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.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

1137 related items for PubMed ID: 26992471

  • 1. Effects of number of training generations on genomic prediction for various traits in a layer chicken population.
    Weng Z, Wolc A, Shen X, Fernando RL, Dekkers JC, Arango J, Settar P, Fulton JE, O'Sullivan NP, Garrick DJ.
    Genet Sel Evol; 2016 Mar 19; 48():22. PubMed ID: 26992471
    [Abstract] [Full Text] [Related]

  • 2. Persistence of accuracy of genomic estimated breeding values over generations in layer chickens.
    Wolc A, Arango J, Settar P, Fulton JE, O'Sullivan NP, Preisinger R, Habier D, Fernando R, Garrick DJ, Dekkers JC.
    Genet Sel Evol; 2011 Jun 21; 43(1):23. PubMed ID: 21693035
    [Abstract] [Full Text] [Related]

  • 3. Accuracy of predicting genomic breeding values for residual feed intake in Angus and Charolais beef cattle.
    Chen L, Schenkel F, Vinsky M, Crews DH, Li C.
    J Anim Sci; 2013 Oct 21; 91(10):4669-78. PubMed ID: 24078618
    [Abstract] [Full Text] [Related]

  • 4. Genomic prediction based on data from three layer lines: a comparison between linear methods.
    Calus MP, Huang H, Vereijken A, Visscher J, Ten Napel J, Windig JJ.
    Genet Sel Evol; 2014 Oct 01; 46(1):57. PubMed ID: 25927219
    [Abstract] [Full Text] [Related]

  • 5. Impact of fitting dominance and additive effects on accuracy of genomic prediction of breeding values in layers.
    Heidaritabar M, Wolc A, Arango J, Zeng J, Settar P, Fulton JE, O'Sullivan NP, Bastiaansen JW, Fernando RL, Garrick DJ, Dekkers JC.
    J Anim Breed Genet; 2016 Oct 01; 133(5):334-46. PubMed ID: 27357473
    [Abstract] [Full Text] [Related]

  • 6. Genomic selection models double the accuracy of predicted breeding values for bacterial cold water disease resistance compared to a traditional pedigree-based model in rainbow trout aquaculture.
    Vallejo RL, Leeds TD, Gao G, Parsons JE, Martin KE, Evenhuis JP, Fragomeni BO, Wiens GD, Palti Y.
    Genet Sel Evol; 2017 Feb 01; 49(1):17. PubMed ID: 28148220
    [Abstract] [Full Text] [Related]

  • 7. Genomic predictions can accelerate selection for resistance against Piscirickettsia salmonis in Atlantic salmon (Salmo salar).
    Bangera R, Correa K, Lhorente JP, Figueroa R, Yáñez JM.
    BMC Genomics; 2017 Jan 31; 18(1):121. PubMed ID: 28143402
    [Abstract] [Full Text] [Related]

  • 8. Genomic prediction ability for beef fatty acid profile in Nelore cattle using different pseudo-phenotypes.
    Chiaia HLJ, Peripolli E, de Oliveira Silva RM, Feitosa FLB, de Lemos MVA, Berton MP, Olivieri BF, Espigolan R, Tonussi RL, Gordo DGM, de Albuquerque LG, de Oliveira HN, Ferrinho AM, Mueller LF, Kluska S, Tonhati H, Pereira ASC, Aguilar I, Baldi F.
    J Appl Genet; 2018 Nov 31; 59(4):493-501. PubMed ID: 30251238
    [Abstract] [Full Text] [Related]

  • 9. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle.
    Bolormaa S, Pryce JE, Kemper K, Savin K, Hayes BJ, Barendse W, Zhang Y, Reich CM, Mason BA, Bunch RJ, Harrison BE, Reverter A, Herd RM, Tier B, Graser HU, Goddard ME.
    J Anim Sci; 2013 Jul 31; 91(7):3088-104. PubMed ID: 23658330
    [Abstract] [Full Text] [Related]

  • 10. Accuracy of genomic selection for a sib-evaluated trait using identity-by-state and identity-by-descent relationships.
    Vela-Avitúa S, Meuwissen TH, Luan T, Ødegård J.
    Genet Sel Evol; 2015 Feb 25; 47(1):9. PubMed ID: 25888184
    [Abstract] [Full Text] [Related]

  • 11. Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires.
    Campos GS, Cardoso FF, Gomes CCG, Domingues R, de Almeida Regitano LC, de Sena Oliveira MC, de Oliveira HN, Carvalheiro R, Albuquerque LG, Miller S, Misztal I, Lourenco D.
    J Anim Sci; 2022 Feb 01; 100(2):. PubMed ID: 35031806
    [Abstract] [Full Text] [Related]

  • 12. Assessment of the genomic prediction accuracy for feed efficiency traits in meat-type chickens.
    Liu T, Luo C, Wang J, Ma J, Shu D, Lund MS, Su G, Qu H.
    PLoS One; 2017 Feb 01; 12(3):e0173620. PubMed ID: 28278209
    [Abstract] [Full Text] [Related]

  • 13. Accuracy of prediction of simulated polygenic phenotypes and their underlying quantitative trait loci genotypes using real or imputed whole-genome markers in cattle.
    Hassani S, Saatchi M, Fernando RL, Garrick DJ.
    Genet Sel Evol; 2015 Dec 23; 47():99. PubMed ID: 26698091
    [Abstract] [Full Text] [Related]

  • 14. Use of a Bayesian model including QTL markers increases prediction reliability when test animals are distant from the reference population.
    Ma P, Lund MS, Aamand GP, Su G.
    J Dairy Sci; 2019 Aug 23; 102(8):7237-7247. PubMed ID: 31155255
    [Abstract] [Full Text] [Related]

  • 15. Accuracy of Igenity genomically estimated breeding values for predicting Australian Angus BREEDPLAN traits.
    Boerner V, Johnston D, Wu XL, Bauck S.
    J Anim Sci; 2015 Feb 23; 93(2):513-21. PubMed ID: 25549982
    [Abstract] [Full Text] [Related]

  • 16. Genomic prediction ability for feed efficiency traits using different models and pseudo-phenotypes under several validation strategies in Nelore cattle.
    Brunes LC, Baldi F, Lopes FB, Narciso MG, Lobo RB, Espigolan R, Costa MFO, Magnabosco CU.
    Animal; 2021 Feb 23; 15(2):100085. PubMed ID: 33573965
    [Abstract] [Full Text] [Related]

  • 17.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 18.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 19.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

  • 20.
    ; . PubMed ID:
    [No Abstract] [Full Text] [Related]

    Page: [Next] [New Search]
    of 57.