The development of inexpensive, whole-genome profiling enables a transition to allele-based breeding using genomic prediction models. These models consider alleles shared between lines to predict phenotypes and select new lines based on estimated breeding values. This approach can leverage highly-unbalanced datasets common to breeding programs. The Southern Regional Performance Nursery (SRPN) is a public nursery established by the USDA-ARS in 1931 to characterize performance and quality of near-release wheat varieties from breeding programs in the US Central Plains. New entries are submitted annually and can be reentered only once. The trial is grown at more than 30 locations each year and lines are evaluated for grain yield, disease resistance, and agronomic traits. Overall genetic gain is measured across years by including common check cultivars for comparison. We have generated whole-genome profiles via genotyping-by-sequencing for 939 SPRN entries dating back to 1992. We measured the diversity within the nursery and have explored its potential use as a GS training population. GS prediction models across years (average r= 0.33) outperformed year-to-year phenotypic correlation for yield (r=0.27) for a majority of the years evaluated, suggesting that genomic selection has the potential to outperform low heritability selection on yield in these highly variable environments. We also examined the predictability of programs using both program-specific and whole-set training populations. Generally, the predictability of a program was similar with both approaches. These results suggest that wheat breeding programs can collaboratively leverage the immense datasets that are generated from regional testing networks.