147 related articles for article (PubMed ID: 34973112)
1. Improvement of genomic prediction in advanced wheat breeding lines by including additive-by-additive epistasis.
Raffo MA; Sarup P; Guo X; Liu H; Andersen JR; Orabi J; Jahoor A; Jensen J
Theor Appl Genet; 2022 Mar; 135(3):965-978. PubMed ID: 34973112
[TBL] [Abstract][Full Text] [Related]
2. Orthogonal Estimates of Variances for Additive, Dominance, and Epistatic Effects in Populations.
Vitezica ZG; Legarra A; Toro MA; Varona L
Genetics; 2017 Jul; 206(3):1297-1307. PubMed ID: 28522540
[TBL] [Abstract][Full Text] [Related]
3. Dominance and epistatic genetic variances for litter size in pigs using genomic models.
Vitezica ZG; Reverter A; Herring W; Legarra A
Genet Sel Evol; 2018 Dec; 50(1):71. PubMed ID: 30577727
[TBL] [Abstract][Full Text] [Related]
4. Genomic Prediction from Multi-Environment Trials of Wheat Breeding.
García-Barrios G; Crespo-Herrera L; Cruz-Izquierdo S; Vitale P; Sandoval-Islas JS; Gerard GS; Aguilar-Rincón VH; Corona-Torres T; Crossa J; Pacheco-Gil RA
Genes (Basel); 2024 Mar; 15(4):. PubMed ID: 38674352
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Genetic estimation of grain yield and its attributes in three wheat (
Attri H; Dey T; Singh B; Kour A
J Genet; 2021; 100():. PubMed ID: 34282738
[TBL] [Abstract][Full Text] [Related]
7. Prediction of Subgenome Additive and Interaction Effects in Allohexaploid Wheat.
Santantonio N; Jannink JL; Sorrells M
G3 (Bethesda); 2019 Mar; 9(3):685-698. PubMed ID: 30455185
[TBL] [Abstract][Full Text] [Related]
8. Genome-wide mapping and prediction suggests presence of local epistasis in a vast elite winter wheat populations adapted to Central Europe.
He S; Reif JC; Korzun V; Bothe R; Ebmeyer E; Jiang Y
Theor Appl Genet; 2017 Apr; 130(4):635-647. PubMed ID: 27995275
[TBL] [Abstract][Full Text] [Related]
9. Epistasis and covariance: how gene interaction translates into genomic relationship.
Martini JW; Wimmer V; Erbe M; Simianer H
Theor Appl Genet; 2016 May; 129(5):963-76. PubMed ID: 26883048
[TBL] [Abstract][Full Text] [Related]
10. Prediction of additive, epistatic, and dominance effects using models accounting for incomplete inbreeding in parental lines of hybrid rye and sugar beet.
Kristensen PS; Sarup P; Fé D; Orabi J; Snell P; Ripa L; Mohlfeld M; Chu TT; Herrström J; Jahoor A; Jensen J
Front Plant Sci; 2023; 14():1193433. PubMed ID: 38162304
[TBL] [Abstract][Full Text] [Related]
11. Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material.
Kristensen PS; Jensen J; Andersen JR; Guzmán C; Orabi J; Jahoor A
Genes (Basel); 2019 Aug; 10(9):. PubMed ID: 31480460
[TBL] [Abstract][Full Text] [Related]
12. Mapping quantitative trait loci with additive effects and additive x additive epistatic interactions for biomass yield, grain yield, and straw yield using a doubled haploid population of wheat (Triticum aestivum L.).
Li ZK; Jiang XL; Peng T; Shi CL; Han SX; Tian B; Zhu ZL; Tian JC
Genet Mol Res; 2014 Feb; 13(1):1412-24. PubMed ID: 24634240
[TBL] [Abstract][Full Text] [Related]
13. Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations.
de Oliveira LF; Brito LF; Marques DBD; da Silva DA; Lopes PS; Dos Santos CG; Johnson JS; Veroneze R
BMC Genom Data; 2023 Dec; 24(1):76. PubMed ID: 38093199
[TBL] [Abstract][Full Text] [Related]
14. Genomic selection in a commercial winter wheat population.
He S; Schulthess AW; Mirdita V; Zhao Y; Korzun V; Bothe R; Ebmeyer E; Reif JC; Jiang Y
Theor Appl Genet; 2016 Mar; 129(3):641-51. PubMed ID: 26747048
[TBL] [Abstract][Full Text] [Related]
15. Use of multiple traits genomic prediction, genotype by environment interactions and spatial effect to improve prediction accuracy in yield data.
Tsai HY; Cericola F; Edriss V; Andersen JR; Orabi J; Jensen JD; Jahoor A; Janss L; Jensen J
PLoS One; 2020; 15(5):e0232665. PubMed ID: 32401769
[TBL] [Abstract][Full Text] [Related]
16. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection.
Robert P; Auzanneau J; Goudemand E; Oury FX; Rolland B; Heumez E; Bouchet S; Le Gouis J; Rincent R
Theor Appl Genet; 2022 Mar; 135(3):895-914. PubMed ID: 34988629
[TBL] [Abstract][Full Text] [Related]
17. A Low Resolution Epistasis Mapping Approach To Identify Chromosome Arm Interactions in Allohexaploid Wheat.
Santantonio N; Jannink JL; Sorrells M
G3 (Bethesda); 2019 Mar; 9(3):675-684. PubMed ID: 30455184
[TBL] [Abstract][Full Text] [Related]
18. Genomic prediction for grain yield and micro-environmental sensitivity in winter wheat.
Raffo MA; Cuyabano BCD; Rincent R; Sarup P; Moreau L; Mary-Huard T; Jensen J
Front Plant Sci; 2022; 13():1075077. PubMed ID: 36816478
[TBL] [Abstract][Full Text] [Related]
19. Genomic prediction of agronomic traits in wheat using different models and cross-validation designs.
Haile TA; Walkowiak S; N'Diaye A; Clarke JM; Hucl PJ; Cuthbert RD; Knox RE; Pozniak CJ
Theor Appl Genet; 2021 Jan; 134(1):381-398. PubMed ID: 33135095
[TBL] [Abstract][Full Text] [Related]
20. Estimation of additive and non-additive genetic effects for fertility and reproduction traits in North American Holstein cattle using genomic information.
Alves K; Brito LF; Baes CF; Sargolzaei M; Robinson JAB; Schenkel FS
J Anim Breed Genet; 2020 May; 137(3):316-330. PubMed ID: 31912573
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]