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| Registros recuperados : 3 | |
| 1. |  | MIYAZAWA, M. A.; BLOCK, M. F. M. Determination of Ca, Mg, K, Mn, Zn, Fe, and P in COFFEE, SOYBEAN. CORN, SUNFLOWER, and PASTURE GRASS LEAF TISSUES BY A HCI EXTRACTION METHOD. Comunication in Soil Science and Plant Anal, ucp. v. 15, n. 2, p. 141 - 148, 1984.| Biblioteca(s): Embrapa Agroindústria Tropical. |
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| 2. | | MIYAZAWA, M.; PAVAN, M. A.; BLOCK, M. F. M. Determination of Ca, Mg, K, Mn, Cu, and P in coffee, soybean, corn, sunflower, and pasture grass leaf tissues by HC1 extraction method. Commun. Soil Sci. Plant Anal., v.15, n.2, p.141-147, 1984.| Biblioteca(s): Embrapa Agrobiologia. |
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| 3. | | MIYAZAWA, M.; PAVAN, M. A.; BLOCK, M. F. M. Determination of Ca, Mg, K, Mn, Cu, Zn, Fe, and P in coffee, soybean, corn, sunflower, and pasture grass leaf tissues by a HCl extraction method. Communications in Soil Science and Plant Analysis, New York, v. 15, n. 2, p. 141-147, 1984.| Biblioteca(s): Embrapa Trigo. |
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| Registros recuperados : 3 | |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Café. Para informações adicionais entre em contato com biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
19/01/2022 |
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Data da última atualização: |
19/01/2022 |
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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: |
SUELA, M. M.; AZEVEDO, C. F.; NASCIMENTO, M.; NASCIMENTO, A. C. C.; RESENDE, M. D. V. de. |
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Afiliação: |
MATHEUS M. SUELA, UFV; CAMILA FERREIRA AZEVEDO, UFV; MOYSÉS NASCIMENTO, UFV; ANA CAROLINA CAMPANA NASCIMENTO, UFV; MARCOS DEON VILELA DE RESENDE, CNPCa. |
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Título: |
Regional heritability mapping and genome-wide association identify loci for rice traits. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Crop Science, v. 62, n. 1, p. 1-48, 2022. |
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DOI: |
https://doi.org/10.1002/csc2.20706 |
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Idioma: |
Inglês |
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Conteúdo: |
Although genome-wide association studies (GWAS) based on single-marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false-positive rates and the limited power to detect associations. Bayesian methods estimated marker effects simultaneously proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM) on the other hand determines the genome region (group of markers) associated with the phenotype, consider population structure and familial relatedness and is more powerful to detect quantitative trait loci (QTL) and reduced false-positive rates than single marker methodologies. A single-marker mixed model (SM-MM), bayesian approach and RHM were used for 11 traits in 413 rice accessions genotyped for 44,100 SNP markers. Using RHM in regions of 0.21Mb and 0.69 Mb, respectively, detected 5 and 7 associated regions with 163 and 569 single nucleotide polymorphisms (SNPs), bayesian method with regions of 0.21Mb and 0.69 Mb detected regions for all traits, whereas SM-MM detected 4 single SNP?trait associations. For the 11 traits, RHM explained around 25-40 and 25-76% using genome regions of 0.21 and 0.69Mb, respectively, and SM-MM using single markers explained 1?7% of the genomic heritability. Regional heritability mapping was more effective than SM-MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the QTL noted in the Q-TARO and Gramene QTL databases. MenosAlthough genome-wide association studies (GWAS) based on single-marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false-positive rates and the limited power to detect associations. Bayesian methods estimated marker effects simultaneously proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM) on the other hand determines the genome region (group of markers) associated with the phenotype, consider population structure and familial relatedness and is more powerful to detect quantitative trait loci (QTL) and reduced false-positive rates than single marker methodologies. A single-marker mixed model (SM-MM), bayesian approach and RHM were used for 11 traits in 413 rice accessions genotyped for 44,100 SNP markers. Using RHM in regions of 0.21Mb and 0.69 Mb, respectively, detected 5 and 7 associated regions with 163 and 569 single nucleotide polymorphisms (SNPs), bayesian method with regions of 0.21Mb and 0.69 Mb detected regions for all traits, whereas SM-MM detected 4 single SNP?trait associations. For the 11 traits, RHM explained around 25-40 and 25-76% using genome regions of 0.21 and 0.69Mb, respectively, and SM-MM using single markers explained 1?7% of the genomic heritability. Regional heritability mapping was more effective than SM-MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the ... Mostrar Tudo |
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Thesagro: |
Arroz; Melhoramento Genético Vegetal. |
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Thesaurus NAL: |
Bayesian theory; Genomics; Plant breeding; Rice. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $ahttps://doi.org/10.1002/csc2.20706$2DOI
100 1 $aSUELA, M. M.
245 $aRegional heritability mapping and genome-wide association identify loci for rice traits.$h[electronic resource]
260 $c2022
520 $aAlthough genome-wide association studies (GWAS) based on single-marker analysis have been widely applied in plant breeding programs, the effectivity of the methodology is still undermined by high false-positive rates and the limited power to detect associations. Bayesian methods estimated marker effects simultaneously proved to be efficient, indicating genes with important effects. Regional heritability mapping (RHM) on the other hand determines the genome region (group of markers) associated with the phenotype, consider population structure and familial relatedness and is more powerful to detect quantitative trait loci (QTL) and reduced false-positive rates than single marker methodologies. A single-marker mixed model (SM-MM), bayesian approach and RHM were used for 11 traits in 413 rice accessions genotyped for 44,100 SNP markers. Using RHM in regions of 0.21Mb and 0.69 Mb, respectively, detected 5 and 7 associated regions with 163 and 569 single nucleotide polymorphisms (SNPs), bayesian method with regions of 0.21Mb and 0.69 Mb detected regions for all traits, whereas SM-MM detected 4 single SNP?trait associations. For the 11 traits, RHM explained around 25-40 and 25-76% using genome regions of 0.21 and 0.69Mb, respectively, and SM-MM using single markers explained 1?7% of the genomic heritability. Regional heritability mapping was more effective than SM-MM in capturing major proportions of genomic heritability. The regions found in this study were within or close to the QTL noted in the Q-TARO and Gramene QTL databases.
650 $aBayesian theory
650 $aGenomics
650 $aPlant breeding
650 $aRice
650 $aArroz
650 $aMelhoramento Genético Vegetal
700 1 $aAZEVEDO, C. F.
700 1 $aNASCIMENTO, M.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aRESENDE, M. D. V. de
773 $tCrop Science$gv. 62, n. 1, p. 1-48, 2022.
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