discuss important features for the success of TRS optimization under different scenarios. In this article, we review the lessons learned from training population optimization in plants and the major challenges associated with the optimization of GS including population size, the relationship between training and test set (TS), update of TRS, and the use of different packages and algorithms for TRS implementation in GS. Finally, we describe general guidelines to improving the rate of genetic improvement by maximizing the use of the TRS optimization in the GS framework.
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Affiliation(s)
- Julio Isidro y Sánchez
- Centro de Biotecnologia y Genómica de Plantas, Instituto Nacional de Investigación y Tecnologia Agraria y Alimentaria, Universidad Politécnica de Madrid, Campus de Montegancedo, Madrid, Spain
| | - Deniz Akdemir
- Animal and Crop Science Division, Agriculture and Food Science Centre, University College Dublin, Dublin, Ireland
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23
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Zhang T, Jiang L, Ruan L, Qian Y, Liang S, Lin F, Lu H, Dai H, Zhao H. Heterotic quantitative trait loci analysis and genomic prediction of seedling biomass-related traits in maize triple testcross populations. Plant Methods 2021; 17:85. [PMID: 34330310 PMCID: PMC8325263 DOI: 10.1186/s13007-021-00785-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 07/23/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Heterosis has been widely used in maize breeding. However, we know little about the heterotic quantitative trait loci and their roles in genomic prediction. In this study, we sought to identify heterotic quantitative trait loci for seedling biomass-related traits using triple testcross design and compare their prediction accuracies by fitting molecular markers and heterotic quantitative trait loci. RESULTS A triple testcross population comprised of 366 genotypes was constructed by crossing each of 122 intermated B73 × Mo17 genotypes with B73, Mo17, and B73 × Mo17. The mid-parent heterosis of seedling biomass-related traits involved in leaf length, leaf width, leaf area, and seedling dry weight displayed a large range, from less than 50 to ~ 150%. Relationships between heterosis of seedling biomass-related traits showed congruency with that between performances. Based on a linkage map comprised of 1631 markers, 14 augmented additive, two augmented dominance, and three dominance × additive epistatic quantitative trait loci for heterosis of seedling biomass-related traits were identified, with each individually explaining 4.1-20.5% of the phenotypic variation. All modes of gene action, i.e., additive, partially dominant, dominant, and overdominant modes were observed. In addition, ten additive × additive and six dominance × dominance epistatic interactions were identified. By implementing the general and special combining ability model, we found that prediction accuracy ranged from 0.29 for leaf length to 0.56 for leaf width. Different number of marker analysis showed that ~ 800 markers almost capture the largest prediction accuracies. When incorporating the heterotic quantitative trait loci into the model, we did not find the significant change of prediction accuracy, with only leaf length showing the marginal improvement by 1.7%. CONCLUSIONS Our results demonstrated that the triple testcross design is suitable for detecting heterotic quantitative trait loci and evaluating the prediction accuracy. Seedling leaf width can be used as the representative trait for seedling prediction. The heterotic quantitative trait loci are not necessary for genomic prediction of seedling biomass-related traits.
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Affiliation(s)
- Tifu Zhang
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Lu Jiang
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Long Ruan
- Institute of Tobacco, Anhui Academy of Agricultural Sciences, Hefei, 230001, China
| | - Yiliang Qian
- Institute of Tobacco, Anhui Academy of Agricultural Sciences, Hefei, 230001, China
| | - Shuaiqiang Liang
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Feng Lin
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Haiyan Lu
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China
| | - Huixue Dai
- Nanjing Institute of Vegetable Sciences, Nanjing, 210042, China
| | - Han Zhao
- Jiangsu Provincial Key Laboratory of Agrobiology, Institute of Germplasm Resources and Biotechnology, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China.
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24
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Rogers AR, Dunne JC, Romay C, Bohn M, Buckler ES, Ciampitti IA, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Hood E, Hooker DC, Knoll J, Lee EC, Lorenz A, Lynch JP, McKay J, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Sekhon R, Singh M, Smith M, Springer N, Thelen K, Thomison P, Thompson A, Tuinstra M, Wallace J, Wisser RJ, Xu W, Gilmour AR, Kaeppler SM, De Leon N, Holland JB. The importance of dominance and genotype-by-environment interactions on grain yield variation in a large-scale public cooperative maize experiment. G3 (Bethesda) 2021; 11:6062399. [PMID: 33585867 DOI: 10.1093/g3journal/jkaa050] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 11/07/2020] [Indexed: 11/12/2022]
Abstract
High-dimensional and high-throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.
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Affiliation(s)
- Anna R Rogers
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA
| | - Jeffrey C Dunne
- Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY 14853, USA.,USDA-ARS Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY 14853, USA
| | | | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA 50011, USA
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA 50131, USA
| | - Sherry Flint-Garcia
- USDA-ARS Plant Genetics Research Unit, University of Missouri, Columbia, MO 65211, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD 57769, USA
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Elizabeth Hood
- College of Agriculture, Arkansas State University, Jonesboro, AR 72467, USA
| | - David C Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON N0P 2C0, Canada
| | - Joseph Knoll
- USDA-ARS Crop Genetics and Breeding Research Unit, Tifton, GA 31793, USA
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph N1G 2W1, Canada
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA 16802, USA
| | - John McKay
- Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, USA
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Patrick S Schnable
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.,Plant Sciences Institute, Iowa State University, Ames, IA 50011, USA
| | - Rajandeep Sekhon
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
| | - Maninder Singh
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Margaret Smith
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nathan Springer
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583, USA
| | - Kurt Thelen
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH 43210, USA
| | - Addie Thompson
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN 55108, USA
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
| | - Jason Wallace
- Department of Crop and Soil Sciences, University of Georgia, Athens GA 30602, USA
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
| | - Wenwei Xu
- Texas A& M AgriLife Research, Texas A& M University, Lubbock, TX 79403, USA
| | | | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - Natalia De Leon
- Department of Agronomy, University of Wisconsin, Madison, WI 53706, USA
| | - James B Holland
- Program in Genetics, North Carolina State University, Raleigh, NC 27695, USA.,Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA.,USDA-ARS Plant Science Research Unit, North Carolina State University, Raleigh, NC 27695-7620, USA
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25
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Fritsche-Neto R, Galli G, Borges KLR, Costa-Neto G, Alves FC, Sabadin F, Lyra DH, Morais PPP, Braatz de Andrade LR, Granato I, Crossa J. Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review. Front Plant Sci 2021; 12:658267. [PMID: 34276721 PMCID: PMC8281958 DOI: 10.3389/fpls.2021.658267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.
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Affiliation(s)
- Roberto Fritsche-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Giovanni Galli
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Karina Lima Reis Borges
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Germano Costa-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Filipe Couto Alves
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States
| | - Felipe Sabadin
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Danilo Hottis Lyra
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom
| | | | | | - Italo Granato
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA), Univ. Montpellier, SupAgro, Montpellier, France
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Carretera México - Veracruz, Texcoco, Mexico
- Colegio de Posgraduado, Montecillo, Mexico
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26
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González-Diéguez D, Legarra A, Charcosset A, Moreau L, Lehermeier C, Teyssèdre S, Vitezica ZG. Genomic prediction of hybrid crops allows disentangling dominance and epistasis. Genetics 2021; 218:iyab026. [PMID: 33864072 PMCID: PMC8128411 DOI: 10.1093/genetics/iyab026] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 02/06/2021] [Indexed: 12/28/2022] Open
Abstract
We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted "residual genetic" line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme-e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.
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Affiliation(s)
| | - Andrés Legarra
- INRAE, INP, UMR 1388 GenPhySE, F-31326 Castanet-Tolosan, France
| | - Alain Charcosset
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE-Le Moulon, INRAE, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
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27
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Abstract
Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.
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Affiliation(s)
- Brian R. Rice
- Department of Crop Sciences, University of Illinois, Urbana, IL USA
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28
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Knoch D, Werner CR, Meyer RC, Riewe D, Abbadi A, Lücke S, Snowdon RJ, Altmann T. Multi-omics-based prediction of hybrid performance in canola. Theor Appl Genet 2021; 134:1147-1165. [PMID: 33523261 PMCID: PMC7973648 DOI: 10.1007/s00122-020-03759-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Accepted: 12/19/2020] [Indexed: 05/05/2023]
Abstract
Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.
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Affiliation(s)
- Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - Christian R. Werner
- The Roslin Institute, University of Edinburgh, Easter Bush, Midlothian, EH25 9RG Scotland, UK
| | - Rhonda C. Meyer
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
| | - David Riewe
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
- Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, 14195 Berlin, Germany
| | - Amine Abbadi
- NPZ Innovation GmbH, Hohenlieth, 24363 Holtsee, Germany
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Sophie Lücke
- Norddeutsche Pflanzenzucht Hans-Georg Lembke KG, Hohenlieth, 24363 Holtsee, Germany
| | - Rod J. Snowdon
- Department of Plant Breeding, IFZ Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University, Heinrich-Buff-Ring 26-32, 35392 Giessen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland, OT Gatersleben Germany
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29
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Jarquin D, de Leon N, Romay C, Bohn M, Buckler ES, Ciampitti I, Edwards J, Ertl D, Flint-Garcia S, Gore MA, Graham C, Hirsch CN, Holland JB, Hooker D, Kaeppler SM, Knoll J, Lee EC, Lawrence-Dill CJ, Lynch JP, Moose SP, Murray SC, Nelson R, Rocheford T, Schnable JC, Schnable PS, Smith M, Springer N, Thomison P, Tuinstra M, Wisser RJ, Xu W, Yu J, Lorenz A. Utility of Climatic Information via Combining Ability Models to Improve Genomic Prediction for Yield Within the Genomes to Fields Maize Project. Front Genet 2021; 11:592769. [PMID: 33763106 PMCID: PMC7982677 DOI: 10.3389/fgene.2020.592769] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 12/21/2020] [Indexed: 11/29/2022] Open
Abstract
Genomic prediction provides an efficient alternative to conventional phenotypic selection for developing improved cultivars with desirable characteristics. New and improved methods to genomic prediction are continually being developed that attempt to deal with the integration of data types beyond genomic information. Modern automated weather systems offer the opportunity to capture continuous data on a range of environmental parameters at specific field locations. In principle, this information could characterize training and target environments and enhance predictive ability by incorporating weather characteristics as part of the genotype-by-environment (G×E) interaction component in prediction models. We assessed the usefulness of including weather data variables in genomic prediction models using a naïve environmental kinship model across 30 environments comprising the Genomes to Fields (G2F) initiative in 2014 and 2015. Specifically four different prediction scenarios were evaluated (i) tested genotypes in observed environments; (ii) untested genotypes in observed environments; (iii) tested genotypes in unobserved environments; and (iv) untested genotypes in unobserved environments. A set of 1,481 unique hybrids were evaluated for grain yield. Evaluations were conducted using five different models including main effect of environments; general combining ability (GCA) effects of the maternal and paternal parents modeled using the genomic relationship matrix; specific combining ability (SCA) effects between maternal and paternal parents; interactions between genetic (GCA and SCA) effects and environmental effects; and finally interactions between the genetics effects and environmental covariates. Incorporation of the genotype-by-environment interaction term improved predictive ability across all scenarios. However, predictive ability was not improved through inclusion of naive environmental covariates in G×E models. More research should be conducted to link the observed weather conditions with important physiological aspects in plant development to improve predictive ability through the inclusion of weather data.
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Affiliation(s)
- Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Natalia de Leon
- Department of Agronomy, University of Wisconsin, Madison, WI, United States
| | - Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States
| | - Martin Bohn
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL, United States
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, NY, United States.,U.S. Department of Agriculture - Agricultural Research Service Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY, United States
| | - Ignacio Ciampitti
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Jode Edwards
- Department of Agronomy, Iowa State University, Ames, IA, United States.,U.S. Department of Agriculture - Agricultural Research Service Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, United States
| | - David Ertl
- Iowa Corn Promotion Board, Johnston, IA, United States
| | - Sherry Flint-Garcia
- U.S. Department of Agriculture - Agricultural Research Service Plant Genetics Research Unit, University of Missouri, Columbia, MO, United States
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Christopher Graham
- Plant Science Department, West River Agricultural Center, South Dakota State University, Rapid City, SD, United States
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
| | - James B Holland
- U.S. Department of Agriculture - Agricultural Research Service Plant Science Research Unit, North Carolina State University, Raleigh, NC, United States
| | - David Hooker
- Department of Plant Agriculture, Ridgetown Campus, University of Guelph, Ridgetown, ON, Canada
| | - Shawn M Kaeppler
- Department of Agronomy, University of Wisconsin, Madison, WI, United States
| | - Joseph Knoll
- U.S. Department of Agriculture - Agricultural Research Service Crop Genetics and Breeding Research Unit, Tifton, GA, United States
| | - Elizabeth C Lee
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Carolyn J Lawrence-Dill
- Department of Agronomy, Iowa State University, Ames, IA, United States.,Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA, United States.,Plant Sciences Institute, Iowa State University, Ames, IA, United States
| | - Jonathan P Lynch
- Department of Plant Science, Penn State University, University Park, PA, United States
| | - Stephen P Moose
- Department of Crop Sciences, University of Illinois at Urban-Champaign, Urbana, IL, United States
| | - Seth C Murray
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | - Rebecca Nelson
- Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, United States
| | - Torbert Rocheford
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE, United States
| | - Patrick S Schnable
- U.S. Department of Agriculture - Agricultural Research Service Corn Insects and Crop Genetics Research Unit, Iowa State University, Ames, IA, United States.,Plant Sciences Institute, Iowa State University, Ames, IA, United States
| | - Margaret Smith
- U.S. Department of Agriculture - Agricultural Research Service Plant, Soil, and Nutrition Research Unit, Cornell University, Ithaca, NY, United States
| | - Nathan Springer
- Department of Plant and Microbial Biology, University of Minnesota, St. Paul, MN, United States
| | - Peter Thomison
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, United States
| | - Mitch Tuinstra
- Department of Agronomy, Purdue University, West Lafayette, IN, United States
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE, United States
| | - Wenwei Xu
- Texas A&M AgriLife Research, Texas A&M University, Lubbock, TX, United States
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, United States
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30
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Gaikpa DS, Kessel B, Presterl T, Ouzunova M, Galiano-Carneiro AL, Mayer M, Melchinger AE, Schön CC, Miedaner T. Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools. Theor Appl Genet 2021; 134:793-805. [PMID: 33274402 PMCID: PMC7925457 DOI: 10.1007/s00122-020-03731-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 11/13/2020] [Indexed: 06/12/2023]
Abstract
KEY MESSAGE High genetic variation in two European maize landraces can be harnessed to improve Gibberella ear rot resistance by integrated genomic tools. Fusarium graminearum (Fg) causes Gibberella ear rot (GER) in maize leading to yield reduction and contamination of grains with several mycotoxins. This study aimed to elucidate the molecular basis of GER resistance among 500 doubled haploid lines derived from two European maize landraces, "Kemater Landmais Gelb" (KE) and "Petkuser Ferdinand Rot" (PE). The two landraces were analyzed individually using genome-wide association studies and genomic selection (GS). The lines were genotyped with a 600-k maize array and phenotyped for GER severity, days to silking, plant height, and seed-set in four environments using artificial infection with a highly aggressive Fg isolate. High genotypic variances and broad-sense heritabilities were found for all traits. Genotype-environment interaction was important throughout. The phenotypic (r) and genotypic ([Formula: see text]) correlations between GER severity and three agronomic traits were low (r = - 0.27 to 0.20; [Formula: see text]= - 0.32 to 0.22). For GER severity, eight QTLs were detected in KE jointly explaining 34% of the genetic variance. In PE, no significant QTLs for GER severity were detected. No common QTLs were found between GER severity and the three agronomic traits. The mean prediction accuracies ([Formula: see text]) of weighted GS (wRR-BLUP) were higher than [Formula: see text] of marker-assisted selection (MAS) and unweighted GS (RR-BLUP) for GER severity. Using KE as the training set and PE as the validation set resulted in very low [Formula: see text] that could be improved by using fixed marker effects in the GS model.
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Affiliation(s)
| | - Bettina Kessel
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Thomas Presterl
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | - Milena Ouzunova
- Kleinwanzlebener Saatzucht (KWS) KWS SAAT SE & Co. KGaA, Einbeck, Germany
| | | | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Population Genetics and Seed Science, University of Hohenheim, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany.
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31
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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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32
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Abstract
Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108 USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108 USA
| | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
| | - Matthew B. Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
| | - Candice N. Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108 USA
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33
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Della Coletta R, Qiu Y, Ou S, Hufford MB, Hirsch CN. How the pan-genome is changing crop genomics and improvement. Genome Biol 2021. [PMID: 33397434 DOI: 10.1186/s13059-020-02224-2228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2023] Open
Abstract
Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.
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Affiliation(s)
- Rafael Della Coletta
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Yinjie Qiu
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA
| | - Shujun Ou
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA
| | - Matthew B Hufford
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, 50011, USA.
| | - Candice N Hirsch
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA.
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34
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Zhao W, Lai X, Liu D, Zhang Z, Ma P, Wang Q, Zhang Z, Pan Y. Applications of Support Vector Machine in Genomic Prediction in Pig and Maize Populations. Front Genet 2020; 11:598318. [PMID: 33343636 PMCID: PMC7744740 DOI: 10.3389/fgene.2020.598318] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Accepted: 11/11/2020] [Indexed: 01/01/2023] Open
Abstract
Genomic prediction (GP) has revolutionized animal and plant breeding. However, better statistical models that can improve the accuracy of GP are required. For this reason, in this study, we explored the genomic-based prediction performance of a popular machine learning method, the Support Vector Machine (SVM) model. We selected the most suitable kernel function and hyperparameters for the SVM model in eight published genomic data sets on pigs and maize. Next, we compared the SVM model with RBF and the linear kernel functions to the two most commonly used genome-enabled prediction models (GBLUP and BayesR) in terms of prediction accuracy, time, and the memory used. The results showed that the SVM model had the best prediction performance in two of the eight data sets, but in general, the predictions of both models were similar. In terms of time, the SVM model was better than BayesR but worse than GBLUP. In terms of memory, the SVM model was better than GBLUP and worse than BayesR in pig data but the same with BayesR in maize data. According to the results, SVM is a competitive method in animal and plant breeding, and there is no universal prediction model.
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Affiliation(s)
- Wei Zhao
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Xueshuang Lai
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Dengying Liu
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Zhenyang Zhang
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Peipei Ma
- Department of Animal Science, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai, China
| | - Qishan Wang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Zhe Zhang
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
| | - Yuchun Pan
- Department of Animal Science, College of Animal Science, Zhejiang University, Hangzhou, China
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35
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Rolling WR, Dorrance AE, McHale LK. Testing methods and statistical models of genomic prediction for quantitative disease resistance to Phytophthora sojae in soybean [Glycine max (L.) Merr] germplasm collections. Theor Appl Genet 2020; 133:3441-3454. [PMID: 32960288 DOI: 10.1007/s00122-020-03679-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 09/04/2020] [Indexed: 06/11/2023]
Abstract
KEY MESSAGE Genomic prediction of quantitative resistance toward Phytophthora sojae indicated that genomic selection may increase breeding efficiency. Statistical model and marker set had minimal effect on genomic prediction with > 1000 markers. Quantitative disease resistance (QDR) toward Phytophthora sojae in soybean is a complex trait controlled by many small-effect loci throughout the genome. Along with the technical and rate-limiting challenges of phenotyping resistance to a root pathogen, the trait complexity can limit breeding efficiency. However, the application of genomic prediction to traits with complex genetic architecture, such as QDR toward P. sojae, is likely to improve breeding efficiency. We provide a novel example of genomic prediction by measuring QDR to P. sojae in two diverse panels of more than 450 plant introductions (PIs) that had previously been genotyped with the SoySNP50K chip. This research was completed in a collection of diverse germplasm and contributes to both an initial assessment of genomic prediction performance and characterization of the soybean germplasm collection. We tested six statistical models used for genomic prediction including Bayesian Ridge Regression; Bayesian LASSO; Bayes A, B, C; and reproducing kernel Hilbert spaces. We also tested how the number and distribution of SNPs included in genomic prediction altered predictive ability by varying the number of markers from less than 50 to more than 34,000 SNPs, including SNPs based on sequential sampling, random sampling, or selections from association analyses. Predictive ability was relatively independent of statistical model and marker distribution, with a diminishing return when more than 1000 SNPs were included in genomic prediction. This work estimated relative efficiency per breeding cycle between 0.57 and 0.83, which may improve the genetic gain for P. sojae QDR in soybean breeding programs.
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Affiliation(s)
- William R Rolling
- Center for Applied Plant Science and Center for Soybean Research, The Ohio State University, Columbus, OH, 43210, US
- Vegetable Crop Research Unit, USDA-ARS, Madison, WI, 53706, US
| | - Anne E Dorrance
- Center for Applied Plant Science and Center for Soybean Research, The Ohio State University, Columbus, OH, 43210, US
- Department of Plant Pathology, The Ohio State University, Wooster, OH, 44691, US
| | - Leah K McHale
- Center for Applied Plant Science and Center for Soybean Research, The Ohio State University, Columbus, OH, 43210, US.
- Department of Horticulture and Crop Science, The Ohio State University, Columbus, OH, 43210, US.
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36
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Wang N, Wang H, Zhang A, Liu Y, Yu D, Hao Z, Ilut D, Glaubitz JC, Gao Y, Jones E, Olsen M, Li X, San Vicente F, Prasanna BM, Crossa J, Pérez-Rodríguez P, Zhang X. Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing. Theor Appl Genet 2020; 133:2869-2879. [PMID: 32607592 PMCID: PMC7782462 DOI: 10.1007/s00122-020-03638-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 06/16/2020] [Indexed: 05/20/2023]
Abstract
Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.
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Affiliation(s)
- Nan Wang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hui Wang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Ao Zhang
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Yubo Liu
- College of Bioscience and Biotechnology, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Diansi Yu
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Crop Breeding and Cultivation Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Zhuanfang Hao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Dan Ilut
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, USA
| | | | - Yanxin Gao
- Institute of Biotechnology, Cornell University, Ithaca, NY, USA
| | - Elizabeth Jones
- Institute of Biotechnology, Cornell University, Ithaca, NY, USA
| | - Michael Olsen
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Nairobi, Kenya
| | - Xinhai Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Felix San Vicente
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Boddupalli M Prasanna
- International Maize and Wheat Improvement Center (CIMMYT), P. O. Box 1041, Nairobi, Kenya
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Xuecai Zhang
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico.
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37
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Seye AI, Bauland C, Charcosset A, Moreau L. Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs. Theor Appl Genet 2020; 133:1995-2010. [PMID: 32185420 DOI: 10.1007/s00122-020-03573-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Accepted: 02/28/2020] [Indexed: 06/10/2023]
Abstract
Simulations showed that hybrid performances issued from an incomplete factorial between segregating families of two heterotic groups enable to calibrate genomic predictions of hybrid value more efficiently than tester-based designs. Genomic selection offers new opportunities to revisit hybrid breeding by replacing extensive phenotyping of hybrid combinations by genomic predictions. A key question remains to identify the best design to calibrate genomic prediction models. We proposed to use single-cross hybrids issued from an incomplete factorial design between segregating populations and compared this strategy with a conventional approach based on topcross evaluation. Two multiparental segregating populations of lines, each specific of one heterotic group, were simulated. Hybrids considered as training sets were generated using either (1) a parental line from the opposite group as tester or (2) following an incomplete factorial design. Different specific combining ability (SCA) proportions were simulated by considering different levels of group divergence and dominance effects for the simulated QTL. For the incomplete factorial design, for a same number of hybrids, we considered different numbers of parental lines and different contributions of lines (one to four) to calibration hybrids. We evaluated for different training set sizes prediction accuracies of new hybrids and genetic gains along three generations. At a given training set size, factorial design was as efficient (considering accuracy) as tester design in additive scenarios, but significantly outperformed tester design when SCA was present. The contribution number of each parental line to the incomplete factorial design had a small impact on accuracies. Our simulations confirmed experimental results and showed that calibrating models on hybrids between two multiparental populations is a cost-efficient way to perform genomic predictions in both groups, opening prospects for revisiting reciprocal recurrent selection schemes.
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Affiliation(s)
- A I Seye
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France
| | - C Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France
| | - A Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France
| | - L Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution - Le Moulon, 91190, Gif-sur-Yvette, France.
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38
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Ramstein GP, Larsson SJ, Cook JP, Edwards JW, Ersoz ES, Flint-Garcia S, Gardner CA, Holland JB, Lorenz AJ, McMullen MD, Millard MJ, Rocheford TR, Tuinstra MR, Bradbury PJ, Buckler ES, Romay MC. Dominance Effects and Functional Enrichments Improve Prediction of Agronomic Traits in Hybrid Maize. Genetics 2020; 215:215-230. [PMID: 32152047 PMCID: PMC7198274 DOI: 10.1534/genetics.120.303025] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 02/26/2020] [Indexed: 01/04/2023] Open
Abstract
Single-cross hybrids have been critical to the improvement of maize (Zea mays L.), but the characterization of their genetic architectures remains challenging. Previous studies of hybrid maize have shown the contribution of within-locus complementation effects (dominance) and their differential importance across functional classes of loci. However, they have generally considered panels of limited genetic diversity, and have shown little benefit from genomic prediction based on dominance or functional enrichments. This study investigates the relevance of dominance and functional classes of variants in genomic models for agronomic traits in diverse populations of hybrid maize. We based our analyses on a diverse panel of inbred lines crossed with two testers representative of the major heterotic groups in the U.S. (1106 hybrids), as well as a collection of 24 biparental populations crossed with a single tester (1640 hybrids). We investigated three agronomic traits: days to silking (DTS), plant height (PH), and grain yield (GY). Our results point to the presence of dominance for all traits, but also among-locus complementation (epistasis) for DTS and genotype-by-environment interactions for GY. Consistently, dominance improved genomic prediction for PH only. In addition, we assessed enrichment of genetic effects in classes defined by genic regions (gene annotation), structural features (recombination rate and chromatin openness), and evolutionary features (minor allele frequency and evolutionary constraint). We found support for enrichment in genic regions and subsequent improvement of genomic prediction for all traits. Our results suggest that dominance and gene annotations improve genomic prediction across diverse populations in hybrid maize.
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Affiliation(s)
| | - Sara J Larsson
- Section of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853
| | - Jason P Cook
- Division of Plant Science, University of Missouri, Columbia, Missouri 56211
| | - Jode W Edwards
- U.S. Department of Agriculture-Agricultural Research Service, Ames, Iowa 50011
| | | | - Sherry Flint-Garcia
- U.S. Department of Agriculture-Agricultural Research Service, University of Missouri, Columbia, Missouri 56211
| | - Candice A Gardner
- U.S. Department of Agriculture-Agricultural Research Service, Ames, Iowa 50011
| | - James B Holland
- U.S. Department of Agriculture-Agricultural Research Service, Department of Crop and Soil Sciences, North Carolina State University, Raleigh, North Carolina 27695
| | - Aaron J Lorenz
- Department of Agronomy and Horticulture, University of Nebraska, Lincoln, Nebraska 68588
| | - Michael D McMullen
- U.S. Department of Agriculture-Agricultural Research Service, University of Missouri, Columbia, Missouri 56211
| | - Mark J Millard
- U.S. Department of Agriculture-Agricultural Research Service, Ames, Iowa 50011
| | | | | | - Peter J Bradbury
- U.S. Department of Agriculture-Agricultural Research Service, Ithaca, New York 14853
| | - Edward S Buckler
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
- U.S. Department of Agriculture-Agricultural Research Service, Ithaca, New York 14853
| | - M Cinta Romay
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
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Jarquin D, Howard R, Liang Z, Gupta SK, Schnable JC, Crossa J. Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds. Front Genet 2020; 10:1294. [PMID: 32038702 PMCID: PMC6993057 DOI: 10.3389/fgene.2019.01294] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 11/25/2019] [Indexed: 11/30/2022] Open
Abstract
Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS).
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Affiliation(s)
- Diego Jarquin
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Reka Howard
- Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Zhikai Liang
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Shashi K Gupta
- International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, India
| | - James C Schnable
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, United States
| | - Jose Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Ciudad de Mexico, Mexico
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Beyene Y, Gowda M, Olsen M, Robbins KR, Pérez-Rodríguez P, Alvarado G, Dreher K, Gao SY, Mugo S, Prasanna BM, Crossa J. Empirical Comparison of Tropical Maize Hybrids Selected Through Genomic and Phenotypic Selections. Front Plant Sci 2019; 10:1502. [PMID: 31824533 PMCID: PMC6883373 DOI: 10.3389/fpls.2019.01502] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 10/29/2019] [Indexed: 05/20/2023]
Abstract
Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost-benefit analysis of GS and PS. For this study, we used two experimental maize data sets (stage I and stage II yield trials). The stage I data set consisted of 1492 doubled haploid (DH) lines genotyped with rAmpSeq SNPs. A subset of these lines (855) representing various DH populations within the stage I cohort was crossed with an individual single-cross tester chosen to complement each population. These testcross hybrids were evaluated in replicated trials under WW and WS conditions for grain yield and other agronomic traits, while the remaining 637 DH lines were predicted using the 855 lines as a training set. The second data set (stage II) consists of 348 DH lines from the first data set. Among these 348 best DH lines, 172 lines selected were solely based on GEBVs, and 176 lines were selected based on phenotypic performance. Each of the 348 DH lines were crossed with three common testers from complementary heterotic groups, and the resulting 1042 testcross hybrids and six commercial checks were evaluated in four to five WW locations and one WS condition in Kenya. For stage I trials, the cross-validated prediction accuracy for grain yield was 0.67 and 0.65 under WW and WS conditions, respectively. We found similar responses to selection using PS and GS for grain yield other agronomic traits under WW and WS conditions. The top 15% of hybrids advanced through GS and PS gave 21%-23% higher grain yield under WW and 51%-52% more grain yield under WS than the mean of the checks. The GS reduced the cost by 32% over the PS with similar selection gains. We concluded that the use of GS for yield under WW and WS conditions in maize can produce selection candidates with similar performance as those generated from conventional PS, but at a lower cost, and therefore, should be incorporated into maize breeding pipelines to increase breeding program efficiency.
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Affiliation(s)
- Yoseph Beyene
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Manje Gowda
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Michael Olsen
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Kelly R. Robbins
- School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | | | - Gregorio Alvarado
- Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kate Dreher
- Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Star Yanxin Gao
- School of Integrative Plant Sciences, Cornell University, Ithaca, NY, United States
| | - Stephen Mugo
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Boddupalli M. Prasanna
- Global Maize Program, International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
| | - Jose Crossa
- Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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41
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Abstract
Genomic prediction has become an increasingly popular tool for hybrid performance evaluation in plant breeding mainly because that it can reduce cost and accelerate a breeding program. In this study, we propose a systematic procedure to predict hybrid performance using a genomic selection (GS) model that takes both additive and dominance marker effects into account. We first demonstrate the advantage of the additive-dominance effects model over the only additive effects model through a simulation study. Based on the additive-dominance model, we predict genomic estimated breeding values (GEBVs) for individual hybrid combinations and their parental lines. The GEBV-based specific combining ability (SCA) for each hybrid and general combining ability (GCA) for its parental lines are then derived to quantify the degree of midparent heterosis (MPH) or better-parent heterosis (BPH) of the hybrid. Finally, we estimate the variance components resulting from additive and dominance gene action effects and heritability using a genomic best linear unbiased predictor (g-BLUP) model. These estimates are used to justify the results of the genomic prediction study. A pumpkin ( spp.) data set is given to illustrate the provided procedure. The data set consists of 320 parental lines with 61,179 collected single nucleotide polymorphism (SNP) markers; 119, 120, and 120 phenotypic values of hybrids on three quantitative traits within maxima Duchesne; and 89, 111, and 90 phenotypic values of hybrids on the same three quantitative traits within Dechesne.
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42
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Technow F. Use of F2 Bulks in Training Sets for Genomic Prediction of Combining Ability and Hybrid Performance. G3 (Bethesda) 2019; 9:1557-1569. [PMID: 30862623 PMCID: PMC6505161 DOI: 10.1534/g3.118.200994] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Accepted: 03/09/2019] [Indexed: 11/18/2022]
Abstract
Developing training sets for genomic prediction in hybrid crops requires producing hybrid seed for a large number of entries. In autogamous crop species (e.g., wheat, rice, rapeseed, cotton) this requires elaborate hybridization systems to prevent self-pollination and presents a significant impediment to the implementation of hybrid breeding in general and genomic selection in particular. An alternative to F1 hybrids are bulks of F2 seed from selfed F1 plants (F1:2). Seed production for F1:2 bulks requires no hybridization system because the number of F1 plants needed for producing enough F1:2 seed for multi-environment testing can be generated by hand-pollination. This study evaluated the suitability of F1:2 bulks for use in training sets for genomic prediction of F1 level general combining ability and hybrid performance, under different degrees of divergence between heterotic groups and modes of gene action, using quantitative genetic theory and simulation of a genomic prediction experiment. The simulation, backed by theory, showed that F1:2 training sets are expected to have a lower prediction accuracy relative to F1 training sets, particularly when heterotic groups have strongly diverged. The accuracy penalty, however, was only modest and mostly because of a lower heritability, rather than because of a difference in F1 and F1:2 genetic values. It is concluded that resorting to F1:2 bulks is, in theory at least, a promising approach to remove the significant complication of a hybridization system from the breeding process.
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Affiliation(s)
- Frank Technow
- Maize Product Development/Systems and Innovation for Breeding and Seed Products, DuPont Pioneer, Tavistock/Ontario, Canada
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43
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Schrag TA, Schipprack W, Melchinger AE. Across-years prediction of hybrid performance in maize using genomics. Theor Appl Genet 2019; 132:933-946. [PMID: 30498894 DOI: 10.1007/s00122-018-3249-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2018] [Accepted: 11/21/2018] [Indexed: 05/20/2023]
Abstract
Inclusion of historical training data improved the genomics-based prediction of performance of maize hybrids, the extent depending on the phenotypic trait and genotype-by-year interaction. Prediction of hybrid performance using existing phenotypic data on previous hybrids combined with molecular data collected on the parent lines allows to identify the most promising candidates from a huge number of possible hybrids at an early stage. Phenotypic data on yield and dry matter of 1970 grain maize hybrids from 19 years of a public breeding program were aggregated considering the underlying structure of factorial sets of hybrids. Pedigree records and 50 K SNP data were collected on their 170 Dent and 127 Flint parent lines. The performance of untested hybrids was predicted by best linear unbiased predictors (BLUP) on basis of pedigree or genomic data. For composition of training sets (TRN) and test sets (TST), three schemes for collecting factorials from specific years were employed which resulted in 490 scenarios. For each scenario, the predictive ability and genomic relationship between TRN and TST hybrids were determined. For extended TRNs, where earlier years were successively added to the TRN, the maximum relationship increased and the predictive ability improved, with the extent of the latter depending on the phenotypic trait and its genotype-by-year interaction. Genomic BLUP outperformed pedigree BLUP and better utilized the early years' data, especially for prediction of hybrids from factorials in a more distant future. This study on hybrid prediction in grain maize illustrated that including historical phenotypic data for training, although consisting of less related genotypes, can improve genomic prediction and enables optimization of hybrid variety development.
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Affiliation(s)
- Tobias A Schrag
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Wolfgang Schipprack
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
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44
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Westhues M, Heuer C, Thaller G, Fernando R, Melchinger AE. Efficient genetic value prediction using incomplete omics data. Theor Appl Genet 2019; 132:1211-1222. [PMID: 30656353 DOI: 10.1007/s00122-018-03273-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 12/21/2018] [Indexed: 05/05/2023]
Abstract
Covering a subset of individuals with a quantitative predictor, while imputing records for all others using pedigree or genomic data, could improve the precision of predictions while controlling for costs. Predicting genetic values with high accuracy is pivotal for effective candidate selection in animal and plant breeding. Novel 'omics'-based predictors have been shown to improve upon established genome-based predictions of important complex traits but require laborious and expensive assays. As a consequence, there are various datasets with full genetic marker coverage of all studied individuals but incomplete coverage with other 'omics' data. In animal breeding, single-step prediction was introduced to efficiently combine pedigree information, collected on a large number of animals, with genomic information, collected on a smaller subset of animals, for breeding value estimation without bias. Using two maize datasets of inbred lines and hybrids, we show that the single-step framework facilitates imputing transcriptomic data, boosting forecasts when their predictive ability exceeds that of pedigree or genomic data. Our results suggest that covering only a subset of inbred lines with 'omics' predictors and imputing all others using pedigree or genomic data could enable breeders to improve trait predictions while keeping costs under control. Employing 'omics' predictors could particularly improve candidate selection in hybrid breeding because the success of forecasts is a strongly convex function of predictive ability.
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Affiliation(s)
- Matthias Westhues
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Claas Heuer
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
- Inguran, LLC dba STGenetics, 22575 SH6 South, Navasota, TX, 77868, USA
| | - Georg Thaller
- Institute of Animal Breeding and Husbandry, Christian-Albrechts-University Kiel, 24098, Kiel, Germany
| | - Rohan Fernando
- Department of Animal Science, Iowa State University, Ames, IA, 50011, USA
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany.
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45
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Jarquín D, Howard R, Graef G, Lorenz A. Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean. Evol Bioinform Online 2019; 15:1176934319831307. [PMID: 30872917 PMCID: PMC6407170 DOI: 10.1177/1176934319831307] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 01/23/2019] [Indexed: 12/15/2022] Open
Abstract
An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and missing marker values and the imputation of missing marker values. In this article, we evaluated how the prediction accuracy is influenced by the combination of 12 MAF values, 27 different percentages of missing marker values, and 2 imputation techniques (IT; naïve and Random Forest (RF)). We constructed a response surface of prediction accuracy values for the two ITs as a function of MAF and percentage of missing marker values using soybean data from the University of Nebraska-Lincoln Soybean Breeding Program. We found that both the genetic architecture of the trait and the IT affect the prediction accuracy implying that we have to be careful how we perform QC on the marker data. For the corresponding combinations MAF-percentage of missing values we observed that implementing the RF imputation increased the number of markers by 2 to 5 times than the simple naïve imputation method that is based on the mean allele dosage of the non-missing values at each loci. We conclude that there is not a unique strategy (combination of the QCs and imputation method) that outperforms the results of the others for all traits.
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Affiliation(s)
- Diego Jarquín
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, USA
| | - Reka Howard
- Department of Statistics, University of Nebraska–Lincoln, Lincoln, NE, USA
| | - George Graef
- Department of Agronomy and Horticulture, University of Nebraska–Lincoln, Lincoln, NE, USA
| | - Aaron Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, Minneapolis, MN, USA
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46
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Guo T, Yu X, Li X, Zhang H, Zhu C, Flint-Garcia S, McMullen MD, Holland JB, Szalma SJ, Wisser RJ, Yu J. Optimal Designs for Genomic Selection in Hybrid Crops. Mol Plant 2019; 12:390-401. [PMID: 30625380 DOI: 10.1016/j.molp.2018.12.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Revised: 12/14/2018] [Accepted: 12/24/2018] [Indexed: 05/05/2023]
Abstract
Improved capacity of genomics and biotechnology has greatly enhanced genetic studies in different areas. Genomic selection exploits the genotype-to-phenotype relationship at the whole-genome level and is being implemented in many crops. Here we show that design-thinking and data-mining techniques can be leveraged to optimize genomic prediction of hybrid performance. We phenotyped a set of 276 maize hybrids generated by crossing founder inbreds of nested association mapping populations for flowering time, ear height, and grain yield. With 10 296 310 SNPs available from the parental inbreds, we explored the patterns of genomic relationships and phenotypic variation to establish training samples based on clustering, graphic network analysis, and genetic mating scheme. Our analysis showed that training set designs outperformed random sampling and earlier methods that either minimize the mean of prediction error variance or maximize the mean of generalized coefficient of determination. Additional analyses of 2556 wheat hybrids from an early-stage hybrid breeding system and 1439 rice hybrids from an established hybrid breeding system validated the approaches. Together, we demonstrated that effective genomic prediction models can be established with a training set 2%-13% of the size of the whole set, enabling an efficient exploration of enormous inference space of genetic combinations.
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Affiliation(s)
- Tingting Guo
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Xiaoqing Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Xianran Li
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Haozhe Zhang
- Department of Statistics, Iowa State University, Ames, IA 50011, USA
| | - Chengsong Zhu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Sherry Flint-Garcia
- USDA-ARS and Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| | - Michael D McMullen
- USDA-ARS and Division of Plant Sciences, University of Missouri, Columbia, MO 65211, USA
| | - James B Holland
- USDA-ARS and Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Stephen J Szalma
- USDA-ARS and Department of Crop and Soil Sciences, North Carolina State University, Raleigh, NC 27695, USA
| | - Randall J Wisser
- Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716, USA
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA.
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47
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Andorf C, Beavis WD, Hufford M, Smith S, Suza WP, Wang K, Woodhouse M, Yu J, Lübberstedt T. Technological advances in maize breeding: past, present and future. Theor Appl Genet 2019; 132:817-849. [PMID: 30798332 DOI: 10.1007/s00122-019-03306-3] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 02/05/2019] [Indexed: 05/18/2023]
Abstract
Maize has for many decades been both one of the most important crops worldwide and one of the primary genetic model organisms. More recently, maize breeding has been impacted by rapid technological advances in sequencing and genotyping technology, transformation including genome editing, doubled haploid technology, parallelled by progress in data sciences and the development of novel breeding approaches utilizing genomic information. Herein, we report on past, current and future developments relevant for maize breeding with regard to (1) genome analysis, (2) germplasm diversity characterization and utilization, (3) manipulation of genetic diversity by transformation and genome editing, (4) inbred line development and hybrid seed production, (5) understanding and prediction of hybrid performance, (6) breeding methodology and (7) synthesis of opportunities and challenges for future maize breeding.
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Affiliation(s)
| | - William D Beavis
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Matthew Hufford
- Department of Ecology, Evolution and Organismal Biology, Iowa State University, Ames, IA, 50011-1010, USA
| | - Stephen Smith
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Walter P Suza
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Kan Wang
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | | | - Jianming Yu
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA
| | - Thomas Lübberstedt
- Department of Agronomy, Iowa State University, Agronomy Hall, Ames, IA, 50011-1010, USA.
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48
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Montesinos-López OA, Martín-Vallejo J, Crossa J, Gianola D, Hernández-Suárez CM, Montesinos-López A, Juliana P, Singh R. A Benchmarking Between Deep Learning, Support Vector Machine and Bayesian Threshold Best Linear Unbiased Prediction for Predicting Ordinal Traits in Plant Breeding. G3 (Bethesda) 2019; 9:601-618. [PMID: 30593512 PMCID: PMC6385991 DOI: 10.1534/g3.118.200998] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Accepted: 12/27/2018] [Indexed: 11/18/2022]
Abstract
Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.
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Affiliation(s)
| | - Javier Martín-Vallejo
- Departamento de Estadística, Universidad de Salamanca, c/Espejo 2, Salamanca, 37007, España
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Ciudad de México, México
| | - Daniel Gianola
- Departments of Animal Sciences, Dairy Science, and Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin 53706
| | | | - Abelardo Montesinos-López
- Departamento de Matemáticas, Centro Universitario de Ciencias Exactas e Ingenierías (CUCEI), Universidad de Guadalajara, 44430, Guadalajara, Jalisco, México
| | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Ciudad de México, México
| | - Ravi Singh
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Ciudad de México, México
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49
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Lyra DH, Galli G, Alves FC, Granato ÍSC, Vidotti MS, Bandeira E Sousa M, Morosini JS, Crossa J, Fritsche-Neto R. Modeling copy number variation in the genomic prediction of maize hybrids. Theor Appl Genet 2019; 132:273-288. [PMID: 30382311 DOI: 10.1007/s00122-018-3215-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 10/20/2018] [Indexed: 06/08/2023]
Abstract
Our study indicates that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids. Moreover, predicting hybrid phenotypes by combining additive-dominance effects with copy variants has the potential to be a viable predictive model. Non-additive effects resulting from the actions of multiple loci may influence trait variation in single-cross hybrids. In addition, complementation of allelic variation could be a valuable contributor to hybrid genetic variation, especially when crossing inbred lines with higher contents of copy gains. With this in mind, we aimed (1) to study the association between copy number variation (CNV) and hybrid phenotype, and (2) to compare the predictive ability (PA) of additive and additive-dominance genomic best linear unbiased prediction model when combined with the effects of CNV in two datasets of maize hybrids (USP and HELIX). In the USP dataset, we observed a significant negative phenotypic correlation of low magnitude between copy number loss and plant height, revealing a tendency that more copy losses lead to lower plants. In the same set, when CNV was combined with the additive plus dominance effects, the PA significantly increased only for plant height under low nitrogen. In this case, CNV effects explicitly capture relatedness between individuals and add extra information to the model. In the HELIX dataset, we observed a pronounced difference in PA between additive (0.50) and additive-dominance (0.71) models for predicting grain yield, suggesting a significant contribution of dominance. We conclude that copy variants may play an essential role in the phenotypic variation of complex traits in maize hybrids, although the inclusion of CNVs into datasets does not return significant gains concerning PA.
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Affiliation(s)
- Danilo Hottis Lyra
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil.
- Department of Computational and Analytical Sciences, Rothamsted Research, West Common, Harpenden, AL52JQ, UK.
| | - Giovanni Galli
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Filipe Couto Alves
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Ítalo Stefanine Correia Granato
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Miriam Suzane Vidotti
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Massaine Bandeira E Sousa
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - Júlia Silva Morosini
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
| | - José Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), 06600, Texcoco, D.F, Mexico
| | - Roberto Fritsche-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo (ESALQ/USP), Piracicaba, São Paulo, Brazil
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Alves FC, Granato ÍSC, Galli G, Lyra DH, Fritsche-Neto R, de Los Campos G. Bayesian analysis and prediction of hybrid performance. Plant Methods 2019; 15:14. [PMID: 30774704 PMCID: PMC6366084 DOI: 10.1186/s13007-019-0388-x] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 01/16/2019] [Indexed: 05/02/2023]
Abstract
BACKGROUND The selection of hybrids is an essential step in maize breeding. However, evaluating a large number of hybrids in field trials can be extremely costly. However, genomic models can be used to predict the expected performance of un-tested genotypes. Bayesian models offer a very flexible framework for hybrid prediction. The Bayesian methodology can be used with parametric and semi-parametric assumptions for additive and non-additive effects. Furthermore, samples from the posterior distribution of Bayesian models can be used to estimate the variance due to general and specific combining abilities even in cases where additive and non-additive effects are not mutually orthogonal. Also, the use of Bayesian models for analysis and prediction of hybrid performance has remained fairly limited. RESULTS We provided an overview of Bayesian parametric and semi-parametric genomic models for prediction of agronomic traits in maize hybrids and discussed how these models can be used to decompose the genotypic variance into components due to general and specific combining ability. We applied the methodology to data from 906 single cross tropical maize hybrids derived from a convergent population. Our results show that: (1) non-additive effects make a sizable contribution to the genetic variance of grain yield; however, the relative importance of non-additive effects was much smaller for ear and plant height; (2) genomic prediction can achieve relatively high accuracy in predicting phenotypes of un-tested hybrids and in pre-screening. CONCLUSIONS Genomic prediction can be a useful tool in pre-screening of hybrids and could contribute to the improvement of the efficiency and efficacy of maize hybrids breeding programs. The Bayesian framework offers a great deal of flexibility in modeling hybrid performance. The methodology can be used to estimate important genetic parameters and render predictions of the expected hybrid performance as well measures of uncertainty about such predictions.
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Affiliation(s)
- Filipe Couto Alves
- 2Department of Epidemiology and Biostatistics, Michigan State University, 775 Woodlot Dr. Office 1315, East Lansing, USA
| | - Ítalo Stefanine Correa Granato
- 3Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Avenida Pádua Dias, No 11, Piracicaba, São Paulo Brazil
| | - Giovanni Galli
- 3Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Avenida Pádua Dias, No 11, Piracicaba, São Paulo Brazil
| | - Danilo Hottis Lyra
- 4Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Roberto Fritsche-Neto
- 3Department of Genetics, "Luiz de Queiroz" College of Agriculture, University of São Paulo, Avenida Pádua Dias, No 11, Piracicaba, São Paulo Brazil
| | - Gustavo de Los Campos
- 1Departments of Epidemiology and Biostatistics, Statistics and Probability and Institute of Quantitative Health Science and Engineering, Michigan State University, 775 Woodlot Dr. Office 1311, East Lansing, USA
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