Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.
Genomic and pedigree predictions for grain yield and agronomic traits were carried out using high density molecular data on a set of 803 spring wheat lines that were evaluated in 5 sites characterized by several environmental co-variables. Seven statistical models were tested using two random cross-validations schemes. Two other prediction problems were studied, namely predicting the lines' performance at one site with another (pairwise-site) and at untested sites (leave-one-site-out). Grain yield ranged from 3.7 to 9.0 t ha(-1) across sites. The best predictability was observed when genotypic and pedigree data were included in the models and their interaction with sites and the environmental co-variables. The leave-one-site-out increased average prediction accuracy over pairwise-site for all the traits, specifically from 0.27 to 0.36 for grain yield. Days to anthesis, maturity, and plant height predictions had high heritability and gave the highest accuracy for prediction models. Genomic and pedigree models coupled with environmental co-variables gave high prediction accuracy due to high genetic correlation between sites. This study provides an example of model prediction considering climate data along-with genomic and pedigree information. Such comprehensive models can be used to achieve rapid enhancement of wheat yield enhancement in current and future climate change scenario.
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Saint Pierre
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Burgueño
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
J
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Crossa
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, INIFAP, México.
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Fuentes Dávila
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, INIFAP, México.
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Figueroa López
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, INIFAP, México.
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Solís Moya
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, INIFAP, México.
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Ireta Moreno
Instituto Nacional de Investigaciones Forestales, Agrícolas y Pecuarias, INIFAP, México.
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VM
Hernández Muela
Universidad Autónoma Agraria Antonio Narro, México.
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VM
Zamora Villa
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Vikram
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Mathews
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Sansaloni
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Sehgal
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, 321 Keim Hall, Lincoln, NE, 68503-0915, USA.
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Jarquin
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
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Wenzl
International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, Texcoco, CP 56237, México.
Sukhwinder
S
Singh
D000383:Agriculture; D001939:Bread; D002523:Edible Grain / Q000235:genetics*; D004777:Environment; D014644:Genetic Variation / Q000235:genetics; D018745:Genome, Plant / Q000235:genetics; D005838:Genotype; D015233:Models, Statistical; D012621:Seasons; D014908:Triticum / Q000235:genetics*; D014887:Weather