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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Silva, F. F. Resende, M. D. V. Lopes, P. S. Guimarães, S. E. F. Martins Filho, S. Glória, L. S. Santos, V. S. Azevedo, C. F. |
| Description | Country affiliation: Brazil Author Affiliation: Santos VS ( Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, MG, Brasil 2santosvinicius@gmail.com.); Martins Filho S ( Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, MG, Brasil.); Resende MD ( Empresa Brasileira de Pesquisa Agropecuária, Universidade Federal de Viçosa, Centro Nacional de Pesquisa de Florestas, Colombo, PR, Brasil.); Azevedo CF ( Departamento de Estatística, Universidade Federal de Viçosa, Viçosa, MG, Brasil.); Lopes PS ( Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, MG, Brasil.); Guimarães SE ( Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, MG, Brasil.); Glória LS ( Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, MG, Brasil.); Silva FF ( Departamento de Zootecnia, Universidade Federal de Viçosa, Viçosa, MG, Brasil.) |
| Abstract | The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed. |
| e-ISSN | 16765680 |
| Journal | Genetics and Molecular Research |
| Issue Number | 4 |
| Volume Number | 14 |
| Language | English |
| Publisher | Fundação de Pesquisas Científicas de Ribeirão Preto |
| Publisher Date | 2015-10-19 |
| Publisher Place | Brazil |
| Access Restriction | Open |
| Subject Keyword | Animal Husbandry Models, Genetic Swine Genetics Abattoirs Animals Bayes Theorem Breeding Computer Simulation Genetic Association Studies Genomics Linear Models Polymorphism, Single Nucleotide Proportional Hazards Models Quantitative Trait, Heritable Regression Analysis Research Support, Non-u.s. Gov't Discipline Genetics Discipline Molecular Biology Discipline Bioinformatics |
| Content Type | Text |
| Resource Type | Article |
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