Diversifying maize genomic selection models

Abstract

Genomic selection (GS) is one of the most powerful tools available for maize breeding. Its use of genome-wide marker data to estimate breeding values translates to increased genetic gains with fewer breeding cycles. In this review, we cover the history of GS and highlight particular milestones during its adaptation to maize breeding. We discuss how GS can be applied to developing superior maize inbreds and hybrids. Additionally, we characterize refinements in GS models that could enable the encapsulation of non-additive genetic effects, genotype by environment interactions, and multiple levels of the biological hierarchy, all of which could ultimately result in more accurate predictions of breeding values. Finally, we suggest the stages in a maize breeding program where it would be beneficial to apply GS. Given the current sophistication of high-throughput phenotypic, genotypic, and other -omic level data currently available to the maize community, now is the time to explore the implications of their incorporation into GS models and thus ensure that genetic gains are being achieved as quickly and efficiently as possible.

Keywords: GBLUP; Genomic selection; Hybrid prediction; Maize; Multi-kernel; Omics.

Fig. 1

Biological hierarchy in genomic selection. Schematic of how various levels of the biological hierarchy of traits could be incorporated into GS models. The availability of the genomic (red), transcriptomic (orange), proteomic (green), and metabolomic (blue) data in maize makes it possible to incorporate multiple levels of the biological hierarchy of an agronomic trait directly into genomic selection (GS) models. Each of the different levels of the biological hierarchy can be used to calculate the correspondingly colored relationship matrices G, T, P, and M. Model terms and abbreviations: Y, observed vector trait values for n individuals; μ, grand mean; 1, n-dimensional vector of 1’s; u, n-dimensional random vector of polygenic effects; Z, incidence matrix relating u to Y; ε, n-dimensional random vector for error terms; MVN, multivariate normal