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Journal Abstract Search

173 related items for PubMed ID: 19048224

  • 1. Molecular marker-based prediction of hybrid performance in maize using unbalanced data from multiple experiments with factorial crosses.
    Schrag TA, Möhring J, Maurer HP, Dhillon BS, Melchinger AE, Piepho HP, Sørensen AP, Frisch M.
    Theor Appl Genet; 2009 Feb; 118(4):741-51. PubMed ID: 19048224
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  • 2. Prediction of hybrid performance in maize using molecular markers and joint analyses of hybrids and parental inbreds.
    Schrag TA, Möhring J, Melchinger AE, Kusterer B, Dhillon BS, Piepho HP, Frisch M.
    Theor Appl Genet; 2010 Jan; 120(2):451-61. PubMed ID: 19916002
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  • 4. Prediction of single-cross hybrid performance for grain yield and grain dry matter content in maize using AFLP markers associated with QTL.
    Schrag TA, Melchinger AE, Sørensen AP, Frisch M.
    Theor Appl Genet; 2006 Oct; 113(6):1037-47. PubMed ID: 16896712
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  • 6. Correlation between parental transcriptome and field data for the characterization of heterosis in Zea mays L.
    Thiemann A, Fu J, Schrag TA, Melchinger AE, Frisch M, Scholten S.
    Theor Appl Genet; 2010 Jan; 120(2):401-13. PubMed ID: 19888564
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  • 7. Genomewide predictions from maize single-cross data.
    Massman JM, Gordillo A, Lorenzana RE, Bernardo R.
    Theor Appl Genet; 2013 Jan; 126(1):13-22. PubMed ID: 22886355
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  • 9. Grouping of tropical mid-altitude maize inbred lines on the basis of yield data and molecular markers.
    Menkir A, Melake-Berhan A, The C, Ingelbrecht I, Adepoju A.
    Theor Appl Genet; 2004 May; 108(8):1582-90. PubMed ID: 14985970
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  • 10. Genome-based prediction of testcross values in maize.
    Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schön CC.
    Theor Appl Genet; 2011 Jul; 123(2):339-50. PubMed ID: 21505832
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  • 11. Novel strategies for genomic prediction of untested single-cross maize hybrids using unbalanced historical data.
    Dias KOG, Piepho HP, Guimarães LJM, Guimarães PEO, Parentoni SN, Pinto MO, Noda RW, Magalhães JV, Guimarães CT, Garcia AAF, Pastina MM.
    Theor Appl Genet; 2020 Feb; 133(2):443-455. PubMed ID: 31758202
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  • 13. Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles.
    Zenke-Philippi C, Thiemann A, Seifert F, Schrag T, Melchinger AE, Scholten S, Frisch M.
    BMC Genomics; 2016 Mar 29; 17():262. PubMed ID: 27025377
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  • 14. Prediction of maize single-cross hybrid performance: support vector machine regression versus best linear prediction.
    Maenhout S, De Baets B, Haesaert G.
    Theor Appl Genet; 2010 Jan 29; 120(2):415-27. PubMed ID: 19904522
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  • 15. Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize.
    Frisch M, Thiemann A, Fu J, Schrag TA, Scholten S, Melchinger AE.
    Theor Appl Genet; 2010 Jan 29; 120(2):441-50. PubMed ID: 19911157
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  • 16. Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects.
    Technow F, Riedelsheimer C, Schrag TA, Melchinger AE.
    Theor Appl Genet; 2012 Oct 29; 125(6):1181-94. PubMed ID: 22733443
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  • 20. 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 29; 208(4):1373-1385. PubMed ID: 29363551
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