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Registro Completo
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Biblioteca(s): |
Embrapa Gado de Corte; Embrapa Pecuária Sudeste. |
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Data corrente: |
07/12/2021 |
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Data da última atualização: |
20/12/2021 |
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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: |
MARTINS, F. B.; MORAES, A. C. L.; AONO, A. H.; FERREIRA, R. C. U.; CHIARI, L.; SIMEÃO, R. M.; BARRIOS, S. C. L.; SANTOS, M. F.; JANK, L.; VALLE, C. B. do; VIGNA, B. B. Z.; SOUZA, A. P. DE. |
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Afiliação: |
FELIPE BITENCOURT MARTINS, Center for Molecular Biology and Genetic Engineering; ALINE COSTA LIMA MORAES, Center for Molecular Biology and Genetic Engineering; ALEXANDRE HILD AONO, Center for Molecular Biology and Genetic Engineering; REBECCA CAROLINE ULBRICHT FERREIRA, Center for Molecular Biology and Genetic Engineering; LUCIMARA CHIARI, CNPGC; ROSANGELA MARIA SIMEAO, CNPGC; SANZIO CARVALHO LIMA BARRIOS, CNPGC; MATEUS FIGUEIREDO SANTOS, CNPGC; LIANA JANK, CNPGC; CACILDA BORGES DO VALLE, CNPGC; BIANCA BACCILI ZANOTTO VIGNA, CPPSE; ANETE PEREIRA DE SOUZA, Center for Molecular Biology and Genetic Engineering; UNICAMP. |
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Título: |
A semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses. |
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Ano de publicação: |
2021 |
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Fonte/Imprenta: |
Frontiers in Plant Science, v.12, article 737919, 2021. |
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Páginas: |
19 p. |
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DOI: |
https://doi.org/10.3389/fpls.2021.737919 |
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Idioma: |
Inglês |
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Conteúdo: |
Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs. MenosArtificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made ava... Mostrar Tudo |
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Palavras-Chave: |
Allele dosage; Apomictic clones; Clustering analysis; GBS; Half sibling; Self fertilization; Shiny. |
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Thesaurus NAL: |
Principal component analysis. |
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Categoria do assunto: |
G Melhoramento Genético |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/228605/1/SemiAutomatedSNP.pdf
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Marc: |
LEADER 02794naa a2200373 a 4500
001 2138071
005 2021-12-20
008 2021 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.3389/fpls.2021.737919$2DOI
100 1 $aMARTINS, F. B.
245 $aA semi-automated SNP-based approach for contaminant identification in biparental Polyploid Populations of tropical forage grasses.$h[electronic resource]
260 $c2021
300 $a19 p.
520 $aArtificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones (ACs), self-fertilized individuals, half-siblings (HSs), and full contaminants (FCs), in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation, and clustering analysis (CA). The combination of these methods allowed for the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.
650 $aPrincipal component analysis
653 $aAllele dosage
653 $aApomictic clones
653 $aClustering analysis
653 $aGBS
653 $aHalf sibling
653 $aSelf fertilization
653 $aShiny
700 1 $aMORAES, A. C. L.
700 1 $aAONO, A. H.
700 1 $aFERREIRA, R. C. U.
700 1 $aCHIARI, L.
700 1 $aSIMEÃO, R. M.
700 1 $aBARRIOS, S. C. L.
700 1 $aSANTOS, M. F.
700 1 $aJANK, L.
700 1 $aVALLE, C. B. do
700 1 $aVIGNA, B. B. Z.
700 1 $aSOUZA, A. P. DE
773 $tFrontiers in Plant Science$gv.12, article 737919, 2021.
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Registro original: |
Embrapa Gado de Corte (CNPGC) |
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