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Bartholomé J, Ospina JO, Sandoval M, Espinosa N, Arcos J, Ospina Y, Frouin J, Beartschi C, Ghneim T, Grenier C. Genomic selection for tolerance to aluminum toxicity in a synthetic population of upland rice. PLoS One 2024; 19:e0307009. [PMID: 39173048 PMCID: PMC11341055 DOI: 10.1371/journal.pone.0307009] [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: 04/15/2024] [Accepted: 06/28/2024] [Indexed: 08/24/2024] Open
Abstract
Over half of the world's arable land is acidic, which constrains cereal production. In South America, different rice-growing regions (Cerrado in Brazil and Llanos in Colombia and Venezuela) are particularly affected due to high aluminum toxicity levels. For this reason, efforts have been made to breed for tolerance to aluminum toxicity using synthetic populations. The breeding program of CIAT-CIRAD is a good example of the use of recurrent selection to increase productivity for the Llanos in Colombia. In this study, we evaluated the performance of genomic prediction models to optimize the breeding scheme by hastening the development of an improved synthetic population and elite lines. We characterized 334 families at the S0:4 generation in two conditions. One condition was the control, managed with liming, while the other had high aluminum toxicity. Four traits were considered: days to flowering (FL), plant height (PH), grain yield (YLD), and zinc concentration in the polished grain (ZN). The population presented a high tolerance to aluminum toxicity, with more than 72% of the families showing a higher yield under aluminum conditions. The performance of the families under the aluminum toxicity condition was predicted using four different models: a single-environment model and three multi-environment models. The multi-environment models differed in the way they integrated genotype-by-environment interactions. The best predictive abilities were achieved using multi-environment models: 0.67 for FL, 0.60 for PH, 0.53 for YLD, and 0.65 for ZN. The gain of multi-environment over single-environment models ranged from 71% for YLD to 430% for FL. The selection of the best-performing families based on multi-trait indices, including the four traits mentioned above, facilitated the identification of suitable families for recombination. This information will be used to develop a new cycle of recurrent selection through genomic selection.
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Affiliation(s)
- Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- Alliance Bioversity CIAT, Cali, Colombia
| | | | | | - Natalia Espinosa
- Alliance Bioversity CIAT, Cali, Colombia
- FEDEARROZ–Fondo Nacional del Arroz, Bogotá, Colombia
| | - Jairo Arcos
- HarvestPlus Program, Alliance Bioversity CIAT, Cali, Colombia
| | | | - Julien Frouin
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Cédric Beartschi
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Thaura Ghneim
- Departamento de Ciencias Biológicas, Universidad ICESI, Cali, Colombia
| | - Cécile Grenier
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
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Inamori M, Kimura T, Mori M, Tarumoto Y, Hattori T, Hayano M, Umeda M, Iwata H. Machine learning for genomic and pedigree prediction in sugarcane. THE PLANT GENOME 2024:e20486. [PMID: 38923818 DOI: 10.1002/tpg2.20486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 05/07/2024] [Accepted: 05/08/2024] [Indexed: 06/28/2024]
Abstract
Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome prediction (GP) models of crops with highly heterozygous polyploid genomes. This study incorporates non-additive genetic effects and pedigree information using machine learning methods to track sugarcane breeding lines and enhance the prediction by assessing the degree of association between genotypes. This study measured the stalk biomass and sugar content of 297 clones from 87 families within a breeding population used in the Japanese sugarcane breeding program. Subsequently, we conducted analyses based on the marker genotypes of 33,149 single-nucleotide polymorphisms. To validate the accuracy of GP in the population, we first predicted the prediction accuracy of the best linear unbiased prediction (BLUP) based on a genomic relationship matrix. Prediction accuracy was assessed using two different cross-validation methods: repeated 10-fold cross-validation and leave-one-family-out cross-validation. The accuracy of GP of the first and second methods ranged from 0.36 to 0.74 and 0.15 to 0.63, respectively. Next, we compared the prediction accuracy of BLUP and two machine learning methods: random forests and simulation annealing ensemble (SAE), a newly developed machine learning method that explicitly models the interaction between variables. Both pedigree and genomic information were utilized as input in these methods. Through repeated 10-fold cross-validation, we found that the accuracy of the machine learning methods consistently surpassed that of BLUP in most cases. In leave-one-family-out cross-validation, SAE demonstrated the highest accuracy among the methods. These results underscore the effectiveness of GP in Japanese sugarcane breeding and highlight the significant potential of machine learning methods.
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Affiliation(s)
- Minoru Inamori
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo, Japan
| | - Tatsuro Kimura
- Toyota Motor Corporation, New Business Planning Division, Agriculture & Biotechnology Business Department, Toyota, Japan
| | - Masaaki Mori
- Toyota Motor Corporation, Environment Affairs and Engineering Management Division, CN Advanced Engineering Development Center, Tokyo, Japan
| | - Yusuke Tarumoto
- NARO Kyushu Okinawa Agricultural Research Center, Tanegashima Sugarcane Breeding Site, Nishinoomote, Japan
| | - Taiichiro Hattori
- NARO Kyushu Okinawa Agricultural Research Center, Tanegashima Sugarcane Breeding Site, Nishinoomote, Japan
- NARO Kyushu Okinawa Agricultural Research Center, Itoman Resident Office, Itoman, Japan
| | - Michiko Hayano
- NARO Kyushu Okinawa Agricultural Research Center, Tanegashima Sugarcane Breeding Site, Nishinoomote, Japan
- NARO Institute for Agro-Environmental Science, Tsukuba, Japan
| | - Makoto Umeda
- NARO Kyushu Okinawa Agricultural Research Center, Tanegashima Sugarcane Breeding Site, Nishinoomote, Japan
| | - 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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Brault C, Segura V, Roques M, Lamblin P, Bouckenooghe V, Pouzalgues N, Cunty C, Breil M, Frouin M, Garcin L, Camps L, Ducasse MA, Romieu C, Masson G, Julliard S, Flutre T, Le Cunff L. Enhancing grapevine breeding efficiency through genomic prediction and selection index. G3 (BETHESDA, MD.) 2024; 14:jkae038. [PMID: 38401528 PMCID: PMC10989862 DOI: 10.1093/g3journal/jkae038] [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: 12/18/2023] [Revised: 02/09/2024] [Accepted: 02/18/2024] [Indexed: 02/26/2024]
Abstract
Grapevine (Vitis vinifera) breeding reaches a critical point. New cultivars are released every year with resistance to powdery and downy mildews. However, the traditional process remains time-consuming, taking 20-25 years, and demands the evaluation of new traits to enhance grapevine adaptation to climate change. Until now, the selection process has relied on phenotypic data and a limited number of molecular markers for simple genetic traits such as resistance to pathogens, without a clearly defined ideotype, and was carried out on a large scale. To accelerate the breeding process and address these challenges, we investigated the use of genomic prediction, a methodology using molecular markers to predict genotypic values. In our study, we focused on 2 existing grapevine breeding programs: Rosé wine and Cognac production. In these programs, several families were created through crosses of emblematic and interspecific resistant varieties to powdery and downy mildews. Thirty traits were evaluated for each program, using 2 genomic prediction methods: Genomic Best Linear Unbiased Predictor and Least Absolute Shrinkage Selection Operator. The results revealed substantial variability in predictive abilities across traits, ranging from 0 to 0.9. These discrepancies could be attributed to factors such as trait heritability and trait characteristics. Moreover, we explored the potential of across-population genomic prediction by leveraging other grapevine populations as training sets. Integrating genomic prediction allowed us to identify superior individuals for each program, using multivariate selection index method. The ideotype for each breeding program was defined collaboratively with representatives from the wine-growing sector.
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Affiliation(s)
- Charlotte Brault
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Vincent Segura
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier 34398, France
| | - Maryline Roques
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Pauline Lamblin
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Virginie Bouckenooghe
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | | | - Constance Cunty
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
- Centre du Rosé, Vidauban 83550, France
| | - Matthieu Breil
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Marina Frouin
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Léa Garcin
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Louise Camps
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Marie-Agnès Ducasse
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
| | - Charles Romieu
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro Montpellier, Montpellier 34398, France
| | - Gilles Masson
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
- Centre du Rosé, Vidauban 83550, France
| | - Sébastien Julliard
- Conservatoire du Vignoble Charentais, Institut de Formation de Richemont, Cherves-Richemont 16370, France
| | - Timothée Flutre
- INRAE, CNRS, AgroParisTech, Université Paris-Saclay, GQE—Le Moulon, Gif-sur-Yvette 91190, France
| | - Loïc Le Cunff
- UMT Geno-Vigne®, IFV, INRAE, Institut Agro Montpellier, Montpellier 34398, France
- Institut Français de la vigne et du vin, Pôle National Matériel Végétal, Le Grau du Roi 30240, France
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Martina M, De Rosa V, Magon G, Acquadro A, Barchi L, Barcaccia G, De Paoli E, Vannozzi A, Portis E. Revitalizing agriculture: next-generation genotyping and -omics technologies enabling molecular prediction of resilient traits in the Solanaceae family. FRONTIERS IN PLANT SCIENCE 2024; 15:1278760. [PMID: 38375087 PMCID: PMC10875072 DOI: 10.3389/fpls.2024.1278760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/22/2024] [Indexed: 02/21/2024]
Abstract
This review highlights -omics research in Solanaceae family, with a particular focus on resilient traits. Extensive research has enriched our understanding of Solanaceae genomics and genetics, with historical varietal development mainly focusing on disease resistance and cultivar improvement but shifting the emphasis towards unveiling resilience mechanisms in genebank-preserved germplasm is nowadays crucial. Collecting such information, might help researchers and breeders developing new experimental design, providing an overview of the state of the art of the most advanced approaches for the identification of the genetic elements laying behind resilience. Building this starting point, we aim at providing a useful tool for tackling the global agricultural resilience goals in these crops.
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Affiliation(s)
- Matteo Martina
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Valeria De Rosa
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Udine, Italy
| | - Gabriele Magon
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Alberto Acquadro
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Lorenzo Barchi
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
| | - Gianni Barcaccia
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Emanuele De Paoli
- Department of Agricultural, Food, Environmental and Animal Sciences (DI4A), University of Udine, Udine, Italy
| | - Alessandro Vannozzi
- Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Laboratory of Plant Genetics and Breeding, University of Padua, Legnaro, Italy
| | - Ezio Portis
- Department of Agricultural, Forest and Food Sciences (DISAFA), Plant Genetics, University of Torino, Grugliasco, Italy
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5
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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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de Verdal H, Baertschi C, Frouin J, Quintero C, Ospina Y, Alvarez MF, Cao TV, Bartholomé J, Grenier C. Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population. RICE (NEW YORK, N.Y.) 2023; 16:43. [PMID: 37758969 PMCID: PMC10533757 DOI: 10.1186/s12284-023-00661-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023]
Abstract
Genomic selection is a worthy breeding method to improve genetic gain in recurrent selection breeding schemes. The integration of multi-generation and multi-location information could significantly improve genomic prediction models in the context of shuttle breeding. The Cirad-CIAT upland rice breeding program applies recurrent genomic selection and seeks to optimize the scheme to increase genetic gain while reducing phenotyping efforts. We used a synthetic population (PCT27) of which S0 plants were all genotyped and advanced by selfing and bulk seed harvest to the S0:2, S0:3, and S0:4 generations. The PCT27 was then divided into two sets. The S0:2 and S0:3 progenies for PCT27A and the S0:4 progenies for PCT27B were phenotyped in two locations: Santa Rosa the target selection location, within the upland rice growing area, and Palmira, the surrogate location, far from the upland rice growing area but easier for experimentation. While the calibration used either one of the two sets phenotyped in one or two locations, the validation population was only the PCT27B phenotyped in Santa Rosa. Five scenarios of genomic prediction and 24 models were performed and compared. Training the prediction model with the PCT27B phenotyped in Santa Rosa resulted in predictive abilities ranging from 0.19 for grain zinc concentration to 0.30 for grain yield. Expanding the training set with the inclusion of the PCT27A resulted in greater predictive abilities for all traits but grain yield, with increases from 5% for plant height to 61% for grain zinc concentration. Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27). For plant height, the model assuming a single G × E variance provided higher accuracy (0.28). The gain in predictive ability for grain yield was the greatest (0.25) when environment-specific variance deviation effect for G × E was considered. While the best scenario was specific to each trait, the results indicated that the gain in predictive ability provided by the multi-location and multi-generation calibration was low. Yet, this approach could lead to increased selection intensity, acceleration of the breeding cycle, and a sizable economic advantage for the program.
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Affiliation(s)
- Hugues de Verdal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France.
| | - Cédric Baertschi
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
| | - Julien Frouin
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
| | - Constanza Quintero
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia
| | - Yolima Ospina
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia
| | | | - Tuong-Vi Cao
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
| | - Jérôme Bartholomé
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia
| | - Cécile Grenier
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, 34398, Montpellier, France.
- Alliance Bioversity-CIAT, A.A.6713, Km 17 Recta Palmira Cali, Cali, Colombia.
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David I, Ricard A. An improved transmissibility model to detect transgenerational transmitted environmental effects. Genet Sel Evol 2023; 55:66. [PMID: 37735633 PMCID: PMC10512618 DOI: 10.1186/s12711-023-00833-y] [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: 04/05/2023] [Accepted: 08/24/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Evolutionary studies have reported that non-genetic information can be inherited across generations (epigenetic marks, microbiota, cultural inheritance). Non-genetic information is considered to be a key element to explain the adaptation of wild species to environmental constraints because it lies at the root of the transgenerational transmission of environmental effects. The "transmissibility model" was proposed several years ago to better predict the transmissible potential of each animal by taking these diverse sources of inheritance into account in a global transmissible potential. We propose to improve this model to account for the influence of the environment on the global transmissible potential as well. This extension of the transmissibility model is the "transmissibility model with environment" that considers a covariance between transmissibility samplings of animals sharing the same environment. The null hypothesis of "no transmitted environmental effect" can be tested by comparing the two models using a likelihood ratio test (LRT). RESULTS We performed simulations that mimicked an experimental design consisting of two lines of animals with one exposed to a particular environment at a given generation. This enabled us to evaluate the performances of the transmissibility model with environment so as to detect and quantify transgenerational transmitted environmental effects. The power and the realized type I error of the LRT were compared to those of a T-test comparing the phenotype of the two lines, three generations after the environmental exposure for different sets of parameters. The power of the LRT ranged from 45 to 94%, whereas that of the T-test was always lower than 26%. In addition, the realized type I error of the T-test was 15% and that of the LRT was 5%, as expected. Variances, the covariance between transmissibility samplings, and path coefficients of transmission estimated with the transmissibility model with environment were close to their true values for all sets of parameters. CONCLUSIONS The transmissibility model with environment is effective in modeling vertical transmission of environmental effects.
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Affiliation(s)
- Ingrid David
- GenPhySE, Université de Toulouse, INRAE, ENVT, 31326, Castanet Tolosan, France.
| | - Anne Ricard
- INRAE, AgroParisTech, GABI, Université Paris Saclay, 78350, Jouy-en-Josas, France
- Département Recherche et Innovation, Institut Français du Cheval et de l'équitation, 61310, Exmes, France
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Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, Seal S, Das M, Banerjee S, Chowdhury S, Adeyemi SB, Chen JT. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes (Basel) 2023; 14:1484. [PMID: 37510388 PMCID: PMC10380062 DOI: 10.3390/genes14071484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.
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Affiliation(s)
- Dwaipayan Sinha
- Department of Botany, Government General Degree College, Mohanpur 721436, India
| | - Arun Kumar Maurya
- Department of Botany, Multanimal Modi College, Modinagar, Ghaziabad 201204, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Muhammad Majeed
- Department of Botany, University of Gujrat, Punjab 50700, Pakistan
| | - Rachna Agarwal
- Applied Genomics Section, Bhabha Atomic Research Centre, Mumbai 400085, India
| | - Rashmi Mukherjee
- Research Center for Natural and Applied Sciences, Department of Botany (UG & PG), Raja Narendralal Khan Women's College, Gope Palace, Midnapur 721102, India
| | - Sharmistha Ganguly
- Department of Dravyaguna, Institute of Post Graduate Ayurvedic Education and Research, Kolkata 700009, India
| | - Robina Aziz
- Department of Botany, Government, College Women University, Sialkot 51310, Pakistan
| | - Manika Bhatia
- TERI School of Advanced Studies, New Delhi 110070, India
| | - Aqsa Majgaonkar
- Department of Botany, St. Xavier's College (Autonomous), Mumbai 400001, India
| | - Sanchita Seal
- Department of Botany, Polba Mahavidyalaya, Polba 712148, India
| | - Moumita Das
- V. Sivaram Research Foundation, Bangalore 560040, India
| | - Swastika Banerjee
- Department of Botany, Kairali College of +3 Science, Champua, Keonjhar 758041, India
| | - Shahana Chowdhury
- Department of Biotechnology, Faculty of Engineering Sciences, German University Bangladesh, TNT Road, Telipara, Chandona Chowrasta, Gazipur 1702, Bangladesh
| | - Sherif Babatunde Adeyemi
- Ethnobotany/Phytomedicine Laboratory, Department of Plant Biology, Faculty of Life Sciences, University of Ilorin, Ilorin P.M.B 1515, Nigeria
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
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Großkinsky DK, Faure JD, Gibon Y, Haslam RP, Usadel B, Zanetti F, Jonak C. The potential of integrative phenomics to harness underutilized crops for improving stress resilience. FRONTIERS IN PLANT SCIENCE 2023; 14:1216337. [PMID: 37409292 PMCID: PMC10318926 DOI: 10.3389/fpls.2023.1216337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Affiliation(s)
- Dominik K. Großkinsky
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| | - Jean-Denis Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
| | - Yves Gibon
- INRAE, Univ. Bordeaux, UMR BFP, Villenave d’Ornon, France
- Bordeaux Metabolome, INRAE, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Björn Usadel
- IBG-4 Bioinformatics, CEPLAS, Forschungszentrum, Jülich, Germany
- Biological Data Science, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, Germany
| | - Federica Zanetti
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Claudia Jonak
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
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John M, Haselbeck F, Dass R, Malisi C, Ricca P, Dreischer C, Schultheiss SJ, Grimm DG. A comparison of classical and machine learning-based phenotype prediction methods on simulated data and three plant species. FRONTIERS IN PLANT SCIENCE 2022; 13:932512. [PMID: 36407627 PMCID: PMC9673477 DOI: 10.3389/fpls.2022.932512] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/25/2022] [Indexed: 06/16/2023]
Abstract
Genomic selection is an integral tool for breeders to accurately select plants directly from genotype data leading to faster and more resource-efficient breeding programs. Several prediction methods have been established in the last few years. These range from classical linear mixed models to complex non-linear machine learning approaches, such as Support Vector Regression, and modern deep learning-based architectures. Many of these methods have been extensively evaluated on different crop species with varying outcomes. In this work, our aim is to systematically compare 12 different phenotype prediction models, including basic genomic selection methods to more advanced deep learning-based techniques. More importantly, we assess the performance of these models on simulated phenotype data as well as on real-world data from Arabidopsis thaliana and two breeding datasets from soy and corn. The synthetic phenotypic data allow us to analyze all prediction models and especially the selected markers under controlled and predefined settings. We show that Bayes B and linear regression models with sparsity constraints perform best under different simulation settings with respect to explained variance. Further, we can confirm results from other studies that there is no superiority of more complex neural network-based architectures for phenotype prediction compared to well-established methods. However, on real-world data, for which several prediction models yield comparable results with slight advantages for Elastic Net, this picture is less clear, suggesting that there is a lot of room for future research.
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Affiliation(s)
- Maura John
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany
| | - Florian Haselbeck
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany
| | | | | | | | | | | | - Dominik G. Grimm
- Technical University of Munich, Campus Straubing for Biotechnology and Sustainability, Bioinformatics, Straubing, Germany
- Weihenstephan-Triesdorf University of Applied Sciences, Bioinformatics, Straubing, Germany
- Technical University of Munich, Department of Informatics, Garching, Germany
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Genomic Prediction Accuracy of Stripe Rust in Six Spring Wheat Populations by Modeling Genotype by Environment Interaction. PLANTS 2022; 11:plants11131736. [PMID: 35807690 PMCID: PMC9269065 DOI: 10.3390/plants11131736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 11/17/2022]
Abstract
Some previous studies have assessed the predictive ability of genome-wide selection on stripe (yellow) rust resistance in wheat, but the effect of genotype by environment interaction (GEI) in prediction accuracies has not been well studied in diverse genetic backgrounds. Here, we compared the predictive ability of a model based on phenotypic data only (M1), the main effect of phenotype and molecular markers (M2), and a model that incorporated GEI (M3) using three cross-validations (CV1, CV2, and CV0) scenarios of interest to breeders in six spring wheat populations. Each population was evaluated at three to eight field nurseries and genotyped with either the DArTseq technology or the wheat 90K single nucleotide polymorphism arrays, of which a subset of 1,058- 23,795 polymorphic markers were used for the analyses. In the CV1 scenario, the mean prediction accuracies of the M1, M2, and M3 models across the six populations varied from −0.11 to −0.07, from 0.22 to 0.49, and from 0.19 to 0.48, respectively. Mean accuracies obtained using the M3 model in the CV1 scenario were significantly greater than the M2 model in two populations, the same in three populations, and smaller in one population. In both the CV2 and CV0 scenarios, the mean prediction accuracies of the three models varied from 0.53 to 0.84 and were not significantly different in all populations, except the Attila/CDC Go in the CV2, where the M3 model gave greater accuracy than both the M1 and M2 models. Overall, the M3 model increased prediction accuracies in some populations by up to 12.4% and decreased accuracy in others by up to 17.4%, demonstrating inconsistent results among genetic backgrounds that require considering each population separately. This is the first comprehensive genome-wide prediction study that investigated details of the effect of GEI on stripe rust resistance across diverse spring wheat populations.
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12
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Robert P, Auzanneau J, Goudemand E, Oury FX, Rolland B, Heumez E, Bouchet S, Le Gouis J, Rincent R. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:895-914. [PMID: 34988629 DOI: 10.1007/s00122-021-04005-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/23/2021] [Indexed: 05/15/2023]
Abstract
Phenomic selection is a promising alternative or complement to genomic selection in wheat breeding. Models combining spectra from different environments maximise the predictive ability of grain yield and heading date of wheat breeding lines. Phenomic selection (PS) is a recent breeding approach similar to genomic selection (GS) except that genotyping is replaced by near-infrared (NIR) spectroscopy. PS can potentially account for non-additive effects and has the major advantage of being low cost and high throughput. Factors influencing GS predictive abilities have been intensively studied, but little is known about PS. We tested and compared the abilities of PS and GS to predict grain yield and heading date from several datasets of bread wheat lines corresponding to the first or second years of trial evaluation from two breeding companies and one research institute in France. We evaluated several factors affecting PS predictive abilities including the possibility of combining spectra collected in different environments. A simple H-BLUP model predicted both traits with prediction ability from 0.26 to 0.62 and with an efficient computation time. Our results showed that the environments in which lines are grown had a crucial impact on predictive ability based on the spectra acquired and was specific to the trait considered. Models combining NIR spectra from different environments were the best PS models and were at least as accurate as GS in most of the datasets. Furthermore, a GH-BLUP model combining genotyping and NIR spectra was the best model of all (prediction ability from 0.31 to 0.73). We demonstrated also that as for GS, the size and the composition of the training set have a crucial impact on predictive ability. PS could therefore replace or complement GS for efficient wheat breeding programs.
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Affiliation(s)
- Pauline Robert
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - Jérôme Auzanneau
- Agri-Obtentions, Ferme de Gauvilliers, 78660, Orsonville, France
| | - Ellen Goudemand
- Florimond-Desprez Veuve & Fils SAS, 3 rue Florimond-Desprez, BP 41, 59242, Cappelle-en-Pévèle, France
| | - François-Xavier Oury
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Bernard Rolland
- INRAE-Agrocampus Ouest-Université Rennes 1, UMR1349, IGEPP, Domaine de la Motte, 35653, Le Rheu, France
| | - Emmanuel Heumez
- INRAE, UE 972, Grandes Cultures Innovation Environnement, 2 Chaussée Brunehaut, 80200, EstréesMons, France
| | - Sophie Bouchet
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Jacques Le Gouis
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France
| | - Renaud Rincent
- Université Paris-Saclay, INRAE, CNRS, AgroParisTech, GQE - Le Moulon, 91190, Gif-sur-Yvette, France.
- INRAE-Université Clermont-Auvergne, UMR1095, GDEC, 5 chemin de Beaulieu, 63000, ClermontFerrand, France.
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13
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Fugeray-Scarbel A, Ben-Sadoun S, Bouchet S, Lemarié S. Analyzing the Economic Effectiveness of Genomic Selection Relative to Conventional Breeding Approaches. Methods Mol Biol 2022; 2467:619-644. [PMID: 35451792 DOI: 10.1007/978-1-0716-2205-6_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Comparing the economic efficiency of alternative strategies for breeding requires to compare the genetic gain obtained with breeding schemes that represent the same total investment. In this chapter, we present a generic method to assess this economic efficiency for alternative breeding schemes. After presenting the baseline framework and the necessity of comparing breeding schemes with equivalent total investment, we propose one illustrative example on wheat breeding. In this application, we compare the use of conventional breeding and genomic selection. With this example, we explain the requirements and the different steps to implement this method. At last, we discuss several extensions of the baseline model.
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Affiliation(s)
| | - Sarah Ben-Sadoun
- INRAE - UCA UMR1095, Genetics Diversity and Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Sophie Bouchet
- INRAE - UCA UMR1095, Genetics Diversity and Ecophysiology of Cereals, Clermont-Ferrand, France
| | - Stéphane Lemarié
- Univ. Grenoble Alpes, INRAE, CNRS, Grenoble INP, GAEL, Grenoble, France.
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