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Registro Completo |
Biblioteca(s): |
Embrapa Mandioca e Fruticultura. |
Data corrente: |
30/08/2000 |
Data da última atualização: |
22/01/2014 |
Autoria: |
CAMARGO, M. N. |
Afiliação: |
MARCELO N. CAMARGO, SNLCS/EMBRAPA. |
Título: |
Comunicado expositivo do mapa de solos do Brasil 1:5.000.000. |
Ano de publicação: |
0 |
Fonte/Imprenta: |
Rio de janeiro: SNLCS/EMBRAPA, s.d. |
Páginas: |
9p. |
Idioma: |
Português |
Conteúdo: |
Realizado pelo Serviço Nacional de levantamento e Conservação de solos da EMBRAPA, encontra-se finalizada a produção de um mapa geral de solos do Brasil. Trata-se do primeiro mapa pedológico abrangente realizado no pais e advém da acentuada necessidade de provimento de informações produzidas por levantamento de solos, efetuados no decorrer dos últimos quase trinta anos. Faculta visão panorâmica dos recursos de solos e revela as grandes linhas de variabilidade prevalente. Incorpora os conhecimentos gerados mais recentemente, quanto a distribuição dos solos, conforme diversidade de natureza, arranjamento espacial e extensão territorial. A metodologia adotada foi do tipo combinado: compilado-exploratório. A execução foi processada mediante desenvolvimento gradativo por aproximações sucessivas. As unidades de mapeamento são, na quase totalidade, associações geográficas (macrossociações) de solos, aproximadamente correspondentes a classes de categoria de Grande Grupo. |
Palavras-Chave: |
Brasil; Solos. |
Thesagro: |
Mapa. |
Categoria do assunto: |
-- |
Marc: |
LEADER 01394nam a2200157 a 4500 001 1639777 005 2014-01-22 008 bl uuuu u0uu1 u #d 100 1 $aCAMARGO, M. N. 245 $aComunicado expositivo do mapa de solos do Brasil 1$b5.000.000. 260 $aRio de janeiro: SNLCS/EMBRAPA, s.d.$c0 300 $a9p. 520 $aRealizado pelo Serviço Nacional de levantamento e Conservação de solos da EMBRAPA, encontra-se finalizada a produção de um mapa geral de solos do Brasil. Trata-se do primeiro mapa pedológico abrangente realizado no pais e advém da acentuada necessidade de provimento de informações produzidas por levantamento de solos, efetuados no decorrer dos últimos quase trinta anos. Faculta visão panorâmica dos recursos de solos e revela as grandes linhas de variabilidade prevalente. Incorpora os conhecimentos gerados mais recentemente, quanto a distribuição dos solos, conforme diversidade de natureza, arranjamento espacial e extensão territorial. A metodologia adotada foi do tipo combinado: compilado-exploratório. A execução foi processada mediante desenvolvimento gradativo por aproximações sucessivas. As unidades de mapeamento são, na quase totalidade, associações geográficas (macrossociações) de solos, aproximadamente correspondentes a classes de categoria de Grande Grupo. 650 $aMapa 653 $aBrasil 653 $aSolos
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Registro original: |
Embrapa Mandioca e Fruticultura (CNPMF) |
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Registro Completo
Biblioteca(s): |
Embrapa Cerrados. |
Data corrente: |
21/12/2017 |
Data da última atualização: |
21/12/2017 |
Tipo da produção científica: |
Artigo em Periódico Indexado |
Circulação/Nível: |
A - 1 |
Autoria: |
TONUSSI, R. L.; SILVA, R. M. de O.; MAGALHAES, A. F. B.; ESPIGOLAN, R.; PERIPOLLI, E.; OLIVIERI, B. F.; FEITOSA, F. L. B.; LEMOS, M. V. A.; BERTON, M. P.; CHIAIA, H. L. J.; PEREIRA, A. S. C.; LOBO, R. B.; BEZERRA, L. A. F.; MAGNABOSCO, C. de U.; LOURENÇO, D. A. L.; AGUILAR, I.; BALDI REY, F. S. |
Afiliação: |
RAFAEL LARA TONUSSI, UNESP; RAFAEL MEDEIROS DE OLIVEIRA SILVA, UNESP; ANA FABRÍCIA BRAGA MAGALHÃES, UNESP; RAFAEL ESPIGOLAN, UNESP; ELISA PERIPOLLI, UNESP; BIANCA FERREIRA OLIVIERI, UNESP; FABIELI LOISE BRAGA FEITOSA, UNESP; MARCOS VINICÍUS ANTUNES LEMOS, UNESP; MARIANA PIATTO BERTON, UNESP; HERMENEGILDO LUCAS JUSTINO CHIAIA, UNESP; ANGELICA SIMONE CRAVO PEREIRA, USP; RAYSILDO BARBOSA LÔBO, ANCP; LUIZ ANTÔNIO FRAMARTINO BEZERRA, USP; CLAUDIO DE ULHOA MAGNABOSCO, CPAC; DANIELA ANDRESSA LINO LOURENÇO, University of Georgia; IGNÁCIO AGUILAR, INIA; FERNANDO SEBASTIÁN BALDI REY, UNESP. |
Título: |
Application of single step genomic BLUP under different uncertain paternity scenarios using simulated data. |
Ano de publicação: |
2017 |
Fonte/Imprenta: |
PLoS ONE, v. 12, n. 9, e0181752, 28 September 2017. |
DOI: |
https://doi.org/10.1371/journal.pone.0181752 |
Idioma: |
Inglês |
Conteúdo: |
The objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty. MenosThe objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population incre... Mostrar Tudo |
Palavras-Chave: |
Best Linear Unbiased Prediction. |
Thesagro: |
Citogenética Animal; Gado de Corte; Hereditariedade; Seleção Fenótipa. |
Categoria do assunto: |
G Melhoramento Genético |
URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/169502/1/Application-of-single-step-genomic-BLUP-under-different-uncertain-paternity-scenarios-using-simulated-data..pdf
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Marc: |
LEADER 04121naa a2200385 a 4500 001 2083177 005 2017-12-21 008 2017 bl uuuu u00u1 u #d 024 7 $ahttps://doi.org/10.1371/journal.pone.0181752$2DOI 100 1 $aTONUSSI, R. L. 245 $aApplication of single step genomic BLUP under different uncertain paternity scenarios using simulated data.$h[electronic resource] 260 $c2017 520 $aThe objective of this study was to investigate the application of BLUP and single step genomic BLUP (ssGBLUP) models in different scenarios of paternity uncertainty with different strategies of scaling the G matrix to match the A22 matrix, using simulated data for beef cattle. Genotypes, pedigree, and phenotypes for age at first calving (AFC) and weight at 550 days (W550) were simulated using heritabilities based on real data (0.12 for AFC and 0.34 for W550). Paternity uncertainty scenarios using 0, 25, 50, 75, and 100% of multiple sires (MS) were studied. The simulated genome had a total length of 2,333 cM, containing 735,293 biallelic markers and 7,000 QTLs randomly distributed over the 29 BTA. It was assumed that QTLs explained 100% of the genetic variance. For QTL, the amount of alleles per loci randomly ranged from two to four. The BLUP model that considers phenotypic and pedigree data, and the ssGBLUP model that combines phenotypic, pedigree and genomic information were used for genetic evaluations. Four ways of scaling the mean of the genomic matrix (G) to match to the mean of the pedigree relationship matrix among genotyped animals (A22) were tested. Accuracy, bias, and inflation were investigated for five groups of animals: ALL = all animals; BULL = only bulls; GEN = genotyped animals; FEM = females; and YOUNG = young males. With the BLUP model, the accuracies of genetic evaluations decreased for both traits as the proportion of unknown sires in the population increased. The EBV accuracy reduction was higher for GEN and YOUNG groups. By analyzing the scenarios for YOUNG (from 0 to 100% of MS), the decrease was 87.8 and 86% for AFC and W550, respectively. When applying the ssGBLUP model, the accuracies of genetic evaluation also decreased as the MS in the pedigree for both traits increased. However, the accuracy reduction was less than those observed for BLUP model. Using the same comparison (scenario 0 to 100% of MS), the accuracies reductions were 38 and 44.6% for AFC and W550, respectively. There were no differences between the strategies for scaling the G matrix for ALL, BULL, and FEM groups under the different scenarios with missing pedigree. These results pointed out that the uninformative part of the A22 matrix and genotyped animals with paternity uncertainty did not influence the scaling of G matrix. On the basis of the results, it is important to have a G matrix in the same scale of the A22 matrix, especially for the evaluation of young animals in situations with missing pedigree information. In these situations, the ssGBLUP model is an appropriate alternative to obtain a more reliable and less biased estimate of breeding values, especially for young animals with few or no phenotypic records. For accurate and unbiased genomic predictions with ssGBLUP, it is necessary to assure that the G matrix is compatible with the A22 matrix, even in situations with paternity uncertainty. 650 $aCitogenética Animal 650 $aGado de Corte 650 $aHereditariedade 650 $aSeleção Fenótipa 653 $aBest Linear Unbiased Prediction 700 1 $aSILVA, R. M. de O. 700 1 $aMAGALHAES, A. F. B. 700 1 $aESPIGOLAN, R. 700 1 $aPERIPOLLI, E. 700 1 $aOLIVIERI, B. F. 700 1 $aFEITOSA, F. L. B. 700 1 $aLEMOS, M. V. A. 700 1 $aBERTON, M. P. 700 1 $aCHIAIA, H. L. J. 700 1 $aPEREIRA, A. S. C. 700 1 $aLOBO, R. B. 700 1 $aBEZERRA, L. A. F. 700 1 $aMAGNABOSCO, C. de U. 700 1 $aLOURENÇO, D. A. L. 700 1 $aAGUILAR, I. 700 1 $aBALDI REY, F. S. 773 $tPLoS ONE$gv. 12, n. 9, e0181752, 28 September 2017.
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