230 related articles for article (PubMed ID: 21566722)
1. Genomic-Enabled Prediction Based on Molecular Markers and Pedigree Using the Bayesian Linear Regression Package in R.
Pérez P; de Los Campos G; Crossa J; Gianola D
Plant Genome; 2010; 3(2):106-116. PubMed ID: 21566722
[TBL] [Abstract][Full Text] [Related]
2. Genome-enabled prediction using the BLR (Bayesian Linear Regression) R-package.
de Los Campos G; Pérez P; Vazquez AI; Crossa J
Methods Mol Biol; 2013; 1019():299-320. PubMed ID: 23756896
[TBL] [Abstract][Full Text] [Related]
3. Predicting quantitative traits with regression models for dense molecular markers and pedigree.
de los Campos G; Naya H; Gianola D; Crossa J; Legarra A; Manfredi E; Weigel K; Cotes JM
Genetics; 2009 May; 182(1):375-85. PubMed ID: 19293140
[TBL] [Abstract][Full Text] [Related]
4. FW: An R Package for Finlay-Wilkinson Regression that Incorporates Genomic/Pedigree Information and Covariance Structures Between Environments.
Lian L; de Los Campos G
G3 (Bethesda); 2015 Dec; 6(3):589-97. PubMed ID: 26715095
[TBL] [Abstract][Full Text] [Related]
5. Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine.
Calleja-Rodriguez A; Pan J; Funda T; Chen Z; Baison J; Isik F; Abrahamsson S; Wu HX
BMC Genomics; 2020 Nov; 21(1):796. PubMed ID: 33198692
[TBL] [Abstract][Full Text] [Related]
6. Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding.
Pérez-Rodríguez P; Gianola D; Weigel KA; Rosa GJ; Crossa J
J Anim Sci; 2013 Aug; 91(8):3522-31. PubMed ID: 23658327
[TBL] [Abstract][Full Text] [Related]
7. Genome-wide regression and prediction with the BGLR statistical package.
Pérez P; de los Campos G
Genetics; 2014 Oct; 198(2):483-95. PubMed ID: 25009151
[TBL] [Abstract][Full Text] [Related]
8. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions.
Ogutu JO; Schulz-Streeck T; Piepho HP
BMC Proc; 2012 May; 6 Suppl 2(Suppl 2):S10. PubMed ID: 22640436
[TBL] [Abstract][Full Text] [Related]
9. A comparison of five methods to predict genomic breeding values of dairy bulls from genome-wide SNP markers.
Moser G; Tier B; Crump RE; Khatkar MS; Raadsma HW
Genet Sel Evol; 2009 Dec; 41(1):56. PubMed ID: 20043835
[TBL] [Abstract][Full Text] [Related]
10. Comparison of Models and Whole-Genome Profiling Approaches for Genomic-Enabled Prediction of Septoria Tritici Blotch, Stagonospora Nodorum Blotch, and Tan Spot Resistance in Wheat.
Juliana P; Singh RP; Singh PK; Crossa J; Rutkoski JE; Poland JA; Bergstrom GC; Sorrells ME
Plant Genome; 2017 Jul; 10(2):. PubMed ID: 28724084
[TBL] [Abstract][Full Text] [Related]
11. Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan.
Tehseen MM; Kehel Z; Sansaloni CP; Lopes MDS; Amri A; Kurtulus E; Nazari K
Plants (Basel); 2021 Mar; 10(3):. PubMed ID: 33809650
[TBL] [Abstract][Full Text] [Related]
12. A Multiple-Trait Bayesian Lasso for Genome-Enabled Analysis and Prediction of Complex Traits.
Gianola D; Fernando RL
Genetics; 2020 Feb; 214(2):305-331. PubMed ID: 31879318
[TBL] [Abstract][Full Text] [Related]
13. Multitrait Bayesian shrinkage and variable selection models with the BGLR-R package.
Pérez-Rodríguez P; de Los Campos G
Genetics; 2022 Aug; 222(1):. PubMed ID: 35924977
[TBL] [Abstract][Full Text] [Related]
14. Genome-enabled methods for predicting litter size in pigs: a comparison.
Tusell L; Pérez-Rodríguez P; Forni S; Wu XL; Gianola D
Animal; 2013 Nov; 7(11):1739-49. PubMed ID: 23880322
[TBL] [Abstract][Full Text] [Related]
15. Application of Bayesian least absolute shrinkage and selection operator (LASSO) and BayesCπ methods for genomic selection in French Holstein and Montbéliarde breeds.
Colombani C; Legarra A; Fritz S; Guillaume F; Croiseau P; Ducrocq V; Robert-Granié C
J Dairy Sci; 2013 Jan; 96(1):575-91. PubMed ID: 23127905
[TBL] [Abstract][Full Text] [Related]
16. An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.
Montesinos-López OA; Montesinos-López A; Luna-Vázquez FJ; Toledo FH; Pérez-Rodríguez P; Lillemo M; Crossa J
G3 (Bethesda); 2019 May; 9(5):1355-1369. PubMed ID: 30819822
[TBL] [Abstract][Full Text] [Related]
17. lme4GS: An R-Package for Genomic Selection.
Caamal-Pat D; Pérez-Rodríguez P; Crossa J; Velasco-Cruz C; Pérez-Elizalde S; Vázquez-Peña M
Front Genet; 2021; 12():680569. PubMed ID: 34220954
[TBL] [Abstract][Full Text] [Related]
18. BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.
Granato I; Cuevas J; Luna-Vázquez F; Crossa J; Montesinos-López O; Burgueño J; Fritsche-Neto R
G3 (Bethesda); 2018 Aug; 8(9):3039-3047. PubMed ID: 30049744
[TBL] [Abstract][Full Text] [Related]
19. Bayesian genomic-enabled prediction as an inverse problem.
Cuevas J; Pérez-Elizalde S; Soberanis V; Pérez-Rodríguez P; Gianola D; Crossa J
G3 (Bethesda); 2014 Aug; 4(10):1991-2001. PubMed ID: 25155273
[TBL] [Abstract][Full Text] [Related]
20. Accounting for Correlation Between Traits in Genomic Prediction.
Montesinos-López OA; Montesinos-López A; Mosqueda-Gonzalez BA; Montesinos-López JC; Crossa J
Methods Mol Biol; 2022; 2467():285-327. PubMed ID: 35451780
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
