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Items: 1 to 50 of 154

1.

High-density genome-wide association study for residual feed intake in Holstein dairy cattle.

Li B, Fang L, Null DJ, Hutchison JL, Connor EE, VanRaden PM, VandeHaar MJ, Tempelman RJ, Weigel KA, Cole JB.

J Dairy Sci. 2019 Dec;102(12):11067-11080. doi: 10.3168/jds.2019-16645. Epub 2019 Sep 25.

PMID:
31563317
2.

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. doi: 10.3168/jds.2019-16820. Epub 2019 Sep 20.

3.

Effect of diet energy density and genomic residual feed intake on prebred dairy heifer feed efficiency, growth, and manure excretion.

Williams KT, Weigel KA, Coblentz WK, Esser NM, Schlesser H, Hoffman PC, Su H, Akins MS.

J Dairy Sci. 2019 May;102(5):4041-4050. doi: 10.3168/jds.2018-15504. Epub 2019 Mar 7.

PMID:
30852010
4.

Genetic Selection for Mastitis Resistance.

Weigel KA, Shook GE.

Vet Clin North Am Food Anim Pract. 2018 Nov;34(3):457-472. doi: 10.1016/j.cvfa.2018.07.001. Review.

PMID:
30316503
5.

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency.

Lu Y, Vandehaar MJ, Spurlock DM, Weigel KA, Armentano LE, Connor EE, Coffey M, Veerkamp RF, de Haas Y, Staples CR, Wang Z, Hanigan MD, Tempelman RJ.

J Dairy Sci. 2018 Apr;101(4):3140-3154. doi: 10.3168/jds.2017-13364. Epub 2018 Feb 1.

6.

A 100-Year Review: Methods and impact of genetic selection in dairy cattle-From daughter-dam comparisons to deep learning algorithms.

Weigel KA, VanRaden PM, Norman HD, Grosu H.

J Dairy Sci. 2017 Dec;100(12):10234-10250. doi: 10.3168/jds.2017-12954. Review.

7.

The genetic and biological basis of feed efficiency in mid-lactation Holstein dairy cows.

Hardie LC, VandeHaar MJ, Tempelman RJ, Weigel KA, Armentano LE, Wiggans GR, Veerkamp RF, de Haas Y, Coffey MP, Connor EE, Hanigan MD, Staples C, Wang Z, Dekkers JCM, Spurlock DM.

J Dairy Sci. 2017 Nov;100(11):9061-9075. doi: 10.3168/jds.2017-12604. Epub 2017 Aug 23.

8.

Prediction of whole-genome risk for selection and management of hyperketonemia in Holstein dairy cattle.

Weigel KA, Pralle RS, Adams H, Cho K, Do C, White HM.

J Anim Breed Genet. 2017 Jun;134(3):275-285. doi: 10.1111/jbg.12259.

PMID:
28508489
9.

Relationships between body condition score change, prior mid-lactation phenotypic residual feed intake, and hyperketonemia onset in transition dairy cows.

Rathbun FM, Pralle RS, Bertics SJ, Armentano LE, Cho K, Do C, Weigel KA, White HM.

J Dairy Sci. 2017 May;100(5):3685-3696. doi: 10.3168/jds.2016-12085. Epub 2017 Mar 16.

10.

Use of genotype × environment interaction model to accommodate genetic heterogeneity for residual feed intake, dry matter intake, net energy in milk, and metabolic body weight in dairy cattle.

Yao C, de Los Campos G, VandeHaar MJ, Spurlock DM, Armentano LE, Coffey M, de Haas Y, Veerkamp RF, Staples CR, Connor EE, Wang Z, Hanigan MD, Tempelman RJ, Weigel KA.

J Dairy Sci. 2017 Mar;100(3):2007-2016. doi: 10.3168/jds.2016-11606. Epub 2017 Jan 18.

11.

Assessing genomic prediction accuracy for Holstein sires using bootstrap aggregation sampling and leave-one-out cross validation.

Mikshowsky AA, Gianola D, Weigel KA.

J Dairy Sci. 2017 Jan;100(1):453-464. doi: 10.3168/jds.2016-11496. Epub 2016 Nov 23.

12.

Modeling genetic and nongenetic variation of feed efficiency and its partial relationships between component traits as a function of management and environmental factors.

Lu Y, Vandehaar MJ, Spurlock DM, Weigel KA, Armentano LE, Staples CR, Connor EE, Wang Z, Coffey M, Veerkamp RF, de Haas Y, Tempelman RJ.

J Dairy Sci. 2017 Jan;100(1):412-427. doi: 10.3168/jds.2016-11491. Epub 2016 Nov 17.

14.

Corrigendum to "Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations-the Netherlands and United States" (J. Dairy Sci. 99:443-457).

Manzanilla-Pech CIV, Veerkamp RF, Tempelman RJ, van Pelt ML, Weigel KA, VandeHaar M, Lawlor TJ, Spurlock DM, Armentano LE, Staples CR, Hanigan M, De Haas Y.

J Dairy Sci. 2016 May;99(5):4095. doi: 10.3168/jds.2016-99-5-4095. Epub 2016 Apr 20. No abstract available.

15.

Genome-wide association mapping and pathway analysis of leukosis incidence in a US Holstein cattle population.

Abdalla EA, Peñagaricano F, Byrem TM, Weigel KA, Rosa GJ.

Anim Genet. 2016 Aug;47(4):395-407. doi: 10.1111/age.12438. Epub 2016 Apr 19.

PMID:
27090879
16.

Improving reliability of genomic predictions for Jersey sires using bootstrap aggregation sampling.

Mikshowsky AA, Gianola D, Weigel KA.

J Dairy Sci. 2016 May;99(5):3632-3645. doi: 10.3168/jds.2015-10715. Epub 2016 Mar 9.

17.

Short communication: Genetic correlation of bovine leukosis incidence with somatic cell score and milk yield in a US Holstein population.

Abdalla EA, Weigel KA, Byrem TM, Rosa GJM.

J Dairy Sci. 2016 Mar;99(3):2005-2009. doi: 10.3168/jds.2015-9833. Epub 2016 Jan 6.

18.

Genetic parameters between feed-intake-related traits and conformation in 2 separate dairy populations--the Netherlands and United States.

Manzanilla-Pech CI, Veerkamp RF, Tempelman RJ, van Pelt ML, Weigel KA, VandeHaar M, Lawlor TJ, Spurlock DM, Armentano LE, Staples CR, Hanigan M, De Haas Y.

J Dairy Sci. 2016 Jan;99(1):443-57. doi: 10.3168/jds.2015-9727. Epub 2015 Nov 5.

19.

An alternative approach to modeling genetic merit of feed efficiency in dairy cattle.

Lu Y, Vandehaar MJ, Spurlock DM, Weigel KA, Armentano LE, Staples CR, Connor EE, Wang Z, Bello NM, Tempelman RJ.

J Dairy Sci. 2015 Sep;98(9):6535-51. doi: 10.3168/jds.2015-9414. Epub 2015 Jul 22.

20.

Using a family-based structure to detect the effects of genomic inbreeding on embryo viability in Holstein cattle.

Bjelland DW, Weigel KA, Coburn AD, Wilson RD.

J Dairy Sci. 2015 Jul;98(7):4934-44. doi: 10.3168/jds.2014-9014. Epub 2015 May 7.

21.

Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors.

Shahinfar S, Guenther JN, Page CD, Kalantari AS, Cabrera VE, Fricke PM, Weigel KA.

J Dairy Sci. 2015 Jun;98(6):3717-28. doi: 10.3168/jds.2014-8255. Epub 2015 Apr 1.

22.

Considerations when combining data from multiple nutrition experiments to estimate genetic parameters for feed efficiency.

Hardie LC, Armentano LE, Shaver RD, VandeHaar MJ, Spurlock DM, Yao C, Bertics SJ, Contreras-Govea FE, Weigel KA.

J Dairy Sci. 2015 Apr;98(4):2727-37. doi: 10.3168/jds.2014-8580. Epub 2015 Feb 7.

23.

Heterogeneity in genetic and nongenetic variation and energy sink relationships for residual feed intake across research stations and countries.

Tempelman RJ, Spurlock DM, Coffey M, Veerkamp RF, Armentano LE, Weigel KA, de Haas Y, Staples CR, Connor EE, Lu Y, VandeHaar MJ.

J Dairy Sci. 2015 Mar;98(3):2013-26. doi: 10.3168/jds.2014.8510. Epub 2015 Jan 9.

24.
25.

Prediction of genetic contributions to complex traits using whole genome sequencing data.

Yao C, Leng N, Weigel KA, Lee KE, Engelman CD, Meyers KJ.

BMC Proc. 2014 Jun 17;8(Suppl 1):S68. doi: 10.1186/1753-6561-8-S1-S68. eCollection 2014.

26.

Applied animal genomics: results from the field.

Van Eenennaam AL, Weigel KA, Young AE, Cleveland MA, Dekkers JC.

Annu Rev Anim Biosci. 2014 Feb;2:105-39. doi: 10.1146/annurev-animal-022513-114119. Epub 2013 Dec 2. Review.

PMID:
25384137
27.

Meta-analysis of candidate gene effects using bayesian parametric and non-parametric approaches.

Wu XL, Gianola D, Rosa GJ, Weigel KA.

J Genomics. 2014 Jan 2;2:1-19. doi: 10.7150/jgen.5054. eCollection 2014.

28.

Enhancing genome-enabled prediction by bagging genomic BLUP.

Gianola D, Weigel KA, Krämer N, Stella A, Schön CC.

PLoS One. 2014 Apr 10;9(4):e91693. doi: 10.1371/journal.pone.0091693. eCollection 2014.

29.

Short communication: genetic evaluation of stillbirth in US Brown Swiss and Jersey cattle.

Yao C, Weigel KA, Cole JB.

J Dairy Sci. 2014;97(4):2474-80. doi: 10.3168/jds.2013-7320. Epub 2014 Feb 6.

30.

Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations.

Pryce JE, Johnston J, Hayes BJ, Sahana G, Weigel KA 2nd, McParland S, Spurlock D, Krattenmacher N, Spelman RJ, Wall E, Calus MP.

J Dairy Sci. 2014 Mar;97(3):1799-811. doi: 10.3168/jds.2013-7368. Epub 2014 Jan 25.

31.

Mating programs including genomic relationships and dominance effects.

Sun C, VanRaden PM, O'Connell JR, Weigel KA, Gianola D.

J Dairy Sci. 2013;96(12):8014-23. doi: 10.3168/jds.2013-6969. Epub 2013 Oct 11.

32.

Random Forests approach for identifying additive and epistatic single nucleotide polymorphisms associated with residual feed intake in dairy cattle.

Yao C, Spurlock DM, Armentano LE, Page CD Jr, VandeHaar MJ, Bickhart DM, Weigel KA.

J Dairy Sci. 2013 Oct;96(10):6716-29. doi: 10.3168/jds.2012-6237. Epub 2013 Aug 9.

33.

Genetic analysis of leukosis incidence in United States Holstein and Jersey populations.

Abdalla EA, Rosa GJ, Weigel KA, Byrem T.

J Dairy Sci. 2013 Sep;96(9):6022-9. doi: 10.3168/jds.2013-6732. Epub 2013 Jul 5.

34.

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data.

Morota G, Koyama M, Rosa GJ, Weigel KA, Gianola D.

Genet Sel Evol. 2013 Jun 13;45:17. doi: 10.1186/1297-9686-45-17.

35.

Genomic architecture of bovine κ-casein and β-lactoglobulin.

Gambra R, Peñagaricano F, Kropp J, Khateeb K, Weigel KA, Lucey J, Khatib H.

J Dairy Sci. 2013 Aug;96(8):5333-43. doi: 10.3168/jds.2012-6324. Epub 2013 Jun 5.

36.

Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding.

Bjelland DW, Weigel KA, Vukasinovic N, Nkrumah JD.

J Dairy Sci. 2013 Jul;96(7):4697-706. doi: 10.3168/jds.2012-6435. Epub 2013 May 16.

37.

Technical note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding.

Pérez-Rodríguez P, Gianola D, Weigel KA, Rosa GJ, Crossa J.

J Anim Sci. 2013 Aug;91(8):3522-31. doi: 10.2527/jas.2012-6162. Epub 2013 May 8.

PMID:
23658327
38.

Comparison of selective genotyping strategies for prediction of breeding values in a population undergoing selection.

Boligon AA, Long N, Albuquerque LG, Weigel KA, Gianola D, Rosa GJ.

J Anim Sci. 2012 Dec;90(13):4716-22. doi: 10.2527/jas.2012-4857.

PMID:
23372045
39.

Inferring quantitative trait pathways associated with bull fertility from a genome-wide association study.

Peñagaricano F, Weigel KA, Rosa GJ, Khatib H.

Front Genet. 2013 Jan 11;3:307. doi: 10.3389/fgene.2012.00307. eCollection 2012.

40.

Short communication: A missense mutation in the PROP1 (prophet of Pit 1) gene affects male fertility and milk production traits in the US Holstein population.

Lan XY, Peñagaricano F, DeJung L, Weigel KA, Khatib H.

J Dairy Sci. 2013 Feb;96(2):1255-7. doi: 10.3168/jds.2012-6019. Epub 2012 Dec 14.

41.

An assessment of linkage disequilibrium in Holstein cattle using a Bayesian network.

Morota G, Valente BD, Rosa GJ, Weigel KA, Gianola D.

J Anim Breed Genet. 2012 Dec;129(6):474-87. doi: 10.1111/jbg.12002. Epub 2012 Sep 13.

PMID:
23148973
42.

Parallel Markov chain Monte Carlo - bridging the gap to high-performance Bayesian computation in animal breeding and genetics.

Wu XL, Sun C, Beissinger TM, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D.

Genet Sel Evol. 2012 Sep 25;44:29. doi: 10.1186/1297-9686-44-29.

43.

Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems.

Shahinfar S, Mehrabani-Yeganeh H, Lucas C, Kalhor A, Kazemian M, Weigel KA.

Comput Math Methods Med. 2012;2012:127130. Epub 2012 Sep 9.

44.

Comparative genomics between fly, mouse, and cattle identifies genes associated with sire conception rate.

Li G, Peñagaricano F, Weigel KA, Zhang Y, Rosa G, Khatib H.

J Dairy Sci. 2012 Oct;95(10):6122-9. doi: 10.3168/jds.2012-5591. Epub 2012 Aug 23.

45.

An ensemble-based approach to imputation of moderate-density genotypes for genomic selection with application to Angus cattle.

Sun C, Wu XL, Weigel KA, Rosa GJ, Bauck S, Woodward BW, Schnabel RD, Taylor JF, Gianola D.

Genet Res (Camb). 2012 Jun;94(3):133-50. doi: 10.1017/S001667231200033X. Epub 2012 Jul 18.

PMID:
22809677
46.

Genome-wide association study identifies candidate markers for bull fertility in Holstein dairy cattle.

Peñagaricano F, Weigel KA, Khatib H.

Anim Genet. 2012 Jul;43 Suppl 1:65-71. doi: 10.1111/j.1365-2052.2012.02350.x.

PMID:
22742504
47.

Potential gains in lifetime net merit from genomic testing of cows, heifers, and calves on commercial dairy farms.

Weigel KA, Hoffman PC, Herring W, Lawlor TJ Jr.

J Dairy Sci. 2012 Apr;95(4):2215-25. doi: 10.3168/jds.2011-4877.

48.

Accuracy of Genome-Enabled Prediction in a Dairy Cattle Population using Different Cross-Validation Layouts.

Pérez-Cabal MA, Vazquez AI, Gianola D, Rosa GJ, Weigel KA.

Front Genet. 2012 Feb 28;3:27. doi: 10.3389/fgene.2012.00027. eCollection 2012.

49.

Predictive ability of alternative models for genetic analysis of clinical mastitis.

Vazquez AI, Perez-Cabal MA, Heringstad B, Rodrigues-Motta M, Rosa GJ, Gianola D, Weigel KA.

J Anim Breed Genet. 2012 Apr;129(2):120-8. doi: 10.1111/j.1439-0388.2011.00950.x. Epub 2011 Sep 27.

PMID:
22394234
50.

A primer on high-throughput computing for genomic selection.

Wu XL, Beissinger TM, Bauck S, Woodward B, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D.

Front Genet. 2011 Feb 24;2:4. doi: 10.3389/fgene.2011.00004. eCollection 2011.

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