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 | Acesso ao texto completo restrito à biblioteca da Embrapa Gado de Leite. Para informações adicionais entre em contato com cnpgl.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
04/12/2014 |
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Data da última atualização: |
05/02/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
OLIVEIRA, F. C. de; BORGES, C. C. H.; ALMEIDA, F. N.; SILVA, F. F. e; VERNEQUE, R. da S.; SILVA, M. V. G. B.; ARBEX, W. A. |
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Afiliação: |
FABRÍZZIO CONDÉ DE OLIVEIRA, UFJF; CARLOS CRISTIANO HASENCLEVER BORGES, UFJF; FERNANDA NASCIMENTO ALMEIDA, FAPEMIG; FABYANO FONSECA E SILVA, UFV; RUI DA SILVA VERNEQUE, CNPGL; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; WAGNER ANTONIO ARBEX, CNPGL. |
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Título: |
SNPs selection using support vector regression and genetic algorithms in GWAS. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
BMC Genomics, v. 15, article S4, 2014. |
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Páginas: |
15 p. |
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Idioma: |
Inglês |
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Notas: |
Suppl. 7. |
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Conteúdo: |
Introduction - This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results- The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions- The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels. MenosIntroduction - This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results- The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions- The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction ... Mostrar Tudo |
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Palavras-Chave: |
Single nucleotide polymorphisms (SNPs); SNPs markers; Support Vector Regression with Pearson Universal. |
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Categoria do assunto: |
G Melhoramento Genético |
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Marc: |
LEADER 02423naa a2200253 a 4500
001 2001715
005 2024-02-05
008 2014 bl uuuu u00u1 u #d
100 1 $aOLIVEIRA, F. C. de
245 $aSNPs selection using support vector regression and genetic algorithms in GWAS.$h[electronic resource]
260 $c2014
300 $a15 p.
500 $aSuppl. 7.
520 $aIntroduction - This paper proposes a new methodology to simultaneously select the most relevant SNPs markers for the characterization of any measurable phenotype described by a continuous variable using Support Vector Regression with Pearson Universal kernel as fitness function of a binary genetic algorithm. The proposed methodology is multi-attribute towards considering several markers simultaneously to explain the phenotype and is based jointly on statistical tools, machine learning and computational intelligence. Results- The suggested method has shown potential in the simulated database 1, with additive effects only, and real database. In this simulated database, with a total of 1,000 markers, and 7 with major effect on the phenotype and the other 993 SNPs representing the noise, the method identified 21 markers. Of this total, 5 are relevant SNPs between the 7 but 16 are false positives. In real database, initially with 50,752 SNPs, we have reduced to 3,073 markers, increasing the accuracy of the model. In the simulated database 2, with additive effects and interactions (epistasis), the proposed method matched to the methodology most commonly used in GWAS. Conclusions- The method suggested in this paper demonstrates the effectiveness in explaining the real phenotype (PTA for milk), because with the application of the wrapper based on genetic algorithm and Support Vector Regression with Pearson Universal, many redundant markers were eliminated, increasing the prediction and accuracy of the model on the real database without quality control filters. The PUK demonstrated that it can replicate the performance of linear and RBF kernels.
653 $aSingle nucleotide polymorphisms (SNPs)
653 $aSNPs markers
653 $aSupport Vector Regression with Pearson Universal
700 1 $aBORGES, C. C. H.
700 1 $aALMEIDA, F. N.
700 1 $aSILVA, F. F. e
700 1 $aVERNEQUE, R. da S.
700 1 $aSILVA, M. V. G. B.
700 1 $aARBEX, W. A.
773 $tBMC Genomics$gv. 15, article S4, 2014.
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Embrapa Gado de Leite (CNPGL) |
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| 2. |  | RIBEIRO, I. G.; CHAGAS, E. A.; SANTOS, V. A. dos; LOZANO, R. B.; SOUSA, O. M.; ARAÚJO, W. F. Efeito da cobertura e substrato na emergência de camu-camu e crescimento de porta-enxertos. In: CONGRESSO BRASILEIRO DE FRUTICULTURA, 22., 2012, Bento Gonçalves. Anais... Bento Gonçalves: SBF, 2012.| Tipo: Artigo em Anais de Congresso |
| Biblioteca(s): Embrapa Roraima. |
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| 3. | | HERRERA, L. A.; SILVA, F. da C.; SANTOS, A. P. dos; SOUSA, O. M. de; SANCHES, E. G. Crescimento compensatório e privação alimentar no desempenho produtivo do robalo-flecha. Pesquisa Agropecuária Brasileira, Brasília, DF, v. 51, n. 6, p. 776-779, jun. 2016. Notas científicas. Título em inglês: Compensatory growth and food deprivation in common snook growth performance.| Biblioteca(s): Embrapa Unidades Centrais. |
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| 4. | | ARAÚJO, M da C. da R.; CHAGAS, E. A.; PINTO, S. T. S.; CHAGAS, P. C.; MOTA FILHO, A. B.; SOUSA, O. M. de. Micropropagation of caçari under different nutritive culture media, antioxidants, and levels of agar and pH. African Journal of Biotechnology, v. 15, n.33, p. 1771-1780, Aug., 2016.| Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
| Biblioteca(s): Embrapa Roraima. |
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| 5. | | QUEIRO, F. B. D. de; CHAGAS, E. A.; SANTOS, V. A. dos; ARAÚJO, M. da C. R.; SOUSA, O. M. de; SOUZA, D. L. Desenvolvimento de mudas de cupuaçuzeiro com diferentes fertilizantes foliares. In: SIMPÓSIO INTERNACIONAL DE BOTÂNICA APLICADA, 2.; SIMPÓSIO NACIONAL DE FRUTÍFERAS E ORNAMENTAIS DO NORTE E NORDESTE, 2., 2013, Manaus. Anais... Manaus: Ufam, 2013.| Tipo: Resumo em Anais de Congresso |
| Biblioteca(s): Embrapa Roraima. |
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