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Items: 11

1.

Maize responsiveness to Azospirillum brasilense: Insights into genetic control, heterosis and genomic prediction.

Vidotti MS, Matias FI, Alves FC, Pérez-Rodríguez P, Beltran GA, Burgueño J, Crossa J, Fritsche-Neto R.

PLoS One. 2019 Jun 7;14(6):e0217571. doi: 10.1371/journal.pone.0217571. eCollection 2019.

2.

Bayesian analysis and prediction of hybrid performance.

Alves FC, Granato ÍSC, Galli G, Lyra DH, Fritsche-Neto R, de Los Campos G.

Plant Methods. 2019 Feb 7;15:14. doi: 10.1186/s13007-019-0388-x. eCollection 2019.

3.

Modeling copy number variation in the genomic prediction of maize hybrids.

Lyra DH, Galli G, Alves FC, Granato ÍSC, Vidotti MS, Bandeira E Sousa M, Morosini JS, Crossa J, Fritsche-Neto R.

Theor Appl Genet. 2019 Jan;132(1):273-288. doi: 10.1007/s00122-018-3215-2. Epub 2018 Oct 31.

PMID:
30382311
4.

Multi-objective optimized genomic breeding strategies for sustainable food improvement.

Akdemir D, Beavis W, Fritsche-Neto R, Singh AK, Isidro-Sánchez J.

Heredity (Edinb). 2019 May;122(5):672-683. doi: 10.1038/s41437-018-0147-1. Epub 2018 Sep 27.

5.

BGGE: A New Package for Genomic-Enabled Prediction Incorporating Genotype × Environment Interaction Models.

Granato I, Cuevas J, Luna-Vázquez F, Crossa J, Montesinos-López O, Burgueño J, Fritsche-Neto R.

G3 (Bethesda). 2018 Aug 30;8(9):3039-3047. doi: 10.1534/g3.118.200435.

6.

Correction to: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

Fritsche-Neto R, Akdemir D, Jannink JL.

Theor Appl Genet. 2018 Jul;131(7):1603. doi: 10.1007/s00122-018-3118-2.

PMID:
29796770
7.

Genomic-Enabled Prediction Kernel Models with Random Intercepts for Multi-environment Trials.

Cuevas J, Granato I, Fritsche-Neto R, Montesinos-Lopez OA, Burgueño J, Bandeira E Sousa M, Crossa J.

G3 (Bethesda). 2018 Mar 28;8(4):1347-1365. doi: 10.1534/g3.117.300454.

8.

Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs.

Fritsche-Neto R, Akdemir D, Jannink JL.

Theor Appl Genet. 2018 May;131(5):1153-1162. doi: 10.1007/s00122-018-3068-8. Epub 2018 Feb 14. Erratum in: Theor Appl Genet. 2018 Jul;131(7):1603. Fristche-Neto, Roberto [corrected to Fritsche-Neto, Roberto].

PMID:
29445844
9.

Genomic-Enabled Prediction in Maize Using Kernel Models with Genotype × Environment Interaction.

Bandeira E Sousa M, Cuevas J, de Oliveira Couto EG, Pérez-Rodríguez P, Jarquín D, Fritsche-Neto R, Burgueño J, Crossa J.

G3 (Bethesda). 2017 Jun 7;7(6):1995-2014. doi: 10.1534/g3.117.042341.

10.

Effects of using phenotypic means and genotypic values in GGE biplot analyses on genotype by environment studies on tropical maize (Zea mays).

Granato IS, Fritsche-Neto R, Resende MD, Silva FF.

Genet Mol Res. 2016 Oct 5;15(4). doi: 10.4238/gmr.15048747.

PMID:
27808373
11.

Genetic Vulnerability and the Relationship of Commercial Germplasms of Maize in Brazil with the Nested Association Mapping Parents.

Andrade LR, Fritsche Neto R, Granato ÍS, Sant'Ana GC, Morais PP, Borém A.

PLoS One. 2016 Oct 25;11(10):e0163739. doi: 10.1371/journal.pone.0163739. eCollection 2016.

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