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Atanda SA, Bandillo N. Genomic-inferred cross-selection methods for multi-trait improvement in a recurrent selection breeding program. PLANT METHODS 2024; 20:133. [PMID: 39218896 PMCID: PMC11367796 DOI: 10.1186/s13007-024-01258-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 08/05/2024] [Indexed: 09/04/2024]
Abstract
The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.
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Affiliation(s)
- Sikiru Adeniyi Atanda
- Agricultural Data Analytics Unit, North Dakota State University, Fargo, ND, 58105-6050, USA.
| | - Nonoy Bandillo
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108-6050, USA.
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2
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Niehoff TAM, Ten Napel J, Bijma P, Pook T, Wientjes YCJ, Hegedűs B, Calus MPL. Improving selection decisions with mating information by accounting for Mendelian sampling variances looking two generations ahead. Genet Sel Evol 2024; 56:41. [PMID: 38773363 PMCID: PMC11107025 DOI: 10.1186/s12711-024-00899-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 04/03/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Breeding programs are judged by the genetic level of animals that are used to disseminate genetic progress. These animals are typically the best ones of the population. To maximise the genetic level of very good animals in the next generation, parents that are more likely to produce top performing offspring need to be selected. The ability of individuals to produce high-performing progeny differs because of differences in their breeding values and gametic variances. Differences in gametic variances among individuals are caused by differences in heterozygosity and linkage. The use of the gametic Mendelian sampling variance has been proposed before, for use in the usefulness criterion or Index5, and in this work, we extend existing approaches by not only considering the gametic Mendelian sampling variance of individuals, but also of their potential offspring. Thus, the criteria developed in this study plan one additional generation ahead. For simplicity, we assumed that the true quantitative trait loci (QTL) effects, genetic map and the haplotypes of all animals are known. RESULTS In this study, we propose a new selection criterion, ExpBVSelGrOff, which describes the genetic level of selected grand-offspring that are produced by selected offspring of a particular mating. We compare our criterion with other published criteria in a stochastic simulation of an ongoing breeding program for 21 generations for proof of concept. ExpBVSelGrOff performed better than all other tested criteria, like the usefulness criterion or Index5 which have been proposed in the literature, without compromising short-term gains. After only five generations, when selection is strong (1%), selection based on ExpBVSelGrOff achieved 5.8% more commercial genetic gain and retained 25% more genetic variance without compromising inbreeding rate compared to selection based only on breeding values. CONCLUSIONS Our proposed selection criterion offers a new tool to accelerate genetic progress for contemporary genomic breeding programs. It retains more genetic variance than previously published criteria that plan less far ahead. Considering future gametic Mendelian sampling variances in the selection process also seems promising for maintaining more genetic variance.
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Grants
- TKI Agri This study was financially supported by the Dutch Ministry of Economic Affairs (TKI Agri & Food Project LWV20054) and the Breed4Food partners Cobb Europe (Colchester, Essex, United Kingdom), CRV (Arnhem, the Netherlands), Hendrix Genetics (Boxmeer, the Net
- Food Project LWV20054 This study was financially supported by the Dutch Ministry of Economic Affairs (TKI Agri & Food Project LWV20054) and the Breed4Food partners Cobb Europe (Colchester, Essex, United Kingdom), CRV (Arnhem, the Netherlands), Hendrix Genetics (Boxmeer, the Net
- This study was financially supported by the Dutch Ministry of Economic Affairs (TKI Agri & Food Project LWV20054) and the Breed4Food partners Cobb Europe (Colchester, Essex, United Kingdom), CRV (Arnhem, the Netherlands), Hendrix Genetics (Boxmeer, the Net
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Affiliation(s)
- Tobias A M Niehoff
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands.
| | - Jan Ten Napel
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands
| | - Piter Bijma
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands
| | - Torsten Pook
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands
| | - Yvonne C J Wientjes
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands
| | - Bernadett Hegedűs
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands
| | - Mario P L Calus
- Animal Breeding and Genomics, Wageningen University and Research, Droevendaalsesteeg 1, 6700AH, Wageningen, The Netherlands
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3
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Hamazaki K, Iwata H. AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1361894. [PMID: 38817943 PMCID: PMC11138345 DOI: 10.3389/fpls.2024.1361894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 03/06/2024] [Indexed: 06/01/2024]
Abstract
Emerging technologies such as genomic selection have been applied to modern plant and animal breeding to increase the speed and efficiency of variety release. However, breeding requires decisions regarding parent selection and mating pairs, which significantly impact the ultimate genetic gain of a breeding scheme. The selection of appropriate parents and mating pairs to increase genetic gain while maintaining genetic diversity is still an urgent need that breeders are facing. This study aimed to determine the best progeny allocation strategies by combining future-oriented simulations and numerical black-box optimization for an improved selection of parents and mating pairs. In this study, we focused on optimizing the allocation of progenies, and the breeding process was regarded as a black-box function whose input is a set of parameters related to the progeny allocation strategies and whose output is the ultimate genetic gain of breeding schemes. The allocation of progenies to each mating pair was parameterized according to a softmax function, whose input is a weighted sum of multiple features for the allocation, including expected genetic variance of progenies and selection criteria such as different types of breeding values, to balance genetic gains and genetic diversity optimally. The weighting parameters were then optimized by the black-box optimization algorithm called StoSOO via future-oriented breeding simulations. Simulation studies to evaluate the potential of our novel method revealed that the breeding strategy based on optimized weights attained almost 10% higher genetic gain than that with an equal allocation of progenies to all mating pairs within just four generations. Among the optimized strategies, those considering the expected genetic variance of progenies could maintain the genetic diversity throughout the breeding process, leading to a higher ultimate genetic gain than those without considering it. These results suggest that our novel method can significantly improve the speed and efficiency of variety development through optimized decisions regarding the selection of parents and mating pairs. In addition, by changing simulation settings, our future-oriented optimization framework for progeny allocation strategies can be easily implemented into general breeding schemes, contributing to accelerated plant and animal breeding with high efficiency.
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Affiliation(s)
| | - Hiroyoshi Iwata
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
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Khanna A, Anumalla M, Ramos J, Cruz MTS, Catolos M, Sajise AG, Gregorio G, Dixit S, Ali J, Islam MR, Singh VK, Rahman MA, Khatun H, Pisano DJ, Bhosale S, Hussain W. Genetic gains in IRRI's rice salinity breeding and elite panel development as a future breeding resource. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:37. [PMID: 38294550 PMCID: PMC10830834 DOI: 10.1007/s00122-024-04545-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 01/05/2024] [Indexed: 02/01/2024]
Abstract
KEY MESSAGE Estimating genetic gains and formulating a future salinity elite breeding panel for rice pave the way for developing better high-yielding salinity tolerant lines with enhanced genetic gains. Genetic gain is a crucial parameter to check the breeding program's success and help optimize future breeding strategies for enhanced genetic gains. To estimate the genetic gains in IRRI's salinity breeding program and identify the best genotypes based on high breeding values for grain yield (kg/ha), we analyzed the historical data from the trials conducted in the IRRI, Philippines and Bangladesh. A two-stage mixed-model approach accounting for experimental design factors and a relationship matrix was fitted to obtain the breeding values for grain yield and estimate genetic trends. A positive genetic trend of 0.1% per annum with a yield advantage of 1.52 kg/ha was observed in IRRI, Philippines. In Bangladesh, we observed a genetic gain of 0.31% per annum with a yield advantage of 14.02 kg/ha. In the released varieties, we observed a genetic gain of 0.12% per annum with a 2.2 kg/ha/year yield advantage in the IRRI, Philippines. For the Bangladesh dataset, a genetic gain of 0.14% per annum with a yield advantage of 5.9 kg/ha/year was observed in the released varieties. Based on breeding values for grain yield, a core set of the top 145 genotypes with higher breeding values of > 2400 kg/ha in the IRRI, Philippines, and > 3500 kg/ha in Bangladesh with a reliability of > 0.4 were selected to develop the elite breeding panel. Conclusively, a recurrent selection breeding strategy integrated with novel technologies like genomic selection and speed breeding is highly required to achieve higher genetic gains in IRRI's salinity breeding programs.
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Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Joie Ramos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Ma Teresa Sta Cruz
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Andres Godwin Sajise
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Glenn Gregorio
- Southeast Asian Regional Center for Graduate Study and Research in Agriculture (SEARCA) and University of Philippines, 4031, Los Baños, Laguna, Philippines
| | - Shalabh Dixit
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Jauhar Ali
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Md Rafiqul Islam
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Vikas Kumar Singh
- IRRI South Asia Regional Center (IRRI-SA Hub), Hyderabad, Telangana, 502324, India
| | - Md Akhlasur Rahman
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Hasina Khatun
- Plant Breeding Division, Bangladesh Rice Research Institute (BRRI), Gazipur, 1701, Bangladesh
| | - Daniel Joseph Pisano
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), 4031, Los Baños, Laguna, Philippines.
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Sanchez D, Allier A, Ben Sadoun S, Mary-Huard T, Bauland C, Palaffre C, Lagardère B, Madur D, Combes V, Melkior S, Bettinger L, Murigneux A, Moreau L, Charcosset A. Assessing the potential of genetic resource introduction into elite germplasm: a collaborative multiparental population for flint maize. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:19. [PMID: 38214870 PMCID: PMC10786986 DOI: 10.1007/s00122-023-04509-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 11/18/2023] [Indexed: 01/13/2024]
Abstract
KEY MESSAGE Implementing a collaborative pre-breeding multi-parental population efficiently identifies promising donor x elite pairs to enrich the flint maize elite germplasm. Genetic diversity is crucial for maintaining genetic gains and ensuring breeding programs' long-term success. In a closed breeding program, selection inevitably leads to a loss of genetic diversity. While managing diversity can delay this loss, introducing external sources of diversity is necessary to bring back favorable genetic variation. Genetic resources exhibit greater diversity than elite materials, but their lower performance levels hinder their use. This is the case for European flint maize, for which elite germplasm has incorporated only a limited portion of the diversity available in landraces. To enrich the diversity of this elite genetic pool, we established an original cooperative maize bridging population that involves crosses between private elite materials and diversity donors to create improved genotypes that will facilitate the incorporation of original favorable variations. Twenty donor × elite BC1S2 families were created and phenotyped for hybrid value for yield related traits. Crosses showed contrasted means and variances and therefore contrasted potential in terms of selection as measured by their usefulness criterion (UC). Average expected mean performance gain over the initial elite material was 5%. The most promising donor for each elite line was identified. Results also suggest that one more generation, i.e., 3 in total, of crossing to the elite is required to fully exploit the potential of a donor. Altogether, our results support the usefulness of incorporating genetic resources into elite flint maize. They call for further effort to create fixed diversity donors and identify those most suitable for each elite program.
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Affiliation(s)
- Dimitri Sanchez
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Antoine Allier
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Syngenta, 12 Chemin de L'Hobit, 31790, Saint-Sauveur, France
| | - Sarah Ben Sadoun
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Tristan Mary-Huard
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
- Université Paris-Saclay, AgroParisTech, INRAE, UMR MIA-Paris Saclay, 91120, Palaiseau, France
| | - Cyril Bauland
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Carine Palaffre
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Bernard Lagardère
- UE 0394 SMH, INRAE, 2297 Route de l'INRA, 40390, Saint-Martin-de-Hinx, France
| | - Delphine Madur
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Valérie Combes
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | | | | | - Alain Murigneux
- Limagrain Europe, 28 Route d'Ennezat, 63720, Chappes, France
| | - Laurence Moreau
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France
| | - Alain Charcosset
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, Génétique Quantitative et Evolution-Le Moulon, 91190, Gif-Sur-Yvette, France.
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6
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Yadav S, Ross EM, Wei X, Powell O, Hivert V, Hickey LT, Atkin F, Deomano E, Aitken KS, Voss-Fels KP, Hayes BJ. Optimising clonal performance in sugarcane: leveraging non-additive effects via mate-allocation strategies. FRONTIERS IN PLANT SCIENCE 2023; 14:1260517. [PMID: 38023905 PMCID: PMC10667552 DOI: 10.3389/fpls.2023.1260517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023]
Abstract
Mate-allocation strategies in breeding programs can improve progeny performance by harnessing non-additive genetic effects. These approaches prioritise predicted progeny merit over parental breeding value, making them particularly appealing for clonally propagated crops such as sugarcane. We conducted a comparative analysis of mate-allocation strategies, exploring utilising non-additive and heterozygosity effects to maximise clonal performance with schemes that solely consider additive effects to optimise breeding value. Using phenotypic and genotypic data from a population of 2,909 clones evaluated in final assessment trials of Australian sugarcane breeding programs, we focused on three important traits: tonnes of cane per hectare (TCH), commercial cane sugar (CCS), and Fibre. By simulating families from all possible crosses (1,225) with 50 progenies each, we predicted the breeding and clonal values of progeny using two models: GBLUP (considering additive effects only) and extended-GBLUP (incorporating additive, non-additive, and heterozygosity effects). Integer linear programming was used to identify the optimal mate-allocation among selected parents. Compared to breeding value-based approaches, mate-allocation strategies based on clonal performance yielded substantial improvements, with predicted progeny values increasing by 57% for TCH, 12% for CCS, and 16% for fibre. Our simulation study highlights the effectiveness of mate-allocation approaches that exploit non-additive and heterozygosity effects, resulting in superior clonal performance. However, there was a notable decline in additive gain, particularly for TCH, likely due to significant epistatic effects. When selecting crosses based on clonal performance for TCH, the inbreeding coefficient of progeny was significantly lower compared to random mating, underscoring the advantages of leveraging non-additive and heterozygosity effects in mitigating inbreeding depression. Thus, mate-allocation strategies are recommended in clonally propagated crops to enhance clonal performance and reduce the negative impacts of inbreeding.
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Affiliation(s)
- Seema Yadav
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Elizabeth M. Ross
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Xianming Wei
- Sugar Research Australia, Mackay, QLD, Australia
| | - Owen Powell
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Valentin Hivert
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Lee T. Hickey
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
| | - Felicity Atkin
- Sugar Research Australia, Meringa Gordonvale, QLD, Australia
| | - Emily Deomano
- Sugar Research Australia, Indooroopilly, QLD, Australia
| | - Karen S. Aitken
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Brisbane, QlD, Australia
| | - Kai P. Voss-Fels
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
- Department of Grapevine Breeding, Hochschule Geisenheim University, Geisenheim, Germany
| | - Ben J. Hayes
- Queensland Alliance for Agriculture and Food Science, The University of Queensland, Brisbane, QLD, Australia
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Danguy des Déserts A, Durand N, Servin B, Goudemand-Dugué E, Alliot JM, Ruiz D, Charmet G, Elsen JM, Bouchet S. Comparison of genomic-enabled cross selection criteria for the improvement of inbred line breeding populations. G3 (BETHESDA, MD.) 2023; 13:jkad195. [PMID: 37625792 PMCID: PMC10627264 DOI: 10.1093/g3journal/jkad195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 03/15/2023] [Accepted: 08/22/2023] [Indexed: 08/27/2023]
Abstract
A crucial step in inbred plant breeding is the choice of mating design to derive high-performing inbred varieties while also maintaining a competitive breeding population to secure sufficient genetic gain in future generations. In practice, the mating design usually relies on crosses involving the best parental inbred lines to ensure high mean progeny performance. This excludes crosses involving lower performing but more complementary parents in terms of favorable alleles. We predicted the ability of crosses to produce putative outstanding progenies (high mean and high variance progeny distribution) using genomic prediction models. This study compared the benefits and drawbacks of 7 genomic cross selection criteria (CSC) in terms of genetic gain for 1 trait and genetic diversity in the next generation. Six CSC were already published, and we propose an improved CSC that can estimate the proportion of progeny above a threshold defined for the whole mating plan. We simulated mating designs optimized using different CSC. The 835 elite parents came from a real breeding program and were evaluated between 2000 and 2016. We applied constraints on parental contributions and genetic similarities between selected parents according to usual breeder practices. Our results showed that CSC based on progeny variance estimation increased the genetic value of superior progenies by up to 5% in the next generation compared to CSC based on the progeny mean estimation (i.e. parental genetic values) alone. It also increased the genetic gain (up to 4%) and/or maintained more genetic diversity at QTLs (up to 4% more genic variance when the marker effects were perfectly estimated).
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Affiliation(s)
- Alice Danguy des Déserts
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 63000 Clermont-Ferrand, Puy de Dôme, Auvergne, France
- INRAE-Université de Toulouse, UMR1388, GenPhySE, 31320 Castanet-Tolosan, Haute-Garonne, Occitanie, France
| | - Nicolas Durand
- ENAC-Ecole Nationale de l'Aviation Civile, 31000 Toulouse, Haute-Garonne, Occitanie, France
| | - Bertrand Servin
- INRAE-Université de Toulouse, UMR1388, GenPhySE, 31320 Castanet-Tolosan, Haute-Garonne, Occitanie, France
| | - Ellen Goudemand-Dugué
- Florimond-Desprez Veuve & Fils SAS, 59242 Cappelle-en-Pévèle, Nord, Hauts-de-France, France
| | - Jean-Marc Alliot
- IRIT-APO, Institut de recherche en informatique de Toulouse - Algorithmes Parallèles et Optimisation, 31000 Toulouse, Haute-Garonne, Occitanie, France
| | - Daniel Ruiz
- INPT-ENSEEIHT, Institut National Polytechnique de Toulouse, École Nationale Supérieure d'Électrotechnique, d'Électronique, d'Informatique, d'Hydraulique et des Télécommunications, 31000 Toulouse, Haute-Garonne, Occitanie, France
| | - Gilles Charmet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 63000 Clermont-Ferrand, Puy de Dôme, Auvergne, France
| | - Jean-Michel Elsen
- INRAE-Université de Toulouse, UMR1388, GenPhySE, 31320 Castanet-Tolosan, Haute-Garonne, Occitanie, France
| | - Sophie Bouchet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 63000 Clermont-Ferrand, Puy de Dôme, Auvergne, France
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8
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Lanzl T, Melchinger AE, Schön CC. Influence of the mating design on the additive genetic variance in plant breeding populations. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:236. [PMID: 37906322 PMCID: PMC10618341 DOI: 10.1007/s00122-023-04447-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 08/14/2023] [Indexed: 11/02/2023]
Abstract
KEY MESSAGE Mating designs determine the realized additive genetic variance in a population sample. Deflated or inflated variances can lead to reduced or overly optimistic assessment of future selection gains. The additive genetic variance [Formula: see text] inherent to a breeding population is a major determinant of short- and long-term genetic gain. When estimated from experimental data, it is not only the additive variances at individual loci (QTL) but also covariances between QTL pairs that contribute to estimates of [Formula: see text]. Thus, estimates of [Formula: see text] depend on the genetic structure of the data source and vary between population samples. Here, we provide a theoretical framework for calculating the expectation and variance of [Formula: see text] from genotypic data of a given population sample. In addition, we simulated breeding populations derived from different numbers of parents (P = 2, 4, 8, 16) and crossed according to three different mating designs (disjoint, factorial and half-diallel crosses). We calculated the variance of [Formula: see text] and of the parameter b reflecting the covariance component in [Formula: see text] standardized by the genic variance. Our results show that mating designs resulting in large biparental families derived from few disjoint crosses carry a high risk of generating progenies exhibiting strong covariances between QTL pairs on different chromosomes. We discuss the consequences of the resulting deflated or inflated [Formula: see text] estimates for phenotypic and genome-based selection as well as for applying the usefulness criterion in selection. We show that already one round of recombination can effectively break negative and positive covariances between QTL pairs induced by the mating design. We suggest to obtain reliable estimates of [Formula: see text] and its components in a population sample by applying statistical methods differing in their treatment of QTL covariances.
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Affiliation(s)
- Tobias Lanzl
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Albrecht E Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
| | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
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9
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Melchinger AE, Frisch M. Genomic prediction in hybrid breeding: II. Reciprocal recurrent genomic selection with full-sib and half-sib families. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:203. [PMID: 37653062 PMCID: PMC10471712 DOI: 10.1007/s00122-023-04446-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 08/09/2023] [Indexed: 09/02/2023]
Abstract
KEY MESSAGE Genomic prediction of GCA effects based on model training with full-sib rather than half-sib families yields higher short- and long-term selection gain in reciprocal recurrent genomic selection for hybrid breeding, if SCA effects are important. Reciprocal recurrent genomic selection (RRGS) is a powerful tool for ensuring sustainable selection progress in hybrid breeding. For training the statistical model, one can use half-sib (HS) or full-sib (FS) families produced by inter-population crosses of candidates from the two parent populations. Our objective was to compare HS-RRGS and FS-RRGS for the cumulative selection gain ([Formula: see text]), the genetic, GCA and SCA variances ([Formula: see text],[Formula: see text], [Formula: see text]) of the hybrid population, and prediction accuracy ([Formula: see text]) for GCA effects across cycles. Using SNP data from maize and wheat, we simulated RRGS programs over 10 cycles, each consisting of four sub-cycles with genomic selection of [Formula: see text] out of 950 candidates in each parent population. Scenarios differed for heritability [Formula: see text] and the proportion [Formula: see text] of traits, training set (TS) size ([Formula: see text]), and maize vs. wheat. Curves of [Formula: see text] over selection cycles showed no crossing of both methods. If [Formula: see text] was high, [Formula: see text] was generally higher for FS-RRGS than HS-RRGS due to higher [Formula: see text]. In contrast, HS-RRGS was superior or on par with FS-RRGS, if [Formula: see text] or [Formula: see text] and [Formula: see text] were low. [Formula: see text] showed a steeper increase and higher selection limit for scenarios with low [Formula: see text], high [Formula: see text] and large [Formula: see text]. [Formula: see text] and even more so [Formula: see text] decreased rapidly over cycles for both methods due to the high selection intensity and the role of the Bulmer effect for reducing [Formula: see text]. Since the TS for FS-RRGS can additionally be used for hybrid prediction, we recommend this method for achieving simultaneously the two major goals in hybrid breeding: population improvement and cultivar development.
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Affiliation(s)
- Albrecht E. Melchinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354 Freising, Germany
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599 Stuttgart, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, 35392 Gießen, Germany
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Miller MJ, Song Q, Fallen B, Li Z. Genomic prediction of optimal cross combinations to accelerate genetic improvement of soybean ( Glycine max). FRONTIERS IN PLANT SCIENCE 2023; 14:1171135. [PMID: 37235007 PMCID: PMC10206060 DOI: 10.3389/fpls.2023.1171135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 04/17/2023] [Indexed: 05/28/2023]
Abstract
Improving yield is a primary soybean breeding goal, as yield is the main determinant of soybean's profitability. Within the breeding process, selection of cross combinations is one of most important elements. Cross prediction will assist soybean breeders in identifying the best cross combinations among parental genotypes prior to crossing, increasing genetic gain and breeding efficiency. In this study optimal cross selection methods were created and applied in soybean and validated using historical data from the University of Georgia soybean breeding program, under multiple training set compositions and marker densities utilizing multiple genomic selection models for marker evaluation. Plant materials consisted of 702 advanced breeding lines evaluated in multiple environments and genotyped using SoySNP6k BeadChips. An additional marker set, the SoySNP3k marker set, was tested in this study as well. Optimal cross selection methods were used to predict the yield of 42 previously made crosses and compared to the performance of the cross's offspring in replicated field trials. The best prediction accuracy was obtained when using Extended Genomic BLUP with the SoySNP6k marker set, consisting of 3,762 polymorphic markers, with an accuracy of 0.56 with a training set maximally related to the crosses predicted and 0.4 in a training set with minimized relatedness to predicted crosses. Prediction accuracy was most significantly impacted by training set relatedness to the predicted crosses, marker density, and the genomic model used to predict marker effects. The usefulness criterion selected had an impact on prediction accuracy within training sets with low relatedness to the crosses predicted. Optimal cross prediction provides a useful method that assists plant breeders in selecting crosses in soybean breeding.
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Affiliation(s)
- Mark J. Miller
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, United States Department of Agriculture - Agricultural Research Service, Beltsville, MD, United States
| | - Benjamin Fallen
- Soybean and Nitrogen Fixation Research Unit, United States Department of Agriculture - Agricultural Research Service, Raleigh, NC, United States
| | - Zenglu Li
- Institute of Plant Breeding, Genetics and Genomics, and Department of Crop and Soil Sciences, University of Georgia, Athens, GA, United States
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11
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Obšteter J, Strachan LK, Bubnič J, Prešern J, Gorjanc G. SIMplyBee: an R package to simulate honeybee populations and breeding programs. Genet Sel Evol 2023; 55:31. [PMID: 37161307 PMCID: PMC10169377 DOI: 10.1186/s12711-023-00798-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/31/2023] [Indexed: 05/11/2023] Open
Abstract
BACKGROUND The Western honeybee is an economically important species globally, but has been experiencing colony losses that lead to economical damage and decreased genetic variability. This situation is spurring additional interest in honeybee breeding and conservation programs. Stochastic simulators are essential tools for rapid and low-cost testing of breeding programs and methods, yet no existing simulator allows for a detailed simulation of honeybee populations. Here we describe SIMplyBee, a holistic simulator of honeybee populations and breeding programs. SIMplyBee is an R package and hence freely available for installation from CRAN http://cran.r-project.org/package=SIMplyBee . IMPLEMENTATION SIMplyBee builds upon the stochastic simulator AlphaSimR that simulates individuals with their corresponding genomes and quantitative genetic values. To enable honeybee-specific simulations, we extended AlphaSimR by developing classes for global simulation parameters, SimParamBee, for a honeybee colony, Colony, and multiple colonies, MultiColony. We also developed functions to address major honeybee specificities: honeybee genome, haplodiploid inheritance, social organisation, complementary sex determination, polyandry, colony events, and quantitative genetics at the individual- and colony-levels. RESULTS We describe its implementation for simulating a honeybee genome, creating a honeybee colony and its members, addressing haplodiploid inheritance and complementary sex determination, simulating colony events, creating and managing multiple colonies at the same time, and obtaining genomic data and honeybee quantitative genetics. Further documentation, available at http://www.SIMplyBee.info , provides details on these operations and describes additional operations related to genomics, quantitative genetics, and other functionalities. DISCUSSION SIMplyBee is a holistic simulator of honeybee populations and breeding programs. It simulates individual honeybees with their genomes, colonies with colony events, and individual- and colony-level genetic and breeding values. Regarding the latter, SIMplyBee takes a user-defined function to combine individual- into colony-level values and hence allows for modeling any type of interaction within a colony. SIMplyBee provides a research platform for testing breeding and conservation strategies and their effect on future genetic gain and genetic variability. Future developments of SIMplyBee will focus on improving the simulation of honeybee genomes, optimizing the simulator's performance, and including spatial awareness in mating functions and phenotype simulation. We invite the honeybee genetics and breeding community to join us in the future development of SIMplyBee.
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Affiliation(s)
- Jana Obšteter
- Department of Animal Science, The Agricultural Institute of Slovenia, Ljubljana, Slovenia.
| | - Laura K Strachan
- The Roslin Institute and Royal (Dick) School of Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
| | - Jernej Bubnič
- Department of Animal Science, The Agricultural Institute of Slovenia, Ljubljana, Slovenia
| | - Janez Prešern
- Department of Animal Science, The Agricultural Institute of Slovenia, Ljubljana, Slovenia
| | - Gregor Gorjanc
- The Roslin Institute and Royal (Dick) School of Veterinary Medicine, The University of Edinburgh, Edinburgh, UK
- Biotechnical Faculty, Department of Animal Science, The University of Ljubljana, Ljubljana, Slovenia
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12
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Iqbal M, Semagn K, Jarquin D, Randhawa H, McCallum BD, Howard R, Aboukhaddour R, Ciechanowska I, Strenzke K, Crossa J, Céron-Rojas JJ, N’Diaye A, Pozniak C, Spaner D. Identification of Disease Resistance Parents and Genome-Wide Association Mapping of Resistance in Spring Wheat. PLANTS (BASEL, SWITZERLAND) 2022; 11:2905. [PMID: 36365358 PMCID: PMC9658635 DOI: 10.3390/plants11212905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/03/2022] [Accepted: 10/25/2022] [Indexed: 06/16/2023]
Abstract
The likelihood of success in developing modern cultivars depend on multiple factors, including the identification of suitable parents to initiate new crosses, and characterizations of genomic regions associated with target traits. The objectives of the present study were to (a) determine the best economic weights of four major wheat diseases (leaf spot, common bunt, leaf rust, and stripe rust) and grain yield for multi-trait restrictive linear phenotypic selection index (RLPSI), (b) select the top 10% cultivars and lines (hereafter referred as genotypes) with better resistance to combinations of the four diseases and acceptable grain yield as potential parents, and (c) map genomic regions associated with resistance to each disease using genome-wide association study (GWAS). A diversity panel of 196 spring wheat genotypes was evaluated for their reaction to stripe rust at eight environments, leaf rust at four environments, leaf spot at three environments, common bunt at two environments, and grain yield at five environments. The panel was genotyped with the Wheat 90K SNP array and a few KASP SNPs of which we used 23,342 markers for statistical analyses. The RLPSI analysis performed by restricting the expected genetic gain for yield displayed significant (p < 0.05) differences among the 3125 economic weights. Using the best four economic weights, a subset of 22 of the 196 genotypes were selected as potential parents with resistance to the four diseases and acceptable grain yield. GWAS identified 37 genomic regions, which included 12 for common bunt, 13 for leaf rust, 5 for stripe rust, and 7 for leaf spot. Each genomic region explained from 6.6 to 16.9% and together accounted for 39.4% of the stripe rust, 49.1% of the leaf spot, 94.0% of the leaf rust, and 97.9% of the common bunt phenotypic variance combined across all environments. Results from this study provide valuable information for wheat breeders selecting parental combinations for new crosses to develop improved germplasm with enhanced resistance to the four diseases as well as the physical positions of genomic regions that confer resistance, which facilitates direct comparisons for independent mapping studies in the future.
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Affiliation(s)
- Muhammad Iqbal
- Department of Agricultural, Food and Nutritional Science, University of Alberta, 4–10 Agriculture-Forestry Centre, Edmonton, AB T6G 2P5, Canada
| | - Kassa Semagn
- Department of Agricultural, Food and Nutritional Science, University of Alberta, 4–10 Agriculture-Forestry Centre, Edmonton, AB T6G 2P5, Canada
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL 32611, USA
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403 1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Brent D. McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, 101 Route 100, Morden, MB R6M 1Y5, Canada
| | - Reka Howard
- Department of Statistics, University of Nebraska—Lincoln, Lincoln, NE 68583, USA
| | - Reem Aboukhaddour
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403 1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Izabela Ciechanowska
- Department of Agricultural, Food and Nutritional Science, University of Alberta, 4–10 Agriculture-Forestry Centre, Edmonton, AB T6G 2P5, Canada
| | - Klaus Strenzke
- Department of Agricultural, Food and Nutritional Science, University of Alberta, 4–10 Agriculture-Forestry Centre, Edmonton, AB T6G 2P5, Canada
| | - José Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera, Veracruz 52640, Mexico
| | - J. Jesus Céron-Rojas
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Km 45 Carretera, Veracruz 52640, Mexico
| | - Amidou N’Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food and Nutritional Science, University of Alberta, 4–10 Agriculture-Forestry Centre, Edmonton, AB T6G 2P5, Canada
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Rembe M, Zhao Y, Wendler N, Oldach K, Korzun V, Reif JC. The Potential of Genome-Wide Prediction to Support Parental Selection, Evaluated with Data from a Commercial Barley Breeding Program. PLANTS 2022; 11:plants11192564. [PMID: 36235430 PMCID: PMC9571379 DOI: 10.3390/plants11192564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/18/2022] [Accepted: 09/23/2022] [Indexed: 11/29/2022]
Abstract
Parental selection is at the beginning and contributes significantly to the success of any breeding work. The value of a cross is reflected in the potential of its progeny population. Breeders invest substantial resources in evaluating progeny to select the best performing genotypes as candidates for variety development. Several proposals have been made to use genomics to support parental selection. These have mostly been evaluated using theoretical considerations or simulation studies. However, evaluations using experimental data have rarely been conducted. In this study, we tested the potential of genomic prediction for predicting the progeny mean, variance, and usefulness criterion using data from an applied breeding population for winter barley. For three traits with genetic architectures at varying levels of complexity, ear emergence, plant height, and grain yield, progeny mean, variance, and usefulness criterion were predicted and validated in scenarios resembling situations in which the described tools shall be used in plant breeding. While the population mean could be predicted with moderate to high prediction abilities amounting to 0.64, 0.21, and 0.39 in ear emergence, plant height, and grain yield, respectively, the prediction of family variance appeared difficult, as reflected in low prediction abilities of 0.41, 0.11, and 0.14, for ear emergence, plant height, and grain yield, respectively. We have shown that identifying superior crosses remains a challenging task and suggest that the success of predicting the usefulness criterion depends strongly on the complexity of the underlying trait.
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Affiliation(s)
- Maximilian Rembe
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
| | - Yusheng Zhao
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
| | - Neele Wendler
- KWS LOCHOW GmbH, Ferdinand-von-Lochow-Str. 5, 29303 Bergen, Germany
| | - Klaus Oldach
- KWS LOCHOW GmbH, Ferdinand-von-Lochow-Str. 5, 29303 Bergen, Germany
| | - Viktor Korzun
- KWS SAAT SE & Co. KGaA, Grimsehlstr. 31, 37574 Einbeck, Germany
| | - Jochen C. Reif
- Department of Breeding Research, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Gatersleben, Germany
- Correspondence:
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14
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Chung PY, Liao CT. Selection of parental lines for plant breeding via genomic prediction. FRONTIERS IN PLANT SCIENCE 2022; 13:934767. [PMID: 35968112 PMCID: PMC9363737 DOI: 10.3389/fpls.2022.934767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 07/01/2022] [Indexed: 06/15/2023]
Abstract
A set of superior parental lines is imperative for the development of high-performing inbred lines in any biparental crossing program for crops. The main objectives of this study are to (a) develop a genomic prediction approach to identify superior parental lines for multi-trait selection, and (b) generate a software package for users to execute the proposed approach before conducting field experiments. According to different breeding goals of the target traits, a novel selection index integrating information from genomic-estimated breeding values (GEBVs) of candidate accessions was proposed to evaluate the composite performance of simulated progeny populations. Two rice (Oryza sativa L.) genome datasets were analyzed to illustrate the potential applications of the proposed approach. One dataset applied to the parental selection for producing inbred lines with satisfactory performance in primary and secondary traits simultaneously. The other one applied to demonstrate the application of producing inbred lines with high adaptability to different environments. Overall, the results showed that incorporating GEBV and genomic diversity into a selection strategy based on the proposed selection index could assist in selecting superior parents to meet the desired breeding goals and increasing long-term genetic gain. An R package, called IPLGP, was generated to facilitate the widespread application of the approach.
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Affiliation(s)
- Ping-Yuan Chung
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
| | - Chen-Tuo Liao
- Department of Agronomy, National Taiwan University, Taipei, Taiwan
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15
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Vanavermaete D, Fostier J, Maenhout S, De Baets B. Deep scoping: a breeding strategy to preserve, reintroduce and exploit genetic variation. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3845-3861. [PMID: 34387711 PMCID: PMC8580937 DOI: 10.1007/s00122-021-03932-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/30/2021] [Indexed: 06/13/2023]
Abstract
The deep scoping method incorporates the use of a gene bank together with different population layers to reintroduce genetic variation into the breeding population, thus maximizing the long-term genetic gain without reducing the short-term genetic gain or increasing the total financial cost. Genomic prediction is often combined with truncation selection to identify superior parental individuals that can pass on favorable quantitative trait locus (QTL) alleles to their offspring. However, truncation selection reduces genetic variation within the breeding population, causing a premature convergence to a sub-optimal genetic value. In order to also increase genetic gain in the long term, different methods have been proposed that better preserve genetic variation. However, when the genetic variation of the breeding population has already been reduced as a result of prior intensive selection, even those methods will not be able to avert such premature convergence. Pre-breeding provides a solution for this problem by reintroducing genetic variation into the breeding population. Unfortunately, as pre-breeding often relies on a separate breeding population to increase the genetic value of wild specimens before introducing them in the elite population, it comes with an increased financial cost. In this paper, on the basis of a simulation study, we propose a new method that reintroduces genetic variation in the breeding population on a continuous basis without the need for a separate pre-breeding program or a larger population size. This way, we are able to introduce favorable QTL alleles into an elite population and maximize the genetic gain in the short as well as in the long term without increasing the financial cost.
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Affiliation(s)
- David Vanavermaete
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, B-9000, Ghent, Belgium.
| | - Jan Fostier
- IDLab, Department of Information Technology, Ghent University - imec, B-9052, Ghent, Belgium
| | | | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, B-9000, Ghent, Belgium
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16
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Wolfe MD, Chan AW, Kulakow P, Rabbi I, Jannink JL. Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices. Genetics 2021; 219:iyab122. [PMID: 34740244 PMCID: PMC8570794 DOI: 10.1093/genetics/iyab122] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 07/13/2021] [Indexed: 11/14/2022] Open
Abstract
Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land, and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects, and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances, and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. Most variance (89%) and covariance (70%) prediction accuracy estimates were greater than zero. The usefulness of crosses was accurately predicted with good correspondence between the predicted and the actual mean performance of family members breeders selected for advancement as new parents and candidate varieties. We also used a directional dominance model to quantify significant inbreeding depression for most traits. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal the new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with top breeding values) and varieties (progeny with top own performance).
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Affiliation(s)
- Marnin D Wolfe
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences,
Cornell University, Ithaca, NY 14850, USA
| | - Ariel W Chan
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences,
Cornell University, Ithaca, NY 14850, USA
| | - Peter Kulakow
- International Institute of Tropical Agriculture (IITA), Ibadan,
Nigeria
| | - Ismail Rabbi
- International Institute of Tropical Agriculture (IITA), Ibadan,
Nigeria
| | - Jean-Luc Jannink
- Section on Plant Breeding and Genetics, School of Integrative Plant Sciences,
Cornell University, Ithaca, NY 14850, USA
- USDA-ARS, Ithaca, NY 14850, USA
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17
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Michel S, Löschenberger F, Ametz C, Bürstmayr H. Genomic selection of parents and crosses beyond the native gene pool of a breeding program. THE PLANT GENOME 2021; 14:e20153. [PMID: 34651462 DOI: 10.1002/tpg2.20153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 08/03/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection has become a valuable tool for selecting cultivar candidates in many plant breeding programs. Genomic selection of elite parents and crossing combinations with germplasm developed outside a breeding program has, however, hardly been explored until now. The aim of this study was to assess the potential of this method for commonly ranking and selecting elite germplasm developed within and beyond a given breeding program. A winter wheat (Triticum aestivum L.) population consisting of 611 in-house and 87 externally developed lines was used to compare training population compositions and statistical models for genomically predicting baking quality in this framework. Augmenting training populations with lines from other breeding programs had a larger influence on the prediction ability than adding in-house generated lines when aiming to commonly rank both germplasm sets. Exploiting preexisting information of secondary correlated traits resulted likewise in more accurate predictions both in empirical analyses and simulations. Genotyping germplasm developed beyond a given breeding program is moreover a convenient way to clarify its relationships with a breeder's own germplasm because pedigree information is oftentimes not available for this purpose. Genomic predictions can thus support a more informed diversity management, especially when integrating simply to phenotype correlated traits to partly circumvent resource reallocations for a costly phenotyping of germplasm from other programs.
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Affiliation(s)
- Sebastian Michel
- Dep. of Agrobiotechnology, IFA-Tulln, Univ. of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430 Tulln, Austria
| | | | - Christian Ametz
- Saatzucht Donau GesmbH & CoKG, Saatzuchtstrasse 11, 2301 Probstdorf, Austria
| | - Hermann Bürstmayr
- Dep. of Agrobiotechnology, IFA-Tulln, Univ. of Natural Resources and Life Sciences Vienna, Konrad-Lorenz-Str. 20, 3430 Tulln, Austria
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Sharma S, Schulthess AW, Bassi FM, Badaeva ED, Neumann K, Graner A, Özkan H, Werner P, Knüpffer H, Kilian B. Introducing Beneficial Alleles from Plant Genetic Resources into the Wheat Germplasm. BIOLOGY 2021; 10:982. [PMID: 34681081 PMCID: PMC8533267 DOI: 10.3390/biology10100982] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/02/2022]
Abstract
Wheat (Triticum sp.) is one of the world's most important crops, and constantly increasing its productivity is crucial to the livelihoods of millions of people. However, more than a century of intensive breeding and selection processes have eroded genetic diversity in the elite genepool, making new genetic gains difficult. Therefore, the need to introduce novel genetic diversity into modern wheat has become increasingly important. This review provides an overview of the plant genetic resources (PGR) available for wheat. We describe the most important taxonomic and phylogenetic relationships of these PGR to guide their use in wheat breeding. In addition, we present the status of the use of some of these resources in wheat breeding programs. We propose several introgression schemes that allow the transfer of qualitative and quantitative alleles from PGR into elite germplasm. With this in mind, we propose the use of a stage-gate approach to align the pre-breeding with main breeding programs to meet the needs of breeders, farmers, and end-users. Overall, this review provides a clear starting point to guide the introgression of useful alleles over the next decade.
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Affiliation(s)
- Shivali Sharma
- Global Crop Diversity Trust, Platz der Vereinten Nationen 7, D-53113 Bonn, Germany; (S.S.); (P.W.)
| | - Albert W. Schulthess
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Filippo M. Bassi
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat 10112, Morocco;
| | - Ekaterina D. Badaeva
- N.I. Vavilov Institute of General Genetics, Russian Academy of Sciences, 119991 Moscow, Russia;
- The Federal Research Center Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (ICG SB RAS), 630090 Novosibirsk, Russia
| | - Kerstin Neumann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Hakan Özkan
- Department of Field Crops, Faculty of Agriculture, University of Çukurova, Adana 01330, Turkey;
| | - Peter Werner
- Global Crop Diversity Trust, Platz der Vereinten Nationen 7, D-53113 Bonn, Germany; (S.S.); (P.W.)
| | - Helmut Knüpffer
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), OT Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; (A.W.S.); (K.N.); (A.G.); (H.K.)
| | - Benjamin Kilian
- Global Crop Diversity Trust, Platz der Vereinten Nationen 7, D-53113 Bonn, Germany; (S.S.); (P.W.)
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Sekine D, Tsuda M, Yabe S, Shimizu T, Machita K, Saruta M, Yamada T, Ishimoto M, Iwata H, Kaga A. Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing. FRONTIERS IN PLANT SCIENCE 2021; 12:729645. [PMID: 34539720 PMCID: PMC8443513 DOI: 10.3389/fpls.2021.729645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 08/06/2021] [Indexed: 06/13/2023]
Abstract
Genomic selection and marker-assisted recurrent selection have been applied to improve quantitative traits in many cross-pollinated crops. However, such selection is not feasible in self-pollinated crops owing to laborious crossing procedures. In this study, we developed a simulation-based selection strategy that makes use of a trait prediction model based on genomic information to predict the phenotype of the progeny for all possible crossing combinations. These predictions are then used to select the best cross combinations for the selection of the given trait. In our simulated experiment, using a biparental initial population with a heritability set to 0.3, 0.6, or 1.0 and the number of quantitative trait loci set to 30 or 100, the genetic gain of the proposed strategy was higher or equal to that of conventional recurrent selection method in the early selection cycles, although the number of cross combinations of the proposed strategy was considerably reduced in each cycle. Moreover, this strategy was demonstrated to increase or decrease seed protein content in soybean recombinant inbred lines using SNP markers. Information on 29 genomic regions associated with seed protein content was used to construct the prediction model and conduct simulation. After two selection cycles, the selected progeny had significantly higher or lower seed protein contents than those from the initial population. These results suggest that our strategy is effective in obtaining superior progeny over a short period with minimal crossing and has the potential to efficiently improve the target quantitative traits in self-pollinated crops.
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Affiliation(s)
- Daisuke Sekine
- Institute of Vegetable and Floriculture Science, National Agriculture and Food Research Organization, Tsu, Japan
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Mai Tsuda
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
- Tsukuba Plant Innovation Research Center, University of Tsukuba, Tsukuba, Japan
| | - Shiori Yabe
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Takehiko Shimizu
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Kayo Machita
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Masayasu Saruta
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Tetsuya Yamada
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Masao Ishimoto
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
| | - Hiroyoshi Iwata
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, Japan
| | - Akito Kaga
- Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Japan
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20
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Auinger HJ, Lehermeier C, Gianola D, Mayer M, Melchinger AE, da Silva S, Knaak C, Ouzunova M, Schön CC. Calibration and validation of predicted genomic breeding values in an advanced cycle maize population. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3069-3081. [PMID: 34117908 PMCID: PMC8354938 DOI: 10.1007/s00122-021-03880-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 05/31/2021] [Indexed: 05/26/2023]
Abstract
Model training on data from all selection cycles yielded the highest prediction accuracy by attenuating specific effects of individual cycles. Expected reliability was a robust predictor of accuracies obtained with different calibration sets. The transition from phenotypic to genome-based selection requires a profound understanding of factors that determine genomic prediction accuracy. We analysed experimental data from a commercial maize breeding programme to investigate if genomic measures can assist in identifying optimal calibration sets for model training. The data set consisted of six contiguous selection cycles comprising testcrosses of 5968 doubled haploid lines genotyped with a minimum of 12,000 SNP markers. We evaluated genomic prediction accuracies in two independent prediction sets in combination with calibration sets differing in sample size and genomic measures (effective sample size, average maximum kinship, expected reliability, number of common polymorphic SNPs and linkage phase similarity). Our results indicate that across selection cycles prediction accuracies were as high as 0.57 for grain dry matter yield and 0.76 for grain dry matter content. Including data from all selection cycles in model training yielded the best results because interactions between calibration and prediction sets as well as the effects of different testers and specific years were attenuated. Among genomic measures, the expected reliability of genomic breeding values was the best predictor of empirical accuracies obtained with different calibration sets. For grain yield, a large difference between expected and empirical reliability was observed in one prediction set. We propose to use this difference as guidance for determining the weight phenotypic data of a given selection cycle should receive in model retraining and for selection when both genomic breeding values and phenotypes are available.
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Affiliation(s)
- Hans-Jürgen Auinger
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | | | - Daniel Gianola
- Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Manfred Mayer
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany
| | - Albrecht E Melchinger
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
| | | | | | | | - Chris-Carolin Schön
- Plant Breeding, TUM School of Life Sciences, Technical University of Munich, 85354, Freising, Germany.
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21
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Covarrubias-Pazaran G, Martini JWR, Quinn M, Atlin G. Strengthening Public Breeding Pipelines by Emphasizing Quantitative Genetics Principles and Open Source Data Management. FRONTIERS IN PLANT SCIENCE 2021; 12:681624. [PMID: 34326855 PMCID: PMC8313805 DOI: 10.3389/fpls.2021.681624] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 06/18/2021] [Indexed: 05/25/2023]
Affiliation(s)
| | - Johannes W. R. Martini
- Genetic Resources Program, International Maize and Wheat Improvement Center, Texcoco, Mexico
| | - Michael Quinn
- Excellence in Breeding Platform, Consultative Group for International Agricultural Research, Texcoco, Mexico
| | - Gary Atlin
- Bill and Melinda Gates Foundation, Seattle, WA, United States
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22
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Fugeray-Scarbel A, Bastien C, Dupont-Nivet M, Lemarié S. Why and How to Switch to Genomic Selection: Lessons From Plant and Animal Breeding Experience. Front Genet 2021; 12:629737. [PMID: 34305998 PMCID: PMC8301370 DOI: 10.3389/fgene.2021.629737] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Accepted: 06/11/2021] [Indexed: 11/25/2022] Open
Abstract
The present study is a transversal analysis of the interest in genomic selection for plant and animal species. It focuses on the arguments that may convince breeders to switch to genomic selection. The arguments are classified into three different “bricks.” The first brick considers the addition of genotyping to improve the accuracy of the prediction of breeding values. The second consists of saving costs and/or shortening the breeding cycle by replacing all or a portion of the phenotyping effort with genotyping. The third concerns population management to improve the choice of parents to either optimize crossbreeding or maintain genetic diversity. We analyse the relevance of these different bricks for a wide range of animal and plant species and sought to explain the differences between species according to their biological specificities and the organization of breeding programs.
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Affiliation(s)
| | | | | | | | - Stéphane Lemarié
- Université Grenoble Alpes, INRAE, CNRS, Grenoble INP, GAEL, Grenoble, France
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23
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Mackay IJ, Cockram J, Howell P, Powell W. Understanding the classics: the unifying concepts of transgressive segregation, inbreeding depression and heterosis and their central relevance for crop breeding. PLANT BIOTECHNOLOGY JOURNAL 2021; 19:26-34. [PMID: 32996672 PMCID: PMC7769232 DOI: 10.1111/pbi.13481] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 09/07/2020] [Accepted: 09/12/2020] [Indexed: 05/12/2023]
Abstract
Transgressive segregation and heterosis are the reasons that plant breeding works. Molecular explanations for both phenomena have been suggested and play a contributing role. However, it is often overlooked by molecular genetic researchers that transgressive segregation and heterosis are most simply explained by dispersion of favorable alleles. Therefore, advances in molecular biology will deliver the most impact on plant breeding when integrated with sources of heritable trait variation - and this will be best achieved within a quantitative genetics framework. An example of the power of quantitative approaches is the implementation of genomic selection, which has recently revolutionized animal breeding. Genomic selection is now being applied to both hybrid and inbred crops and is likely to be the major source of improvement in plant breeding practice over the next decade. Breeders' ability to efficiently apply genomic selection methodologies is due to recent technology advances in genotyping and sequencing. Furthermore, targeted integration of additional molecular data (such as gene expression, gene copy number and methylation status) into genomic prediction models may increase their performance. In this review, we discuss and contextualize a suite of established quantitative genetics themes relating to hybrid vigour, transgressive segregation and their central relevance to plant breeding, with the aim of informing crop researchers outside of the quantitative genetics discipline of their relevance and importance to crop improvement. Better understanding between molecular and quantitative disciplines will increase the potential for further improvements in plant breeding methodologies and so help underpin future food security.
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Affiliation(s)
- Ian J. Mackay
- SRUC (Scotland’s Rural College)EdinburghUK
- IMplant ConsultancyChelmsfordUK
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24
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Heslot N, Feoktistov V. Optimization of Selective Phenotyping and Population Design for Genomic Prediction. JOURNAL OF AGRICULTURAL, BIOLOGICAL AND ENVIRONMENTAL STATISTICS 2020. [DOI: 10.1007/s13253-020-00415-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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25
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Preservation of Genetic Variation in a Breeding Population for Long-Term Genetic Gain. G3-GENES GENOMES GENETICS 2020; 10:2753-2762. [PMID: 32513654 PMCID: PMC7407475 DOI: 10.1534/g3.120.401354] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Genomic selection has been successfully implemented in plant and animal breeding. The transition of parental selection based on phenotypic characteristics to genomic selection (GS) has reduced breeding time and cost while accelerating the rate of genetic progression. Although breeding methods have been adapted to include genomic selection, parental selection often involves truncation selection, selecting the individuals with the highest genomic estimated breeding values (GEBVs) in the hope that favorable properties will be passed to their offspring. This ensures genetic progression and delivers offspring with high genetic values. However, several favorable quantitative trait loci (QTL) alleles risk being eliminated from the breeding population during breeding. We show that this could reduce the mean genetic value that the breeding population could reach in the long term with up to 40%. In this paper, by means of a simulation study, we propose a new method for parental mating that is able to preserve the genetic variation in the breeding population, preventing premature convergence of the genetic values to a local optimum, thus maximizing the genetic values in the long term. We do not only prevent the fixation of several unfavorable QTL alleles, but also demonstrate that the genetic values can be increased by up to 15 percentage points compared with truncation selection.
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26
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Keller B, Ariza-Suarez D, de la Hoz J, Aparicio JS, Portilla-Benavides AE, Buendia HF, Mayor VM, Studer B, Raatz B. Genomic Prediction of Agronomic Traits in Common Bean ( Phaseolus vulgaris L.) Under Environmental Stress. FRONTIERS IN PLANT SCIENCE 2020; 11:1001. [PMID: 32774338 PMCID: PMC7381332 DOI: 10.3389/fpls.2020.01001] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 06/18/2020] [Indexed: 05/19/2023]
Abstract
In plant and animal breeding, genomic prediction models are established to select new lines based on genomic data, without the need for laborious phenotyping. Prediction models can be trained on recent or historic phenotypic data and increasingly available genotypic data. This enables the adoption of genomic selection also in under-used legume crops such as common bean. Beans are an important staple food in the tropics and mainly grown by smallholders under limiting environmental conditions such as drought or low soil fertility. Therefore, genotype-by-environment interactions (G × E) are an important consideration when developing new bean varieties. However, G × E are often not considered in genomic prediction models nor are these models implemented in current bean breeding programs. Here we show the prediction abilities of four agronomic traits in common bean under various environmental stresses based on twelve field trials. The dataset includes 481 elite breeding lines characterized by 5,820 SNP markers. Prediction abilities over all twelve trials ranged between 0.6 and 0.8 for yield and days to maturity, respectively, predicting new lines into new seasons. In all four evaluated traits, the prediction abilities reached about 50-80% of the maximum accuracies given by phenotypic correlations and heritability. Predictions under drought and low phosphorus stress were up to 10 and 20% improved when G × E were included in the model, respectively. Our results demonstrate the potential of genomic selection to increase the genetic gain in common bean breeding. Prediction abilities improved when more phenotypic data was available and G × E could be accounted for. Furthermore, the developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.
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Affiliation(s)
- Beat Keller
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Daniel Ariza-Suarez
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Juan de la Hoz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Johan Steven Aparicio
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | | | - Hector Fabio Buendia
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Victor Manuel Mayor
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
| | - Bruno Studer
- Molecular Plant Breeding, Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Bodo Raatz
- Bean Program, Agrobiodiversity Area, International Center for Tropical Agriculture (CIAT), Cali, Colombia
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27
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Fikere M, Barbulescu DM, Malmberg MM, Maharjan P, Salisbury PA, Kant S, Panozzo J, Norton S, Spangenberg GC, Cogan NOI, Daetwyler HD. Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola ( Brassica napus L.). PLANTS (BASEL, SWITZERLAND) 2020; 9:E719. [PMID: 32517116 PMCID: PMC7356366 DOI: 10.3390/plants9060719] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/29/2020] [Accepted: 06/02/2020] [Indexed: 02/02/2023]
Abstract
Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015-2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.
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Affiliation(s)
- Mulusew Fikere
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, QLD 4072, Australia
| | - Denise M. Barbulescu
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
| | - M. Michelle Malmberg
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Pankaj Maharjan
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
| | - Phillip A. Salisbury
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
- Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Surya Kant
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
- Centre for Agricultural Innovation, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Joe Panozzo
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
- Centre for Agricultural Innovation, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Sally Norton
- Agriculture Victoria, Grains Innovation Park, Horsham, VIC 3400, Australia; (D.M.B.); (P.M.); (S.K.); (J.P.); (S.N.)
| | - German C. Spangenberg
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Noel O. I. Cogan
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
| | - Hans D. Daetwyler
- School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia; (M.F.); (M.M.M.); (G.C.S.); (N.O.I.C.)
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia;
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28
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Allier A, Teyssèdre S, Lehermeier C, Charcosset A, Moreau L. Genomic prediction with a maize collaborative panel: identification of genetic resources to enrich elite breeding programs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2020; 133:201-215. [PMID: 31595338 DOI: 10.1007/s00122-019-03451-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 09/28/2019] [Indexed: 05/02/2023]
Abstract
Collaborative diversity panels and genomic prediction seem relevant to identify and harness genetic resources for polygenic trait-specific enrichment of elite germplasms. In plant breeding, genetic diversity is important to maintain the pace of genetic gain and the ability to respond to new challenges in a context of climatic and social expectation changes. Many genetic resources are accessible to breeders but cannot all be considered for broadening the genetic diversity of elite germplasm. This study presents the use of genomic predictions trained on a collaborative diversity panel, which assembles genetic resources and elite lines, to identify resources to enrich an elite germplasm. A maize collaborative panel (386 lines) was considered to estimate genome-wide marker effects. Relevant predictive abilities (0.40-0.55) were observed on a large population of private elite materials, which supported the interest of such a collaborative panel for diversity management perspectives. Grain-yield estimated marker effects were used to select a donor that best complements an elite recipient at individual loci or haplotype segments, or that is expected to give the best-performing progeny with the elite. Among existing and new criteria that were compared, some gave more weight to the donor-elite complementarity than to the donor value, and appeared more adapted to long-term objective. We extended this approach to the selection of a set of donors complementing an elite population. We defined a crossing plan between identified donors and elite recipients. Our results illustrated how collaborative projects based on diversity panels including both public resources and elite germplasm can contribute to a better characterization of genetic resources in view of their use to enrich elite germplasm.
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Affiliation(s)
- Antoine Allier
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
- RAGT2n, Genetics and Analytics Unit, 12510, Druelle, France
| | | | | | - Alain Charcosset
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE - Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, 91190, Gif-sur-Yvette, France.
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29
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Bijma P, Wientjes YCJ, Calus MPL. Breeding Top Genotypes and Accelerating Response to Recurrent Selection by Selecting Parents with Greater Gametic Variance. Genetics 2020; 214:91-107. [PMID: 31772074 PMCID: PMC6944402 DOI: 10.1534/genetics.119.302643] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 11/21/2019] [Indexed: 11/18/2022] Open
Abstract
Because of variation in linkage phase and heterozygosity among individuals, some individuals produce genetically more variable gametes than others. With the availability of genomic EBVs (GEBVs) or estimates of SNP-effects together with phased genotypes, differences in gametic variability can be quantified by simulating a set of virtual gametes of each selection candidate. Previous results in dairy cattle show that gametic variance can be large. Here, we show that breeders can increase the probability of breeding a top-ranking genotype and response to recurrent selection by selecting parents that produce more variable gametes, using the index [Formula: see text] where [Formula: see text] is the standardized normal truncation point belonging to selected proportion p, and SDgGEBV is the SD of the GEBV of an individual's gametes. Benefits of the index were considerably larger in an ongoing selection program with equilibrium genetic parameters than in an initially unselected population. Superiority of the index over selection on GEBV increased strongly with the magnitude of the [Formula: see text] indicating that benefits of the index may vary considerably among populations. Compared to selection on ordinary GEBV, the probability of breeding a top-ranking individual can be increased by ∼36%, and response to selection by ∼3.6% when selection is strong (P = 0.001) based on values for the Holstein-Friesian dairy cattle population. Two-stage selection, with a preselection on GEBV and a final selection on the index, considerably reduced computational requirements with little loss of benefits. Response to multiple generations of selection and inheritance of the SDgEBV require further study.
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Affiliation(s)
- Piter Bijma
- Wageningen University and Research, Animal Breeding and Genomics, 6708PB Wageningen, The Netherlands
| | - Yvonne C J Wientjes
- Wageningen University and Research, Animal Breeding and Genomics, 6708PB Wageningen, The Netherlands
| | - Mario P L Calus
- Wageningen University and Research, Animal Breeding and Genomics, 6708PB Wageningen, The Netherlands
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30
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Allier A, Lehermeier C, Charcosset A, Moreau L, Teyssèdre S. Improving Short- and Long-Term Genetic Gain by Accounting for Within-Family Variance in Optimal Cross-Selection. Front Genet 2019; 10:1006. [PMID: 31737033 PMCID: PMC6828944 DOI: 10.3389/fgene.2019.01006] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 09/20/2019] [Indexed: 12/30/2022] Open
Abstract
The implementation of genomic selection in recurrent breeding programs raises the concern that a higher inbreeding rate could compromise the long-term genetic gain. An optimized mating strategy that maximizes the performance in progeny and maintains diversity for long-term genetic gain is therefore essential. The optimal cross-selection approach aims at identifying the optimal set of crosses that maximizes the expected genetic value in the progeny under a constraint on genetic diversity in the progeny. Optimal cross-selection usually does not account for within-family selection, i.e., the fact that only a selected fraction of each family is used as parents of the next generation. In this study, we consider within-family variance accounting for linkage disequilibrium between quantitative trait loci to predict the expected mean performance and the expected genetic diversity in the selected progeny of a set of crosses. These predictions rely on the usefulness criterion parental contribution (UCPC) method. We compared UCPC-based optimal cross-selection and the optimal cross-selection approach in a long-term simulated recurrent genomic selection breeding program considering overlapping generations. UCPC-based optimal cross-selection proved to be more efficient to convert the genetic diversity into short- and long-term genetic gains than optimal cross-selection. We also showed that, using the UCPC-based optimal cross-selection, the long-term genetic gain can be increased with only a limited reduction of the short-term commercial genetic gain.
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Affiliation(s)
- Antoine Allier
- GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
- Genetics and Analytics Unit, RAGT2n, Druelle, France
| | | | - Alain Charcosset
- GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Laurence Moreau
- GQE-Le Moulon, INRA, Univ. Paris-Sud, CNRS, AgroParisTech, Université Paris-Saclay, Gif-sur-Yvette, France
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31
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Neyhart JL, Lorenz AJ, Smith KP. Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses. G3 (BETHESDA, MD.) 2019; 9:3153-3165. [PMID: 31358561 PMCID: PMC6778794 DOI: 10.1534/g3.119.400406] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2019] [Accepted: 07/25/2019] [Indexed: 12/22/2022]
Abstract
The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations - an important breeding step - that will deliver more favorable genetic correlations (rG ). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11-27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.
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Affiliation(s)
- Jeffrey L Neyhart
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
| | - Aaron J Lorenz
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
| | - Kevin P Smith
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN 55108
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Usefulness Criterion and Post-selection Parental Contributions in Multi-parental Crosses: Application to Polygenic Trait Introgression. G3-GENES GENOMES GENETICS 2019; 9:1469-1479. [PMID: 30819823 PMCID: PMC6505154 DOI: 10.1534/g3.119.400129] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Predicting the usefulness of crosses in terms of expected genetic gain and genetic diversity is of interest to secure performance in the progeny and to maintain long-term genetic gain in plant breeding. A wide range of crossing schemes are possible including large biparental crosses, backcrosses, four-way crosses, and synthetic populations. In silico progeny simulations together with genome-based prediction of quantitative traits can be used to guide mating decisions. However, the large number of multi-parental combinations can hinder the use of simulations in practice. Analytical solutions have been proposed recently to predict the distribution of a quantitative trait in the progeny of biparental crosses using information of recombination frequency and linkage disequilibrium between loci. Here, we extend this approach to obtain the progeny distribution of more complex crosses including two to four parents. Considering agronomic traits and parental genome contribution as jointly multivariate normally distributed traits, the usefulness criterion parental contribution (UCPC) enables to (i) evaluate the expected genetic gain for agronomic traits, and at the same time (ii) evaluate parental genome contributions to the selected fraction of progeny. We validate and illustrate UCPC in the context of multiple allele introgression from a donor into one or several elite recipients in maize (Zea mays L.). Recommendations regarding the interest of two-way, three-way, and backcrosses were derived depending on the donor performance. We believe that the computationally efficient UCPC approach can be useful for mate selection and allocation in many plant and animal breeding contexts.
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Santos DJA, Cole JB, Lawlor TJ, VanRaden PM, Tonhati H, Ma L. Variance of gametic diversity and its application in selection programs. J Dairy Sci 2019; 102:5279-5294. [PMID: 30981488 DOI: 10.3168/jds.2018-15971] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Accepted: 02/27/2019] [Indexed: 11/19/2022]
Abstract
The variance of gametic diversity ( σgamete2) can be used to find individuals that more likely produce progeny with extreme breeding values. The aim of this study was to obtain this variance for individuals from routine genomic evaluations, and to apply gametic variance in a selection criterion in conjunction with breeding values to improve genetic progress. An analytical approach was developed to estimate σgamete2 by the sum of binomial variances of all individual quantitative trait loci across the genome. Simulation was used to verify the predictability of this variance in a range of scenarios. The accuracy of prediction ranged from 0.49 to 0.85, depending on the scenario and model used. Compared with sequence data, SNP data are sufficient for estimating σgamete2 Results also suggested that markers with low minor allele frequency and the covariance between markers should be included in the estimation. To incorporate σgamete2 into selective breeding programs, we proposed a new index, relative predicted transmitting ability, which better utilizes the genetic potential of individuals than traditional predicted transmitting ability. Simulation with a small genome showed an additional genetic gain of up to 16% in 10 generations, depending on the number of quantitative trait loci and selection intensity. Finally, we applied σgamete2 to the US genomic evaluations for Holstein and Jersey cattle. As expected, the DGAT1 gene had a strong effect on the estimation of σgamete2 for several production traits. However, inbreeding had a small impact on gametic variability, with greater effect for more polygenic traits. In conclusion, gametic variance, a potentially important parameter for selection programs, can be easily computed and is useful for improving genetic progress and controlling genetic diversity.
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Affiliation(s)
- D J A Santos
- Department of Animal and Avian Sciences, University of Maryland, College Park 20742; Departamento de Zootecinia, Universidade Estadual Paulista, Jaboticabal, 14884-900, Brazil.
| | - J B Cole
- Henry A. Wallace Beltsville Agricultural Research Center, Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - T J Lawlor
- Holstein Association USA, Brattleboro, VT 05302-0808
| | - P M VanRaden
- Henry A. Wallace Beltsville Agricultural Research Center, Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705-2350
| | - H Tonhati
- Departamento de Zootecinia, Universidade Estadual Paulista, Jaboticabal, 14884-900, Brazil
| | - L Ma
- Department of Animal and Avian Sciences, University of Maryland, College Park 20742.
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Steiner B, Michel S, Maccaferri M, Lemmens M, Tuberosa R, Buerstmayr H. Exploring and exploiting the genetic variation of Fusarium head blight resistance for genomic-assisted breeding in the elite durum wheat gene pool. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2019; 132:969-988. [PMID: 30506523 PMCID: PMC6449325 DOI: 10.1007/s00122-018-3253-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 11/27/2018] [Indexed: 05/09/2023]
Abstract
KEY MESSAGE Genomic selection had a higher selection response for FHB resistance than phenotypic selection, while association mapping identified major QTL on chromosome 3B unaffected by plant height and flowering date. Fusarium head blight (FHB) is one of the most destructive diseases of durum wheat. Hence, minimizing losses in yield, quality and avoiding contamination with mycotoxins are of pivotal importance, as durum wheat is mostly used for human consumption. While growing resistant varieties is the most promising approach for controlling this fungal disease, FHB resistance breeding in durum wheat is hampered by the limited variation in the elite gene pool and difficulties in efficiently combining the numerous small-effect resistance quantitative trait loci (QTL) in the same line. We evaluated an international collection of 228 genotyped durum wheat cultivars for FHB resistance over 3 years to investigate the genetic architecture and potential of genomic-assisted breeding for FHB resistance in durum wheat. Plant height was strongly positively correlated with FHB resistance and led to co-localization of plant height and resistance QTL. Nevertheless, a major QTL on chromosome 3B independent of plant height was identified in the same chromosomal interval as reported for the prominent hexaploid resistance QTL Fhb1, though haplotype analysis highlighted the distinctiveness of both QTL. Comparison between phenotypic and genomic selection for FHB resistance revealed a superior prediction ability of the former. However, simulated selection experiments resulted in higher selection responses when using genomic breeding values for early generation selection. An earlier identification of the most promising lines and crossing parents was feasible with a genomic selection index, which suggested a much faster short-term population improvement than previously possible in durum wheat, complementing long-term strategies with exotic resistance donors.
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Affiliation(s)
- Barbara Steiner
- Department of Agrobiotechnology (IFA-Tulln), Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
| | - Sebastian Michel
- Department of Agrobiotechnology (IFA-Tulln), Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430, Tulln, Austria.
| | - Marco Maccaferri
- Department of Agricultural and Food Sciences, University of Bologna, 40127, Bologna, Italy
| | - Marc Lemmens
- Department of Agrobiotechnology (IFA-Tulln), Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
| | - Roberto Tuberosa
- Department of Agricultural and Food Sciences, University of Bologna, 40127, Bologna, Italy
| | - Hermann Buerstmayr
- Department of Agrobiotechnology (IFA-Tulln), Institute of Biotechnology in Plant Production, University of Natural Resources and Life Sciences, Vienna (BOKU), Konrad-Lorenz-Str. 20, 3430, Tulln, Austria
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Multi-objective optimized genomic breeding strategies for sustainable food improvement. Heredity (Edinb) 2018; 122:672-683. [PMID: 30262841 PMCID: PMC6461918 DOI: 10.1038/s41437-018-0147-1] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Revised: 08/09/2018] [Accepted: 08/22/2018] [Indexed: 01/07/2023] Open
Abstract
The purpose of breeding programs is to obtain sustainable gains in multiple traits while controlling the loss of genetic variation. The decisions at each breeding cycle involve multiple, usually competing, objectives; these complex decisions can be supported by the insights that are gained by applying multi-objective optimization principles to breeding. The discussion in this manuscript includes the definition of several multi-objective optimized breeding approaches within the phenotypic or genomic breeding frameworks and the comparison of these approaches with the standard multi-trait breeding schemes such as tandem selection, independent culling and index selection. Proposed methods are demonstrated with two empirical data sets and simulations. In addition, we have described several graphical tools that can aid breeders in arriving at a compromise decision. The results show that the proposed methodology is a viable approach to answer several real breeding problems. In simulations, the newly proposed methods resulted in gains larger than the methods previously proposed including index selection: Compared to the best alternative breeding strategy, the gains from multi-objective optimized parental proportions approaches were about 20–30% higher at the end of long-term simulations of breeding cycles. In addition, the flexibility of the multi-objective optimized breeding strategies were displayed with methods and examples covering non-dominated selection, assignment of optimal parental proportions, using genomewide marker effects in producing optimal mating designs, and finally in selection of training populations for genomic prediction.
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Osthushenrich T, Frisch M, Zenke-Philippi C, Jaiser H, Spiller M, Cselényi L, Krumnacker K, Boxberger S, Kopahnke D, Habekuß A, Ordon F, Herzog E. Prediction of Means and Variances of Crosses With Genome-Wide Marker Effects in Barley. FRONTIERS IN PLANT SCIENCE 2018; 9:1899. [PMID: 30627135 PMCID: PMC6309237 DOI: 10.3389/fpls.2018.01899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 12/07/2018] [Indexed: 05/10/2023]
Abstract
Background: The expected genetic variance is an important criterion for the selection of crossing partners which will produce superior combinations of genotypes in their progeny. The advent of molecular markers has opened up new vistas for obtaining precise predictors for the genetic variance of a cross, but fast prediction methods that allow plant breeders to select crossing partners based on already available data from their breeding programs without complicated calculations or simulation of breeding populations are still lacking. The main objective of the present study was to demonstrate the practical applicability of an analytical approach for the selection of superior cross combinations with experimental data from a barley breeding program. We used genome-wide marker effects to predict the yield means and genetic variances of 14 DH families resulting from crosses of four donor lines with five registered elite varieties with the genotypic information of the parental lines. For the validation of the predicted parameters, the analytical approach was extended by the masking variance as a major component of phenotypic variance. The predicted parameters were used to fit normal distribution curves of the phenotypic values and to conduct an Anderson-Darling goodness-of-fit test for the observed phenotypic data of the 14 DH families from the field trial. Results: There was no evidence that the observed phenotypic values deviated from the predicted phenotypic normal distributions in 13 out of 14 crosses. The correlations between the observed and the predicted means and the observed and predicted variances were r = 0.95 and r = 0.34, respectively. After removing two crosses with downward outliers in the phenotypic data, the correlation between the observed and predicted variances increased to r = 0.76. A ranking of the 14 crosses based on the sum of predicted mean and genetic variance identified the 50% best crosses from the field trial correctly. Conclusions: We conclude that the prediction accuracy of the presented approach is sufficiently high to identify superior crosses even with limited phenotypic data. We therefore expect that the analytical approach based on genome-wide marker effects is applicable in a wide range of breeding programs.
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Affiliation(s)
- Tanja Osthushenrich
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | - Matthias Frisch
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | - Carola Zenke-Philippi
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
| | - Heidi Jaiser
- Saatzucht Josef Breun GmbH & Co. KG, Herzogenaurach, Germany
| | | | - László Cselényi
- W. von Borries-Eckendorf GmbH & Co. KG, Leopoldshöhe, Germany
| | | | | | - Doris Kopahnke
- Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute, Quedlinburg, Germany
| | - Antje Habekuß
- Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute, Quedlinburg, Germany
| | - Frank Ordon
- Institute for Resistance Research and Stress Tolerance, Julius Kühn-Institute, Quedlinburg, Germany
| | - Eva Herzog
- Institute of Agronomy and Plant Breeding II, Justus Liebig University, Gießen, Germany
- *Correspondence: Eva Herzog
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