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| 1. |  | 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.| Biblioteca(s): Embrapa Florestas. |
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 | Acesso ao texto completo restrito à biblioteca da Embrapa Florestas. Para informações adicionais entre em contato com cnpf.biblioteca@embrapa.br. |
Registro Completo
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
10/06/2015 |
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Data da última atualização: |
02/03/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
VERARDO, L. L.; SILVA, F. F.; VARONA, L.; RESENDE, M. D. V. de; BASTIAANSEN, J. W. M.; LOPES, P. S.; GUIMARÃES, S. E. F. |
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Afiliação: |
UFV; UFV; Universidad de Zaragoza; MARCOS DEON VILELA DE RESENDE, CNPF; Wageningen University; UFV; UFV. |
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Título: |
Bayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
Journal of Applied Genetics, v. 56, n. 1, p. 123-132, Feb. 2015. |
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DOI: |
10.1007/s13353-014-0240-y |
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Idioma: |
Inglês |
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Conteúdo: |
The genetic improvement of reproductive traits such as the number of teats is essential to the success of the pig industry. As opposite to most SNP association studies that consider continuous phenotypes under Gaussian assumptions, this trait is characterized as a discrete variable, which could potentially follow other distributions, such as the Poisson. Therefore, in order to access the complexity of a counting random regression considering all SNPs simultaneously as covariate under a GWAS modeling, the Bayesian inference tools become necessary. Currently, another point that deserves to be highlighted in GWAS is the genetic dissection of complex phenotypes through candidate genes network derived from significant SNPs. We present a full Bayesian treatment of SNP association analysis for number of teats assuming alternatively Gaussian and Poisson distributions for this trait. Under this framework, significant SNP effects were identified by hypothesis tests using 95 % highest posterior density intervals. These SNPs were used to construct associated candidate genes network aiming to explain the genetic mechanism behind this reproductive trait. The Bayesian model comparisons based on deviance posterior distribution indicated the superiority of Gaussian model. In general, our results suggest the presence of 19 significant SNPs, which mapped 13 genes. Besides, we predicted gene interactions through networks that are consistent with the mammals known breast biology (e.g., development of prolactin receptor signaling, and cell proliferation), captured known regulation binding sites, and provided candidate genes for that trait (e.g., TINAGL1 and ICK). MenosThe genetic improvement of reproductive traits such as the number of teats is essential to the success of the pig industry. As opposite to most SNP association studies that consider continuous phenotypes under Gaussian assumptions, this trait is characterized as a discrete variable, which could potentially follow other distributions, such as the Poisson. Therefore, in order to access the complexity of a counting random regression considering all SNPs simultaneously as covariate under a GWAS modeling, the Bayesian inference tools become necessary. Currently, another point that deserves to be highlighted in GWAS is the genetic dissection of complex phenotypes through candidate genes network derived from significant SNPs. We present a full Bayesian treatment of SNP association analysis for number of teats assuming alternatively Gaussian and Poisson distributions for this trait. Under this framework, significant SNP effects were identified by hypothesis tests using 95 % highest posterior density intervals. These SNPs were used to construct associated candidate genes network aiming to explain the genetic mechanism behind this reproductive trait. The Bayesian model comparisons based on deviance posterior distribution indicated the superiority of Gaussian model. In general, our results suggest the presence of 19 significant SNPs, which mapped 13 genes. Besides, we predicted gene interactions through networks that are consistent with the mammals known breast biology (e.g., developme... Mostrar Tudo |
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Palavras-Chave: |
Counting data; Inferência Bayesiana; SNP association; Teta de porco; Trato reprodutivo. |
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Thesagro: |
Gene; Melhoramento Genético Animal; Suíno. |
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Thesaurus NAL: |
genes; reproductive traits. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 02629naa a2200325 a 4500
001 2017279
005 2016-03-02
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1007/s13353-014-0240-y$2DOI
100 1 $aVERARDO, L. L.
245 $aBayesian GWAS and network analysis revealed new candidate genes for number of teats in pigs.$h[electronic resource]
260 $c2015
520 $aThe genetic improvement of reproductive traits such as the number of teats is essential to the success of the pig industry. As opposite to most SNP association studies that consider continuous phenotypes under Gaussian assumptions, this trait is characterized as a discrete variable, which could potentially follow other distributions, such as the Poisson. Therefore, in order to access the complexity of a counting random regression considering all SNPs simultaneously as covariate under a GWAS modeling, the Bayesian inference tools become necessary. Currently, another point that deserves to be highlighted in GWAS is the genetic dissection of complex phenotypes through candidate genes network derived from significant SNPs. We present a full Bayesian treatment of SNP association analysis for number of teats assuming alternatively Gaussian and Poisson distributions for this trait. Under this framework, significant SNP effects were identified by hypothesis tests using 95 % highest posterior density intervals. These SNPs were used to construct associated candidate genes network aiming to explain the genetic mechanism behind this reproductive trait. The Bayesian model comparisons based on deviance posterior distribution indicated the superiority of Gaussian model. In general, our results suggest the presence of 19 significant SNPs, which mapped 13 genes. Besides, we predicted gene interactions through networks that are consistent with the mammals known breast biology (e.g., development of prolactin receptor signaling, and cell proliferation), captured known regulation binding sites, and provided candidate genes for that trait (e.g., TINAGL1 and ICK).
650 $agenes
650 $areproductive traits
650 $aGene
650 $aMelhoramento Genético Animal
650 $aSuíno
653 $aCounting data
653 $aInferência Bayesiana
653 $aSNP association
653 $aTeta de porco
653 $aTrato reprodutivo
700 1 $aSILVA, F. F.
700 1 $aVARONA, L.
700 1 $aRESENDE, M. D. V. de
700 1 $aBASTIAANSEN, J. W. M.
700 1 $aLOPES, P. S.
700 1 $aGUIMARÃES, S. E. F.
773 $tJournal of Applied Genetics$gv. 56, n. 1, p. 123-132, Feb. 2015.
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