287 related articles for article (PubMed ID: 27821057)
21. Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets.
de Haas Y; Calus MP; Veerkamp RF; Wall E; Coffey MP; Daetwyler HD; Hayes BJ; Pryce JE
J Dairy Sci; 2012 Oct; 95(10):6103-12. PubMed ID: 22863091
[TBL] [Abstract][Full Text] [Related]
22. Variation in residual feed intake in Holstein-Friesian dairy heifers in southern Australia.
Williams YJ; Pryce JE; Grainger C; Wales WJ; Linden N; Porker M; Hayes BJ
J Dairy Sci; 2011 Sep; 94(9):4715-25. PubMed ID: 21854946
[TBL] [Abstract][Full Text] [Related]
23. Short communication: Validation of genomic breeding value predictions for feed intake and feed efficiency traits.
Pryce JE; Gonzalez-Recio O; Thornhill JB; Marett LC; Wales WJ; Coffey MP; de Haas Y; Veerkamp RF; Hayes BJ
J Dairy Sci; 2014; 97(1):537-42. PubMed ID: 24239085
[TBL] [Abstract][Full Text] [Related]
24. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.
Erbe M; Hayes BJ; Matukumalli LK; Goswami S; Bowman PJ; Reich CM; Mason BA; Goddard ME
J Dairy Sci; 2012 Jul; 95(7):4114-29. PubMed ID: 22720968
[TBL] [Abstract][Full Text] [Related]
25. Using genomics to enhance selection of novel traits in North American dairy cattle.
Chesnais JP; Cooper TA; Wiggans GR; Sargolzaei M; Pryce JE; Miglior F
J Dairy Sci; 2016 Mar; 99(3):2413-2427. PubMed ID: 26778318
[TBL] [Abstract][Full Text] [Related]
26. Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits.
Aliloo H; Pryce JE; González-Recio O; Cocks BG; Hayes BJ
Genet Sel Evol; 2016 Feb; 48():8. PubMed ID: 26830030
[TBL] [Abstract][Full Text] [Related]
27. 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; 15(2):100085. PubMed ID: 33573965
[TBL] [Abstract][Full Text] [Related]
28. Application of neural networks with back-propagation to genome-enabled prediction of complex traits in Holstein-Friesian and German Fleckvieh cattle.
Ehret A; Hochstuhl D; Gianola D; Thaller G
Genet Sel Evol; 2015 Mar; 47(1):22. PubMed ID: 25886037
[TBL] [Abstract][Full Text] [Related]
29. Inclusion of herdmate data improves genomic prediction for milk-production and feed-efficiency traits within North American dairy herds.
Schultz NE; Weigel KA
J Dairy Sci; 2019 Dec; 102(12):11081-11091. PubMed ID: 31548069
[TBL] [Abstract][Full Text] [Related]
30. Predicted accuracy of and response to genomic selection for new traits in dairy cattle.
Calus MP; de Haas Y; Pszczola M; Veerkamp RF
Animal; 2013 Feb; 7(2):183-91. PubMed ID: 23031684
[TBL] [Abstract][Full Text] [Related]
31. The impact of training on data from genetically-related lines on the accuracy of genomic predictions for feed efficiency traits in pigs.
Aliakbari A; Delpuech E; Labrune Y; Riquet J; Gilbert H
Genet Sel Evol; 2020 Oct; 52(1):57. PubMed ID: 33028194
[TBL] [Abstract][Full Text] [Related]
32. 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; 48():22. PubMed ID: 26992471
[TBL] [Abstract][Full Text] [Related]
33. Effect of breed composition on phenotypic residual feed intake and growth in Angus, Brahman, and Angus x Brahman crossbred cattle.
Elzo MA; Riley DG; Hansen GR; Johnson DD; Myer RO; Coleman SW; Chase CC; Wasdin JG; Driver JD
J Anim Sci; 2009 Dec; 87(12):3877-86. PubMed ID: 19684277
[TBL] [Abstract][Full Text] [Related]
34. Genome-wide association study and genomic evaluation of feed efficiency traits in Japanese Black cattle using single-step genomic best linear unbiased prediction method.
Takeda M; Uemoto Y; Inoue K; Ogino A; Nozaki T; Kurogi K; Yasumori T; Satoh M
Anim Sci J; 2020; 91(1):e13316. PubMed ID: 31769129
[TBL] [Abstract][Full Text] [Related]
35. The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers.
van den Berg I; Ho PN; Luke TDW; Haile-Mariam M; Bolormaa S; Pryce JE
J Dairy Sci; 2021 Feb; 104(2):2008-2017. PubMed ID: 33358169
[TBL] [Abstract][Full Text] [Related]
36. Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies.
Calus MP; de Haas Y; Veerkamp RF
J Dairy Sci; 2013 Oct; 96(10):6703-15. PubMed ID: 23891299
[TBL] [Abstract][Full Text] [Related]
37. Comparing genomic prediction accuracy from purebred, crossbred and combined purebred and crossbred reference populations in sheep.
Moghaddar N; Swan AA; van der Werf JH
Genet Sel Evol; 2014 Sep; 46(1):58. PubMed ID: 25927315
[TBL] [Abstract][Full Text] [Related]
38. Genomic-polygenic evaluation of Angus-Brahman multibreed cattle for feed efficiency and postweaning growth using the Illumina 3K chip.
Elzo MA; Lamb GC; Johnson DD; Thomas MG; Misztal I; Rae DO; Martinez CA; Wasdin JG; Driver JD
J Anim Sci; 2012 Aug; 90(8):2488-97. PubMed ID: 22785165
[TBL] [Abstract][Full Text] [Related]
39. Prediction accuracy for a simulated maternally affected trait of beef cattle using different genomic evaluation models.
Lourenco DA; Misztal I; Wang H; Aguilar I; Tsuruta S; Bertrand JK
J Anim Sci; 2013 Sep; 91(9):4090-8. PubMed ID: 23893997
[TBL] [Abstract][Full Text] [Related]
40. Genotyping strategies for genomic selection in small dairy cattle populations.
Jiménez-Montero JA; González-Recio O; Alenda R
Animal; 2012 Aug; 6(8):1216-24. PubMed ID: 23217224
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
