Genomic prediction in CIMMYT maize and wheat breeding programs

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.

Figure 1

Heat map of the G matrix for the data set with 306 wheat lines genotyped with 1717 DArTs markers.

Figure 2

Heat map of the G matrix for the data set with 599 wheat lines genotyped with 1279 DArTs markers.

Figure 3

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).

Figure 4

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.

Figure 5

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).