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Items: 1 to 50 of 90

1.

The use of mid-infrared spectra to map genes affecting milk composition.

Benedet A, Ho PN, Xiang R, Bolormaa S, De Marchi M, Goddard ME, Pryce JE.

J Dairy Sci. 2019 Jun 6. pii: S0022-0302(19)30485-0. doi: 10.3168/jds.2018-15890. [Epub ahead of print]

PMID:
31178181
2.

Corrigendum to "On the value of the phenotypes in the genomic era" (J. Dairy Sci. 97:7905-7915).

Gonzalez-Recio O, Coffey MP, Pryce JE.

J Dairy Sci. 2019 Jun;102(6):5764. doi: 10.3168/jds.2019-102-6-5764. No abstract available.

PMID:
31101393
3.

Prediction of blood β-hydroxybutyrate content and occurrence of hyperketonemia in early-lactation, pasture-grazed dairy cows using milk infrared spectra.

Bonfatti V, Turner SA, Kuhn-Sherlock B, Luke TDW, Ho PN, Phyn CVC, Pryce JE.

J Dairy Sci. 2019 Jul;102(7):6466-6476. doi: 10.3168/jds.2018-15988. Epub 2019 May 10.

PMID:
31079906
4.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle.

Tiplady KM, Sherlock RG, Littlejohn MD, Pryce JE, Davis SR, Garrick DJ, Spelman RJ, Harris BL.

J Dairy Sci. 2019 Jul;102(7):6357-6372. doi: 10.3168/jds.2018-16144. Epub 2019 Apr 25.

5.

Fine-mapping sequence mutations with a major effect on oligosaccharide content in bovine milk.

Liu Z, Wang T, Pryce JE, MacLeod IM, Hayes BJ, Chamberlain AJ, Jagt CV, Reich CM, Mason BA, Rochfort S, Cocks BG.

Sci Rep. 2019 Feb 14;9(1):2137. doi: 10.1038/s41598-019-38488-9.

6.

Whole rumen metagenome sequencing allows classifying and predicting feed efficiency and intake levels in cattle.

Delgado B, Bach A, Guasch I, González C, Elcoso G, Pryce JE, Gonzalez-Recio O.

Sci Rep. 2019 Jan 9;9(1):11. doi: 10.1038/s41598-018-36673-w.

7.

Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra.

Luke TDW, Rochfort S, Wales WJ, Bonfatti V, Marett L, Pryce JE.

J Dairy Sci. 2019 Feb;102(2):1747-1760. doi: 10.3168/jds.2018-15103. Epub 2018 Dec 26.

8.

Comparing allele specific expression and local expression quantitative trait loci and the influence of gene expression on complex trait variation in cattle.

Khansefid M, Pryce JE, Bolormaa S, Chen Y, Millen CA, Chamberlain AJ, Vander Jagt CJ, Goddard ME.

BMC Genomics. 2018 Nov 3;19(1):793. doi: 10.1186/s12864-018-5181-0.

9.

Genetic evaluation of gestation length and its use in managing calving patterns.

Haile-Mariam M, Pryce JE.

J Dairy Sci. 2019 Jan;102(1):476-487. doi: 10.3168/jds.2018-14981. Epub 2018 Oct 19.

PMID:
30343913
10.

Putative bovine topological association domains and CTCF binding motifs can reduce the search space for causative regulatory variants of complex traits.

Wang M, Hancock TP, Chamberlain AJ, Vander Jagt CJ, Pryce JE, Cocks BG, Goddard ME, Hayes BJ.

BMC Genomics. 2018 May 24;19(1):395. doi: 10.1186/s12864-018-4800-0.

11.

The cost-benefit of genomic testing of heifers and using sexed semen in pasture-based dairy herds.

Newton JE, Hayes BJ, Pryce JE.

J Dairy Sci. 2018 Jul;101(7):6159-6173. doi: 10.3168/jds.2017-13476. Epub 2018 Apr 26.

12.

Components of the covariances between reproductive performance traits and milk protein concentration and milk yield in dairy cows.

Morton JM, Pryce JE, Haile-Mariam M.

J Dairy Sci. 2018 Jun;101(6):5227-5239. doi: 10.3168/jds.2017-13268. Epub 2018 Mar 15.

13.

Invited review: Genetics and claw health: Opportunities to enhance claw health by genetic selection.

Heringstad B, Egger-Danner C, Charfeddine N, Pryce JE, Stock KF, Kofler J, Sogstad AM, Holzhauer M, Fiedler A, Müller K, Nielsen P, Thomas G, Gengler N, de Jong G, Ødegård C, Malchiodi F, Miglior F, Alsaaod M, Cole JB.

J Dairy Sci. 2018 Jun;101(6):4801-4821. doi: 10.3168/jds.2017-13531. Epub 2018 Mar 7. Review.

14.

Symposium review: Building a better cow-The Australian experience and future perspectives.

Pryce JE, Nguyen TTT, Axford M, Nieuwhof G, Shaffer M.

J Dairy Sci. 2018 Apr;101(4):3702-3713. doi: 10.3168/jds.2017-13377. Epub 2018 Feb 14.

15.

Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake.

Khansefid M, Millen CA, Chen Y, Pryce JE, Chamberlain AJ, Vander Jagt CJ, Gondro C, Goddard ME.

J Anim Sci. 2017 Nov;95(11):4764-4775. doi: 10.2527/jas2016.1320.

16.

Genetic parameters for health traits using data collected from genomic information nucleus herds.

Abdelsayed M, Haile-Mariam M, Pryce JE.

J Dairy Sci. 2017 Dec;100(12):9643-9655. doi: 10.3168/jds.2017-12960. Epub 2017 Oct 4.

17.

Application of a Bayesian non-linear model hybrid scheme to sequence data for genomic prediction and QTL mapping.

Wang T, Chen YP, MacLeod IM, Pryce JE, Goddard ME, Hayes BJ.

BMC Genomics. 2017 Aug 15;18(1):618. doi: 10.1186/s12864-017-4030-x.

18.

Short communication: Implementation of a breeding value for heat tolerance in Australian dairy cattle.

Nguyen TTT, Bowman PJ, Haile-Mariam M, Nieuwhof GJ, Hayes BJ, Pryce JE.

J Dairy Sci. 2017 Sep;100(9):7362-7367. doi: 10.3168/jds.2017-12898. Epub 2017 Jul 12.

19.

Putative enhancer sites in the bovine genome are enriched with variants affecting complex traits.

Wang M, Hancock TP, MacLeod IM, Pryce JE, Cocks BG, Hayes BJ.

Genet Sel Evol. 2017 Jul 6;49(1):56. doi: 10.1186/s12711-017-0331-4.

20.
21.

Invited review: Inbreeding in the genomics era: Inbreeding, inbreeding depression, and management of genomic variability.

Howard JT, Pryce JE, Baes C, Maltecca C.

J Dairy Sci. 2017 Aug;100(8):6009-6024. doi: 10.3168/jds.2017-12787. Epub 2017 Jun 7. Review.

22.

Geno-Diver: A combined coalescence and forward-in-time simulator for populations undergoing selection for complex traits.

Howard JT, Tiezzi F, Pryce JE, Maltecca C.

J Anim Breed Genet. 2017 Dec;134(6):553-563. doi: 10.1111/jbg.12277. Epub 2017 May 2.

PMID:
28464287
23.

Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems.

Haile-Mariam M, Pryce JE.

J Dairy Sci. 2017 May;100(5):3754-3766. doi: 10.3168/jds.2016-11952. Epub 2017 Mar 9.

24.

Including nonadditive genetic effects in mating programs to maximize dairy farm profitability.

Aliloo H, Pryce JE, González-Recio O, Cocks BG, Goddard ME, Hayes BJ.

J Dairy Sci. 2017 Feb;100(2):1203-1222. doi: 10.3168/jds.2016-11261. Epub 2016 Dec 9.

25.

Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle.

Manzanilla-Pech CI, De Haas Y, Hayes BJ, Veerkamp RF, Khansefid M, Donoghue KA, Arthur PF, Pryce JE.

J Anim Sci. 2016 Oct;94(10):4151-4166. doi: 10.2527/jas.2016-0431.

PMID:
27898855
26.

New breeding objectives and selection indices for the Australian dairy industry.

Byrne TJ, Santos BFS, Amer PR, Martin-Collado D, Pryce JE, Axford M.

J Dairy Sci. 2016 Oct;99(10):8146-8167. doi: 10.3168/jds.2015-10747. Epub 2016 Aug 10.

27.

Invited review: Opportunities for genetic improvement of metabolic diseases.

Pryce JE, Parker Gaddis KL, Koeck A, Bastin C, Abdelsayed M, Gengler N, Miglior F, Heringstad B, Egger-Danner C, Stock KF, Bradley AJ, Cole JB.

J Dairy Sci. 2016 Sep;99(9):6855-6873. doi: 10.3168/jds.2016-10854. Epub 2016 Jun 29. Review.

28.

Genomic selection for tolerance to heat stress in Australian dairy cattle.

Nguyen TTT, Bowman PJ, Haile-Mariam M, Pryce JE, Hayes BJ.

J Dairy Sci. 2016 Apr;99(4):2849-2862. doi: 10.3168/jds.2015-9685.

29.

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 1;48:8. doi: 10.1186/s12711-016-0186-0.

30.

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. doi: 10.3168/jds.2015-9970. Epub 2016 Jan 6.

31.

Rare Variants in Transcript and Potential Regulatory Regions Explain a Small Percentage of the Missing Heritability of Complex Traits in Cattle.

Gonzalez-Recio O, Daetwyler HD, MacLeod IM, Pryce JE, Bowman PJ, Hayes BJ, Goddard ME.

PLoS One. 2015 Dec 7;10(12):e0143945. doi: 10.1371/journal.pone.0143945. eCollection 2015.

32.

Differentially Expressed Genes in Endometrium and Corpus Luteum of Holstein Cows Selected for High and Low Fertility Are Enriched for Sequence Variants Associated with Fertility.

Moore SG, Pryce JE, Hayes BJ, Chamberlain AJ, Kemper KE, Berry DP, McCabe M, Cormican P, Lonergan P, Fair T, Butler ST.

Biol Reprod. 2016 Jan;94(1):19. doi: 10.1095/biolreprod.115.132951. Epub 2015 Nov 25.

PMID:
26607721
33.

Improving the reliability of female fertility breeding values using type and milk yield traits that predict energy status in Australian Holstein cattle.

González-Recio O, Haile-Mariam M, Pryce JE.

J Dairy Sci. 2016 Jan;99(1):493-504. doi: 10.3168/jds.2015-10001. Epub 2015 Nov 5.

34.
35.

Hot topic: Definition and implementation of a breeding value for feed efficiency in dairy cows.

Pryce JE, Gonzalez-Recio O, Nieuwhof G, Wales WJ, Coffey MP, Hayes BJ, Goddard ME.

J Dairy Sci. 2015 Oct;98(10):7340-50. doi: 10.3168/jds.2015-9621. Epub 2015 Aug 5.

36.

Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian Holstein cattle.

Haile-Mariam M, Pryce JE.

J Dairy Sci. 2015 Oct;98(10):7364-79. doi: 10.3168/jds.2015-9537. Epub 2015 Aug 5.

37.

Validation of markers with non-additive effects on milk yield and fertility in Holstein and Jersey cows.

Aliloo H, Pryce JE, González-Recio O, Cocks BG, Hayes BJ.

BMC Genet. 2015 Jul 22;16:89. doi: 10.1186/s12863-015-0241-9.

38.

Genomic prediction of dry matter intake in dairy cattle from an international data set consisting of research herds in Europe, North America, and Australasia.

de Haas Y, Pryce JE, Calus MP, Wall E, Berry DP, Løvendahl P, Krattenmacher N, Miglior F, Weigel K, Spurlock D, Macdonald KA, Hulsegge B, Veerkamp RF.

J Dairy Sci. 2015 Sep;98(9):6522-34. doi: 10.3168/jds.2014-9257. Epub 2015 Jul 15.

39.

Non-additive genetic variation in growth, carcass and fertility traits of beef cattle.

Bolormaa S, Pryce JE, Zhang Y, Reverter A, Barendse W, Hayes BJ, Goddard ME.

Genet Sel Evol. 2015 Apr 2;47:26. doi: 10.1186/s12711-015-0114-8.

40.

Characterizing homozygosity across United States, New Zealand and Australian Jersey cow and bull populations.

Howard JT, Maltecca C, Haile-Mariam M, Hayes BJ, Pryce JE.

BMC Genomics. 2015 Mar 15;16:187. doi: 10.1186/s12864-015-1352-4.

41.

Analyzing the heterogeneity of farmers' preferences for improvements in dairy cow traits using farmer typologies.

Martin-Collado D, Byrne TJ, Amer PR, Santos BF, Axford M, Pryce JE.

J Dairy Sci. 2015 Jun;98(6):4148-61. doi: 10.3168/jds.2014-9194. Epub 2015 Apr 8.

42.

Including overseas performance information in genomic evaluations of Australian dairy cattle.

Haile-Mariam M, Pryce JE, Schrooten C, Hayes BJ.

J Dairy Sci. 2015 May;98(5):3443-59. doi: 10.3168/jds.2014-8785. Epub 2015 Mar 12.

43.

On the value of the phenotypes in the genomic era.

Gonzalez-Recio O, Coffey MP, Pryce JE.

J Dairy Sci. 2014 Dec;97(12):7905-15. doi: 10.3168/jds.2014-8125. Epub 2014 Oct 13. Erratum in: J Dairy Sci. 2019 Jun;102(6):5764.

44.

Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle.

Pryce JE, Haile-Mariam M, Goddard ME, Hayes BJ.

Genet Sel Evol. 2014 Nov 18;46:71. doi: 10.1186/s12711-014-0071-7.

45.

Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits.

Egger-Danner C, Cole JB, Pryce JE, Gengler N, Heringstad B, Bradley A, Stock KF.

Animal. 2015 Feb;9(2):191-207. doi: 10.1017/S1751731114002614. Epub 2014 Nov 12. Review.

46.

Estimation of genomic breeding values for residual feed intake in a multibreed cattle population.

Khansefid M, Pryce JE, Bolormaa S, Miller SP, Wang Z, Li C, Goddard ME.

J Anim Sci. 2014 Aug;92(8):3270-83. doi: 10.2527/jas.2014-7375.

PMID:
25074450
47.

Genetic parameters across lactation for feed intake, fat- and protein-corrected milk, and liveweight in first-parity Holstein cattle.

Manzanilla Pech CI, Veerkamp RF, Calus MP, Zom R, van Knegsel A, Pryce JE, De Haas Y.

J Dairy Sci. 2014 Sep;97(9):5851-62. doi: 10.3168/jds.2014-8165. Epub 2014 Jul 11.

48.

Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows.

McParland S, Lewis E, Kennedy E, Moore SG, McCarthy B, O'Donovan M, Butler ST, Pryce JE, Berry DP.

J Dairy Sci. 2014 Sep;97(9):5863-71. doi: 10.3168/jds.2014-8214. Epub 2014 Jul 3.

49.

Short communication: Heterosis by environment and genotype by environment interactions for protein yield in Danish Jerseys.

Norberg E, Madsen P, Su G, Pryce JE, Jensen J, Kargo M.

J Dairy Sci. 2014 Jul;97(7):4557-61. doi: 10.3168/jds.2013-7693. Epub 2014 May 16.

50.

Genetic variants in mammary development, prolactin signalling and involution pathways explain considerable variation in bovine milk production and milk composition.

Raven LA, Cocks BG, Goddard ME, Pryce JE, Hayes BJ.

Genet Sel Evol. 2014 Apr 29;46:29. doi: 10.1186/1297-9686-46-29.

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