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.
4. Accuracy of Genomic Selection in a Rice Synthetic Population Developed for Recurrent Selection Breeding. Grenier C; Cao TV; Ospina Y; Quintero C; Châtel MH; Tohme J; Courtois B; Ahmadi N PLoS One; 2015; 10(8):e0136594. PubMed ID: 26313446 [TBL] [Abstract][Full Text] [Related]
5. Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize. Gevartosky R; Carvalho HF; Costa-Neto G; Montesinos-López OA; Crossa J; Fritsche-Neto R BMC Plant Biol; 2023 Jan; 23(1):10. PubMed ID: 36604618 [TBL] [Abstract][Full Text] [Related]
6. Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat. Gill HS; Halder J; Zhang J; Brar NK; Rai TS; Hall C; Bernardo A; Amand PS; Bai G; Olson E; Ali S; Turnipseed B; Sehgal SK Front Plant Sci; 2021; 12():709545. PubMed ID: 34490011 [TBL] [Abstract][Full Text] [Related]
7. Genomic selection and association mapping in rice (Oryza sativa): effect of trait genetic architecture, training population composition, marker number and statistical model on accuracy of rice genomic selection in elite, tropical rice breeding lines. Spindel J; Begum H; Akdemir D; Virk P; Collard B; Redoña E; Atlin G; Jannink JL; McCouch SR PLoS Genet; 2015 Feb; 11(2):e1004982. PubMed ID: 25689273 [TBL] [Abstract][Full Text] [Related]
8. 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]
9. Economical optimization of a breeding scheme by selective phenotyping of the calibration set in a multi-trait context: application to bread making quality. Ben-Sadoun S; Rincent R; Auzanneau J; Oury FX; Rolland B; Heumez E; Ravel C; Charmet G; Bouchet S Theor Appl Genet; 2020 Jul; 133(7):2197-2212. PubMed ID: 32303775 [TBL] [Abstract][Full Text] [Related]
10. Accuracy of genomic selection in European maize elite breeding populations. Zhao Y; Gowda M; Liu W; Würschum T; Maurer HP; Longin FH; Ranc N; Reif JC Theor Appl Genet; 2012 Mar; 124(4):769-76. PubMed ID: 22075809 [TBL] [Abstract][Full Text] [Related]
11. Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice. Bhandari A; Bartholomé J; Cao-Hamadoun TV; Kumari N; Frouin J; Kumar A; Ahmadi N PLoS One; 2019; 14(5):e0208871. PubMed ID: 31059529 [TBL] [Abstract][Full Text] [Related]
12. Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat. Lozada DN; Mason RE; Sarinelli JM; Brown-Guedira G BMC Genet; 2019 Nov; 20(1):82. PubMed ID: 31675927 [TBL] [Abstract][Full Text] [Related]
13. Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice ( Labroo MR; Ali J; Aslam MU; de Asis EJ; Dela Paz MA; Sevilla MA; Lipka AE; Studer AJ; Rutkoski JE Front Genet; 2021; 12():692870. PubMed ID: 34276796 [TBL] [Abstract][Full Text] [Related]
14. Genomic Prediction of Grain Yield and Drought-Adaptation Capacity in Sorghum Is Enhanced by Multi-Trait Analysis. Velazco JG; Jordan DR; Mace ES; Hunt CH; Malosetti M; van Eeuwijk FA Front Plant Sci; 2019; 10():997. PubMed ID: 31417601 [TBL] [Abstract][Full Text] [Related]
15. Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. Rutkoski J; Poland J; Mondal S; Autrique E; Pérez LG; Crossa J; Reynolds M; Singh R G3 (Bethesda); 2016 Sep; 6(9):2799-808. PubMed ID: 27402362 [TBL] [Abstract][Full Text] [Related]
16. Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. Lado B; Vázquez D; Quincke M; Silva P; Aguilar I; Gutiérrez L Theor Appl Genet; 2018 Dec; 131(12):2719-2731. PubMed ID: 30232499 [TBL] [Abstract][Full Text] [Related]
17. Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes. Guo J; Khan J; Pradhan S; Shahi D; Khan N; Avci M; Mcbreen J; Harrison S; Brown-Guedira G; Murphy JP; Johnson J; Mergoum M; Esten Mason R; Ibrahim AMH; Sutton R; Griffey C; Babar MA Genes (Basel); 2020 Oct; 11(11):. PubMed ID: 33126620 [TBL] [Abstract][Full Text] [Related]
18. Multi-Trait Genomic Prediction Models Enhance the Predictive Ability of Grain Trace Elements in Rice. Muvunyi BP; Zou W; Zhan J; He S; Ye G Front Genet; 2022; 13():883853. PubMed ID: 35812754 [TBL] [Abstract][Full Text] [Related]
19. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. Baba T; Momen M; Campbell MT; Walia H; Morota G PLoS One; 2020; 15(2):e0228118. PubMed ID: 32012182 [TBL] [Abstract][Full Text] [Related]
20. Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat. Shahi D; Guo J; Pradhan S; Khan J; Avci M; Khan N; McBreen J; Bai G; Reynolds M; Foulkes J; Babar MA BMC Genomics; 2022 Apr; 23(1):298. PubMed ID: 35413795 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]