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Journal Abstract Search

325 related items for PubMed ID: 29363551

  • 1. Beyond Genomic Prediction: Combining Different Types of omics Data Can Improve Prediction of Hybrid Performance in Maize.
    Schrag TA, Westhues M, Schipprack W, Seifert F, Thiemann A, Scholten S, Melchinger AE.
    Genetics; 2018 Apr; 208(4):1373-1385. PubMed ID: 29363551
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  • 2. Omics-based hybrid prediction in maize.
    Westhues M, Schrag TA, Heuer C, Thaller G, Utz HF, Schipprack W, Thiemann A, Seifert F, Ehret A, Schlereth A, Stitt M, Nikoloski Z, Willmitzer L, Schön CC, Scholten S, Melchinger AE.
    Theor Appl Genet; 2017 Sep; 130(9):1927-1939. PubMed ID: 28647896
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  • 3. Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids.
    Acosta-Pech R, Crossa J, de Los Campos G, Teyssèdre S, Claustres B, Pérez-Elizalde S, Pérez-Rodríguez P.
    Theor Appl Genet; 2017 Jul; 130(7):1431-1440. PubMed ID: 28401254
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  • 6. Across-years prediction of hybrid performance in maize using genomics.
    Schrag TA, Schipprack W, Melchinger AE.
    Theor Appl Genet; 2019 Apr; 132(4):933-946. PubMed ID: 30498894
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  • 7. Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize.
    Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE.
    Genetics; 2014 Aug; 197(4):1343-55. PubMed ID: 24850820
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  • 9. Genomic prediction applied to multiple traits and environments in second season maize hybrids.
    de Oliveira AA, Resende MFR, Ferrão LFV, Amadeu RR, Guimarães LJM, Guimarães CT, Pastina MM, Margarido GRA.
    Heredity (Edinb); 2020 Aug; 125(1-2):60-72. PubMed ID: 32472060
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  • 11. Hybrid breeding of rice via genomic selection.
    Cui Y, Li R, Li G, Zhang F, Zhu T, Zhang Q, Ali J, Li Z, Xu S.
    Plant Biotechnol J; 2020 Jan; 18(1):57-67. PubMed ID: 31124256
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  • 12. Phenotypic Data from Inbred Parents Can Improve Genomic Prediction in Pearl Millet Hybrids.
    Liang Z, Gupta SK, Yeh CT, Zhang Y, Ngu DW, Kumar R, Patil HT, Mungra KD, Yadav DV, Rathore A, Srivastava RK, Gupta R, Yang J, Varshney RK, Schnable PS, Schnable JC.
    G3 (Bethesda); 2018 Jul 02; 8(7):2513-2522. PubMed ID: 29794163
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  • 13. Genome-Wide Association Study of Yield Component Traits in Intermediate Wheatgrass and Implications in Genomic Selection and Breeding.
    Bajgain P, Zhang X, Anderson JA.
    G3 (Bethesda); 2019 Aug 08; 9(8):2429-2439. PubMed ID: 31147390
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  • 15. Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel.
    LaPorte MF, Suwarno WB, Hannok P, Koide A, Bradbury P, Crossa J, Palacios-Rojas N, Diepenbrock CH.
    G3 (Bethesda); 2024 May 07; 14(5):. PubMed ID: 38427914
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  • 16. Genomic Prediction of Complex Traits in an Allogamous Annual Crop: The Case of Maize Single-Cross Hybrids.
    Martins Oliveira IC, Bernardeli A, Soler Guilhen JH, Pastina MM.
    Methods Mol Biol; 2022 May 07; 2467():543-567. PubMed ID: 35451790
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  • 18. Genomic predictability of interconnected biparental maize populations.
    Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE.
    Genetics; 2013 Jun 07; 194(2):493-503. PubMed ID: 23535384
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  • 19. Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.
    Bandeira E Sousa M, Cuevas J, de Oliveira Couto EG, Pérez-Rodríguez P, Jarquín D, Fritsche-Neto R, Burgueño J, Crossa J.
    G3 (Bethesda); 2017 Jun 07; 7(6):1995-2014. PubMed ID: 28455415
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