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Registro Completo |
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Biblioteca(s): |
Embrapa Café. |
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Data corrente: |
08/12/2023 |
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Data da última atualização: |
08/12/2023 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FIALHO, I. C.; AZEVEDO, C. F.; NASCIMENTO, A. C. C.; TEIXEIRA, F. R. F.; RESENDE, M. D. V. de; NASCIMENTO, M. |
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Afiliação: |
IZABELA CLARA FIALHO, UNIVERSIDADE FEDERAL DE VIÇOSA; CAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; FILIPE RIBEIRO FORMIGA TEIXEIRA, UNIVERSIDADE FEDERAL DO PIAUÍ; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSÉS NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA. |
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Título: |
Factor analysis applied in genomic prediction considering different density marker panels in rice. |
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Ano de publicação: |
2023 |
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Fonte/Imprenta: |
Euphytica, v. 219, article 88, 2023. |
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DOI: |
https://doi.org/10.1007/s10681-023-03214-0 |
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Idioma: |
Inglês |
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Conteúdo: |
The study objective was to evaluate the application of factor analysis (FA) on genomic prediction considering different density marker panels. The FA transforms phenotype traits in latent variables (factor scores), called pseudo-phenotype in this study. The Genomic Best Linear Unbiased Prediction method was applied to the Oriza sativa L phenotype traits. The dataset contains twenty-two phenotypic traits and 36,901 SNPs (Single Nucleotide Polymorphism) from 413 genotypes. The results obtained indicate that combining the factor analysis and the genomic prediction with different density marker panels was efficient. The analysis presented similar values for predictive ability, considering the phenotypes and pseudo-phenotypes (in both analyses, there was variation between 0.60 and 0.80), high agreement of SNPs with major effects, and high agreement between the best and worst selected individuals considering phenotypes and pseudo-phenotypes analysis. |
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Thesaurus Nal: |
Genomics; Marker-assisted selection; Phenotype; Prediction; Rice. |
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Categoria do assunto: |
-- |
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Marc: |
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024 7 $ahttps://doi.org/10.1007/s10681-023-03214-0$2DOI
100 1 $aFIALHO, I. C.
245 $aFactor analysis applied in genomic prediction considering different density marker panels in rice.$h[electronic resource]
260 $c2023
520 $aThe study objective was to evaluate the application of factor analysis (FA) on genomic prediction considering different density marker panels. The FA transforms phenotype traits in latent variables (factor scores), called pseudo-phenotype in this study. The Genomic Best Linear Unbiased Prediction method was applied to the Oriza sativa L phenotype traits. The dataset contains twenty-two phenotypic traits and 36,901 SNPs (Single Nucleotide Polymorphism) from 413 genotypes. The results obtained indicate that combining the factor analysis and the genomic prediction with different density marker panels was efficient. The analysis presented similar values for predictive ability, considering the phenotypes and pseudo-phenotypes (in both analyses, there was variation between 0.60 and 0.80), high agreement of SNPs with major effects, and high agreement between the best and worst selected individuals considering phenotypes and pseudo-phenotypes analysis.
650 $aGenomics
650 $aMarker-assisted selection
650 $aPhenotype
650 $aPrediction
650 $aRice
700 1 $aAZEVEDO, C. F.
700 1 $aNASCIMENTO, A. C. C.
700 1 $aTEIXEIRA, F. R. F.
700 1 $aRESENDE, M. D. V. de
700 1 $aNASCIMENTO, M.
773 $tEuphytica$gv. 219, article 88, 2023.
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