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Registro Completo |
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Biblioteca(s): |
Embrapa Pecuária Sudeste; Embrapa Pesca e Aquicultura. |
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Data corrente: |
09/07/2019 |
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Data da última atualização: |
08/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
FERREIRA FILHO, D.; BUENO FILHO, J. S. de S.; REGITANO, L. C. de A.; ALENCAR, M. M. de; ALVES, R. R.; BAENA, M. M.; MEIRELLES, S. L. C. |
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Afiliação: |
Diógenes Ferreira Filho, UFRRJ; Júio Sílvio de Sousa Bueno Filho, UFLA; LUCIANA CORREIA DE ALMEIDA REGITANO, CPPSE; MAURICIO MELLO DE ALENCAR, CPPSE; ROSIANA RODRIGUES ALVES, CNPASA; Marielle Moura Baena, UFLA; Sarah Laguna Conceição Meirelles, UFLA. |
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Título: |
Tournaments between markers as a strategy to enhance genomic predictions. |
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Ano de publicação: |
2019 |
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Fonte/Imprenta: |
Plos One, v. 14, n. 7, e0219448, p. 1-17, 2019. |
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DOI: |
10.1371/journal.pone.0219448 |
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Idioma: |
Inglês |
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Conteúdo: |
Analysis of a large number of markers is crucial in both genome-wide association studies (GWAS) and genome-wide selection (GWS). However there are two methodological issues that restrict statistical analysis: high dimensionality (p>>n) and multicollinearity. Although there are methodologies that can be used to fit models for data with high dimensionality (eg,the Bayesian Lasso), a big problem that can occurs in this cases is that the predictive ability of the model should perform well for the individuals used to fit the model, but should not perform well for other individuals, restricting the applicability of the model. This problem can be circumvent by applying some selection methodology to reduce the number of markers (but keeping the markers associated with the phenotypic trait) before adjusting a model to predict GBVs. We revisit a tournament-based strategy between marker samples, where each sample has good statistical properties for estimation: n>p and low collinearity. Such tournaments are elaborated using multiple linear regression to eliminate markers. This method is adapted from previous works found in the literature. We used simulated data as well as real data derived from a study with SNPs in beef cattle. Tournament strategies not only circumvent the p>>n issue, but also minimize spurious associations. For real data, when we selected a few more than 20 markers, we obtained correlations greater than 0.70 between predicted Genomic Breeding Values (GBVs) and phenotypes in validation groups of a cross-validation scheme; and when we selected a larger number of markers (more than 100), the correlations exceeded 0.90, showing the efficiency in identifying relevant SNPs (or segregations) for both GWAS and GWS. In the simulation study, we obtained similar results. MenosAnalysis of a large number of markers is crucial in both genome-wide association studies (GWAS) and genome-wide selection (GWS). However there are two methodological issues that restrict statistical analysis: high dimensionality (p>>n) and multicollinearity. Although there are methodologies that can be used to fit models for data with high dimensionality (eg,the Bayesian Lasso), a big problem that can occurs in this cases is that the predictive ability of the model should perform well for the individuals used to fit the model, but should not perform well for other individuals, restricting the applicability of the model. This problem can be circumvent by applying some selection methodology to reduce the number of markers (but keeping the markers associated with the phenotypic trait) before adjusting a model to predict GBVs. We revisit a tournament-based strategy between marker samples, where each sample has good statistical properties for estimation: n>p and low collinearity. Such tournaments are elaborated using multiple linear regression to eliminate markers. This method is adapted from previous works found in the literature. We used simulated data as well as real data derived from a study with SNPs in beef cattle. Tournament strategies not only circumvent the p>>n issue, but also minimize spurious associations. For real data, when we selected a few more than 20 markers, we obtained correlations greater than 0.70 between predicted Genomic Breeding Values (GBVs) and phenotyp... Mostrar Tudo |
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Palavras-Chave: |
Genome-wide; Genomic Breeding Values; GWAS; GWS; SNPs. |
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Thesagro: |
Genoma; Genótipo; Marcador Genético; Seleção Genética. |
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Thesaurus Nal: |
Genetic markers; Genomics; Genotyping. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/199471/1/Tournaments-between-markers-as-a-strategy-correcao.pdf
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Marc: |
LEADER 02769naa a2200349 a 4500
001 2110534
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008 2019 bl uuuu u00u1 u #d
024 7 $a10.1371/journal.pone.0219448$2DOI
100 1 $aFERREIRA FILHO, D.
245 $aTournaments between markers as a strategy to enhance genomic predictions.$h[electronic resource]
260 $c2019
520 $aAnalysis of a large number of markers is crucial in both genome-wide association studies (GWAS) and genome-wide selection (GWS). However there are two methodological issues that restrict statistical analysis: high dimensionality (p>>n) and multicollinearity. Although there are methodologies that can be used to fit models for data with high dimensionality (eg,the Bayesian Lasso), a big problem that can occurs in this cases is that the predictive ability of the model should perform well for the individuals used to fit the model, but should not perform well for other individuals, restricting the applicability of the model. This problem can be circumvent by applying some selection methodology to reduce the number of markers (but keeping the markers associated with the phenotypic trait) before adjusting a model to predict GBVs. We revisit a tournament-based strategy between marker samples, where each sample has good statistical properties for estimation: n>p and low collinearity. Such tournaments are elaborated using multiple linear regression to eliminate markers. This method is adapted from previous works found in the literature. We used simulated data as well as real data derived from a study with SNPs in beef cattle. Tournament strategies not only circumvent the p>>n issue, but also minimize spurious associations. For real data, when we selected a few more than 20 markers, we obtained correlations greater than 0.70 between predicted Genomic Breeding Values (GBVs) and phenotypes in validation groups of a cross-validation scheme; and when we selected a larger number of markers (more than 100), the correlations exceeded 0.90, showing the efficiency in identifying relevant SNPs (or segregations) for both GWAS and GWS. In the simulation study, we obtained similar results.
650 $aGenetic markers
650 $aGenomics
650 $aGenotyping
650 $aGenoma
650 $aGenótipo
650 $aMarcador Genético
650 $aSeleção Genética
653 $aGenome-wide
653 $aGenomic Breeding Values
653 $aGWAS
653 $aGWS
653 $aSNPs
700 1 $aBUENO FILHO, J. S. de S.
700 1 $aREGITANO, L. C. de A.
700 1 $aALENCAR, M. M. de
700 1 $aALVES, R. R.
700 1 $aBAENA, M. M.
700 1 $aMEIRELLES, S. L. C.
773 $tPlos One$gv. 14, n. 7, e0219448, p. 1-17, 2019.
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Registro original: |
Embrapa Pecuária Sudeste (CPPSE) |
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