Genomic selection allows the prediction of genetic values using SNP markers distributed across the genome. Its effectiveness depends on factors such as trait heritability, genetic similarity between training and validation sets, and population structure. Although results in homogeneous populations have been promising, its application in diverse germplasm remains a challenge. This study evaluates the predictive capacity of genomic best linear unbiased prediction models applied to agronomic and biochemical-structural traits related to stover quality in two maize populations: a diversity panel and a multiparental advanced generation inter-cross (MAGIC) population. Higher heritability was observed in the panel, especially for flowering traits (h2 ≥ 0.88), with high intra-population predictive abilities (PA = 0.15-0.75) for most traits, compared to MAGIC (PA = 0.14-0.37). However, when applying the models from one population to another (cross-population prediction), the predictive ability was drastically reduced for most traits (PA < 0.05), possibly due to differences in allele frequencies and phases of linkage disequilibrium. Combining both populations in a single training set did not improve prediction (PA = 0.13-0.74) and even reduced it in some cases. These results indicate that genetic heterogeneity and differences in linkage disequilibrium between populations compromise the stability of marker effects. Therefore, it is critical to optimize the training set composition by considering genetic relatedness and population structure to improve the efficiency of genomic selection in diverse germplasm.
López-Malvar et al. (Sun,) studied this question.