| Genomic selection in plant breeding: methods, models, and perspectives J Crossa, P Pérez-Rodríguez, J Cuevas, O Montesinos-López, D Jarquín, ... Trends in plant science 22 (11), 961-975, 2017 | 1453 | 2017 |
| A reaction norm model for genomic selection using high-dimensional genomic and environmental data D Jarquín, J Crossa, X Lacaze, P Du Cheyron, J Daucourt, J Lorgeou, ... Theoretical and applied genetics 127, 595-607, 2014 | 594 | 2014 |
| Genotyping by sequencing for genomic prediction in a soybean breeding population D Jarquín, K Kocak, L Posadas, K Hyma, J Jedlicka, G Graef, A Lorenz BMC genomics 15, 1-10, 2014 | 251 | 2014 |
| A population structure and genome‐wide association analysis on the USDA soybean germplasm collection N Bandillo, D Jarquin, Q Song, R Nelson, P Cregan, J Specht, A Lorenz The Plant Genome 8 (3), plantgenome2015.04.0024, 2015 | 218 | 2015 |
| Genomic prediction of gene bank wheat landraces J Crossa, D Jarquín, J Franco, P Pérez-Rodríguez, J Burgueño, ... G3: Genes, Genomes, Genetics 6 (7), 1819-1834, 2016 | 190 | 2016 |
| A chickpea genetic variation map based on the sequencing of 3,366 genomes RK Varshney, M Roorkiwal, S Sun, P Bajaj, A Chitikineni, M Thudi, ... Nature 599 (7886), 622-627, 2021 | 167 | 2021 |
| Increasing genomic‐enabled prediction accuracy by modeling genotype× environment interactions in Kansas wheat D Jarquín, C Lemes da Silva, RC Gaynor, J Poland, A Fritz, R Howard, ... The plant genome 10 (2), plantgenome2016.12.0130, 2017 | 151 | 2017 |
| The effect of artificial selection on phenotypic plasticity in maize JL Gage, D Jarquin, C Romay, A Lorenz, ES Buckler, S Kaeppler, ... Nature communications 8 (1), 1348, 2017 | 146 | 2017 |
| Prospects of genomic prediction in the USDA soybean germplasm collection: historical data creates robust models for enhancing selection of accessions D Jarquin, J Specht, A Lorenz G3: Genes, Genomes, Genetics 6 (8), 2329-2341, 2016 | 103 | 2016 |
| Genomic prediction enhanced sparse testing for multi-environment trials D Jarquin, R Howard, J Crossa, Y Beyene, M Gowda, JWR Martini, ... G3: Genes, Genomes, Genetics 10 (8), 2725-2739, 2020 | 100 | 2020 |
| Genomic-enabled prediction in maize using kernel models with genotype× environment interaction M Bandeira e Sousa, J Cuevas, EG de Oliveira Couto, P Pérez-Rodríguez, ... G3: Genes, Genomes, Genetics 7 (6), 1995-2014, 2017 | 95 | 2017 |
| Genomic selection in preliminary yield trials in a winter wheat breeding program V Belamkar, MJ Guttieri, W Hussain, D Jarquín, I El-Basyoni, J Poland, ... G3: Genes, Genomes, Genetics 8 (8), 2735-2747, 2018 | 94 | 2018 |
| Genome-wide analysis of grain yield stability and environmental interactions in a multiparental soybean population A Xavier, D Jarquin, R Howard, V Ramasubramanian, JE Specht, ... G3: Genes, Genomes, Genetics 8 (2), 519-529, 2018 | 92 | 2018 |
| Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype× environment interaction on prediction accuracy in chickpea M Roorkiwal, D Jarquin, MK Singh, PM Gaur, C Bharadwaj, A Rathore, ... Scientific reports 8 (1), 11701, 2018 | 85 | 2018 |
| Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones C Saint Pierre, J Burgueño, J Crossa, G Fuentes Dávila, ... Scientific reports 6 (1), 27312, 2016 | 83 | 2016 |
| Deep kernel and deep learning for genome-based prediction of single traits in multienvironment breeding trials J Crossa, JWR Martini, D Gianola, P Pérez-Rodríguez, D Jarquin, ... Frontiers in genetics 10, 1168, 2019 | 80 | 2019 |
| Maize genomes to fields (G2F): 2014–2017 field seasons: genotype, phenotype, climatic, soil, and inbred ear image datasets BA McFarland, N AlKhalifah, M Bohn, J Bubert, ES Buckler, I Ciampitti, ... BMC research notes 13, 1-6, 2020 | 66 | 2020 |
| Genomic prediction with pedigree and genotype× environment interaction in spring wheat grown in South and West Asia, North Africa, and Mexico S Sukumaran, J Crossa, D Jarquin, M Lopes, MP Reynolds G3: Genes, Genomes, Genetics 7 (2), 481-495, 2017 | 61 | 2017 |
| Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project D Jarquin, N De Leon, C Romay, M Bohn, ES Buckler, I Ciampitti, ... Frontiers in genetics 11, 592769, 2021 | 60 | 2021 |
| A genomic selection index applied to simulated and real data JJ Ceron-Rojas, J Crossa, VN Arief, K Basford, J Rutkoski, D Jarquín, ... G3: Genes, Genomes, Genetics 5 (10), 2155-2164, 2015 | 56 | 2015 |
Jose CrossaBiometricianПотвърден имейл адрес: cgiar.org