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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

Heat map of the genomic relationship matrix G of five wheat populations: PBW343/Pavon76, PBW343/Juchi, PBW343/Kingbird, PBW343/K-Nyangumi and PBW343/Muu. The numbers indicate the average values of the corresponding elements of G within and between populations (from Ornella et al., 2012).
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fig3: Heat map of the genomic relationship matrix G of five wheat populations: PBW343/Pavon76, PBW343/Juchi, PBW343/Kingbird, PBW343/K-Nyangumi and PBW343/Muu. The numbers indicate the average values of the corresponding elements of G within and between populations (from Ornella et al., 2012).

Mentions: As depicted in Figure 3, there are five clearly distinct yet related (half-sib) populations; however, lines in the Juchi population do not seem to be closely related. Results of the pairwise prediction of one population using the other are shown in Table 3. The accuracy of predictions of one population using another population was relatively high, except for PBW343/K-Nyangumi when predicted by PBW343/Juchi (using BL, 0.14) and PBW343/Juchi when predicted by PBW343/K-Nyangumi (using GBLUP, 0.14). Furthermore, Table 4 indicates that prediction of stem rust data in each individual populations using stem rust data from the other four populations gave relatively high correlations, except for population PBW343/Juchi, which, as shown by Ornella et al. (2012), does not have lines that are closely related among themselves or to lines in other populations.


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)

Heat map of the genomic relationship matrix G of five wheat populations: PBW343/Pavon76, PBW343/Juchi, PBW343/Kingbird, PBW343/K-Nyangumi and PBW343/Muu. The numbers indicate the average values of the corresponding elements of G within and between populations (from Ornella et al., 2012).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3: Heat map of the genomic relationship matrix G of five wheat populations: PBW343/Pavon76, PBW343/Juchi, PBW343/Kingbird, PBW343/K-Nyangumi and PBW343/Muu. The numbers indicate the average values of the corresponding elements of G within and between populations (from Ornella et al., 2012).
Mentions: As depicted in Figure 3, there are five clearly distinct yet related (half-sib) populations; however, lines in the Juchi population do not seem to be closely related. Results of the pairwise prediction of one population using the other are shown in Table 3. The accuracy of predictions of one population using another population was relatively high, except for PBW343/K-Nyangumi when predicted by PBW343/Juchi (using BL, 0.14) and PBW343/Juchi when predicted by PBW343/K-Nyangumi (using GBLUP, 0.14). Furthermore, Table 4 indicates that prediction of stem rust data in each individual populations using stem rust data from the other four populations gave relatively high correlations, except for population PBW343/Juchi, which, as shown by Ornella et al. (2012), does not have lines that are closely related among themselves or to lines in other populations.

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