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Khanna A, Anumalla M, Catolos M, Bhosale S, Jarquin D, Hussain W. Optimizing predictions in IRRI's rice drought breeding program by leveraging 17 years of historical data and pedigree information. Front Plant Sci 2022; 13:983818. [PMID: 36204059 PMCID: PMC9530897 DOI: 10.3389/fpls.2022.983818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/08/2022] [Indexed: 06/16/2023]
Abstract
Prediction models based on pedigree and/or molecular marker information are now an inextricable part of the crop breeding programs and have led to increased genetic gains in many crops. Optimization of IRRI's rice drought breeding program is crucial for better implementation of selections based on predictions. Historical datasets with precise and robust pedigree information have been a great resource to help optimize the prediction models in the breeding programs. Here, we leveraged 17 years of historical drought data along with the pedigree information to predict the new lines or environments and dissect the G × E interactions. Seven models ranging from basic to proposed higher advanced models incorporating interactions, and genotypic specific effects were used. These models were tested with three cross-validation schemes (CV1, CV2, and CV0) to assess the predictive ability of tested and untested lines in already observed environments and tested lines in novel or new environments. In general, the highest prediction abilities were obtained when the model accounting interactions between pedigrees (additive) and environment were included. The CV0 scheme (predicting unobserved or novel environments) reveals very low predictive abilities among the three schemes. CV1 and CV2 schemes that borrow information from the target and correlated environments have much higher predictive abilities. Further, predictive ability was lower when predicting lines in non-stress conditions using drought data as training set and/or vice-versa. When predicting the lines using the data sets under the same conditions (stress or non-stress data sets), much better prediction accuracy was obtained. These results provide conclusive evidence that modeling G × E interactions are important in predictions. Thus, considering G × E interactions would help to build enhanced genomic or pedigree-based prediction models in the rice breeding program. Further, it is crucial to borrow the correlated information from other environments to improve prediction accuracy.
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Affiliation(s)
- Apurva Khanna
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Mahender Anumalla
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Margaret Catolos
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Sankalp Bhosale
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Diego Jarquin
- Agronomy Department, University of Florida, Gainesville, FL, United States
| | - Waseem Hussain
- Rice Breeding Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
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Crespo-Herrera LA, Crossa J, Huerta-Espino J, Mondal S, Velu G, Juliana P, Vargas M, Pérez-Rodríguez P, Joshi AK, Braun HJ, Singh RP. Target Population of Environments for Wheat Breeding in India: Definition, Prediction and Genetic Gains. Front Plant Sci 2021; 12:638520. [PMID: 34108977 PMCID: PMC8181127 DOI: 10.3389/fpls.2021.638520] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 04/06/2021] [Indexed: 05/02/2023]
Abstract
In this study, we defined the target population of environments (TPE) for wheat breeding in India, the largest wheat producer in South Asia, and estimated the correlated response to the selection and prediction ability of five selection environments (SEs) in Mexico. We also estimated grain yield (GY) gains in each TPE. Our analysis used meteorological, soil, and GY data from the international Elite Spring Wheat Yield Trials (ESWYT) distributed by the International Maize and Wheat Improvement Center (CIMMYT) from 2001 to 2016. We identified three TPEs: TPE 1, the optimally irrigated Northwestern Plain Zone; TPE 2, the optimally irrigated, heat-stressed North Eastern Plains Zone; and TPE 3, the drought-stressed Central-Peninsular Zone. The correlated response to selection ranged from 0.4 to 0.9 within each TPE. The highest prediction accuracies for GY per TPE were derived using models that included genotype-by-environment interaction and/or meteorological information and their interaction with the lines. The highest prediction accuracies for TPEs 1, 2, and 3 were 0.37, 0.46, and 0.51, respectively, and the respective GY gains were 118, 46, and 123 kg/ha/year. These results can help fine-tune the breeding of elite wheat germplasm with stable yields to reduce farmers' risk from year-to-year environmental variation in India's wheat lands, which cover 30 million ha, account for 100 million tons of grain or more each year, and provide food and livelihoods for hundreds of millions of farmers and consumers in South Asia.
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Affiliation(s)
- Leonardo Abdiel Crespo-Herrera
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- *Correspondence: Leonardo Abdiel Crespo-Herrera,
| | - Jose Crossa
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
- Colegio de Post-Graduados, Texcoco, Mexico
| | - Julio Huerta-Espino
- Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Campo Experimental Valle de México, México, Mexico
| | - Suchismita Mondal
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Govindan Velu
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Philomin Juliana
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
| | - Mateo Vargas
- Programa de Protección Vegetal, Universidad Autónoma Chapingo, Texcoco, Mexico
| | | | - Arun Kumar Joshi
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), New Delhi, India
| | - Hans Joachim Braun
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Ravi Prakash Singh
- Global Wheat Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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