These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

156 related articles for article (PubMed ID: 38674352)

  • 1. Genomic Prediction from Multi-Environment Trials of Wheat Breeding.
    García-Barrios G; Crespo-Herrera L; Cruz-Izquierdo S; Vitale P; Sandoval-Islas JS; Gerard GS; Aguilar-Rincón VH; Corona-Torres T; Crossa J; Pacheco-Gil RA
    Genes (Basel); 2024 Mar; 15(4):. PubMed ID: 38674352
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Phenomic selection in wheat breeding: prediction of the genotype-by-environment interaction in multi-environment breeding trials.
    Robert P; Goudemand E; Auzanneau J; Oury FX; Rolland B; Heumez E; Bouchet S; Caillebotte A; Mary-Huard T; Le Gouis J; Rincent R
    Theor Appl Genet; 2022 Oct; 135(10):3337-3356. PubMed ID: 35939074
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.
    Krause MR; González-Pérez L; Crossa J; Pérez-Rodríguez P; Montesinos-López O; Singh RP; Dreisigacker S; Poland J; Rutkoski J; Sorrells M; Gore MA; Mondal S
    G3 (Bethesda); 2019 Apr; 9(4):1231-1247. PubMed ID: 30796086
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.
    Tsai HY; Cericola F; Edriss V; Andersen JR; Orabi J; Jensen JD; Jahoor A; Janss L; Jensen J
    PLoS One; 2020; 15(5):e0232665. PubMed ID: 32401769
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.
    Sukumaran S; Crossa J; Jarquin D; Lopes M; Reynolds MP
    G3 (Bethesda); 2017 Feb; 7(2):481-495. PubMed ID: 27903632
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model.
    Lopez-Cruz M; Crossa J; Bonnett D; Dreisigacker S; Poland J; Jannink JL; Singh RP; Autrique E; de los Campos G
    G3 (Bethesda); 2015 Feb; 5(4):569-82. PubMed ID: 25660166
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments.
    Howard R; Gianola D; Montesinos-López O; Juliana P; Singh R; Poland J; Shrestha S; Pérez-Rodríguez P; Crossa J; Jarquín D
    G3 (Bethesda); 2019 Sep; 9(9):2925-2934. PubMed ID: 31300481
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data.
    Costa-Neto G; Crespo-Herrera L; Fradgley N; Gardner K; Bentley AR; Dreisigacker S; Fritsche-Neto R; Montesinos-López OA; Crossa J
    G3 (Bethesda); 2023 Feb; 13(2):. PubMed ID: 36454213
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Bayesian Genomic Prediction with Genotype × Environment Interaction Kernel Models.
    Cuevas J; Crossa J; Montesinos-López OA; Burgueño J; Pérez-Rodríguez P; de Los Campos G
    G3 (Bethesda); 2017 Jan; 7(1):41-53. PubMed ID: 27793970
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis.
    Raffo MA; Sarup P; Guo X; Liu H; Andersen JR; Orabi J; Jahoor A; Jensen J
    Theor Appl Genet; 2022 Mar; 135(3):965-978. PubMed ID: 34973112
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials.
    Dias KODG; Gezan SA; Guimarães CT; Nazarian A; da Costa E Silva L; Parentoni SN; de Oliveira Guimarães PE; de Oliveira Anoni C; Pádua JMV; de Oliveira Pinto M; Noda RW; Ribeiro CAG; de Magalhães JV; Garcia AAF; de Souza JC; Guimarães LJM; Pastina MM
    Heredity (Edinb); 2018 Jul; 121(1):24-37. PubMed ID: 29472694
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Accounting for Genotype-by-Environment Interactions and Residual Genetic Variation in Genomic Selection for Water-Soluble Carbohydrate Concentration in Wheat.
    Ovenden B; Milgate A; Wade LJ; Rebetzke GJ; Holland JB
    G3 (Bethesda); 2018 May; 8(6):1909-1919. PubMed ID: 29661842
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Genome-Enabled Estimates of Additive and Nonadditive Genetic Variances and Prediction of Apple Phenotypes Across Environments.
    Kumar S; Molloy C; Muñoz P; Daetwyler H; Chagné D; Volz R
    G3 (Bethesda); 2015 Oct; 5(12):2711-8. PubMed ID: 26497141
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Genome-enabled prediction for sparse testing in multi-environmental wheat trials.
    Crespo-Herrera L; Howard R; Piepho HP; Pérez-Rodríguez P; Montesinos-Lopez O; Burgueño J; Singh R; Mondal S; Jarquín D; Crossa J
    Plant Genome; 2021 Nov; 14(3):e20151. PubMed ID: 34510790
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Epistasis and covariance: how gene interaction translates into genomic relationship.
    Martini JW; Wimmer V; Erbe M; Simianer H
    Theor Appl Genet; 2016 May; 129(5):963-76. PubMed ID: 26883048
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Increasing Genomic-Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat.
    Jarquín D; Lemes da Silva C; Gaynor RC; Poland J; Fritz A; Howard R; Battenfield S; Crossa J
    Plant Genome; 2017 Jul; 10(2):. PubMed ID: 28724062
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Genomic selection in a commercial winter wheat population.
    He S; Schulthess AW; Mirdita V; Zhao Y; Korzun V; Bothe R; Ebmeyer E; Reif JC; Jiang Y
    Theor Appl Genet; 2016 Mar; 129(3):641-51. PubMed ID: 26747048
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Sparse testing using genomic prediction improves selection for breeding targets in elite spring wheat.
    Atanda SA; Govindan V; Singh R; Robbins KR; Crossa J; Bentley AR
    Theor Appl Genet; 2022 Jun; 135(6):1939-1950. PubMed ID: 35348821
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Genomic prediction for rust resistance in diverse wheat landraces.
    Daetwyler HD; Bansal UK; Bariana HS; Hayden MJ; Hayes BJ
    Theor Appl Genet; 2014 Aug; 127(8):1795-803. PubMed ID: 24965887
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Modeling Epistasis in Genomic Selection.
    Jiang Y; Reif JC
    Genetics; 2015 Oct; 201(2):759-68. PubMed ID: 26219298
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.