eprintid: 10280
rev_number: 19
eprint_status: archive
userid: 1305
dir: disk0/00/01/02/80
datestamp: 2017-11-16 09:59:50
lastmod: 2018-08-28 04:04:48
status_changed: 2017-11-16 09:59:50
type: article
metadata_visibility: show
contact_email: Library-ICRISAT@CGIAR.ORG
creators_name: Crossa, J
creators_name: Pérez-Rodríguez, P
creators_name: Cuevas, J
creators_name: Montesinos-López, O
creators_name: Jarquín, D
creators_name: de los Campos, G
creators_name: Burgueño, J
creators_name: González-Camacho, J M
creators_name: Pérez-Elizalde, S
creators_name: Beyene, Y
creators_name: Dreisigacker, S
creators_name: Singh, R
creators_name: Zhang, X
creators_name: Gowda, M
creators_name: Roorkiwal, M
creators_name: Rutkoski, J
creators_name: Varshney, R K
icrisatcreators_name: Roorkiwal, M
icrisatcreators_name: Varshney, R K
affiliation: International Maize and Wheat Improvement Center (CIMMYT) (Mexico City)
affiliation: Colegio de Postgraduados, Montecillo (Texcoco)
affiliation: Universidad de Quintana Roo (Quintana Roo)
affiliation: Facultad de Telemática, Universidad de Colima (Colima)
affiliation: Department of Agronomy and Horticulture, University of Nebraska-Lincoln (Lincoln)
affiliation: Department of Epidemiology & Biostatistics, Michigan State University (East Lansing)
affiliation: ICRISAT (Patancheru)
affiliation: International Rice Research Institute (Los Banos)
country: Mexico
country: USA
country: India
country: Philippines
title: Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
ispublished: pub
subjects: F12
subjects: PLB1
subjects: s2.13
subjects: s2.8
subjects: s3000
divisions: CRPS3
full_text_status: restricted
keywords: genomic selection; genomic-enabled prediction accuracy; model complexity; models for genomic genotype × environment interaction; genomic selection and genetic gains in crop breeding populations
note: The authors are grateful to their colleagues, collaborators, and by using
field and lab technician from CIMMYT and
ICRISAT, as well as scientists in National Programs who collected the valuable data used in the various studies. The
authors would like to thank Kevin Pixley, Daniel Gianola, and four anonymous reviewers for their positive, careful, and
detailed reviews of the manuscript; their comments, editing, and suggestions significantly improved the quality and
readability of the manuscript. The authors also thank Michael Listman for editing the revised version of the manuscript.
abstract: Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype × environment (G × E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.
date: 2017-11
date_type: published
publication: Trends in Plant Science
volume: 22
number: 11
publisher: Elsevier
pagerange: 961-975
id_number: 10.1016/j.tplants.2017.08.011
refereed: TRUE
issn: 13601385
official_url: http://dx.doi.org/10.1016/j.tplants.2017.08.011
related_url_url: https://scholar.google.co.in/scholar?hl=en&as_sdt=0%2C5&q=+Genomic+Selection+in+Plant+Breeding%3A+Methods%2C+Models%2C+and+Perspectives&btnG=
related_url_type: pub
citation:   Crossa, J and Pérez-Rodríguez, P and Cuevas, J and Montesinos-López, O and Jarquín, D and de los Campos, G and Burgueño, J and González-Camacho, J M and Pérez-Elizalde, S and Beyene, Y and Dreisigacker, S and Singh, R and Zhang, X and Gowda, M and Roorkiwal, M and Rutkoski, J and Varshney, R K  (2017) Genomic Selection in Plant Breeding: Methods, Models, and Perspectives.  Trends in Plant Science, 22 (11).  pp. 961-975.  ISSN 13601385     
document_url: http://oar.icrisat.org/10280/1/Genomic%20Selection%20in%20Plant%20Breeding%20Methods%2C%20Models%2C%20and%20Perspectives.pdf