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Registro Completo |
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Biblioteca(s): |
Embrapa Meio Norte / UEP-Parnaíba. |
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Data corrente: |
28/01/1994 |
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Data da última atualização: |
28/01/2011 |
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Autoria: |
HELENTJARIS, T.; SLOCUM, M.; WRIGHT, S.; SCHAEFER. A.; NIENHUIS, J. |
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Título: |
Construction of genetics linkage maps in maize and tomato using restriction fragment length polymosphisms. |
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Ano de publicação: |
1986 |
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Fonte/Imprenta: |
Theoretical and Applied Genetics, v. 72, p. 761-769, 1986. |
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Idioma: |
Inglês |
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Palavras-Chave: |
Fragmento de restrição; Maize; Mapa de ligação; Tomato. |
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Thesagro: |
Genética; Melhoramento; Milho; Tomate. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00705naa a2200253 a 4500
001 1074124
005 2011-01-28
008 1986 bl --- 0-- u #d
100 1 $aHELENTJARIS, T.
245 $aConstruction of genetics linkage maps in maize and tomato using restriction fragment length polymosphisms.
260 $c1986
650 $aGenética
650 $aMelhoramento
650 $aMilho
650 $aTomate
653 $aFragmento de restrição
653 $aMaize
653 $aMapa de ligação
653 $aTomato
700 1 $aSLOCUM, M.
700 1 $aWRIGHT, S.
700 1 $aSCHAEFER. A.
700 1 $aNIENHUIS, J.
773 $tTheoretical and Applied Genetics$gv. 72, p. 761-769, 1986.
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Registro original: |
Embrapa Meio Norte / UEP-Parnaíba (CPAMN-UEPP) |
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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. |
Registro Completo
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
19/12/2018 |
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Data da última atualização: |
24/01/2023 |
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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: |
FONSECA, P. A. S.; LEAL, T. P.; SANTOS, F. C.; GOUVEIA, M. H.; ID-LAHOUCINE, S.; ROSSE, I. C.; VENTURA, R. V.; BRUNELI, F. A. T.; MACHADO, M. A.; PEIXOTO, M. G. C. D.; TARAZONA-SANTOS, E.; CARVALHO, M. R. S. |
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Afiliação: |
PABLO A. S. FONSECA, UFMG; THIAGO P. LEAL, UFMG; FERNANDA C. SANTOS, UFMG; MATEUS H. GOUVEIA, UFMG; SAMIR ID-LAHOUCINE, University of Guelph, Guelph, Canada; IZINARA C. ROSSE, UFMG; RICARDO V. VENTURA, University of Guelph, Guelph, Canada; Beef Improvement Opportunities, Guelph, Canada; FRANK ANGELO TOMITA BRUNELI, CNPGL; MARCO ANTONIO MACHADO, CNPGL; MARIA GABRIELA CAMPOLINA D PEIXOTO, CNPGL; EDUARDO TARAZONA?SANTOS, UFMG; MARIA RAQUEL S. CARVALHO, UFMG. |
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Título: |
Reducing cryptic relatedness in genomic data sets via a central node exclusion algorithm. |
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Ano de publicação: |
2018 |
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Fonte/Imprenta: |
Molecular Ecology Resources, v. 18, n. 3, 2018. |
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DOI: |
10.1111/1755-0998.12746 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract Cryptic relatedness is a confounding factor in genetic diversity and genetic association studies. Development of strategies to reduce cryptic relatedness in a sample is a crucial step for downstream genetic analyses. This study uses a node selection algorithm, based on network degrees of centrality, to evaluate its applicability and impact on evaluation of genetic diversity and population stratification. 1,036 Guzerá (Bos indicus) females were genotyped using Illumina Bovine SNP50 v2 BeadChip. Four strategies were compared. The first and second strategies consist on a iterative exclusion of most related individuals based on PLINK kinship coefficient (φij) and VanRaden's φij, respectively. The third and fourth strategies were based on a node selection algorithm. The fourth strategy, Network G matrix, preserved the larger number of individuals with a better diversity and representation from the initial sample. Determining the most probable number of populations was directly affected by the kinship metric. Network G matrix was the better strategy for reducing relatedness due to producing a larger sample, with more distant individuals, a more similar distribution when compared with the full data set in the MDS plots and keeping a better representation of the population structure. Resampling strategies using VanRaden's φij as a relationship metric was better to infer the relationships among individuals. Moreover, the resampling strategies directly impact the genomic inflation values in genomewide association studies. The use of the node selection algorithm also implies better selection of the most central individuals to be removed, providing a more representative sample. MenosAbstract Cryptic relatedness is a confounding factor in genetic diversity and genetic association studies. Development of strategies to reduce cryptic relatedness in a sample is a crucial step for downstream genetic analyses. This study uses a node selection algorithm, based on network degrees of centrality, to evaluate its applicability and impact on evaluation of genetic diversity and population stratification. 1,036 Guzerá (Bos indicus) females were genotyped using Illumina Bovine SNP50 v2 BeadChip. Four strategies were compared. The first and second strategies consist on a iterative exclusion of most related individuals based on PLINK kinship coefficient (φij) and VanRaden's φij, respectively. The third and fourth strategies were based on a node selection algorithm. The fourth strategy, Network G matrix, preserved the larger number of individuals with a better diversity and representation from the initial sample. Determining the most probable number of populations was directly affected by the kinship metric. Network G matrix was the better strategy for reducing relatedness due to producing a larger sample, with more distant individuals, a more similar distribution when compared with the full data set in the MDS plots and keeping a better representation of the population structure. Resampling strategies using VanRaden's φij as a relationship metric was better to infer the relationships among individuals. Moreover, the resampling strategies directly impact t... Mostrar Tudo |
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Palavras-Chave: |
Bovine; Cryptic relatedness; Genetic diversity; Population genetic structure. |
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Thesaurus NAL: |
Inbreeding. |
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Categoria do assunto: |
L Ciência Animal e Produtos de Origem Animal |
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Marc: |
LEADER 02671naa a2200325 a 4500
001 2102041
005 2023-01-24
008 2018 bl uuuu u00u1 u #d
024 7 $a10.1111/1755-0998.12746$2DOI
100 1 $aFONSECA, P. A. S.
245 $aReducing cryptic relatedness in genomic data sets via a central node exclusion algorithm.$h[electronic resource]
260 $c2018
520 $aAbstract Cryptic relatedness is a confounding factor in genetic diversity and genetic association studies. Development of strategies to reduce cryptic relatedness in a sample is a crucial step for downstream genetic analyses. This study uses a node selection algorithm, based on network degrees of centrality, to evaluate its applicability and impact on evaluation of genetic diversity and population stratification. 1,036 Guzerá (Bos indicus) females were genotyped using Illumina Bovine SNP50 v2 BeadChip. Four strategies were compared. The first and second strategies consist on a iterative exclusion of most related individuals based on PLINK kinship coefficient (φij) and VanRaden's φij, respectively. The third and fourth strategies were based on a node selection algorithm. The fourth strategy, Network G matrix, preserved the larger number of individuals with a better diversity and representation from the initial sample. Determining the most probable number of populations was directly affected by the kinship metric. Network G matrix was the better strategy for reducing relatedness due to producing a larger sample, with more distant individuals, a more similar distribution when compared with the full data set in the MDS plots and keeping a better representation of the population structure. Resampling strategies using VanRaden's φij as a relationship metric was better to infer the relationships among individuals. Moreover, the resampling strategies directly impact the genomic inflation values in genomewide association studies. The use of the node selection algorithm also implies better selection of the most central individuals to be removed, providing a more representative sample.
650 $aInbreeding
653 $aBovine
653 $aCryptic relatedness
653 $aGenetic diversity
653 $aPopulation genetic structure
700 1 $aLEAL, T. P.
700 1 $aSANTOS, F. C.
700 1 $aGOUVEIA, M. H.
700 1 $aID-LAHOUCINE, S.
700 1 $aROSSE, I. C.
700 1 $aVENTURA, R. V.
700 1 $aBRUNELI, F. A. T.
700 1 $aMACHADO, M. A.
700 1 $aPEIXOTO, M. G. C. D.
700 1 $aTARAZONA-SANTOS, E.
700 1 $aCARVALHO, M. R. S.
773 $tMolecular Ecology Resources$gv. 18, n. 3, 2018.
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