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Items: 28

1.

Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models.

Momen M, Ayatollahi Mehrgardi A, Amiri Roudbar M, Kranis A, Mercuri Pinto R, Valente BD, Morota G, Rosa GJM, Gianola D.

Front Genet. 2018 Oct 9;9:455. doi: 10.3389/fgene.2018.00455. eCollection 2018.

2.

Quantifying genomic connectedness and prediction accuracy from additive and non-additive gene actions.

Momen M, Morota G.

Genet Sel Evol. 2018 Sep 17;50(1):45. doi: 10.1186/s12711-018-0415-9.

3.

Do stronger measures of genomic connectedness enhance prediction accuracies across management units?

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

J Anim Sci. 2018 Nov 21;96(11):4490-4500. doi: 10.1093/jas/sky316.

4.

Predictive ability of genome-assisted statistical models under various forms of gene action.

Momen M, Mehrgardi AA, Sheikhi A, Kranis A, Tusell L, Morota G, Rosa GJM, Gianola D.

Sci Rep. 2018 Aug 17;8(1):12309. doi: 10.1038/s41598-018-30089-2.

5.

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.

6.

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.

7.

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.

8.

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.

9.

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
10.

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.

11.

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.

12.

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.

13.

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.

14.

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.

15.

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.

16.

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.

17.

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.

18.

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.

19.

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
20.

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.

21.

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.

22.

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.

23.

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
24.

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
25.

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.

26.

Evaluation of linkage disequilibrium in wheat with an L1-regularized sparse Markov network.

Morota G, Gianola D.

Theor Appl Genet. 2013 Aug;126(8):1991-2002. doi: 10.1007/s00122-013-2112-y. Epub 2013 May 10.

PMID:
23661079
27.

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
28.

Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model.

Bueno Filho JS, Morota G, Tran Q, Maenner MJ, Vera-Cala LM, Engelman CD, Meyers KJ.

BMC Proc. 2011 Nov 29;5 Suppl 9:S93. doi: 10.1186/1753-6561-5-S9-S93.

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