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 *

169 related articles for article (PubMed ID: 38453705)

  • 1. Portability of genomic predictions trained on sparse factorial designs across two maize silage breeding cycles.
    Lorenzi A; Bauland C; Pin S; Madur D; Combes V; Palaffre C; Guillaume C; Touzy G; Mary-Huard T; Charcosset A; Moreau L
    Theor Appl Genet; 2024 Mar; 137(3):75. PubMed ID: 38453705
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Genomic prediction of hybrid performance: comparison of the efficiency of factorial and tester designs used as training sets in a multiparental connected reciprocal design for maize silage.
    Lorenzi A; Bauland C; Mary-Huard T; Pin S; Palaffre C; Guillaume C; Lehermeier C; Charcosset A; Moreau L
    Theor Appl Genet; 2022 Sep; 135(9):3143-3160. PubMed ID: 35918515
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Revisiting hybrid breeding designs using genomic predictions: simulations highlight the superiority of incomplete factorials between segregating families over topcross designs.
    Seye AI; Bauland C; Charcosset A; Moreau L
    Theor Appl Genet; 2020 Jun; 133(6):1995-2010. PubMed ID: 32185420
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Reciprocal Genetics: Identifying QTL for General and Specific Combining Abilities in Hybrids Between Multiparental Populations from Two Maize (
    Giraud H; Bauland C; Falque M; Madur D; Combes V; Jamin P; Monteil C; Laborde J; Palaffre C; Gaillard A; Blanchard P; Charcosset A; Moreau L
    Genetics; 2017 Nov; 207(3):1167-1180. PubMed ID: 28971957
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Quantitative trait loci mapping in hybrids between Dent and Flint maize multiparental populations reveals group-specific QTL for silage quality traits with variable pleiotropic effects on yield.
    Seye AI; Bauland C; Giraud H; Mechin V; Reymond M; Charcosset A; Moreau L
    Theor Appl Genet; 2019 May; 132(5):1523-1542. PubMed ID: 30734114
    [TBL] [Abstract][Full Text] [Related]  

  • 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
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Linkage Analysis and Association Mapping QTL Detection Models for Hybrids Between Multiparental Populations from Two Heterotic Groups: Application to Biomass Production in Maize (
    Giraud H; Bauland C; Falque M; Madur D; Combes V; Jamin P; Monteil C; Laborde J; Palaffre C; Gaillard A; Blanchard P; Charcosset A; Moreau L
    G3 (Bethesda); 2017 Nov; 7(11):3649-3657. PubMed ID: 28963164
    [TBL] [Abstract][Full Text] [Related]  

  • 8. 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; 2467():543-567. PubMed ID: 35451790
    [TBL] [Abstract][Full Text] [Related]  

  • 9. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction.
    Lehermeier C; Krämer N; Bauer E; Bauland C; Camisan C; Campo L; Flament P; Melchinger AE; Menz M; Meyer N; Moreau L; Moreno-González J; Ouzunova M; Pausch H; Ranc N; Schipprack W; Schönleben M; Walter H; Charcosset A; Schön CC
    Genetics; 2014 Sep; 198(1):3-16. PubMed ID: 25236445
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Optimization of training sets for genomic prediction of early-stage single crosses in maize.
    Kadam DC; Rodriguez OR; Lorenz AJ
    Theor Appl Genet; 2021 Feb; 134(2):687-699. PubMed ID: 33398385
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Maximizing efficiency of genomic selection in CIMMYT's tropical maize breeding program.
    Atanda SA; Olsen M; Burgueño J; Crossa J; Dzidzienyo D; Beyene Y; Gowda M; Dreher K; Zhang X; Prasanna BM; Tongoona P; Danquah EY; Olaoye G; Robbins KR
    Theor Appl Genet; 2021 Jan; 134(1):279-294. PubMed ID: 33037897
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Genomic prediction of northern corn leaf blight resistance in maize with combined or separated training sets for heterotic groups.
    Technow F; Bürger A; Melchinger AE
    G3 (Bethesda); 2013 Feb; 3(2):197-203. PubMed ID: 23390596
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Genomic prediction of hybrid crops allows disentangling dominance and epistasis.
    González-Diéguez D; Legarra A; Charcosset A; Moreau L; Lehermeier C; Teyssèdre S; Vitezica ZG
    Genetics; 2021 May; 218(1):. PubMed ID: 33864072
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance.
    Fritsche-Neto R; Ali J; De Asis EJ; Allahgholipour M; Labroo MR
    Theor Appl Genet; 2023 Dec; 137(1):3. PubMed ID: 38085288
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Beyond Genomic Prediction: Combining Different Types of
    Schrag TA; Westhues M; Schipprack W; Seifert F; Thiemann A; Scholten S; Melchinger AE
    Genetics; 2018 Apr; 208(4):1373-1385. PubMed ID: 29363551
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.).
    Weiß TM; Zhu X; Leiser WL; Li D; Liu W; Schipprack W; Melchinger AE; Hahn V; Würschum T
    G3 (Bethesda); 2022 Mar; 12(3):. PubMed ID: 35100379
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Forecasting the accuracy of genomic prediction with different selection targets in the training and prediction set as well as truncation selection.
    Schopp P; Riedelsheimer C; Utz HF; Schön CC; Melchinger AE
    Theor Appl Genet; 2015 Nov; 128(11):2189-201. PubMed ID: 26231985
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.