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Items: 1 to 20 of 24

1.

Linkage disequilibrium in Brazilian Santa Inês breed, Ovis aries.

Alvarenga AB, Rovadoscki GA, Petrini J, Coutinho LL, Morota G, Spangler ML, Pinto LFB, Carvalho GGP, Mourão GB.

Sci Rep. 2018 Jun 11;8(1):8851. doi: 10.1038/s41598-018-27259-7.

2.

Estimates of genomic heritability and genome-wide association study for fatty acids profile in Santa Inês sheep.

Rovadoscki GA, Pertile SFN, Alvarenga AB, Cesar ASM, Pértille F, Petrini J, Franzo V, Soares WVB, Morota G, Spangler ML, Pinto LFB, Carvalho GGP, Lanna DPD, Coutinho LL, Mourão GB.

BMC Genomics. 2018 May 21;19(1):375. doi: 10.1186/s12864-018-4777-8.

3.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

Morota G, Ventura RV, Silva FF, Koyama M, Fernando SC.

J Anim Sci. 2018 Apr 14;96(4):1540-1550. doi: 10.1093/jas/sky014.

PMID:
29385611
4.

ShinyGPAS: interactive genomic prediction accuracy simulator based on deterministic formulas.

Morota G.

Genet Sel Evol. 2017 Dec 20;49(1):91. doi: 10.1186/s12711-017-0368-4.

5.

Comparing strategies for selection of low-density SNPs for imputation-mediated genomic prediction in U. S. Holsteins.

He J, Xu J, Wu XL, Bauck S, Lee J, Morota G, Kachman SD, Spangler ML.

Genetica. 2018 Apr;146(2):137-149. doi: 10.1007/s10709-017-0004-9. Epub 2017 Dec 14.

PMID:
29243001
6.

Predicting bull fertility using genomic data and biological information.

Abdollahi-Arpanahi R, Morota G, Peñagaricano F.

J Dairy Sci. 2017 Dec;100(12):9656-9666. doi: 10.3168/jds.2017-13288. Epub 2017 Oct 4.

7.

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units.

Yu H, Spangler ML, Lewis RM, Morota G.

G3 (Bethesda). 2017 Oct 5;7(10):3543-3556. doi: 10.1534/g3.117.300151.

8.

Medical Subject Heading (MeSH) annotations illuminate maize genetics and evolution.

Beissinger TM, Morota G.

Plant Methods. 2017 Feb 23;13:8. doi: 10.1186/s13007-017-0159-5. eCollection 2017.

9.

MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity Among Functional Terms and Gene Products in Chicken.

Morota G, Beissinger TM, Peñagaricano F.

G3 (Bethesda). 2016 Aug 9;6(8):2447-53. doi: 10.1534/g3.116.031096.

10.

Differential contribution of genomic regions to marked genetic variation and prediction of quantitative traits in broiler chickens.

Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D.

Genet Sel Evol. 2016 Feb 3;48:10. doi: 10.1186/s12711-016-0187-z.

11.

Prediction of Plant Height in Arabidopsis thaliana Using DNA Methylation Data.

Hu Y, Morota G, Rosa GJ, Gianola D.

Genetics. 2015 Oct;201(2):779-93. doi: 10.1534/genetics.115.177204. Epub 2015 Aug 6.

12.

An application of MeSH enrichment analysis in livestock.

Morota G, Peñagaricano F, Petersen JL, Ciobanu DC, Tsuyuzaki K, Nikaido I.

Anim Genet. 2015 Aug;46(4):381-7. doi: 10.1111/age.12307. Epub 2015 Jun 2.

13.

The Causal Meaning of Genomic Predictors and How It Affects Construction and Comparison of Genome-Enabled Selection Models.

Valente BD, Morota G, Peñagaricano F, Gianola D, Weigel K, Rosa GJ.

Genetics. 2015 Jun;200(2):483-94. doi: 10.1534/genetics.114.169490. Epub 2015 Apr 23.

14.

MeSH ORA framework: R/Bioconductor packages to support MeSH over-representation analysis.

Tsuyuzaki K, Morota G, Ishii M, Nakazato T, Miyazaki S, Nikaido I.

BMC Bioinformatics. 2015 Feb 15;16:45. doi: 10.1186/s12859-015-0453-z.

15.

Assessment of bagging GBLUP for whole-genome prediction of broiler chicken traits.

Abdollahi-Arpanahi R, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D.

J Anim Breed Genet. 2015 Jun;132(3):218-28. doi: 10.1111/jbg.12131. Epub 2015 Mar 1.

PMID:
25727456
16.

Kernel-based whole-genome prediction of complex traits: a review.

Morota G, Gianola D.

Front Genet. 2014 Oct 16;5:363. doi: 10.3389/fgene.2014.00363. eCollection 2014. Review.

17.

Kernel-based variance component estimation and whole-genome prediction of pre-corrected phenotypes and progeny tests for dairy cow health traits.

Morota G, Boddhireddy P, Vukasinovic N, Gianola D, Denise S.

Front Genet. 2014 Mar 24;5:56. doi: 10.3389/fgene.2014.00056. eCollection 2014.

18.

Genome-enabled prediction of quantitative traits in chickens using genomic annotation.

Morota G, Abdollahi-Arpanahi R, Kranis A, Gianola D.

BMC Genomics. 2014 Feb 7;15:109. doi: 10.1186/1471-2164-15-109.

19.

Dissection of additive genetic variability for quantitative traits in chickens using SNP markers.

Abdollahi-Arpanahi R, Pakdel A, Nejati-Javaremi A, Moradi Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D.

J Anim Breed Genet. 2014 Jun;131(3):183-93. doi: 10.1111/jbg.12079. Epub 2014 Jan 25.

PMID:
24460953
20.

Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens.

Abdollahi-Arpanahi R, Nejati-Javaremi A, Pakdel A, Moradi-Shahrbabak M, Morota G, Valente BD, Kranis A, Rosa GJ, Gianola D.

J Anim Breed Genet. 2014 Apr;131(2):123-33. doi: 10.1111/jbg.12075. Epub 2014 Jan 8.

PMID:
24397350

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