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Registro Completo |
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Biblioteca(s): |
Embrapa Florestas. |
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Data corrente: |
24/08/2015 |
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Data da última atualização: |
25/02/2016 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
AZEVEDO, C. F.; RESENDE, M. D. V. de; SILVA, F. F. e; VIANA, J. M. S.; VALENTE, M. S. F.; RESENDE JUNIOR, M. F. R.; MUÑOZ, P. |
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Afiliação: |
Camila Ferreira Azevedo, UFV; MARCOS DEON VILELA DE RESENDE, CNPF; Fabyano Fonseca e Silva, UFV; José Marcelo Soriano Viana, UFV; Magno Sávio Ferreira Valente, UFV; Márcio Fernando Ribeiro Resende Jr, Florida Innovation Hub; Patricio Muñoz, University of Florida. |
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Título: |
Ridge, Lasso and Bayesian additive dominance genomic models. |
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Ano de publicação: |
2015 |
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Fonte/Imprenta: |
BMC Genetics, v. 16, art. 105, Aug. 2015. 13 p. |
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DOI: |
10.1186/s12863-015-0264-2 |
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Idioma: |
Inglês |
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Conteúdo: |
Background: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models. MenosBackground: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS ... Mostrar Tudo |
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Palavras-Chave: |
Bayesian methods; Dominance genomic models; Genética quantitativa; Lasso methods; Melhoramento genético; Modelo Bayesiano; Selection accuracy. |
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Thesagro: |
Parâmetro Genético. |
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Categoria do assunto: |
-- |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/128510/1/2015-API-Deon-Ridge.pdf
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Marc: |
LEADER 02783naa a2200301 a 4500
001 2022575
005 2016-02-25
008 2015 bl uuuu u00u1 u #d
024 7 $a10.1186/s12863-015-0264-2$2DOI
100 1 $aAZEVEDO, C. F.
245 $aRidge, Lasso and Bayesian additive dominance genomic models.$h[electronic resource]
260 $c2015
520 $aBackground: A complete approach for genome-wide selection (GWS) involves reliable statistical genetics models and methods. Reports on this topic are common for additive genetic models but not for additive-dominance models. The objective of this paper was (i) to compare the performance of 10 additive-dominance predictive models (including current models and proposed modifications), fitted using Bayesian, Lasso and Ridge regression approaches; and (ii) to decompose genomic heritability and accuracy in terms of three quantitative genetic information sources, namely, linkage disequilibrium (LD), co-segregation (CS) and pedigree relationships or family structure (PR). The simulation study considered two broad sense heritability levels (0.30 and 0.50, associated with narrow sense heritabilities of 0.20 and 0.35, respectively) and two genetic architectures for traits (the first consisting of small gene effects and the second consisting of a mixed inheritance model with five major genes). Results: G-REML/G-BLUP and a modified Bayesian/Lasso (called BayesA*B* or t-BLASSO) method performed best in the prediction of genomic breeding as well as the total genotypic values of individuals in all four scenarios (two heritabilities x two genetic architectures). The BayesA*B*-type method showed a better ability to recover the dominance variance/additive variance ratio. Decomposition of genomic heritability and accuracy revealed the following descending importance order of information: LD, CS and PR not captured by markers, the last two being very close. Conclusions: Amongst the 10 models/methods evaluated, the G-BLUP, BAYESA*B* (−2,8) and BAYESA*B* (4,6) methods presented the best results and were found to be adequate for accurately predicting genomic breeding and total genotypic values as well as for estimating additive and dominance in additive-dominance genomic models.
650 $aParâmetro Genético
653 $aBayesian methods
653 $aDominance genomic models
653 $aGenética quantitativa
653 $aLasso methods
653 $aMelhoramento genético
653 $aModelo Bayesiano
653 $aSelection accuracy
700 1 $aRESENDE, M. D. V. de
700 1 $aSILVA, F. F. e
700 1 $aVIANA, J. M. S.
700 1 $aVALENTE, M. S. F.
700 1 $aRESENDE JUNIOR, M. F. R.
700 1 $aMUÑOZ, P.
773 $tBMC Genetics$gv. 16, art. 105, Aug. 2015. 13 p.
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Registro original: |
Embrapa Florestas (CNPF) |
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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
19/05/2014 |
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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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Circulação/Nível: |
A - 1 |
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Autoria: |
UTSUNOMIYA, Y. T.; CARMO, A. S.; NEVES, H. H. R.; CARVALHEIRO, R.; MATOS, M. C.; ZAVAREZ, L. B.; ITO, P. K. R. K.; O'BRIEN, A. M. P.; SOLKNER, J.; PORTO-NETO, L. R.; SCHENKEL, F. S.; McEWAN, J.; COLE, J. B.; SILVA, M. V. G. B.; VAN TASSELL, C. P.; SONSTEGARD, T. S.; GARCIA, J. F. |
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Afiliação: |
YURI T. UTSUNOMIYA, UNESP; ADRIANA S. CARMO, UNESP; HAROLDO H. R. NEVES, UNESP; ROBERTO CARVALHEIRO, UNESP; GenSys; MARCIA C. MATOS, UNESP; LUDMILLA B. ZAVAREZ, UNESP; PIER K. R. K. ITO, UNESP; ANA M. PEREZ O'BRIEN, University of Natural Resources and Life Sciences, Vienna, Austria; JOHANN SOLKNER, University of Natural Resources and Life Sciences, Vienna, Austria; LAERCIO R. PORTO-NETO, CSIRO, Queensland; FLAVIO S. SCHENKEL, University of Guelph, Guelph, Ontario; JOHN McEWAN, AgResearch, Invermay, Mosgiel, Otago, New Zealand; JOHN B. COLE, ARS-USDA; MARCOS VINICIUS GUALBERTO B SILVA, CNPGL; CURTIS P. VAN TASSELL, ARS-USDA; TAD S. SONSTEGARD, ARS-USDA; JOSE FERNANDO GARCIA, UNESP. |
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Título: |
Genome-wide mapping of loci explaining variance in scrotal circumference in Nellore Cattle. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Plos One v. 9, n. 2, p. 1-9, 2014. |
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DOI: |
https://doi.org/10.1371/journal.pone.0088561 |
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Idioma: |
Inglês |
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Conteúdo: |
The reproductive performance of bulls has a high impact on the beef cattle industry. Scrotal circumference (SC) is the most recorded reproductive trait in beef herds, and is used as a major selection criterion to improve precocity and fertility. The characterization of genomic regions affecting SC can contribute to the identification of diagnostic markers for reproductive performance and uncover molecular mechanisms underlying complex aspects of bovine reproductive biology. In this paper, we report a genome-wide scan for chromosome segments explaining differences in SC, using data of 861 Nellore bulls (Bos indicus) genotyped for over 777,000 single nucleotide polymorphisms. Loci that excel from the genome background were identified on chromosomes 4, 6, 7, 10, 14, 18 and 21. The majority of these regions were previously found to be associated with reproductive and body size traits in cattle. The signal on chromosome 14 replicates the pleiotropic quantitative trait locus encompassing PLAG1 that affects male fertility in cattle and stature in several species. Based on intensive literature mining, SP4, MAGEL2, SH3RF2, PDE5A and SNAI2 are proposed as novel candidate genes for SC, as they affect growth and testicular size in other animal models. These findings contribute to linking reproductive phenotypes to gene functions, and may offer new insights on the molecular biology of male fertility. |
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Palavras-Chave: |
Genome-wide; Raça Canchim; Race Nellore. |
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Thesagro: |
Gado de corte. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/102293/1/Artigo-MVinicius-journal.plosone.0088561.pdf
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Marc: |
LEADER 02473naa a2200373 a 4500
001 1986515
005 2024-02-05
008 2014 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1371/journal.pone.0088561$2DOI
100 1 $aUTSUNOMIYA, Y. T.
245 $aGenome-wide mapping of loci explaining variance in scrotal circumference in Nellore Cattle.$h[electronic resource]
260 $c2014
520 $aThe reproductive performance of bulls has a high impact on the beef cattle industry. Scrotal circumference (SC) is the most recorded reproductive trait in beef herds, and is used as a major selection criterion to improve precocity and fertility. The characterization of genomic regions affecting SC can contribute to the identification of diagnostic markers for reproductive performance and uncover molecular mechanisms underlying complex aspects of bovine reproductive biology. In this paper, we report a genome-wide scan for chromosome segments explaining differences in SC, using data of 861 Nellore bulls (Bos indicus) genotyped for over 777,000 single nucleotide polymorphisms. Loci that excel from the genome background were identified on chromosomes 4, 6, 7, 10, 14, 18 and 21. The majority of these regions were previously found to be associated with reproductive and body size traits in cattle. The signal on chromosome 14 replicates the pleiotropic quantitative trait locus encompassing PLAG1 that affects male fertility in cattle and stature in several species. Based on intensive literature mining, SP4, MAGEL2, SH3RF2, PDE5A and SNAI2 are proposed as novel candidate genes for SC, as they affect growth and testicular size in other animal models. These findings contribute to linking reproductive phenotypes to gene functions, and may offer new insights on the molecular biology of male fertility.
650 $aGado de corte
653 $aGenome-wide
653 $aRaça Canchim
653 $aRace Nellore
700 1 $aCARMO, A. S.
700 1 $aNEVES, H. H. R.
700 1 $aCARVALHEIRO, R.
700 1 $aMATOS, M. C.
700 1 $aZAVAREZ, L. B.
700 1 $aITO, P. K. R. K.
700 1 $aO'BRIEN, A. M. P.
700 1 $aSOLKNER, J.
700 1 $aPORTO-NETO, L. R.
700 1 $aSCHENKEL, F. S.
700 1 $aMcEWAN, J.
700 1 $aCOLE, J. B.
700 1 $aSILVA, M. V. G. B.
700 1 $aVAN TASSELL, C. P.
700 1 $aSONSTEGARD, T. S.
700 1 $aGARCIA, J. F.
773 $tPlos One$gv. 9, n. 2, p. 1-9, 2014.
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Embrapa Gado de Leite (CNPGL) |
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