Genome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorWI-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorNational Council of Technological and Scientific Development (CNPq)-
Autor(es): dc.creatorAlves, Anderson Antonio Carvalho [UNESP]-
Autor(es): dc.creatorda Costa, Rebeka Magalhães [UNESP]-
Autor(es): dc.creatorBresolin, Tiago-
Autor(es): dc.creatorFernandes Júnior, Gerardo Alves [UNESP]-
Autor(es): dc.creatorEspigolan, Rafael-
Autor(es): dc.creatorRibeiro, André Mauric Frossard-
Autor(es): dc.creatorCarvalheiro, Roberto [UNESP]-
Autor(es): dc.creatorde Albuquerque, Lucia Galvão [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:34:53Z-
Data de disponibilização: dc.date.available2022-02-22T00:34:53Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1093/jas/skaa179-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/201920-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/201920-
Descrição: dc.descriptionThe aim of this study was to compare the predictive performance of the Genomic Best Linear Unbiased Predictor (GBLUP) and machine learning methods (Random Forest, RF; Support Vector Machine, SVM; Artificial Neural Network, ANN) in simulated populations presenting different levels of dominance effects. Simulated genome comprised 50k SNP and 300 QTL, both biallelic and randomly distributed across 29 autosomes. A total of six traits were simulated considering different values for the narrow and broad-sense heritability. In the purely additive scenario with low heritability (h2 = 0.10), the predictive ability obtained using GBLUP was slightly higher than the other methods whereas ANN provided the highest accuracies for scenarios with moderate heritability (h2 = 0.30). The accuracies of dominance deviations predictions varied from 0.180 to 0.350 in GBLUP extended for dominance effects (GBLUP-D), from 0.06 to 0.185 in RF and they were null using the ANN and SVM methods. Although RF has presented higher accuracies for total genetic effect predictions, the mean-squared error values in such a model were worse than those observed for GBLUP-D in scenarios with large additive and dominance variances. When applied to prescreen important regions, the RF approach detected QTL with high additive and/or dominance effects. Among machine learning methods, only the RF was capable to cover implicitly dominance effects without increasing the number of covariates in the model, resulting in higher accuracies for the total genetic and phenotypic values as the dominance ratio increases. Nevertheless, whether the interest is to infer directly on dominance effects, GBLUP-D could be a more suitable method.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Animal Science Faculty of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Animal Sciences University of Wisconsin WI-
Descrição: dc.descriptionDepartment of Animal Science Faculty of Animal Science and Food Engineering University of Sao Paulo-
Descrição: dc.descriptionNational Council of Technological and Scientific Development (CNPq)-
Descrição: dc.descriptionDepartment of Animal Science Faculty of Agricultural and Veterinary Sciences Sao Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationJournal of animal science-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectArtificial Neural Network-
Palavras-chave: dc.subjectgenomic selection-
Palavras-chave: dc.subjectnonadditive effects-
Palavras-chave: dc.subjectRandom Forest-
Palavras-chave: dc.subjectSupport Vector Machine-
Título: dc.titleGenome-wide prediction for complex traits under the presence of dominance effects in simulated populations using GBLUP and machine learning methods-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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