Genomic Selection Using Maize Ex‐Plant Variety Protection Germplasm for the Prediction of Nitrogen‐Use Traits

Abstract

Maize (Zea mays L) yield increases associated with better usage of N fertilizer, (i.e., increased N use efficiency [NUE]), will require innovative breeding efforts. Genomic selection (GS) for N‐use traits (e.g., uptake or utilization efficiency) may speed up the breeding cycle of programs targeting NUE in maize. We evaluated the GS accuracy of 12 N‐use traits for training populations (TPs) varying in composition (TC) and size, predicted yield performance under different N fertilizer rates, and investigated the usefulness of GS for NUE in maize breeding programs. A total of 552 maize hybrids were planted under low (0 kg N ha−1) and high N fertilizer (252 kg N ha−1) conditions across 10 environments. Training composition scenarios included T0 (hybrids in which none of the parents were included in the random subset of inbreds), T1 (hybrids in which one of their parents were included in the random subset of inbreds), and T2 (hybrids in which both of their parents were included in the random subset of inbreds). Training population sizes ranged from 10 to 40 or 30 to 90 hybrids, depending on the N‐use trait. Across different TC, TP sizes, and N‐use traits, GS accuracy ranged from −0.12 to 0.78 and was greatest with larger TP sizes when both parents of untested hybrids appeared in the training and validation sets (T2 hybrids). Moreover, GS accuracy in response to different TC and TP sizes was dependent on the N‐use trait. Successful breeding for N stress tolerance or improved yield response to N fertilizer level will require selection of specific N‐use traits.

Theoretical Unification and Practical Integration of Conventional Methods and Genomic Selection in Plant Breeding

“…So far, most publications on GS in plant breeding are based on simulations or proof of concept using existing data from historical variety trials, and by researchers other than practical plant breeders. Reports on experimental application of GS have started to appear, e.g., in barley [13], winter wheat [14], and maize [15]. Thus, it seems that GS in plant breeding is now transitioning from theory and technology development to practical implementation.…”

Utility of anthesis–silking interval information to predict grain yield under water and nitrogen limited conditions

“…Mastrodomenico et al. (2019) and Dias et al. (2018) also reported the use of secondary traits and stress‐related traits; the authors highlight the advantage of genomic prediction, and the effectiveness depends on the type of trait and the composition of the training population.…”

Improving Genomic Selection With Quantitative Trait Loci and Nonadditive Effects Revealed by Empirical Evidence in Maize

“…GS, the theoretical and practical application of marker-assisted selection, has been widely implemented in animal and plant molecular breeding with the accumulation of genotypic and phenotypic data in commercial and experimental breeding programs (Nirea and Meuwissen, 2017; Raoul et al, 2017; Xu et al, 2018; Mastrodomenico et al, 2019; Rezende et al, 2019; Sarinelli et al, 2019; Yuan et al, 2019). Several factors, such as population size, population structure, marker density, heritability, statistical models, and genetic relationships between training and breeding populations, affect prediction accuracy (Schopp et al, 2017; Zhang et al, 2017a; Cerrudo et al, 2018; Edwards et al, 2019; Zhang et al, 2019).…”