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Registro Completo |
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Biblioteca(s): |
Embrapa Gado de Leite. |
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Data corrente: |
25/04/2022 |
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Data da última atualização: |
25/04/2022 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
LEAL, T. P.; FURLAN, V. C.; GOUVEIA, M. H.; DUARTE, J. M. S.; FONSECA, P. A. S.; TOU, R.; SCLIAR, M. de O.; ARAUJO, G. S. de; COSTA, L. F.; ZOLINI, C.; PEIXOTO, M. G. C. D.; CARVALHO, M. R. dos S.; LIMA-COSTA, M. F.; GILMAN, R. H.; TARAZONA-SANTOS, E.; RODRIGUES, M. R. |
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Afiliação: |
THIAGO PEIXOTO LEAL, Universidade Federal de Minas Gerais; VINICIUS C. FURLAN, Universidade Federal de Minas Gerais; MATEUS HENRIQUE GOUVEIA, Universidade Federal de Minas Gerais; JULIA MARIA SARAIVA DUARTE, Universidade Federal de Minas Gerais; PABLO A. S. FONSECA, Universidade Federal de Minas Gerais; RAFAEL TOU, Universidade Federal de Minas Gerais; MARILIA DE OLIVEIRA SCLIAR, Universidade de São Paulo; GILDERLANIO SANTANA DE ARAUJO, Universidade Federal do Pará; LUCAS F. COSTA, Universidade Federal de Minas Gerais; CAMILA ZOLINI, Universidade Federal de Minas Gerais; MARIA GABRIELA CAMPOLINA D PEIXOTO, CNPGL; MARIA RAQUEL SANTOS CARVALHO, Universidade Federal de Minas Gerais; MARIA FERNANDA LIMA-COSTA, Fundação Oswaldo Cruz; ROBERT H. GILMAN, Universidad Peruana Cayetano Heredia; EDUARDO TARAZONA-SANTOS, Universidade Federal de Minas Gerais; MAÍRA RIBEIRO RODRIGUES, Universidade Federal de Minas Gerais. |
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Título: |
NAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses. |
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Ano de publicação: |
2022 |
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Fonte/Imprenta: |
Computational and Structural Biotechnology Journal, v. 20, p. 1821-1828, 2022. |
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DOI: |
https://doi.org/10.1016/j.csbj.2022.04.009 |
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Idioma: |
Inglês |
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Conteúdo: |
Genetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study. |
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Palavras-Chave: |
Complex network theory; Genealogies simulator; Genetic kinship; Genética de populações; Parentesco genético; Simulador de genealogias; Teoria de redes complexas. |
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Thesagro: |
Genética. |
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Thesaurus Nal: |
Population genetics. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02661naa a2200421 a 4500
001 2142362
005 2022-04-25
008 2022 bl uuuu u00u1 u #d
024 7 $ahttps://doi.org/10.1016/j.csbj.2022.04.009$2DOI
100 1 $aLEAL, T. P.
245 $aNAToRA, a relatedness-pruning method to minimize the loss of dataset size in genetic and omics analyses.$h[electronic resource]
260 $c2022
520 $aGenetic and omics analyses frequently require independent observations, which is not guaranteed in real datasets. When relatedness cannot be accounted for, solutions involve removing related individuals (or observations) and, consequently, a reduction of available data. We developed a network-based relatedness-pruning method that minimizes dataset reduction while removing unwanted relationships in a dataset. It uses node degree centrality metric to identify highly connected nodes (or individuals) and implements heuristics that approximate the minimal reduction of a dataset to allow its application to complex datasets. When compared with two other popular population genetics methodologies (PLINK and KING), NAToRA shows the best combination of removing all relatives while keeping the largest possible number of individuals in all datasets tested and also, with similar effects on the allele frequency spectrum and Principal Component Analysis than PLINK and KING. NAToRA is freely available, both as a standalone tool that can be easily incorporated as part of a pipeline, and as a graphical web tool that allows visualization of the relatedness networks. NAToRA also accepts a variety of relationship metrics as input, which facilitates its use. We also release a genealogies simulator software used for different tests performed in this study.
650 $aPopulation genetics
650 $aGenética
653 $aComplex network theory
653 $aGenealogies simulator
653 $aGenetic kinship
653 $aGenética de populações
653 $aParentesco genético
653 $aSimulador de genealogias
653 $aTeoria de redes complexas
700 1 $aFURLAN, V. C.
700 1 $aGOUVEIA, M. H.
700 1 $aDUARTE, J. M. S.
700 1 $aFONSECA, P. A. S.
700 1 $aTOU, R.
700 1 $aSCLIAR, M. de O.
700 1 $aARAUJO, G. S. de
700 1 $aCOSTA, L. F.
700 1 $aZOLINI, C.
700 1 $aPEIXOTO, M. G. C. D.
700 1 $aCARVALHO, M. R. dos S.
700 1 $aLIMA-COSTA, M. F.
700 1 $aGILMAN, R. H.
700 1 $aTARAZONA-SANTOS, E.
700 1 $aRODRIGUES, M. R.
773 $tComputational and Structural Biotechnology Journal$gv. 20, p. 1821-1828, 2022.
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Registro original: |
Embrapa Gado de Leite (CNPGL) |
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