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Genomic Selection

Traditional plant breeding programs rely mainly on phenotypes being evaluated in several environments; selection and recombination are based solely on the resulting data plus pedigree information, when available. Marker assisted selection (MAS) uses molecular markers in linkage disequilibrium (LD) with QTL. Genomic selection (GS) is a new approach for improving quantitative traits in large plant breeding populations that uses whole‐genome molecular markers (high density markers and high‐throughput genotyping). Genomic prediction combines marker data with phenotypic and pedigree data (when available) in an attempt to increase the accuracy of the prediction of breeding and genotypic values.

In practice, GS is applied in a population that is different from the reference population in which the marker effects were estimated. Genomic selection uses two types of datasets: a training set and a validation set. The training set is the reference population in which the marker effects were estimated; it contains: (1) phenotypic information from relevant breeding germplasm evaluated over a range of environmental conditions; (2) molecular marker scores; and (3) pedigree information or kinship. Hence, marker effects are estimated based on the training set using certain statistical methods to incorporate this information; the genomic breeding value or genetic values of new genotypes are predicted based only on the marker effect. The validation set contains the selection candidates (derived from the reference population) that have been genotyped (but not phenotyped) and selected based on marker effects estimated in the training set.

For quantitative traits, selection based on marker effects alone has dramatically changed standard practices used in plant and animal breeding. However, in public plant breeding programs, the benefits of GS have been studied only through computer simulation. Since marker technology is continuously reducing the cost per data point and increasing the number of available markers, genotyping is currently less costly than phenotyping in an applied plant breeding program. Besides accelerating the selection cycles, GS offers the opportunity to increase the selection gains per unit of time. Therefore, it is believed that alternating progeny field testing with selection based only on markers should increase the genetic gains per unit. However, unresolved questions, such as how much (if any) genetic diversity will be diminished by this combination of phenotypic and GS, remain.

Selection based solely on marker effects is becoming a focal point for many public plant breeders in Africa, Asia, and Latin America. Several of these breeding programs have been historical partners of the Global Maize and Wheat CIMMYT programs. The implementation of GS in these breeding programs should help speed up genetic gains and, as a result, improved, higher yielding, broadly adapted, and stable genotypes will be delivered at a much faster rate. CIMMYT breeding programs have initiated studies aimed at applying GS to speed up genetic gains for breeding programs in Africa and other parts of the world in an attempt to help resource‐poor farmer obtain improved varieties, lines, and hybrids of maize and wheat. Private and public donors, specially the Melinda and Bill Gates Foundation, are funding the Global Maize Program project ''Drought Tolerant Maize for Africa'' and have supported the application of GS in several of those breeding programs.

The software and data generated at CIMMYT was used to evaluate the potential of GS for several traits in Wheat and Maize.


Bernardo, R., and J. Yu. 2007. Prospects for genome-wide selection for quantitative traits in maize. Crop Science 47: 1082-1090. Download

de los Campos, G., Naya, H., Gianola, D., Crossa, J., Legarra, A., Manfredi, E., Weigel, K. and Cotes, J.M. 2009. Predicting quantitative traits with regression models for dense molecular markers and pedigrees. Genetics 182, 375 - 385. Download

Gianola, D. and J.B.C.H.M van Kaam. 2008 Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178, 2289-2303. Download

Goddard, M.E. and B.J. Hayes. 2007 Genomic selection. J. Anim. Breed. Genet. 124, 323-330. Download

Gonzalez-Recio, O., D. Gianola, N. Long, K. Wiegel, G.J.M. Rosa et al. 2008 Non parametric methods for incorporating genomic information into genetic evaluation: An application to mortality in broilers. Genetics 178:2305-2313. Download

Habier, D., R.L. Fernando, and J.C.M. Deckkers. 2009 Genomic selection using low-density marker panels. Genetics 182:343-353. Download

Heffner, E. L., M.R. Sorrels, and J-L. Jannink. 2009. Genomic selection for crop improvement. Crop Science 49:1-12. Download

Meuwissen, T. H. E., Hayes, B. J. and Goddard, M. E. 2001. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829. Download

Piepho, H.P. 2009 Ridge regression and extensions for genomewide selection in maize. Crop Sci. 49:1165-1179 Download

VanRaden, P.M., C.P. Van Tassell, G.R. Wiggans, T.S. Sonstegard, R.D. Schnabel, J.F. et al. 2008. Invited review: Reliability of genomic predictions for North American Holstein bulls. J. of Dairy Science 92, 16-24. Download

Wong, C. and R. Bernardo, 2008. Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations. Theoretical and Applied Genetics 116:815-824. Download

Zhong, S., J.C.M. Dekker, R.L. Fernando, and J-L. Jannink. 2009. Factors affecting accuracy from genomic selection in populations derived from multiple inbred lines: A barley case study. Genetics 182:355-364. Download


Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers Download

Genomic prediction of quantitative traits in plant breeding using molecular markers and pedigree (Book chapter) Download

Genomic-enabled prediction based on molecular markers and pedigree using the BLR package in R Download

Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods Download


An R-Package for Genomic Selection using dense molecular markers and pedigree. Download the source code here or download it from the R website.

Contact us

José Crossa (e-mail)
Biometrics and Statistics, Crop Research Informatics Lab, CIMMYT, Mexico.

Gustavo de los Campos (e-mail)
Department of Biostatistics, University of Alabama-Birmingham, USA, Biometrics and Statistics, Crop Research Informatics Lab, CIMMYT, Mexico.

Peter Wenzl (e-mail)
Manager, Crop Research Informatics Lab, CIMMYT, Mexico.

Gary Atlin (e-mail)
Global Maize Program, CIMMYT, Mexico.

Yann Manes (e-mail)
Global Wheat Program, CIMMYT, Mexico.

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