Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.

Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.

The methodology used by the International Maize and Wheat Improvement Center (CIMMYT) to develop and improve its maize (Zea mays L.) germplasm involves evaluation of families or experimental varieties in extensive international testing trials. The genotype‐environmental interaction is produced by differential genotypic responses to varied environmental conditions. Its effect is to limit the accuracy of yield estimates and complicate the identification of specific genotypes for specific environments. The objective of this study was to use the Additive Main effects and Multiplicative Interaction (AMMI) method, with additive effects for genotypes and environments and multiplicative terms for genotype‐environment interaction, for analyzing data from two international maize cultivar trials. Results from the first trial were: (i) predictive assessment selected AMMI with one principal component axis, (ii) AMMI increased the precision of yield estimates equivalent to increasing the number of replications by a factor of 4.30, (iii) AMMI provided much insight into genotype‐environment interactions, and (iv) AMMI selected a different highest‐yielding genotype than did treatment means in 72% of the environments. Results for the second trial were that predictive assessment selects the AMMI with none of the principal component axes, which increased precision equivalent to increase the number of replications by a factor of 2.59.

SA genotype main effect plus genotype × environment interaction (GGE) biplot graphically displays the genotypic main effect (G) and the genotype × environment interaction (GE) of the multienvironment trial (MET) data and facilitates visual evaluation of both the genotypes and the environments. This paper compares the merits of two types of GGE biplots in MET data analysis. The first type is constructed by the least squares solution of the sites regression model (SREG2), with the first two principal components as the primary and secondary effects, respectively. The second type is constructed by Man‐del's solution for sites regression as the primary effect and the first principal component extracted from the regression residual as the secondary effect (SREGM+1). Results indicate that both the SREG2 biplot and the SREGM+1 biplot are equally effective in displaying the “which‐won‐where” pattern of the MET data, although the SREG2 biplot explains slightly more GGE variation. The SREGM+1 biplot is more desirable, however, in that it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.

We report a map of 4.97 million single-nucleotide polymorphisms of the chickpea from whole-genome resequencing of 429 lines sampled from 45 countries. We identified 122 candidate regions with 204 genes under selection during chickpea breeding. Our data suggest the Eastern Mediterranean as the primary center of origin and migration route of chickpea from the Mediterranean/Fertile Crescent to Central Asia, and probably in parallel from Central Asia to East Africa (Ethiopia) and South Asia (India). Genome-wide association studies identified 262 markers and several candidate genes for 13 traits. Our study establishes a foundation for large-scale characterization of germplasm and population genomics, and a resource for trait dissection, accelerating genetic gains in future chickpea breeding.