340 related articles for article (PubMed ID: 23629460)
1. Sensitivity to prior specification in Bayesian genome-based prediction models.
Lehermeier C; Wimmer V; Albrecht T; Auinger HJ; Gianola D; Schmid VJ; Schön CC
Stat Appl Genet Mol Biol; 2013 Jun; 12(3):375-91. PubMed ID: 23629460
[TBL] [Abstract][Full Text] [Related]
2. Improving the computational efficiency of fully Bayes inference and assessing the effect of misspecification of hyperparameters in whole-genome prediction models.
Yang W; Chen C; Tempelman RJ
Genet Sel Evol; 2015 Mar; 47(1):13. PubMed ID: 25885894
[TBL] [Abstract][Full Text] [Related]
3. Comparison of Genomic Selection Models to Predict Flowering Time and Spike Grain Number in Two Hexaploid Wheat Doubled Haploid Populations.
Thavamanikumar S; Dolferus R; Thumma BR
G3 (Bethesda); 2015 Jul; 5(10):1991-8. PubMed ID: 26206349
[TBL] [Abstract][Full Text] [Related]
4. Using Bayesian Multilevel Whole Genome Regression Models for Partial Pooling of Training Sets in Genomic Prediction.
Technow F; Totir LR
G3 (Bethesda); 2015 May; 5(8):1603-12. PubMed ID: 26024866
[TBL] [Abstract][Full Text] [Related]
5. Improving resistance to the European corn borer: a comprehensive study in elite maize using QTL mapping and genome-wide prediction.
Foiada F; Westermeier P; Kessel B; Ouzunova M; Wimmer V; Mayerhofer W; Presterl T; Dilger M; Kreps R; Eder J; Schön CC
Theor Appl Genet; 2015 May; 128(5):875-91. PubMed ID: 25758357
[TBL] [Abstract][Full Text] [Related]
6. Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment.
Lorenz AJ
G3 (Bethesda); 2013 Mar; 3(3):481-91. PubMed ID: 23450123
[TBL] [Abstract][Full Text] [Related]
7. Estimation of physiological genomic estimated breeding values (PGEBV) combining full hyperspectral and marker data across environments for grain yield under combined heat and drought stress in tropical maize (Zea mays L.).
Trachsel S; Dhliwayo T; Gonzalez Perez L; Mendoza Lugo JA; Trachsel M
PLoS One; 2019; 14(3):e0212200. PubMed ID: 30893307
[TBL] [Abstract][Full Text] [Related]
8. Genome-wide prediction of maize single-cross performance, considering non-additive genetic effects.
Santos JP; Pereira HD; Von Pinho RG; Pires LP; Camargos RB; Balestre M
Genet Mol Res; 2015 Dec; 14(4):18471-84. PubMed ID: 26782495
[TBL] [Abstract][Full Text] [Related]
9. Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and years.
Albrecht T; Auinger HJ; Wimmer V; Ogutu JO; Knaak C; Ouzunova M; Piepho HP; Schön CC
Theor Appl Genet; 2014 Jun; 127(6):1375-86. PubMed ID: 24723140
[TBL] [Abstract][Full Text] [Related]
10. Comparison between genomic predictions using daughter yield deviation and conventional estimated breeding value as response variables.
Guo G; Lund MS; Zhang Y; Su G
J Anim Breed Genet; 2010 Dec; 127(6):423-32. PubMed ID: 21077966
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Factors affecting GEBV accuracy with single-step Bayesian models.
Zhou L; Mrode R; Zhang S; Zhang Q; Li B; Liu JF
Heredity (Edinb); 2018 Jan; 120(2):100-109. PubMed ID: 29167557
[TBL] [Abstract][Full Text] [Related]
13. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes.
Kizilkaya K; Fernando RL; Garrick DJ
J Anim Sci; 2010 Feb; 88(2):544-51. PubMed ID: 19820059
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Salinity stress tolerance prediction for biomass-related traits in maize (Zea mays L.) using genome-wide markers.
Singh V; Krause M; Sandhu D; Sekhon RS; Kaundal A
Plant Genome; 2023 Dec; 16(4):e20385. PubMed ID: 37667417
[TBL] [Abstract][Full Text] [Related]
16. Ridge, Lasso and Bayesian additive-dominance genomic models.
Azevedo CF; de Resende MD; E Silva FF; Viana JM; Valente MS; Resende MF; Muñoz P
BMC Genet; 2015 Aug; 16():105. PubMed ID: 26303864
[TBL] [Abstract][Full Text] [Related]
17. 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
[TBL] [Abstract][Full Text] [Related]
18. Evaluation of genome-wide selection efficiency in maize nested association mapping populations.
Guo Z; Tucker DM; Lu J; Kishore V; Gay G
Theor Appl Genet; 2012 Feb; 124(2):261-75. PubMed ID: 21938474
[TBL] [Abstract][Full Text] [Related]
19. 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
[TBL] [Abstract][Full Text] [Related]
20. Implementing a QTL detection study (GWAS) using genomic prediction methodology.
Garrick DJ; Fernando RL
Methods Mol Biol; 2013; 1019():275-98. PubMed ID: 23756895
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
