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Items: 1 to 50 of 188

1.

Crossbred evaluations using single-step genomic BLUP and algorithm for proven and young with different sources of data1.

Pocrnic I, Lourenco DAL, Chen CY, Herring WO, Misztal I.

J Anim Sci. 2019 Apr 3;97(4):1513-1522. doi: 10.1093/jas/skz042.

2.

Modeling pedigree accuracy and uncertain parentage in single-step genomic evaluations of simulated and US Holstein datasets.

Bradford HL, Masuda Y, Cole JB, Misztal I, VanRaden PM.

J Dairy Sci. 2019 Mar;102(3):2308-2318. doi: 10.3168/jds.2018-15419. Epub 2019 Jan 11.

3.

International bull evaluations by genomic BLUP with a prediction population.

Fragomeni B, Masuda Y, Bradford HL, Lourenco DAL, Misztal I.

J Dairy Sci. 2019 Mar;102(3):2330-2335. doi: 10.3168/jds.2018-15554. Epub 2019 Jan 11.

4.

Modeling missing pedigree in single-step genomic BLUP.

Bradford HL, Masuda Y, VanRaden PM, Legarra A, Misztal I.

J Dairy Sci. 2019 Mar;102(3):2336-2346. doi: 10.3168/jds.2018-15434. Epub 2019 Jan 11.

5.

Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle.

Oliveira HR, Lourenco DAL, Masuda Y, Misztal I, Tsuruta S, Jamrozik J, Brito LF, Silva FF, Schenkel FS.

J Dairy Sci. 2019 Mar;102(3):2365-2377. doi: 10.3168/jds.2018-15466. Epub 2019 Jan 11.

6.

Development of genomic predictions for harvest and carcass weight in channel catfish.

Garcia ALS, Bosworth B, Waldbieser G, Misztal I, Tsuruta S, Lourenco DAL.

Genet Sel Evol. 2018 Dec 14;50(1):66. doi: 10.1186/s12711-018-0435-5.

7.

Genetics and genomics of reproductive disorders in Canadian Holstein cattle.

Guarini AR, Lourenco DAL, Brito LF, Sargolzaei M, Baes CF, Miglior F, Misztal I, Schenkel FS.

J Dairy Sci. 2019 Feb;102(2):1341-1353. doi: 10.3168/jds.2018-15038. Epub 2018 Nov 22.

PMID:
30471913
8.

Bias in heritability estimates from genomic restricted maximum likelihood methods under different genotyping strategies.

Cesarani A, Pocrnic I, Macciotta NPP, Fragomeni BO, Misztal I, Lourenco DAL.

J Anim Breed Genet. 2019 Jan;136(1):40-50. doi: 10.1111/jbg.12367. Epub 2018 Nov 13.

PMID:
30426582
9.

Comparison of genomic predictions for lowly heritable traits using multi-step and single-step genomic best linear unbiased predictor in Holstein cattle.

Guarini AR, Lourenco DAL, Brito LF, Sargolzaei M, Baes CF, Miglior F, Misztal I, Schenkel FS.

J Dairy Sci. 2018 Sep;101(9):8076-8086. doi: 10.3168/jds.2017-14193. Epub 2018 Jun 21.

PMID:
29935829
10.

Differing genetic trend estimates from traditional and genomic evaluations of genotyped animals as evidence of preselection bias in US Holsteins.

Masuda Y, VanRaden PM, Misztal I, Lawlor TJ.

J Dairy Sci. 2018 Jun;101(6):5194-5206. doi: 10.3168/jds.2017-13310. Epub 2018 Mar 21.

11.

Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach.

Zhang X, Tsuruta S, Andonov S, Lourenco DAL, Sapp RL, Wang C, Misztal I.

Poult Sci. 2018 May 1;97(5):1511-1518. doi: 10.3382/ps/pex431.

12.

Reaction norm for yearling weight in beef cattle using single-step genomic evaluation.

Oliveira DP, Lourenco DAL, Tsuruta S, Misztal I, Santos DJA, de Araújo Neto FR, Aspilcueta-Borquis RR, Baldi F, Carvalheiro R, de Camargo GMF, Albuquerque LG, Tonhati H.

J Anim Sci. 2018 Feb 15;96(1):27-34. doi: 10.1093/jas/skx006.

13.

Erratum to: Incorporation of causative quantitative trait nucleotides in single-step GBLUP.

Fragomeni BO, Lourenco DAL, Masuda Y, Legarra A, Misztal I.

Genet Sel Evol. 2017 Aug 24;49(1):65. doi: 10.1186/s12711-017-0341-2. No abstract available.

14.

Implications of SNP weighting on single-step genomic predictions for different reference population sizes.

Lourenco DAL, Fragomeni BO, Bradford HL, Menezes IR, Ferraz JBS, Aguilar I, Tsuruta S, Misztal I.

J Anim Breed Genet. 2017 Dec;134(6):463-471. doi: 10.1111/jbg.12288. Epub 2017 Aug 22.

PMID:
28833593
15.

Technical note: Impact of pedigree depth on convergence of single-step genomic BLUP in a purebred swine population.

Pocrnic I, Lourenco DAL, Bradford HL, Chen CY, Misztal I.

J Anim Sci. 2017 Aug;95(8):3391-3395. doi: 10.2527/jas.2017.1581.

PMID:
28805917
16.

Incorporation of causative quantitative trait nucleotides in single-step GBLUP.

Fragomeni BO, Lourenco DAL, Masuda Y, Legarra A, Misztal I.

Genet Sel Evol. 2017 Jul 26;49(1):59. doi: 10.1186/s12711-017-0335-0. Erratum in: Genet Sel Evol. 2017 Aug 24;49(1):65.

17.

Genomic analysis of cow mortality and milk production using a threshold-linear model.

Tsuruta S, Lourenco DAL, Misztal I, Lawlor TJ.

J Dairy Sci. 2017 Sep;100(9):7295-7305. doi: 10.3168/jds.2017-12665. Epub 2017 Jun 21.

18.

Selection of core animals in the Algorithm for Proven and Young using a simulation model.

Bradford HL, Pocrnić I, Fragomeni BO, Lourenco DAL, Misztal I.

J Anim Breed Genet. 2017 Dec;134(6):545-552. doi: 10.1111/jbg.12276. Epub 2017 May 2.

PMID:
28464315
19.

BREEDING AND GENETICS SYMPOSIUM: Resilience and lessons from studies in genetics of heat stress.

Misztal I.

J Anim Sci. 2017 Apr;95(4):1780-1787. doi: 10.2527/jas.2016.0953.

PMID:
28464095
20.

Accuracy of breeding values in small genotyped populations using different sources of external information-A simulation study.

Andonov S, Lourenco DAL, Fragomeni BO, Masuda Y, Pocrnic I, Tsuruta S, Misztal I.

J Dairy Sci. 2017 Jan;100(1):395-401. doi: 10.3168/jds.2016-11335. Epub 2016 Oct 27.

21.

Metafounders are related to F st fixation indices and reduce bias in single-step genomic evaluations.

Garcia-Baccino CA, Legarra A, Christensen OF, Misztal I, Pocrnic I, Vitezica ZG, Cantet RJ.

Genet Sel Evol. 2017 Mar 10;49(1):34. doi: 10.1186/s12711-017-0309-2.

22.

Technical note: Avoiding the direct inversion of the numerator relationship matrix for genotyped animals in single-step genomic best linear unbiased prediction solved with the preconditioned conjugate gradient.

Masuda Y, Misztal I, Legarra A, Tsuruta S, Lourenco DA, Fragomeni BO, Aguilar I.

J Anim Sci. 2017 Jan;95(1):49-52. doi: 10.2527/jas.2016.0699.

PMID:
28177357
23.

Genome-Wide Association Study for Carcass Traits in an Experimental Nelore Cattle Population.

Medeiros de Oliveira Silva R, Bonvino Stafuzza N, de Oliveira Fragomeni B, Miguel Ferreira de Camargo G, Matos Ceacero T, Noely Dos Santos Gonçalves Cyrillo J, Baldi F, Augusti Boligon A, Zerlotti Mercadante ME, Lino Lourenco D, Misztal I, Galvão de Albuquerque L.

PLoS One. 2017 Jan 24;12(1):e0169860. doi: 10.1371/journal.pone.0169860. eCollection 2017.

24.

Using single-step genomic best linear unbiased predictor to enhance the mitigation of seasonal losses due to heat stress in pigs.

Fragomeni BO, Lourenco DA, Tsuruta S, Bradford HL, Gray KA, Huang Y, Misztal I.

J Anim Sci. 2016 Dec;94(12):5004-5013. doi: 10.2527/jas.2016-0820.

PMID:
28046178
25.

Modeling response to heat stress in pigs from nucleus and commercial farms in different locations in the United States.

Fragomeni BO, Lourenco DA, Tsuruta S, Andonov S, Gray K, Huang Y, Misztal I.

J Anim Sci. 2016 Nov;94(11):4789-4798. doi: 10.2527/jas.2016-0536.

PMID:
27898949
26.

Sexual dimorphism in livestock species selected for economically important traits.

van der Heide EM, Lourenco DA, Chen CY, Herring WO, Sapp RL, Moser DW, Tsuruta S, Masuda Y, Ducro BJ, Misztal I.

J Anim Sci. 2016 Sep;94(9):3684-3692. doi: 10.2527/jas.2016-0393.

PMID:
27898906
27.

Accuracies of genomic prediction of feed efficiency traits using different prediction and validation methods in an experimental Nelore cattle population.

Silva RM, Fragomeni BO, Lourenco DA, Magalhães AF, Irano N, Carvalheiro R, Canesin RC, Mercadante ME, Boligon AA, Baldi FS, Misztal I, Albuquerque LG.

J Anim Sci. 2016 Sep;94(9):3613-3623. doi: 10.2527/jas.2016-0401.

PMID:
27898889
28.

Regional and seasonal analyses of weights in growing Angus cattle.

Bradford HL, Fragomeni BO, Bertrand JK, Lourenco DA, Misztal I.

J Anim Sci. 2016 Oct;94(10):4369-4375. doi: 10.2527/jas.2016-0683.

PMID:
27898859
29.

Genetic evaluations for growth heat tolerance in Angus cattle.

Bradford HL, Fragomeni BO, Bertrand JK, Lourenco DA, Misztal I.

J Anim Sci. 2016 Oct;94(10):4143-4150. doi: 10.2527/jas.2016-0707.

PMID:
27898850
30.

Invited review: efficient computation strategies in genomic selection.

Misztal I, Legarra A.

Animal. 2017 May;11(5):731-736. doi: 10.1017/S1751731116002366. Epub 2016 Nov 21. Review.

PMID:
27869042
31.
32.

Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS.

Zhang X, Lourenco D, Aguilar I, Legarra A, Misztal I.

Front Genet. 2016 Aug 19;7:151. doi: 10.3389/fgene.2016.00151. eCollection 2016.

33.

Evaluation of Genome-Enabled Selection for Bacterial Cold Water Disease Resistance Using Progeny Performance Data in Rainbow Trout: Insights on Genotyping Methods and Genomic Prediction Models.

Vallejo RL, Leeds TD, Fragomeni BO, Gao G, Hernandez AG, Misztal I, Welch TJ, Wiens GD, Palti Y.

Front Genet. 2016 May 27;7:96. doi: 10.3389/fgene.2016.00096. eCollection 2016.

34.

Crossbreed evaluations in single-step genomic best linear unbiased predictor using adjusted realized relationship matrices.

Lourenco DA, Tsuruta S, Fragomeni BO, Chen CY, Herring WO, Misztal I.

J Anim Sci. 2016 Mar;94(3):909-19. doi: 10.2527/jas.2015-9748.

PMID:
27065253
35.

Is genomic selection now a mature technology?

Misztal I.

J Anim Breed Genet. 2016 Apr;133(2):81-2. doi: 10.1111/jbg.12209. No abstract available.

PMID:
26995216
36.

The Dimensionality of Genomic Information and Its Effect on Genomic Prediction.

Pocrnic I, Lourenco DA, Masuda Y, Legarra A, Misztal I.

Genetics. 2016 May;203(1):573-81. doi: 10.1534/genetics.116.187013. Epub 2016 Mar 4.

37.

Genomic BLUP including additive and dominant variation in purebreds and F1 crossbreds, with an application in pigs.

Vitezica ZG, Varona L, Elsen JM, Misztal I, Herring W, Legarra A.

Genet Sel Evol. 2016 Jan 29;48:6. doi: 10.1186/s12711-016-0185-1.

38.

Implementation of genomic recursions in single-step genomic best linear unbiased predictor for US Holsteins with a large number of genotyped animals.

Masuda Y, Misztal I, Tsuruta S, Legarra A, Aguilar I, Lourenco DAL, Fragomeni BO, Lawlor TJ.

J Dairy Sci. 2016 Mar;99(3):1968-1974. doi: 10.3168/jds.2015-10540. Epub 2016 Jan 21.

39.

Inexpensive Computation of the Inverse of the Genomic Relationship Matrix in Populations with Small Effective Population Size.

Misztal I.

Genetics. 2016 Feb;202(2):401-9. doi: 10.1534/genetics.115.182089. Epub 2015 Nov 19.

40.

Technical note: Acceleration of sparse operations for average-information REML analyses with supernodal methods and sparse-storage refinements.

Masuda Y, Aguilar I, Tsuruta S, Misztal I.

J Anim Sci. 2015 Oct;93(10):4670-4. doi: 10.2527/jas.2015-9395.

PMID:
26523559
41.

Accuracy of estimated breeding values with genomic information on males, females, or both: an example on broiler chicken.

Lourenco DA, Fragomeni BO, Tsuruta S, Aguilar I, Zumbach B, Hawken RJ, Legarra A, Misztal I.

Genet Sel Evol. 2015 Jul 2;47:56. doi: 10.1186/s12711-015-0137-1.

42.

Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus.

Lourenco DA, Tsuruta S, Fragomeni BO, Masuda Y, Aguilar I, Legarra A, Bertrand JK, Amen TS, Wang L, Moser DW, Misztal I.

J Anim Sci. 2015 Jun;93(6):2653-62. doi: 10.2527/jas.2014-8836.

PMID:
26115253
43.

Genotype by environment interactions on culling rates and 305-day milk yield of Holstein cows in 3 US regions.

Tsuruta S, Lourenco DA, Misztal I, Lawlor TJ.

J Dairy Sci. 2015 Aug;98(8):5796-805. doi: 10.3168/jds.2014-9242. Epub 2015 May 28.

44.

Correlations between purebred and crossbred body weight traits in Limousin and Limousin-Angus populations.

Lukaszewicz M, Davis R, Bertrand JK, Misztal I, Tsuruta S.

J Anim Sci. 2015 Apr;93(4):1490-3. doi: 10.2527/jas.2014-8285.

PMID:
26020170
45.

Ancestral Relationships Using Metafounders: Finite Ancestral Populations and Across Population Relationships.

Legarra A, Christensen OF, Vitezica ZG, Aguilar I, Misztal I.

Genetics. 2015 Jun;200(2):455-68. doi: 10.1534/genetics.115.177014. Epub 2015 Apr 14.

46.

Hot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes.

Fragomeni BO, Lourenco DA, Tsuruta S, Masuda Y, Aguilar I, Legarra A, Lawlor TJ, Misztal I.

J Dairy Sci. 2015 Jun;98(6):4090-4. doi: 10.3168/jds.2014-9125. Epub 2015 Apr 8.

47.

Use of genomic recursions and algorithm for proven and young animals for single-step genomic BLUP analyses--a simulation study.

Fragomeni BO, Lourenco DA, Tsuruta S, Masuda Y, Aguilar I, Misztal I.

J Anim Breed Genet. 2015 Oct;132(5):340-5. doi: 10.1111/jbg.12161. Epub 2015 Apr 10.

PMID:
25857518
48.

Quality control of genotypes using heritability estimates of gene content at the marker.

Forneris NS, Legarra A, Vitezica ZG, Tsuruta S, Aguilar I, Misztal I, Cantet RJ.

Genetics. 2015 Mar;199(3):675-81. doi: 10.1534/genetics.114.173559. Epub 2015 Jan 6.

49.

Changes in variance explained by top SNP windows over generations for three traits in broiler chicken.

Fragomeni Bde O, Misztal I, Lourenco DL, Aguilar I, Okimoto R, Muir WM.

Front Genet. 2014 Oct 1;5:332. doi: 10.3389/fgene.2014.00332. eCollection 2014.

50.

Differences between genomic-based and pedigree-based relationships in a chicken population, as a function of quality control and pedigree links among individuals.

Wang H, Misztal I, Legarra A.

J Anim Breed Genet. 2014 Dec;131(6):445-51. doi: 10.1111/jbg.12109. Epub 2014 Jul 15.

PMID:
25039816

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