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Wilson S, Zheng C, Maliepaard C, Mulder HA, Visser RGF, van Eeuwijk F. Multienvironment genomic prediction in tetraploid potato. G3 (BETHESDA, MD.) 2024; 14:jkae011. [PMID: 38243613 PMCID: PMC10989893 DOI: 10.1093/g3journal/jkae011] [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: 04/27/2023] [Revised: 10/06/2023] [Accepted: 10/20/2023] [Indexed: 01/21/2024]
Abstract
Multienvironment genomic prediction was applied to tetraploid potato using 147 potato varieties, tested for 2 years, in 3 locations representative of 3 distinct regions in Europe. Different prediction scenarios were investigated to help breeders predict genotypic performance in the regions from one year to the next, for genotypes that were tested this year (scenario 1), as well as new genotypes (scenario 3). In scenario 2, we predicted new genotypes for any one of the 6 trials, using all the information that is available. The choice of prediction model required assessment of the variance-covariance matrix in a mixed model that takes into account heterogeneity of genetic variances and correlations. This was done for each analyzed trait (tuber weight, tuber length, and dry matter) where examples of both limited and higher degrees of heterogeneity was observed. This explains why dry matter did not need complex multienvironment modeling to combine environments and increase prediction ability, while prediction in tuber weight, improved only when models were flexible enough to capture the heterogeneous variances and covariances between environments. We also found that the prediction abilities in a target trial condition decreased, if trials with a low genetic correlation to the target were included when training the model. Genomic prediction in tetraploid potato can work once there is clarity about the prediction scenario, a suitable training set is created, and a multienvironment prediction model is chosen based on the patterns of G×E indicated by the genetic variances and covariances.
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Affiliation(s)
- Stefan Wilson
- Biometris, Wageningen University & Research Centre, Wageningen, PB 6708, The Netherlands
| | - Chaozhi Zheng
- Biometris, Wageningen University & Research Centre, Wageningen, PB 6708, The Netherlands
| | - Chris Maliepaard
- Plant Breeding, Wageningen University and Research, Wageningen, PB 6708, The Netherlands
| | - Han A Mulder
- Wageningen University and Research Animal Breeding and Genomics, Wageningen, AH 6700, The Netherlands
| | - Richard G F Visser
- Plant Breeding, Wageningen University and Research, Wageningen, PB 6708, The Netherlands
| | - Fred van Eeuwijk
- Biometris, Wageningen University & Research Centre, Wageningen, PB 6708, The Netherlands
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Prado GS, Rocha DC, dos Santos LN, Contiliani DF, Nobile PM, Martinati-Schenk JC, Padilha L, Maluf MP, Lubini G, Pereira TC, Monteiro-Vitorello CB, Creste S, Boscariol-Camargo RL, Takita MA, Cristofani-Yaly M, de Souza AA. CRISPR technology towards genome editing of the perennial and semi-perennial crops citrus, coffee and sugarcane. FRONTIERS IN PLANT SCIENCE 2024; 14:1331258. [PMID: 38259920 PMCID: PMC10801916 DOI: 10.3389/fpls.2023.1331258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 12/14/2023] [Indexed: 01/24/2024]
Abstract
Gene editing technologies have opened up the possibility of manipulating the genome of any organism in a predicted way. CRISPR technology is the most used genome editing tool and, in agriculture, it has allowed the expansion of possibilities in plant biotechnology, such as gene knockout or knock-in, transcriptional regulation, epigenetic modification, base editing, RNA editing, prime editing, and nucleic acid probing or detection. This technology mostly depends on in vitro tissue culture and genetic transformation/transfection protocols, which sometimes become the major challenges for its application in different crops. Agrobacterium-mediated transformation, biolistics, plasmid or RNP (ribonucleoprotein) transfection of protoplasts are some of the commonly used CRISPR delivery methods, but they depend on the genotype and target gene for efficient editing. The choice of the CRISPR system (Cas9, Cas12), CRISPR mechanism (plasmid or RNP) and transfection technique (Agrobacterium spp., PEG solution, lipofection) directly impacts the transformation efficiency and/or editing rate. Besides, CRISPR/Cas technology has made countries rethink regulatory frameworks concerning genetically modified organisms and flexibilize regulatory obstacles for edited plants. Here we present an overview of the state-of-the-art of CRISPR technology applied to three important crops worldwide (citrus, coffee and sugarcane), considering the biological, methodological, and regulatory aspects of its application. In addition, we provide perspectives on recently developed CRISPR tools and promising applications for each of these crops, thus highlighting the usefulness of gene editing to develop novel cultivars.
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Affiliation(s)
- Guilherme Souza Prado
- Citrus Research Center “Sylvio Moreira” – Agronomic Institute (IAC), Cordeirópolis, Brazil
| | - Dhiôvanna Corrêia Rocha
- Citrus Research Center “Sylvio Moreira” – Agronomic Institute (IAC), Cordeirópolis, Brazil
- Institute of Biology, State University of Campinas (Unicamp), Campinas, Brazil
| | - Lucas Nascimento dos Santos
- Citrus Research Center “Sylvio Moreira” – Agronomic Institute (IAC), Cordeirópolis, Brazil
- Institute of Biology, State University of Campinas (Unicamp), Campinas, Brazil
| | - Danyel Fernandes Contiliani
- Sugarcane Research Center – Agronomic Institute (IAC), Ribeirão Preto, Brazil
- Ribeirão Preto Medical School, University of São Paulo (USP), Ribeirão Preto, Brazil
| | - Paula Macedo Nobile
- Sugarcane Research Center – Agronomic Institute (IAC), Ribeirão Preto, Brazil
| | | | - Lilian Padilha
- Coffee Center of the Agronomic Institute of Campinas (IAC), Campinas, Brazil
- Embrapa Coffee, Brazilian Agricultural Research Corporation, Brasília, Federal District, Brazil
| | - Mirian Perez Maluf
- Coffee Center of the Agronomic Institute of Campinas (IAC), Campinas, Brazil
- Embrapa Coffee, Brazilian Agricultural Research Corporation, Brasília, Federal District, Brazil
| | - Greice Lubini
- Sugarcane Research Center – Agronomic Institute (IAC), Ribeirão Preto, Brazil
- Department of Biology, Faculty of Philosophy, Sciences and Letters at Ribeirão Preto, University of São Paulo (USP), Ribeirão Preto, Brazil
| | - Tiago Campos Pereira
- Ribeirão Preto Medical School, University of São Paulo (USP), Ribeirão Preto, Brazil
- Department of Biology, Faculty of Philosophy, Sciences and Letters at Ribeirão Preto, University of São Paulo (USP), Ribeirão Preto, Brazil
| | | | - Silvana Creste
- Sugarcane Research Center – Agronomic Institute (IAC), Ribeirão Preto, Brazil
- Ribeirão Preto Medical School, University of São Paulo (USP), Ribeirão Preto, Brazil
| | | | - Marco Aurélio Takita
- Citrus Research Center “Sylvio Moreira” – Agronomic Institute (IAC), Cordeirópolis, Brazil
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Azevedo CF, Ferrão LFV, Benevenuto J, de Resende MDV, Nascimento M, Nascimento ACC, Munoz PR. Using visual scores for genomic prediction of complex traits in breeding programs. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 137:9. [PMID: 38102495 DOI: 10.1007/s00122-023-04512-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 11/21/2023] [Indexed: 12/17/2023]
Abstract
KEY MESSAGE An approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.
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Affiliation(s)
- Camila Ferreira Azevedo
- Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA
| | - Luis Felipe Ventorim Ferrão
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA
| | - Juliana Benevenuto
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA
| | - Marcos Deon Vilela de Resende
- Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
- Department of Forestry Engineering, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
- Embrapa Café, Brasília, Distrito Federal, Brazil
| | - Moyses Nascimento
- Statistics Department, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil
| | | | - Patricio R Munoz
- Horticultural Sciences Department, Blueberry Breeding and Genomics Lab, University of Florida, Gainesville, FL, USA.
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Covre AM, da Silva FA, Oliosi G, Correa CCG, Viana AP, Partelli FL. Multi-Environment and Multi-Year Bayesian Analysis Approach in Coffee canephora. PLANTS (BASEL, SWITZERLAND) 2022; 11:3274. [PMID: 36501314 PMCID: PMC9741437 DOI: 10.3390/plants11233274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Revised: 10/11/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
This work aimed to use the Bayesian approach to discriminate 43 genotypes of Coffea canephora cv. Conilon, which were cultivated in two producing regions to identify the most stable and productive genotypes. The experiment was a randomized block design with three replications and seven plants per plot, carried out in the south of Bahia and the north of Espírito Santo, environments with different climatic conditions, and evaluated during four harvests. The proposed Bayesian methodology was implemented in R language, using the MCMCglmm package. This approach made it possible to find great genetic divergence between the materials, and detect significant effects for both genotype, environment, and year, but the hyper-parametrized models (block effect) presented problems of singularity and convergence. It was also possible to detect a few differences between crops within the same environment. With a model with lower residual, it was possible to recommend the most productive genotypes for both environments: LB1, AD1, Peneirão, Z21, and P2.
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Affiliation(s)
- André Monzoli Covre
- Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil
| | - Flavia Alves da Silva
- Laboratório de Melhoramento Genético Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Campos dos Goytacazes 28013-602, RJ, Brazil
| | - Gleison Oliosi
- Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil
| | - Caio Cezar Guedes Correa
- Departamento de Agronomia, Universidade Federal do Espírito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil
| | - Alexandre Pio Viana
- Laboratório de Melhoramento Genético Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Campos dos Goytacazes 28013-602, RJ, Brazil
| | - Fabio Luiz Partelli
- Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil
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Seyum EG, Bille NH, Abtew WG, Munyengwa N, Bell JM, Cros D. Genomic selection in tropical perennial crops and plantation trees: a review. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:58. [PMID: 37313015 PMCID: PMC10248687 DOI: 10.1007/s11032-022-01326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 09/06/2022] [Indexed: 06/15/2023]
Abstract
To overcome the multiple challenges currently faced by agriculture, such as climate change and soil deterioration, more efficient plant breeding strategies are required. Genomic selection (GS) is crucial for the genetic improvement of quantitative traits, as it can increase selection intensity, shorten the generation interval, and improve selection accuracy for traits that are difficult to phenotype. Tropical perennial crops and plantation trees are of major economic importance and have consequently been the subject of many GS articles. In this review, we discuss the factors that affect GS accuracy (statistical models, linkage disequilibrium, information concerning markers, relatedness between training and target populations, the size of the training population, and trait heritability) and the genetic gain expected in these species. The impact of GS will be particularly strong in tropical perennial crops and plantation trees as they have long breeding cycles and constrained selection intensity. Future GS prospects are also discussed. High-throughput phenotyping will allow constructing of large training populations and implementing of phenomic selection. Optimized modeling is needed for longitudinal traits and multi-environment trials. The use of multi-omics, haploblocks, and structural variants will enable going beyond single-locus genotype data. Innovative statistical approaches, like artificial neural networks, are expected to efficiently handle the increasing amounts of heterogeneous multi-scale data. Targeted recombinations on sites identified from profiles of marker effects have the potential to further increase genetic gain. GS can also aid re-domestication and introgression breeding. Finally, GS consortia will play an important role in making the best of these opportunities. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-022-01326-4.
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Affiliation(s)
- Essubalew Getachew Seyum
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Ngalle Hermine Bille
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - Wosene Gebreselassie Abtew
- Department of Horticulture and Plant Sciences, College of Agriculture and Veterinary Medicine, Jimma University, P.O. Box 307, Jimma, Ethiopia
| | - Norman Munyengwa
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072 Australia
| | - Joseph Martin Bell
- Department of Plant Biology and Physiology, Faculty of Sciences, University of Yaoundé I, Yaoundé, Cameroon
| | - David Cros
- CIRAD, UMR AGAP Institut, 34398 Montpellier, France
- UMR AGAP Institut, CIRAD, INRAE, Univ. Montpellier, Institut Agro, 34398 Montpellier, France
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Murad Leite Andrade MH, Acharya JP, Benevenuto J, de Bem Oliveira I, Lopez Y, Munoz P, Resende MFR, Rios EF. Genomic prediction for canopy height and dry matter yield in alfalfa using family bulks. THE PLANT GENOME 2022; 15:e20235. [PMID: 35818699 DOI: 10.1002/tpg2.20235] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 04/30/2022] [Indexed: 06/15/2023]
Abstract
Genomic selection (GS) has proven to be an effective method to increase genetic gain rates and accelerate breeding cycles in many crop species. However, its implementation requires large investments to phenotype of the training population and for routine genotyping. Alfalfa (Medicago sativa L.) is one of the major cultivated forage legumes, showing high-quality nutritional value. Alfalfa breeding is usually carried out by phenotypic recurrent selection and is commonly done at the family level. The application of GS in alfalfa could be simplified and less costly by genotyping and phenotyping families in bulks. For this study, an alfalfa reference population composed of 142 full-sib and 35 half-sib families was bulk-genotyped using target enrichment sequencing and phenotyped for dry matter yield (DMY) and canopy height (CH) in Florida, USA. Genotyping of the family bulks with 17,707 targeted probes resulted in 114,945 single-nucleotide polymorphisms. The markers revealed a population structure that matched the mating design, and the linkage disequilibrium slowly decayed in this breeding population. After exploring multiple prediction scenarios, a strategy was proposed including data from multiple harvests and accounting for the G×E in the training population, which led to a higher predictive ability of up to 38 and 24% for DMY and CH, respectively. Although this study focused on the implementation of GS in alfalfa families, the bulk methodology and the prediction schemes used herein could guide future studies in alfalfa and other crops bred in bulks.
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Affiliation(s)
| | - Janam P Acharya
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Juliana Benevenuto
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | | | - Yolanda Lopez
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Patricio Munoz
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Marcio F R Resende
- Horticultural Sciences Dep., Univ. of Florida, Gainesville, FL, 32611, USA
| | - Esteban F Rios
- Agronomy Dep., Univ. of Florida, Gainesville, FL, 32611, USA
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Paixão PTM, Nascimento ACC, Nascimento M, Azevedo CF, Oliveira GF, da Silva FL, Caixeta ET. Factor analysis applied in genomic selection studies in the breeding of Coffea canephora. EUPHYTICA: NETHERLANDS JOURNAL OF PLANT BREEDING 2022; 218:42. [PMID: 35310815 PMCID: PMC8918905 DOI: 10.1007/s10681-022-02998-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
Brazil stands out worldwide in the production of coffee. The observed increases in its productivity and morpho agronomic traits are the results of the improvement of several methodologies applied in obtaining improved cultivars, among which the predictive methods of genetic value stand out. These contribute significantly to the selection of higher genotypes, increasing the genetic gain per unit time. In this context, genomic-wide selection (GWS) is a tool that stands out, since it allows predicting the future phenotype of an individual based only on molecular information. Performing joint selection of traits is the interest of most breeding programs, and factor analysis (FA) has been used to assist in this end. The aim of this study was to evaluate the use of FA in the context of GWS, in genotypes of Coffea canephora. It was found that FA was efficient to elucidate the relationships between the traits and generate new variables. The factors formed can assist in the selection, as in addition to allowing joint interpretations, they present good estimates of predictive capacity, heritability and accuracy. Furthermore, high agreement was observed between the individuals selected based on the factors and those selected considering the individual traits. Additionally, it was observed agreement between the top 10% individuals selected based on the "vigor factor" and each variable individually. However, the selection based on "vigor factor" presented individuals with more suitable size from the phytotechnical point of view.
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Affiliation(s)
| | | | - Moysés Nascimento
- Department of Statistics, Universidade Federal de Viçosa (UFV), Viçosa, MG Brazil
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Brault C, Segura V, This P, Le Cunff L, Flutre T, François P, Pons T, Péros JP, Doligez A. Across-population genomic prediction in grapevine opens up promising prospects for breeding. HORTICULTURE RESEARCH 2022; 9:uhac041. [PMID: 35184162 PMCID: PMC9070645 DOI: 10.1093/hr/uhac041] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 02/01/2022] [Indexed: 05/15/2023]
Abstract
Crop breeding involves two selection steps: choosing progenitors and selecting individuals within progenies. Genomic prediction, based on genome-wide marker estimation of genetic values, could facilitate these steps. However, its potential usefulness in grapevine (Vitis vinifera L.) has only been evaluated in non-breeding contexts mainly through cross-validation within a single population. We tested across-population genomic prediction in a more realistic breeding configuration, from a diversity panel to ten bi-parental crosses connected within a half-diallel mating design. Prediction quality was evaluated over 15 traits of interest (related to yield, berry composition, phenology and vigour), for both the average genetic value of each cross (cross mean) and the genetic values of individuals within each cross (individual values). Genomic prediction in these conditions was found useful: for cross mean, average per-trait predictive ability was 0.6, while per-cross predictive ability was halved on average, but reached a maximum of 0.7. Mean predictive ability for individual values within crosses was 0.26, about half the within-half-diallel value taken as a reference. For some traits and/or crosses, these across-population predictive ability values are promising for implementing genomic selection in grapevine breeding. This study also provided key insights on variables affecting predictive ability. Per-cross predictive ability was well predicted by genetic distance between parents and when this predictive ability was below 0.6, it was improved by training set optimization. For individual values, predictive ability mostly depended on trait-related variables (magnitude of the cross effect and heritability). These results will greatly help designing grapevine breeding programs assisted by genomic prediction.
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Affiliation(s)
- Charlotte Brault
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Institut Français de la Vigne et du Vin, F-34398 Montpellier, France
| | - Vincent Segura
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Patrice This
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Loïc Le Cunff
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
- Institut Français de la Vigne et du Vin, F-34398 Montpellier, France
| | - Timothée Flutre
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE – Le Moulon, 91190, Gif-sur-Yvette, France
| | - Pierre François
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Thierry Pons
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Jean-Pierre Péros
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
| | - Agnès Doligez
- UMT Geno-Vigne®, IFV-INRAE-Institut Agro, F-34398 Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France
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Alkimim ER, Caixeta ET, Sousa TV, Gois IB, Lopes da Silva F, Sakiyama NS, Zambolim L, Alves RS, de Resende MDV. Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits. PLoS One 2021; 16:e0260997. [PMID: 34965248 PMCID: PMC8716045 DOI: 10.1371/journal.pone.0260997] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 11/20/2021] [Indexed: 12/03/2022] Open
Abstract
Breeding programs of the species Coffea canephora rely heavily on the significant genetic variability between and within its two varietal groups (conilon and robusta). The use of hybrid families and individuals has been less common. The objectives of this study were to evaluate parents and families from the populations of conilon, robusta, and its hybrids and to define the best breeding and selection strategies for productivity and disease resistance traits. As such, 71 conilon clones, 56 robusta clones, and 20 hybrid families were evaluated over several years for the following traits: vegetative vigor, incidence of rust and cercosporiosis, fruit ripening time, fruit size, plant height, canopy diameter, and yield per plant. Components of variance and genetic parameters were estimated via residual maximum likelihood (REML) and genotypic values were predicted via best linear unbiased prediction (BLUP). Genetic variability among parents (clones) and hybrid families was detected for most of the evaluated traits. The Mulamba-Rank index suggests potential gains up to 17% for the genotypic aggregate of traits in the hybrid population. An intrapopulation recurrent selection within the hybrid population would be the best breeding strategy because the genetic variability, narrow and broad senses heritabilities and selective accuracies for important traits were maximized in the crossed population. Besides, such strategy is simple, low cost and quicker than the concurrent reciprocal recurrent selection in the two parental populations, and this maximizes the genetic gain for unit of time.
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Affiliation(s)
| | - Eveline Teixeira Caixeta
- Brazilian Agricultural Research Corporation—Embrapa Café, Viçosa, MG, Brazil
- * E-mail: (ETC); (MDVR)
| | | | | | | | | | | | - Rodrigo Silva Alves
- National Institute of Coffee Science and Technology—INCT Café, Lavras, MG, Brazil
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Vanden Abeele S, Janssens SB, Asimonyio Anio J, Bawin Y, Depecker J, Kambale B, Mwanga Mwanga I, Ebele T, Ntore S, Stoffelen P, Vandelook F. Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation. AMERICAN JOURNAL OF BOTANY 2021; 108:2425-2434. [PMID: 34634128 PMCID: PMC9305747 DOI: 10.1002/ajb2.1769] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 09/02/2021] [Accepted: 09/07/2021] [Indexed: 06/13/2023]
Abstract
PREMISE Many cultivated coffee varieties descend from Coffea canephora, commonly known as Robusta coffee. The Congo Basin has a century-long history of Robusta coffee cultivation and breeding, and is hypothesized to be the region of origin of many of the cultivated Robusta varieties. Since little is known about the genetic composition of C. canephora in this region, we assessed the genetic diversity of wild and cultivated C. canephora shrubs in the Democratic Republic of the Congo. METHODS Using 18 microsatellite markers, we studied the genetic composition of wild and backyard-grown C. canephora shrubs in the Tshopo and Ituri provinces and multiple accessions from the INERA Yangambi Coffee Collection. We assessed genetic clustering patterns, genetic diversity, and genetic differentiation between populations. RESULTS Genetic differentiation was relatively strong between wild and cultivated C. canephora shrubs, and both gene pools harbored multiple unique alleles. Strong genetic differentiation was also observed between wild populations. The level of genetic diversity in wild populations was similar to that of the INERA Yangambi Coffee Collection, but local wild genotypes were mostly missing from that collection. Shrubs grown in the backyards were genetically similar to the breeding material from INERA Yangambi. CONCLUSIONS Most C. canephora that is grown in local backyards originated from INERA breeding programs, while a few shrubs were obtained directly from surrounding forests. The INERA Yangambi Coffee Collection could benefit from an enrichment with local wild genotypes to increase the genetic resources available for breeding purposes and to support ex situ conservation.
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Affiliation(s)
| | - Steven B. Janssens
- Meise Botanic Garden, Nieuwelaan 38Meise1860Belgium
- Department of BiologyKU LeuvenBelgium
| | - Justin Asimonyio Anio
- Centre pour la Surveillance de la Biodiversité et Université de KisanganiKisanganiDR Congo
| | - Yves Bawin
- Meise Botanic Garden, Nieuwelaan 38Meise1860Belgium
- Department of BiologyKU LeuvenBelgium
| | - Jonas Depecker
- Meise Botanic Garden, Nieuwelaan 38Meise1860Belgium
- Department of BiologyKU LeuvenBelgium
| | - Bienfait Kambale
- Centre pour la Surveillance de la Biodiversité et Université de KisanganiKisanganiDR Congo
| | | | - Tshimi Ebele
- Institut National des Études et Recherches Agronomique, DR CongoDR Congo
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11
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Brault C, Doligez A, Cunff L, Coupel-Ledru A, Simonneau T, Chiquet J, This P, Flutre T. Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection of drought-related traits in grapevine. G3-GENES GENOMES GENETICS 2021; 11:6325507. [PMID: 34544146 PMCID: PMC8496232 DOI: 10.1093/g3journal/jkab248] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022]
Abstract
Viticulture has to cope with climate change and to decrease pesticide inputs, while maintaining yield and wine quality. Breeding is a key lever to meet this challenge, and genomic prediction a promising tool to accelerate breeding programs. Multivariate methods are potentially more accurate than univariate ones. Moreover, some prediction methods also provide marker selection, thus allowing quantitative trait loci (QTLs) detection and the identification of positional candidate genes. To study both genomic prediction and QTL detection for drought-related traits in grapevine, we applied several methods, interval mapping (IM) as well as univariate and multivariate penalized regression, in a bi-parental progeny. With a dense genetic map, we simulated two traits under four QTL configurations. The penalized regression method Elastic Net (EN) for genomic prediction, and controlling the marginal False Discovery Rate on EN selected markers to prioritize the QTLs. Indeed, penalized methods were more powerful than IM for QTL detection across various genetic architectures. Multivariate prediction did not perform better than its univariate counterpart, despite strong genetic correlation between traits. Using 14 traits measured in semi-controlled conditions under different watering conditions, penalized regression methods proved very efficient for intra-population prediction whatever the genetic architecture of the trait, with predictive abilities reaching 0.68. Compared to a previous study on the same traits, these methods applied on a denser map found new QTLs controlling traits linked to drought tolerance and provided relevant candidate genes. Overall, these findings provide a strong evidence base for implementing genomic prediction in grapevine breeding.
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Affiliation(s)
- Charlotte Brault
- Institut Français de la Vigne et du Vin, Montpellier F-34398, France.,UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Agnès Doligez
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Le Cunff
- Institut Français de la Vigne et du Vin, Montpellier F-34398, France.,UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Aude Coupel-Ledru
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier 34000, France
| | - Thierry Simonneau
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier 34000, France
| | | | - Patrice This
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier F-34398, France.,UMT Geno-Vigne®, IFV-INRAE-Institut Agro, Montpellier F-34398, France
| | - Timothée Flutre
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE-Le Moulon, Gif-sur-Yvette 91190, France
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12
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Rabier C, Grusea S. Prediction in high‐dimensional linear models and application to genomic selection under imperfect linkage disequilibrium. J R Stat Soc Ser C Appl Stat 2021. [DOI: 10.1111/rssc.12496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Charles‐Elie Rabier
- ISE‐MUMR 5554CNRSIRDUniversité de Montpellier France
- IMAGUMR 5149CNRSUniversité de Montpellier France
- LIRMMUMR 5506CNRSUniversité de Montpellier France
| | - Simona Grusea
- Institut de Mathématiques de Toulouse Université de ToulouseINSA de Toulouse France
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13
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Ferrão LFV, Amadeu RR, Benevenuto J, de Bem Oliveira I, Munoz PR. Genomic Selection in an Outcrossing Autotetraploid Fruit Crop: Lessons From Blueberry Breeding. FRONTIERS IN PLANT SCIENCE 2021; 12:676326. [PMID: 34194453 PMCID: PMC8236943 DOI: 10.3389/fpls.2021.676326] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 05/12/2021] [Indexed: 05/17/2023]
Abstract
Blueberry (Vaccinium corymbosum and hybrids) is a specialty crop with expanding production and consumption worldwide. The blueberry breeding program at the University of Florida (UF) has greatly contributed to expanding production areas by developing low-chilling cultivars better adapted to subtropical and Mediterranean climates of the globe. The breeding program has historically focused on recurrent phenotypic selection. As an autopolyploid, outcrossing, perennial, long juvenile phase crop, blueberry breeding cycles are costly and time consuming, which results in low genetic gains per unit of time. Motivated by applying molecular markers for a more accurate selection in the early stages of breeding, we performed pioneering genomic selection studies and optimization for its implementation in the blueberry breeding program. We have also addressed some complexities of sequence-based genotyping and model parametrization for an autopolyploid crop, providing empirical contributions that can be extended to other polyploid species. We herein revisited some of our previous genomic selection studies and showed for the first time its application in an independent validation set. In this paper, our contribution is three-fold: (i) summarize previous results on the relevance of model parametrizations, such as diploid or polyploid methods, and inclusion of dominance effects; (ii) assess the importance of sequence depth of coverage and genotype dosage calling steps; (iii) demonstrate the real impact of genomic selection on leveraging breeding decisions by using an independent validation set. Altogether, we propose a strategy for using genomic selection in blueberry, with the potential to be applied to other polyploid species of a similar background.
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Affiliation(s)
- Luís Felipe V. Ferrão
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Rodrigo R. Amadeu
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Juliana Benevenuto
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Ivone de Bem Oliveira
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
- Hortifrut North America, Inc., Estero, FL, United States
| | - Patricio R. Munoz
- Blueberry Breeding and Genomics Lab, Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
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14
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Cortés AJ, López-Hernández F. Harnessing Crop Wild Diversity for Climate Change Adaptation. Genes (Basel) 2021; 12:783. [PMID: 34065368 PMCID: PMC8161384 DOI: 10.3390/genes12050783] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/28/2021] [Accepted: 05/19/2021] [Indexed: 12/20/2022] Open
Abstract
Warming and drought are reducing global crop production with a potential to substantially worsen global malnutrition. As with the green revolution in the last century, plant genetics may offer concrete opportunities to increase yield and crop adaptability. However, the rate at which the threat is happening requires powering new strategies in order to meet the global food demand. In this review, we highlight major recent 'big data' developments from both empirical and theoretical genomics that may speed up the identification, conservation, and breeding of exotic and elite crop varieties with the potential to feed humans. We first emphasize the major bottlenecks to capture and utilize novel sources of variation in abiotic stress (i.e., heat and drought) tolerance. We argue that adaptation of crop wild relatives to dry environments could be informative on how plant phenotypes may react to a drier climate because natural selection has already tested more options than humans ever will. Because isolated pockets of cryptic diversity may still persist in remote semi-arid regions, we encourage new habitat-based population-guided collections for genebanks. We continue discussing how to systematically study abiotic stress tolerance in these crop collections of wild and landraces using geo-referencing and extensive environmental data. By uncovering the genes that underlie the tolerance adaptive trait, natural variation has the potential to be introgressed into elite cultivars. However, unlocking adaptive genetic variation hidden in related wild species and early landraces remains a major challenge for complex traits that, as abiotic stress tolerance, are polygenic (i.e., regulated by many low-effect genes). Therefore, we finish prospecting modern analytical approaches that will serve to overcome this issue. Concretely, genomic prediction, machine learning, and multi-trait gene editing, all offer innovative alternatives to speed up more accurate pre- and breeding efforts toward the increase in crop adaptability and yield, while matching future global food demands in the face of increased heat and drought. In order for these 'big data' approaches to succeed, we advocate for a trans-disciplinary approach with open-source data and long-term funding. The recent developments and perspectives discussed throughout this review ultimately aim to contribute to increased crop adaptability and yield in the face of heat waves and drought events.
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Affiliation(s)
- Andrés J. Cortés
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia;
- Departamento de Ciencias Forestales, Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Sede Medellín, Medellín 050034, Colombia
| | - Felipe López-Hernández
- Corporación Colombiana de Investigación Agropecuaria AGROSAVIA, C.I. La Selva, Km 7 Vía Rionegro, Las Palmas, Rionegro 054048, Colombia;
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15
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Rabier CE, Delmas C. The SgenoLasso and its cousins for selective genotyping and extreme sampling: application to association studies and genomic selection. STATISTICS-ABINGDON 2021. [DOI: 10.1080/02331888.2021.1881785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Charles-Elie Rabier
- ISEM, CNRS, EPHE, IRD, Université de Montpellier, Montpellier, France
- IMAG, CNRS, Université de Montpellier, Montpellier, France
- LIRMM, CNRS, Université de Montpellier, Montpellier, France
| | - Céline Delmas
- INRAE, UR MIAT, Université de Toulouse, Castanet-Tolosan, France
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16
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Dos Santos JPR, Fernandes SB, McCoy S, Lozano R, Brown PJ, Leakey ADB, Buckler ES, Garcia AAF, Gore MA. Novel Bayesian Networks for Genomic Prediction of Developmental Traits in Biomass Sorghum. G3 (BETHESDA, MD.) 2020; 10:769-781. [PMID: 31852730 PMCID: PMC7003104 DOI: 10.1534/g3.119.400759] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Accepted: 12/15/2019] [Indexed: 11/23/2022]
Abstract
The ability to connect genetic information between traits over time allow Bayesian networks to offer a powerful probabilistic framework to construct genomic prediction models. In this study, we phenotyped a diversity panel of 869 biomass sorghum (Sorghum bicolor (L.) Moench) lines, which had been genotyped with 100,435 SNP markers, for plant height (PH) with biweekly measurements from 30 to 120 days after planting (DAP) and for end-of-season dry biomass yield (DBY) in four environments. We evaluated five genomic prediction models: Bayesian network (BN), Pleiotropic Bayesian network (PBN), Dynamic Bayesian network (DBN), multi-trait GBLUP (MTr-GBLUP), and multi-time GBLUP (MTi-GBLUP) models. In fivefold cross-validation, prediction accuracies ranged from 0.46 (PBN) to 0.49 (MTr-GBLUP) for DBY and from 0.47 (DBN, DAP120) to 0.75 (MTi-GBLUP, DAP60) for PH. Forward-chaining cross-validation further improved prediction accuracies of the DBN, MTi-GBLUP and MTr-GBLUP models for PH (training slice: 30-45 DAP) by 36.4-52.4% relative to the BN and PBN models. Coincidence indices (target: biomass, secondary: PH) and a coincidence index based on lines (PH time series) showed that the ranking of lines by PH changed minimally after 45 DAP. These results suggest a two-level indirect selection method for PH at harvest (first-level target trait) and DBY (second-level target trait) could be conducted earlier in the season based on ranking of lines by PH at 45 DAP (secondary trait). With the advance of high-throughput phenotyping technologies, our proposed two-level indirect selection framework could be valuable for enhancing genetic gain per unit of time when selecting on developmental traits.
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Affiliation(s)
- Jhonathan P R Dos Santos
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil
| | | | | | - Roberto Lozano
- Plant Breeding and Genetics Section, School of Integrative Plant Science
| | - Patrick J Brown
- Section of Agricultural Plant Biology, Department of Plant Sciences, University of California Davis, 95616, and
| | - Andrew D B Leakey
- Department of Crop Science
- Institute for Genomic Biology
- Department of Plant Biology, University of Illinois at Urbana Champaign, 61801
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science
- United States Department of Agriculture, Agricultural Research Service, R. W. Holley Center, Ithaca, New York 14853
- Institute for Genomic Diversity, Cornell University, Ithaca, New York 14853
| | - Antonio A F Garcia
- Department of Genetics, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, SP, Brazil,
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science,
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