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Registro Completo |
Biblioteca(s): |
Embrapa Florestas; Embrapa Gado de Leite. |
Data corrente: |
17/05/2017 |
Data da última atualização: |
27/01/2023 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Autoria: |
SILVA, F. F. e; ZAMBRANO, M. F. B.; VARONA, L.; GLÓRIA, L. S.; LOPES, P. S.; SILVA, M. V. G. B.; ARBEX, W. A.; LÁZARO, S. F.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F. |
Afiliação: |
Fabyano Fonseca e Silva, UFV/VIÇOSA; Maria Fernanda Betancur Zambrano, UFV/VIÇOSA; Luis Varona, University of Zaragoza; Leonardo Siqueira Glória, UFV/VIÇOSA; Paulo Sávio Lopes, UFV; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; WAGNER ANTONIO ARBEX, CNPGL; Sirlene Fernandes Lázaro, UFV/VIÇOSA; MARCOS DEON VILELA DE RESENDE, CNPF; Simone Eliza Facioni Guimarães, UFV/VIÇOSA. |
Título: |
Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
Scientia Agricola, v. 74, n. 1, 2017. |
Páginas: |
7 P. |
Idioma: |
Inglês Português |
Conteúdo: |
Genome association analyses have been successful in identifying quantitative trait
loci (QTLs) for pig body weights measured at a single age. However, when considering the whole
weight trajectories over time in the context of genome association analyses, it is important to
look at the markers that affect growth curve parameters. The easiest way to consider them is
via the two-step method, in which the growth curve parameters and marker effects are estimated
separately, thereby resulting in a reduction of the statistical power and the precision of
estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint
modeling of the individual growth curves and marker effects. Our aim was to propose a genome
association analysis for growth curves in pigs based on NMM as well as to compare it with the
traditional two-step method. In addition, we also aimed to identify the nearest candidate genes
related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a
higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct
way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all
significant SNPs from the two-step method were also reported in the NMM analysis. The ontology
of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs
(simultaneously affecting A and K) allows us to make inferences with regards to their contribution
to the pig growth process in the population studied. MenosGenome association analyses have been successful in identifying quantitative trait
loci (QTLs) for pig body weights measured at a single age. However, when considering the whole
weight trajectories over time in the context of genome association analyses, it is important to
look at the markers that affect growth curve parameters. The easiest way to consider them is
via the two-step method, in which the growth curve parameters and marker effects are estimated
separately, thereby resulting in a reduction of the statistical power and the precision of
estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint
modeling of the individual growth curves and marker effects. Our aim was to propose a genome
association analysis for growth curves in pigs based on NMM as well as to compare it with the
traditional two-step method. In addition, we also aimed to identify the nearest candidate genes
related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a
higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct
way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all
significant SNPs from the two-step method were also reported in the NMM analysis. The ontology
of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs
(simultaneously affecting A and K) allows us to make inferences with regards to their contribution
... Mostrar Tudo |
Palavras-Chave: |
Longitudinal data; SNP markers. |
Thesaurus Nal: |
body weight. |
Categoria do assunto: |
-- L Ciência Animal e Produtos de Origem Animal |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/161512/1/Cnpgl-2017-SciAgric-Silva-Genome.pdf
|
Marc: |
LEADER 02345naa a2200277 a 4500 001 2072227 005 2023-01-27 008 2017 bl uuuu u00u1 u #d 100 1 $aSILVA, F. F. e 245 $aGenome association study through nonlinear mixed models revealed new candidate genes for pig growth curves.$h[electronic resource] 260 $c2017 300 $a7 P. 520 $aGenome association analyses have been successful in identifying quantitative trait loci (QTLs) for pig body weights measured at a single age. However, when considering the whole weight trajectories over time in the context of genome association analyses, it is important to look at the markers that affect growth curve parameters. The easiest way to consider them is via the two-step method, in which the growth curve parameters and marker effects are estimated separately, thereby resulting in a reduction of the statistical power and the precision of estimates. One efficient solution is to adopt nonlinear mixed models (NMM), which enables a joint modeling of the individual growth curves and marker effects. Our aim was to propose a genome association analysis for growth curves in pigs based on NMM as well as to compare it with the traditional two-step method. In addition, we also aimed to identify the nearest candidate genes related to significant SNP (single nucleotide polymorphism) markers. The NMM presented a higher number of significant SNPs for adult weight (A) and maturity rate (K), and provided a direct way to test SNP significance simultaneously for both the A and K parameters. Furthermore, all significant SNPs from the two-step method were also reported in the NMM analysis. The ontology of the three candidate genes (SH3BGRL2, MAPK14, and MYL9) derived from significant SNPs (simultaneously affecting A and K) allows us to make inferences with regards to their contribution to the pig growth process in the population studied. 650 $abody weight 653 $aLongitudinal data 653 $aSNP markers 700 1 $aZAMBRANO, M. F. B. 700 1 $aVARONA, L. 700 1 $aGLÓRIA, L. S. 700 1 $aLOPES, P. S. 700 1 $aSILVA, M. V. G. B. 700 1 $aARBEX, W. A. 700 1 $aLÁZARO, S. F. 700 1 $aRESENDE, M. D. V. de 700 1 $aGUIMARÃES, S. E. F. 773 $tScientia Agricola$gv. 74, n. 1, 2017.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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Registros recuperados : 6 | |
1. | | VENTURA, H. T.; SILVA, F. F e; VARONA, L.; FIGUEIREDO, E. A. P. de; COSTA, E. V.; SILVA, L. P. da; VENTURA. R.; LOPES, P. S. Comparing multi-trait Poisson and Gaussian Bayesian models for genetic evaluation of litter traits in pigs. Livestock Science, v. 176, p. 47-53, 2015.Tipo: Artigo em Periódico Indexado | Circulação/Nível: B - 1 |
Biblioteca(s): Embrapa Suínos e Aves. |
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2. | | VERARDO, L. L.; SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BASTIAANSEN, J. W. M.; LOPES, P. S.; GUIMARÃES, S. E. F. Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs. Journal of Applied Genetics, v. 56, n. 1, p. 123-132, Feb. 2015.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 2 |
Biblioteca(s): Embrapa Florestas. |
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3. | | BRITO, L. C.; CASELLAS, J.; VARONA, L; LOPES, P. S.; VENTURA, H. T.; PEIXOTO, M. G. C. D.; LÁZARO, S. F.; SILVA, F. F. Genetic evaluation of age at first calving for Guzerá beef cattle using linear, threshold, and survival Bayesian models. Journal of Animal Science, v. 96, n. 7, p. 2517-2524, 2018.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Gado de Leite. |
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4. | | ARBEX, W. A.; SILVA, F. F. e; SILVA, M. V. G. B.; BORGES, C. C. H.; OLIVEIRA, F. C. de; VARONA, L.; VERNEQUE, R. da S. Decision Support in Attribute Selection with Machine Learning Approach. In: CONFERENCIA IBÉRICA DE SISTEMAS Y TECNOLOGÍAS DE INFORMACION, 9., 2014, Barcelona. Actas... Barcelona: Aisti; Salle, 2014. CISTI 2014Tipo: Artigo em Anais de Congresso |
Biblioteca(s): Embrapa Gado de Leite. |
| |
6. | | SILVA, F. F. e; ZAMBRANO, M. F. B.; VARONA, L.; GLÓRIA, L. S.; LOPES, P. S.; SILVA, M. V. G. B.; ARBEX, W. A.; LÁZARO, S. F.; RESENDE, M. D. V. de; GUIMARÃES, S. E. F. Genome association study through nonlinear mixed models revealed new candidate genes for pig growth curves. Scientia Agricola, v. 74, n. 1, 2017. 7 P.Tipo: Artigo em Periódico Indexado | Circulação/Nível: A - 1 |
Biblioteca(s): Embrapa Florestas; Embrapa Gado de Leite. |
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Registros recuperados : 6 | |
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