Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.
By Jose Crossa, P. Pérez-Rodríguez, Jaime Cuevas, O. Montesinos-López, D. Jarquín, G. de los Campos, Juan Burgueño, J. M. González-Camacho, S. Pérez-Elizalde, Y. Beyene and 7 more. Trends in Plant Science, 2017
1,416 citations recorded by the source corpus.
Subfields: Genetics, Plant Science
View Paper: https://www.semanticscholar.org/paper/e00f603720a5fc3a5461e620ea82fe41efa7a82e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Genetics
- Ben J. Hayes
- T. Lebedeva
- A. Chernodubov
- V. Lebedev
- K. Shestibratov
- Michael E. Goddard
- Jean-Luc Jannink
- M. Thomson
In Plant Science
- K. Aitken
- Ben J. Hayes
- T. Lebedeva
- A. Chernodubov
- T. Hirayama
- Farid Melgani
- V. Lebedev
- K. Shestibratov
Citations in the corpus
- Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective (2018, positive): “Moreover, a model of the relationship between the genotype/phenotype map of individuals in a breeding population can be used to compute genome-estimated breeding values to select the best parents for new crosses in genomic selection in crop breeding [7, 8].”
- Accelerating Genetic Gain in Sugarcane Breeding Using Genomic Selection (2020, background): “Pre-breeding programs could significantly benefit from GS approaches because they could help to prioritize accessions and track introgressions on the molecular level [128].”
- Genomic Selection for Forest Tree Improvement: Methods, Achievements and Perspectives (2020, background): “Ultimately, this will significantly increase genetic gain per unit time [20,21].”