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 | Acesso ao texto completo restrito à biblioteca da Embrapa Milho e Sorgo. Para informações adicionais entre em contato com cnpms.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
20/05/2025 |
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Data da última atualização: |
20/05/2025 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
RIBEIRO, P. C. O.; HOWARD, R.; JARQUIN, D.; OLIVEIRA, I. C. M.; CHAVES, S.; CARNEIRO, P. C. S.; SOUZA, V. F.; SCHAFFERT, R. E.; DAMASCENO, C. M. B.; PARRELLA, R. A. da C.; DIAS, K. O. G.; PASTINA, M. M. |
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Afiliação: |
PEDRO C. O. RIBEIRO, UNIVERSIDADE FEDERAL DE VIÇOSA; REKA HOWARD, UNIVERSITY OF NEBRASKA; DIEGO JARQUIN, UNIVERSITY OF FLORIDA; ISADORA C. M. OLIVEIRA; SAULO CHAVES, UNIVERSIDADE FEDERAL DE VIÇOSA; PEDRO C. S. CARNEIRO, UNIVERSIDADE FEDERAL DE VIÇOSA; VANDER F. SOUZA; ROBERT EUGENE SCHAFFERT, CNPMS; CYNTHIA MARIA BORGES DAMASCENO, CNPMS; RAFAEL AUGUSTO DA COSTA PARRELLA, CNPMS; KAIO OLIMPIO G. DIAS, UNIVERSIDADE FEDERAL DE VIÇOSA; MARIA MARTA PASTINA, CNPMS. |
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Título: |
Prediction of biomass sorghum hybrids using environmental feature‑enriched genomic combining ability models in tropical environments. |
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Ano de publicação: |
2025 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 138, article 113, 2025. |
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DOI: |
https://doi.org/10.1007/s00122-025-04895-y |
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Idioma: |
Inglês |
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Conteúdo: |
Gathering environmental and genomic information can beneft the breeding of sorghum hybrids by overcoming complications imposed by the genotype-by-environment interaction (GEI). In this study, we explored the value of combining environmental features (EFs) and genomic data to enhance predictions for biomass sorghum hybrid breeding, addressing GEI complexities. We also investigated if considering specifc time windows for EFs improves the prediction. We used a historical dataset from a tropical biomass sorghum breeding program featuring 253 genotypes across 64 trials. Initially, a frst-stage analysis was performed to obtain the adjusted means (EBLUEs) and scrutinize the impact of 29 EFs (geographic, climatic, and soil-related EFs) on GEI. Subsequently, in the second-stage analysis, we used data from 221 hybrids that had both parents genotyped to evaluate the predictive ability and assertiveness of 12 models with diferent efects. The most relevant EFs included soil organic carbon, insolation on a horizontal surface, longitude, temperature at dew point, and nitrogen content. Across three cross-validation scenarios (CV1, CV0, and CV00), the most efective model encompassed main combining ability efects, GEI, and G휔I (genotype-by-specifc environmental efects interaction), utilizing an environmental kinship matrix (Ω) derived from mean EF values. Only in CV2, a model with a similar structure but utilizing Ω from specifc time windows outperformed others. Our fndings highlight the potential of integrating environmental and genomic data to refne predictive models for optimizing biomass sorghum hybrid breeding strategies. MenosGathering environmental and genomic information can beneft the breeding of sorghum hybrids by overcoming complications imposed by the genotype-by-environment interaction (GEI). In this study, we explored the value of combining environmental features (EFs) and genomic data to enhance predictions for biomass sorghum hybrid breeding, addressing GEI complexities. We also investigated if considering specifc time windows for EFs improves the prediction. We used a historical dataset from a tropical biomass sorghum breeding program featuring 253 genotypes across 64 trials. Initially, a frst-stage analysis was performed to obtain the adjusted means (EBLUEs) and scrutinize the impact of 29 EFs (geographic, climatic, and soil-related EFs) on GEI. Subsequently, in the second-stage analysis, we used data from 221 hybrids that had both parents genotyped to evaluate the predictive ability and assertiveness of 12 models with diferent efects. The most relevant EFs included soil organic carbon, insolation on a horizontal surface, longitude, temperature at dew point, and nitrogen content. Across three cross-validation scenarios (CV1, CV0, and CV00), the most efective model encompassed main combining ability efects, GEI, and G휔I (genotype-by-specifc environmental efects interaction), utilizing an environmental kinship matrix (Ω) derived from mean EF values. Only in CV2, a model with a similar structure but utilizing Ω from specifc time windows outperformed others. Our fndings highlight the pote... Mostrar Tudo |
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Palavras-Chave: |
Combinação genômica; Interação genótipo-ambiente. |
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Thesagro: |
Biomassa; Hibrido; Melhoramento Vegetal; Sorgo. |
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Categoria do assunto: |
F Plantas e Produtos de Origem Vegetal |
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Marc: |
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Registro original: |
Embrapa Milho e Sorgo (CNPMS) |
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