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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

Correlations between predicted and observed performance in environment 1 (E1) and average of environments 2, 3 and 4 (E2 3 4) obtained in CV2 using only pedigree (a), only genomics (b) or using pedigree+genomics (c)-based models with different specifications for the residual and genetic covariance matrices (FA=GE modeled using the factor analytic model; no FA=GE not modeled) (adapted from Burgueño et al., 2012).
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fig5: Correlations between predicted and observed performance in environment 1 (E1) and average of environments 2, 3 and 4 (E2 3 4) obtained in CV2 using only pedigree (a), only genomics (b) or using pedigree+genomics (c)-based models with different specifications for the residual and genetic covariance matrices (FA=GE modeled using the factor analytic model; no FA=GE not modeled) (adapted from Burgueño et al., 2012).

Mentions: Figure 5 shows that the impact of modeling GE in CV2 is marked in environments E2–E4, but not in E1; this is because genetic values in E2–E4 have high genetic correlations, whereas genetic values in E1 exhibit low genetic correlations with those from E2–E4. For correlated environments E2–E4, the benefits in predictive ability come from borrowing information from correlated environments by modeling GE and by using information regarding pedigree and genomic relationships. Interestingly, Burgueño et al. (2012) also examined the predictive accuracy of models without using pedigree and genomic relationships, and showed that to predict the environments that cause a great deal of the GE, such as environment E1 (and E4), modeling GE using information on genomic relationships and/or pedigree gives better prediction accuracy than when the GE is not modeled and the information on genomic and pedigree is ignored.


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)

Correlations between predicted and observed performance in environment 1 (E1) and average of environments 2, 3 and 4 (E2 3 4) obtained in CV2 using only pedigree (a), only genomics (b) or using pedigree+genomics (c)-based models with different specifications for the residual and genetic covariance matrices (FA=GE modeled using the factor analytic model; no FA=GE not modeled) (adapted from Burgueño et al., 2012).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig5: Correlations between predicted and observed performance in environment 1 (E1) and average of environments 2, 3 and 4 (E2 3 4) obtained in CV2 using only pedigree (a), only genomics (b) or using pedigree+genomics (c)-based models with different specifications for the residual and genetic covariance matrices (FA=GE modeled using the factor analytic model; no FA=GE not modeled) (adapted from Burgueño et al., 2012).
Mentions: Figure 5 shows that the impact of modeling GE in CV2 is marked in environments E2–E4, but not in E1; this is because genetic values in E2–E4 have high genetic correlations, whereas genetic values in E1 exhibit low genetic correlations with those from E2–E4. For correlated environments E2–E4, the benefits in predictive ability come from borrowing information from correlated environments by modeling GE and by using information regarding pedigree and genomic relationships. Interestingly, Burgueño et al. (2012) also examined the predictive accuracy of models without using pedigree and genomic relationships, and showed that to predict the environments that cause a great deal of the GE, such as environment E1 (and E4), modeling GE using information on genomic relationships and/or pedigree gives better prediction accuracy than when the GE is not modeled and the information on genomic and pedigree is ignored.

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