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Genomic prediction in CIMMYT maize and wheat breeding programs.

Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K - Heredity (Edinb) (2013)

Bottom Line: However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible.When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments.Further research on the quantification of breeding value components for GS in plant breeding populations is required.

View Article: PubMed Central - PubMed

Affiliation: Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.

ABSTRACT
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

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Related in: MedlinePlus

Mean correlations (across four environments) between predicted and observed grain yield values derived from models using only pedigree, only genomics and pedigree+genomic for two cross-validation schemes (CV1 and CV2) (adapted from Burgueño et al., 2012). Cross-validation CV1 predicts genotypes that have never been evaluated in any environment, and cross-validation CV2 predicts genotypes that were evaluated in some environments but not in other environments.
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fig4: Mean correlations (across four environments) between predicted and observed grain yield values derived from models using only pedigree, only genomics and pedigree+genomic for two cross-validation schemes (CV1 and CV2) (adapted from Burgueño et al., 2012). Cross-validation CV1 predicts genotypes that have never been evaluated in any environment, and cross-validation CV2 predicts genotypes that were evaluated in some environments but not in other environments.

Mentions: Burgueño et al. (2012) found that the predictive ability of genomic-based models was higher than that of pedigree-based models. Their results confirmed the superiority of pedigree+genomic models for GS over pedigree-based predictions or genomic-based predictions alone. Predicting the performance of newly developed lines that have never been evaluated in the field (CV1) is more challenging than predicting the performance of lines that have been evaluated in different but correlated environments (CV2). Figure 4 shows correlations obtained from multi-environment models that model GE using the FA model in two cross-validations schemes (CV1 and CV2). Correlations for CV1 do not change much and those in CV2 were 31, 17.5 and 21.8% greater than those obtained in CV1, indicating the importance of having information from correlated environments when predicting performance.


Genomic prediction in CIMMYT maize and wheat breeding programs.

Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K - Heredity (Edinb) (2013)

Mean correlations (across four environments) between predicted and observed grain yield values derived from models using only pedigree, only genomics and pedigree+genomic for two cross-validation schemes (CV1 and CV2) (adapted from Burgueño et al., 2012). Cross-validation CV1 predicts genotypes that have never been evaluated in any environment, and cross-validation CV2 predicts genotypes that were evaluated in some environments but not in other environments.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860161&req=5

fig4: Mean correlations (across four environments) between predicted and observed grain yield values derived from models using only pedigree, only genomics and pedigree+genomic for two cross-validation schemes (CV1 and CV2) (adapted from Burgueño et al., 2012). Cross-validation CV1 predicts genotypes that have never been evaluated in any environment, and cross-validation CV2 predicts genotypes that were evaluated in some environments but not in other environments.
Mentions: Burgueño et al. (2012) found that the predictive ability of genomic-based models was higher than that of pedigree-based models. Their results confirmed the superiority of pedigree+genomic models for GS over pedigree-based predictions or genomic-based predictions alone. Predicting the performance of newly developed lines that have never been evaluated in the field (CV1) is more challenging than predicting the performance of lines that have been evaluated in different but correlated environments (CV2). Figure 4 shows correlations obtained from multi-environment models that model GE using the FA model in two cross-validations schemes (CV1 and CV2). Correlations for CV1 do not change much and those in CV2 were 31, 17.5 and 21.8% greater than those obtained in CV1, indicating the importance of having information from correlated environments when predicting performance.

Bottom Line: However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible.When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments.Further research on the quantification of breeding value components for GS in plant breeding populations is required.

View Article: PubMed Central - PubMed

Affiliation: Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Mexico, Mexico.

ABSTRACT
Genomic selection (GS) has been implemented in animal and plant species, and is regarded as a useful tool for accelerating genetic gains. Varying levels of genomic prediction accuracy have been obtained in plants, depending on the prediction problem assessed and on several other factors, such as trait heritability, the relationship between the individuals to be predicted and those used to train the models for prediction, number of markers, sample size and genotype × environment interaction (GE). The main objective of this article is to describe the results of genomic prediction in International Maize and Wheat Improvement Center's (CIMMYT's) maize and wheat breeding programs, from the initial assessment of the predictive ability of different models using pedigree and marker information to the present, when methods for implementing GS in practical global maize and wheat breeding programs are being studied and investigated. Results show that pedigree (population structure) accounts for a sizeable proportion of the prediction accuracy when a global population is the prediction problem to be assessed. However, when the prediction uses unrelated populations to train the prediction equations, prediction accuracy becomes negligible. When genomic prediction includes modeling GE, an increase in prediction accuracy can be achieved by borrowing information from correlated environments. Several questions on how to incorporate GS into CIMMYT's maize and wheat programs remain unanswered and subject to further investigation, for example, prediction within and between related bi-parental crosses. Further research on the quantification of breeding value components for GS in plant breeding populations is required.

Show MeSH
Related in: MedlinePlus