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| 18. |  | COSTA, M. A.; TORNISIELO, V. L.; REGITANO, B. Mobilidade do paclobutrasol em um solo franco-arenoso cultivado com manga no Nordeste brasileiro. Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 32, n. 5, p. 2177-2182, set./out., 2008. Separata de Revista Brasileira de Ciência do Solo, Viçosa, MG, v. 32, n. 5, set./out., 2008.| Biblioteca(s): Embrapa Solos / UEP-Recife. |
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Registro Completo
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Biblioteca(s): |
Embrapa Milho e Sorgo. |
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Data corrente: |
05/10/2023 |
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Data da última atualização: |
09/10/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 1 |
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Autoria: |
CHANDNANI, R.; QIN, T.; YE, H.; HU, H.; PANJVANI, K.; TOKIZAWA, M.; MACIAS, J. M.; MEDINA, A. A.; BERNARDINO, K. C.; PRADIER, P.-L.; BANIK, P.; MOONEY, A.; MAGALHAES, J. V. de; NGUYEN, H. T.; KOCHIAN, L. V. |
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Afiliação: |
RAHUL CHANDNANI, UNIVERSITY OF SASKATCHEWAN; TONGFEI QIN, UNIVERSITY OF SASKATCHEWAN; HENG YE, UNIVERSITY OF MISSOURI; HAIFEI HU, UNIVERSITY OF WESTERN AUSTRALIA; KARIM PANJVANI, UNIVERSITY OF SASKATCHEWAN; MUTSUTOMO TOKIZAWA, UNIVERSITY OF SASKATCHEWAN; JAVIER MORA MACIAS, UNIVERSITY OF SASKATCHEWAN; ALMA ARMENTA MEDINA, UNIVERSITY OF SASKATCHEWAN; KARINE C. BERNARDINO; PIERRE-LUC PRADIER UNIVERSITY OF SASKATCHEWAN, UNIVERSITY OF SASKATCHEWAN; PANKAJ BANIK, UNIVERSITY OF SASKATCHEWAN; ASHLYN MOONEY, UNIVERSITY OF SASKATCHEWAN; JURANDIR VIEIRA DE MAGALHAES, CNPMS; HENRY T. NGUYEN, UNIVERSITY OF MISSOURI; LEON V. KOCHIAN, UNIVERSITY OF SASKATCHEWAN. |
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Título: |
Application of an improved 2-dimensional high-throughput soybean root phenotyping platform to identify novel genetic variants regulating root architecture traits. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Plant Phenomics, v. 5, article 97, 2023. |
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DOI: |
https://doi.org/10.34133/plantphenomics.0097 |
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Idioma: |
Inglês |
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Conteúdo: |
Nutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots. MenosNutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (... Mostrar Tudo |
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Thesagro: |
Genética Vegetal; Raiz; Seleção Fenótipa; Soja. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1157100/1/Application-of-an-improved-2-dimensional-high-throughput-soybean-root.pdf
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Marc: |
LEADER 02746naa a2200349 a 4500
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024 7 $ahttps://doi.org/10.34133/plantphenomics.0097$2DOI
100 1 $aCHANDNANI, R.
245 $aApplication of an improved 2-dimensional high-throughput soybean root phenotyping platform to identify novel genetic variants regulating root architecture traits.$h[electronic resource]
260 $c2023
520 $aNutrient-efficient root system architecture (RSA) is becoming an important breeding objective for generating crop varieties with improved nutrient and water acquisition efficiency. Genetic variants shaping soybean RSA is key in improving nutrient and water acquisition. Here, we report on the use of an improved 2-dimensional high-throughput root phenotyping platform that minimizes background noise by imaging pouch-grown root systems submerged in water. We also developed a background image cleaning Python pipeline that computationally removes images of small pieces of debris and filter paper fibers, which can be erroneously quantified as root tips. This platform was used to phenotype root traits in 286 soybean lines genotyped with 5.4 million single-nucleotide polymorphisms. There was a substantially higher correlation in manually counted number of root tips with computationally quantified root tips (95% correlation), when the background was cleaned of nonroot materials compared to root images without the background corrected (79%). Improvements in our RSA phenotyping pipeline significantly reduced overestimation of the root traits influenced by the number of root tips. Genome-wide association studies conducted on the root phenotypic data and quantitative gene expression analysis of candidate genes resulted in the identification of 3 putative positive regulators of root system depth, total root length and surface area, and root system volume and surface area of thicker roots (DOF1-like zinc finger transcription factor, protein of unknown function, and C2H2 zinc finger protein). We also identified a putative negative regulator (gibberellin 20 oxidase 3) of the total number of lateral roots.
650 $aGenética Vegetal
650 $aRaiz
650 $aSeleção Fenótipa
650 $aSoja
700 1 $aQIN, T.
700 1 $aYE, H.
700 1 $aHU, H.
700 1 $aPANJVANI, K.
700 1 $aTOKIZAWA, M.
700 1 $aMACIAS, J. M.
700 1 $aMEDINA, A. A.
700 1 $aBERNARDINO, K. C.
700 1 $aPRADIER, P.-L.
700 1 $aBANIK, P.
700 1 $aMOONEY, A.
700 1 $aMAGALHAES, J. V. de
700 1 $aNGUYEN, H. T.
700 1 $aKOCHIAN, L. V.
773 $tPlant Phenomics$gv. 5, article 97, 2023.
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Embrapa Milho e Sorgo (CNPMS) |
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