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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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Pereira de Castro A, Breseghello F, Furtini IV, Utumi MM, Pereira JA, Cao TV, Bartholomé J. Population improvement via recurrent selection drives genetic gain in upland rice breeding. Heredity (Edinb) 2023; 131:201-210. [PMID: 37407693 PMCID: PMC10462700 DOI: 10.1038/s41437-023-00636-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 06/16/2023] [Accepted: 06/19/2023] [Indexed: 07/07/2023] Open
Abstract
One of the main challenges of breeding programs is to identify superior genotypes from a large number of candidates. By gradually increasing the frequency of favorable alleles in the breeding population, recurrent selection improves the population mean for target traits, increasing the chance to identify promising genotypes. In rice, population improvement through recurrent selection has been used very little to date, except in Latin America. At Embrapa (Brazilian Agricultural Research Corporation), the upland rice breeding program is conducted in two phases: population improvement followed by product development. In this study, the CNA6 population, evaluated over five cycles (3 to 7) of selection, including 20 field trials, was used to assess the realized genetic gain. A high rate of genetic gain was observed for grain yield, at 215 kg.ha-1 per cycle or 67.8 kg.ha-1 per year (3.08%). The CNA6 population outperformed the controls only for the last cycle, with a yield difference of 1128 kg.ha-1. An analysis of the product development pipeline, based on 29 advanced yield trials with lines derived from cycles 3 to 6, showed that lines derived from the CNA6 population had high grain yield, but did not outperform the controls. These results demonstrate that the application of recurrent selection to a breeding population with sufficient genetic variability can result in significant genetic gains for quantitative traits, such as grain yield. The integration of this strategy into a two-phase breeding program also makes it possible to increase quantitative traits while selecting for other traits of interest.
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Affiliation(s)
| | | | | | | | | | - Tuong-Vi Cao
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
| | - Jérôme Bartholomé
- AGAP Institut, Univ Montpellier, CIRAD, INRAE, Montpellier SupAgro, Montpellier, France
- CIRAD, UMR AGAP Institut, F-34398, Montpellier, France
- Alliance Bioversity-CIAT, Cali, Colombia
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Nguyen VH, Morantte RIZ, Lopena V, Verdeprado H, Murori R, Ndayiragije A, Katiyar SK, Islam MR, Juma RU, Flandez-Galvez H, Glaszmann JC, Cobb JN, Bartholomé J. Multi-environment Genomic Selection in Rice Elite Breeding Lines. RICE (NEW YORK, N.Y.) 2023; 16:7. [PMID: 36752880 PMCID: PMC9908796 DOI: 10.1186/s12284-023-00623-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Assessing the performance of elite lines in target environments is essential for breeding programs to select the most relevant genotypes. One of the main complexities in this task resides in accounting for the genotype by environment interactions. Genomic prediction models that integrate information from multi-environment trials and environmental covariates can be efficient tools in this context. The objective of this study was to assess the predictive ability of different genomic prediction models to optimize the use of multi-environment information. We used 111 elite breeding lines representing the diversity of the international rice research institute breeding program for irrigated ecosystems. The lines were evaluated for three traits (days to flowering, plant height, and grain yield) in 15 environments in Asia and Africa and genotyped with 882 SNP markers. We evaluated the efficiency of genomic prediction to predict untested environments using seven multi-environment models and three cross-validation scenarios. RESULTS The elite lines were found to belong to the indica group and more specifically the indica-1B subgroup which gathered improved material originating from the Green Revolution. Phenotypic correlations between environments were high for days to flowering and plant height (33% and 54% of pairwise correlation greater than 0.5) but low for grain yield (lower than 0.2 in most cases). Clustering analyses based on environmental covariates separated Asia's and Africa's environments into different clusters or subclusters. The predictive abilities ranged from 0.06 to 0.79 for days to flowering, 0.25-0.88 for plant height, and - 0.29-0.62 for grain yield. We found that models integrating genotype-by-environment interaction effects did not perform significantly better than models integrating only main effects (genotypes and environment or environmental covariates). The different cross-validation scenarios showed that, in most cases, the use of all available environments gave better results than a subset. CONCLUSION Multi-environment genomic prediction models with main effects were sufficient for accurate phenotypic prediction of elite lines in targeted environments. These results will help refine the testing strategy to update the genomic prediction models to improve predictive ability.
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Affiliation(s)
- Van Hieu Nguyen
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Rose Imee Zhella Morantte
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Vitaliano Lopena
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Holden Verdeprado
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Rosemary Murori
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Alexis Ndayiragije
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Sanjay Kumar Katiyar
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Md Rafiqul Islam
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Roselyne Uside Juma
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
| | - Hayde Flandez-Galvez
- Institute of Crop Science, College of Agriculture and Food Science, University of the Philippines, Los Baños, Laguna, Philippines
| | - Jean-Christophe Glaszmann
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Joshua N Cobb
- Rice Breeding Innovation Platform, International Rice Research Institute, DAPO, Box7777, Metro Manila, Philippines
- RiceTec. Inc, PO Box 1305, Alvin, TX, 77512, USA
| | - Jérôme Bartholomé
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France.
- CIRAD, UMR AGAP Institut, Cali, Colombia.
- Alliance Bioversity-CIAT, Cali, Colombia.
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Rakotondramanana M, Tanaka R, Pariasca-Tanaka J, Stangoulis J, Grenier C, Wissuwa M. Genomic prediction of zinc-biofortification potential in rice gene bank accessions. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2265-2278. [PMID: 35618915 PMCID: PMC9271118 DOI: 10.1007/s00122-022-04110-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/19/2022] [Indexed: 06/15/2023]
Abstract
A genomic prediction model successfully predicted grain Zn concentrations in 3000 gene bank accessions and this was verified experimentally with selected potential donors having high on-farm grain-Zn in Madagascar. Increasing zinc (Zn) concentrations in edible parts of food crops, an approach termed Zn-biofortification, is a global breeding objective to alleviate micro-nutrient malnutrition. In particular, infants in countries like Madagascar are at risk of Zn deficiency because their dominant food source, rice, contains insufficient Zn. Biofortified rice varieties with increased grain Zn concentrations would offer a solution and our objective is to explore the genotypic variation present among rice gene bank accessions and to possibly identify underlying genetic factors through genomic prediction and genome-wide association studies (GWAS). A training set of 253 rice accessions was grown at two field sites in Madagascar to determine grain Zn concentrations and grain yield. A multi-locus GWAS analysis identified eight loci. Among these, QTN_11.3 had the largest effect and a rare allele increased grain Zn concentrations by 15%. A genomic prediction model was developed from the above training set to predict Zn concentrations of 3000 sequenced rice accessions. Predicted concentrations ranged from 17.1 to 40.2 ppm with a prediction accuracy of 0.51. An independent confirmation with 61 gene bank seed samples provided high correlations (r = 0.74) between measured and predicted values. Accessions from the aus sub-species had the highest predicted grain Zn concentrations and these were confirmed in additional field experiments, with one potential donor having more than twice the grain Zn compared to a local check variety. We conclude utilizing donors from the aus sub-species and employing genomic selection during the breeding process is the most promising approach to raise grain Zn concentrations in rice.
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Affiliation(s)
- Mbolatantely Rakotondramanana
- Rice Research Department, The National Center for Applied Research on Rural Development (FOFIFA), 101, Antananarivo, Madagascar
| | - Ryokei Tanaka
- Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo, 113-8657, Japan
| | - Juan Pariasca-Tanaka
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan
| | - James Stangoulis
- College of Science and Engineering, Flinders University, Bedford Park, SA, 5042, Australia
| | - Cécile Grenier
- CIRAD, INRAE, Institut Agro, UMR AGAP Institut, Univ Montpellier, 34398, Montpellier, France
| | - Matthias Wissuwa
- Crop, Livestock and Environment Division, Japan International Research Center for Agricultural Sciences (JIRCAS), 1-1 Ohwashi, Tsukuba, Ibaraki, 305-8686, Japan.
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