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Best matches for Jarquín D[au]:

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Crossa J et al. Trends Plant Sci. (2017)

The effect of artificial selection on phenotypic plasticity in maize. Gage JL et al. Nat Commun. (2017)

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection. Bandillo NB et al. Plant Genome. (2017)

Search results

Items: 23

1.

Genome-Wide Association and Gene Co-expression Network Analyses Reveal Complex Genetics of Resistance to Goss's Wilt of Maize.

Singh A, Li G, Brohammer AB, Jarquin D, Hirsch CN, Alfano JR, Lorenz AJ.

G3 (Bethesda). 2019 Jul 30. pii: g3.400347.2019. doi: 10.1534/g3.119.400347. [Epub ahead of print]

2.

Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments.

Howard R, Gianola D, Montesinos-López O, Juliana P, Singh R, Poland J, Shrestha S, Pérez-Rodríguez P, Crossa J, Jarquín D.

G3 (Bethesda). 2019 Sep 4;9(9):2925-2934. doi: 10.1534/g3.119.400508.

3.

Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model.

Howard R, Jarquin D.

Evol Bioinform Online. 2019 Mar 29;15:1176934319840026. doi: 10.1177/1176934319840026. eCollection 2019.

4.

Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean.

Jarquín D, Howard R, Graef G, Lorenz A.

Evol Bioinform Online. 2019 Mar 7;15:1176934319831307. doi: 10.1177/1176934319831307. eCollection 2019. Review.

5.

Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype × environment interaction on prediction accuracy in chickpea.

Roorkiwal M, Jarquin D, Singh MK, Gaur PM, Bharadwaj C, Rathore A, Howard R, Srinivasan S, Jain A, Garg V, Kale S, Chitikineni A, Tripathi S, Jones E, Robbins KR, Crossa J, Varshney RK.

Sci Rep. 2018 Aug 3;8(1):11701. doi: 10.1038/s41598-018-30027-2.

6.

Genomic-enabled Prediction Accuracies Increased by Modeling Genotype × Environment Interaction in Durum Wheat.

Sukumaran S, Jarquin D, Crossa J, Reynolds M.

Plant Genome. 2018 Jul;11(2). doi: 10.3835/plantgenome2017.12.0112.

7.

Genomic Selection in Preliminary Yield Trials in a Winter Wheat Breeding Program.

Belamkar V, Guttieri MJ, Hussain W, Jarquín D, El-Basyoni I, Poland J, Lorenz AJ, Baenziger PS.

G3 (Bethesda). 2018 Jul 31;8(8):2735-2747. doi: 10.1534/g3.118.200415.

8.

Genome-Wide Analysis of Grain Yield Stability and Environmental Interactions in a Multiparental Soybean Population.

Xavier A, Jarquin D, Howard R, Ramasubramanian V, Specht JE, Graef GL, Beavis WD, Diers BW, Song Q, Cregan PB, Nelson R, Mian R, Shannon JG, McHale L, Wang D, Schapaugh W, Lorenz AJ, Xu S, Muir WM, Rainey KM.

G3 (Bethesda). 2018 Feb 2;8(2):519-529. doi: 10.1534/g3.117.300300.

9.

The effect of artificial selection on phenotypic plasticity in maize.

Gage JL, Jarquin D, Romay C, Lorenz A, Buckler ES, Kaeppler S, Alkhalifah N, Bohn M, Campbell DA, Edwards J, Ertl D, Flint-Garcia S, Gardiner J, Good B, Hirsch CN, Holland J, Hooker DC, Knoll J, Kolkman J, Kruger G, Lauter N, Lawrence-Dill CJ, Lee E, Lynch J, Murray SC, Nelson R, Petzoldt J, Rocheford T, Schnable J, Schnable PS, Scully B, Smith M, Springer NM, Srinivasan S, Walton R, Weldekidan T, Wisser RJ, Xu W, Yu J, de Leon N.

Nat Commun. 2017 Nov 7;8(1):1348. doi: 10.1038/s41467-017-01450-2.

10.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.

Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X, Gowda M, Roorkiwal M, Rutkoski J, Varshney RK.

Trends Plant Sci. 2017 Nov;22(11):961-975. doi: 10.1016/j.tplants.2017.08.011. Epub 2017 Sep 28. Review.

PMID:
28965742
11.

Genome-wide Association Mapping of Qualitatively Inherited Traits in a Germplasm Collection.

Bandillo NB, Lorenz AJ, Graef GL, Jarquin D, Hyten DL, Nelson RL, Specht JE.

Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.06.0054.

12.

Increasing Genomic-Enabled Prediction Accuracy by Modeling Genotype × Environment Interactions in Kansas Wheat.

Jarquín D, Lemes da Silva C, Gaynor RC, Poland J, Fritz A, Howard R, Battenfield S, Crossa J.

Plant Genome. 2017 Jul;10(2). doi: 10.3835/plantgenome2016.12.0130.

13.

Interaction between FTO rs9939609 and the Native American-origin ABCA1 rs9282541 affects BMI in the admixed Mexican population.

Villalobos-Comparán M, Antuna-Puente B, Villarreal-Molina MT, Canizales-Quinteros S, Velázquez-Cruz R, León-Mimila P, Villamil-Ramírez H, González-Barrios JA, Merino-García JL, Thompson-Bonilla MR, Jarquin D, Sánchez-Hernández OE, Rodríguez-Arellano ME, Posadas-Romero C, Vargas-Alarcón G, Campos-Pérez F, Quiterio M, Salmerón-Castro J, Carnevale A, Romero-Hidalgo S.

BMC Med Genet. 2017 May 2;18(1):46. doi: 10.1186/s12881-017-0410-y.

14.

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.

15.

Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.

Sukumaran S, Crossa J, Jarquin D, Lopes M, Reynolds MP.

G3 (Bethesda). 2017 Feb 9;7(2):481-495. doi: 10.1534/g3.116.036251.

16.

Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.

Saint Pierre C, Burgueño J, Crossa J, Fuentes Dávila G, Figueroa López P, Solís Moya E, Ireta Moreno J, Hernández Muela VM, Zamora Villa VM, Vikram P, Mathews K, Sansaloni C, Sehgal D, Jarquin D, Wenzl P, Singh S.

Sci Rep. 2016 Jun 17;6:27312. doi: 10.1038/srep27312.

17.
18.

Genomic Prediction of Gene Bank Wheat Landraces.

Crossa J, Jarquín D, Franco J, Pérez-Rodríguez P, Burgueño J, Saint-Pierre C, Vikram P, Sansaloni C, Petroli C, Akdemir D, Sneller C, Reynolds M, Tattaris M, Payne T, Guzman C, Peña RJ, Wenzl P, Singh S.

G3 (Bethesda). 2016 Jul 7;6(7):1819-34. doi: 10.1534/g3.116.029637.

19.

A Genomic Selection Index Applied to Simulated and Real Data.

Ceron-Rojas JJ, Crossa J, Arief VN, Basford K, Rutkoski J, Jarquín D, Alvarado G, Beyene Y, Semagn K, DeLacy I.

G3 (Bethesda). 2015 Aug 18;5(10):2155-64. doi: 10.1534/g3.115.019869.

20.

Genotyping by sequencing for genomic prediction in a soybean breeding population.

Jarquín D, Kocak K, Posadas L, Hyma K, Jedlicka J, Graef G, Lorenz A.

BMC Genomics. 2014 Aug 29;15:740. doi: 10.1186/1471-2164-15-740.

21.

A reaction norm model for genomic selection using high-dimensional genomic and environmental data.

Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M, Burgueño J, de los Campos G.

Theor Appl Genet. 2014 Mar;127(3):595-607. doi: 10.1007/s00122-013-2243-1. Epub 2013 Dec 12.

22.

Use of active management of the third stage of labour in seven developing countries.

Stanton C, Armbruster D, Knight R, Ariawan I, Gbangbade S, Getachew A, Portillo JA, Jarquin D, Marin F, Mfinanga S, Vallecillo J, Johnson H, Sintasath D.

Bull World Health Organ. 2009 Mar;87(3):207-15.

23.

FIGO Save the Mothers Initiative: the Central America and USA collaboration.

Curet LB, Foster-Rosales A, Hale R, Kestler E, Medina C, Altamirano L, Reyes C, Jarquin D.

Int J Gynaecol Obstet. 2003 Feb;80(2):213-21.

PMID:
12566201

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