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

José Crossa, Paulino Pérez-Rodríguez, Jaime Cuevas, Osval Montesinos-López, Diego Jarquín, Gustavo de los Campos, Juan Burgueño, Juan M. González-Camacho, Sergio Pérez-Elizalde, Yoseph Beyene, Susanne Dreisigacker, Ravi Singh, Xuecai Zhang, Manje Gowda, Manish Roorkiwal, Jessica Rutkoski, Rajeev K. Varshney

Research output: Contribution to journalReview article

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. In recent years, the global climate has changed, resulting in drastic fluctuations in rainfall patterns and increasing temperature. Sudden climate changes can cause significant economic losses to countries worldwide. Genetic improvement of several economically important crops during the 20th century using phenotypic, pedigree, and performance data was very successful. However, signs of grain yield stagnation in some crops, especially in drought-stressed and semi-arid regions, are evident. Genomic selection offers the opportunity to increase grain production in less time. International Maize and Wheat Improvement Center (CIMMYT) maize breeding research in Sub-Saharan Africa, India, and Mexico has shown that genomic selection can reduce the breeding interval cycle to at least half the conventional time and produces lines that, in hybrid combinations, significantly increase grain yield performance over that of commercial checks. Public and private investment in crop genomic selection research should increase to successfully develop in less time germplasm that is adapted to sudden climate change.

Original languageEnglish (US)
Pages (from-to)961-975
Number of pages15
JournalTrends in Plant Science
Volume22
Issue number11
DOIs
StatePublished - Nov 2017
Externally publishedYes

Fingerprint

breeding methods
plant breeding
marker-assisted selection
genomics
breeding
genetic improvement
prediction
crops
pedigree
corn
grain yield
climate change
hyperspectral imagery
gene banks
genotype-environment interaction
Sub-Saharan Africa
grain crops
arid zones
gene flow
germplasm

Keywords

  • genomic selection
  • genomic selection and genetic gains in crop breeding populations
  • genomic-enabled prediction accuracy
  • model complexity
  • models for genomic genotype × environment interaction

ASJC Scopus subject areas

  • Plant Science

Cite this

Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., ... Varshney, R. K. (2017). Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends in Plant Science, 22(11), 961-975. https://doi.org/10.1016/j.tplants.2017.08.011

Genomic Selection in Plant Breeding : Methods, Models, and Perspectives. / Crossa, José; Pérez-Rodríguez, Paulino; Cuevas, Jaime; Montesinos-López, Osval; Jarquín, Diego; de los Campos, Gustavo; Burgueño, Juan; González-Camacho, Juan M.; Pérez-Elizalde, Sergio; Beyene, Yoseph; Dreisigacker, Susanne; Singh, Ravi; Zhang, Xuecai; Gowda, Manje; Roorkiwal, Manish; Rutkoski, Jessica; Varshney, Rajeev K.

In: Trends in Plant Science, Vol. 22, No. 11, 11.2017, p. 961-975.

Research output: Contribution to journalReview article

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 2017, 'Genomic Selection in Plant Breeding: Methods, Models, and Perspectives', Trends in Plant Science, vol. 22, no. 11, pp. 961-975. https://doi.org/10.1016/j.tplants.2017.08.011
Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G et al. Genomic Selection in Plant Breeding: Methods, Models, and Perspectives. Trends in Plant Science. 2017 Nov;22(11):961-975. https://doi.org/10.1016/j.tplants.2017.08.011
Crossa, José ; Pérez-Rodríguez, Paulino ; Cuevas, Jaime ; Montesinos-López, Osval ; Jarquín, Diego ; de los Campos, Gustavo ; Burgueño, Juan ; González-Camacho, Juan M. ; Pérez-Elizalde, Sergio ; Beyene, Yoseph ; Dreisigacker, Susanne ; Singh, Ravi ; Zhang, Xuecai ; Gowda, Manje ; Roorkiwal, Manish ; Rutkoski, Jessica ; Varshney, Rajeev K. / Genomic Selection in Plant Breeding : Methods, Models, and Perspectives. In: Trends in Plant Science. 2017 ; Vol. 22, No. 11. pp. 961-975.
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