Use este identificador para citar ou linkar para este item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1160409
Registro completo de metadados
Campo DCValorIdioma
dc.contributor.authorAZEVEDO, C. F.
dc.contributor.authorFERRÃO, L. F. V.
dc.contributor.authorBENEVENUTO, J.
dc.contributor.authorRESENDE, M. D. V. de
dc.contributor.authorNASCIMENTO, M.
dc.contributor.authorNASCIMENTO, A. C. C.
dc.contributor.authorMUNOZ, P. R.
dc.date.accessioned2024-01-03T13:32:22Z-
dc.date.available2024-01-03T13:32:22Z-
dc.date.created2024-01-03
dc.date.issued2024
dc.identifier.citationTheoretical and Applied Genetics, v. 137, n. 1, 2024.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1160409-
dc.descriptionAn approach for handling visual scores with potential errors and subjectivity in scores was evaluated in simulated and blueberry recurrent selection breeding schemes to assist breeders in their decision-making. Most genomic prediction methods are based on assumptions of normality due to their simplicity and ease of implementation. However, in plant and animal breeding, continuous traits are often visually scored as categorical traits and analyzed as a Gaussian variable, thus violating the normality assumption, which could affect the prediction of breeding values and the estimation of genetic parameters. In this study, we examined the main challenges of visual scores for genomic prediction and genetic parameter estimation using mixed models, Bayesian, and machine learning methods. We evaluated these approaches using simulated and real breeding data sets. Our contribution in this study is a five-fold demonstration: (i) collecting data using an intermediate number of categories (1-3 and 1-5) is the best strategy, even considering errors associated with visual scores; (ii) Linear Mixed Models and Bayesian Linear Regression are robust to the normality violation, but marginal gains can be achieved when using Bayesian Ordinal Regression Models (BORM) and Random Forest Classification; (iii) genetic parameters are better estimated using BORM; (iv) our conclusions using simulated data are also applicable to real data in autotetraploid blueberry; and (v) a comparison of continuous and categorical phenotypes found that investing in the evaluation of 600-1000 categorical data points with low error, when it is not feasible to collect continuous phenotypes, is a strategy for improving predictive abilities. Our findings suggest the best approaches for effectively using visual scores traits to explore genetic information in breeding programs and highlight the importance of investing in the training of evaluator teams and in high-quality phenotyping.
dc.language.isoeng
dc.rightsopenAccess
dc.titleUsing visual scores for genomic prediction of complex traits in breeding programs.
dc.typeArtigo de periódico
dc.subject.nalthesaurusPlant breeding
dc.subject.nalthesaurusAnimal breeding
dc.subject.nalthesaurusBayesian theory
dc.subject.nalthesaurusGenome
dc.subject.nalthesaurusInheritance (genetics)
dc.subject.nalthesaurusPhenotype
dc.format.extent216 p.
riaa.ainfo.id1160409
riaa.ainfo.lastupdate2024-01-03
dc.identifier.doihttps://doi.org/10.1007/s00122-023-04512-w
dc.contributor.institutionCAMILA FERREIRA AZEVEDO, UNIVERSIDADE FEDERAL DE VIÇOSA; LUIS FELIPE VENTORIM FERRÃO, UNIVERSITY OF FLORID; JULIANA BENEVENUTO, UNIVERSITY OF FLORID; MARCOS DEON VILELA DE RESENDE, CNPCa; MOYSES NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; ANA CAROLINA CAMPANA NASCIMENTO, UNIVERSIDADE FEDERAL DE VIÇOSA; PATRICIO R. MUNOZ, UNIVERSITY OF FLORID.
Aparece nas coleções:Artigo em periódico indexado (SAPC)

Arquivos associados a este item:
Arquivo TamanhoFormato 
Using-visual-scores-for-genomic-prediction.pdf3,97 MBAdobe PDFVisualizar/Abrir
Mostrar registro simples do item Visualizar estatísticas

FacebookTwitterDeliciousLinkedInGoogle BookmarksMySpace