Genomic prediction in CIMMYT maize and wheat breeding programs

TLDR

Genomic selection has been applied in animal and plant species, yet its accuracy in plants varies with trait heritability, training–testing relationships, marker density, sample size, genotype‑environment interaction, and population structure, and many questions remain about its implementation in CIMMYT’s maize and wheat programs. The study aims to describe genomic prediction results in CIMMYT’s maize and wheat breeding programs. The authors evaluated predictive ability of various models that combine pedigree and marker data and explored practical methods for implementing GS in global maize and wheat breeding programs. Pedigree information contributes substantially to prediction accuracy in global populations, but accuracy becomes negligible when training on unrelated populations, while incorporating genotype–environment interaction improves accuracy by borrowing information from correlated environments.

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.