Genomic prediction accuracy is affected by population size, trait heritability, relatedness of training and validation populations, marker density, and genetic architecture. Nested association mapping (NAM) populations have advantages in many of these features compared with biparental families and may be an effective strategy for increasing prediction accuracy. The classic NAM design was modified to create a two-row spring malting barley (Hordeum vulgare L.) population of 1341 F 3 :F 4 lines in

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... seven families that was phenotyped for heading date, plant height, leaf rust, spot blotch, pre-harvest sprouting, and grain protein. Quantitative trait loci (QTL) were detected for plant height, leaf rust, pre-harvest sprouting, and spot blotch with genome-wide association analyses. Prediction accuracies were assessed in validation populations consisting of a single family or multiple families. Across-family prediction accuracy (.607-.811) generally surpassed within-family prediction accuracy, particularly for traits with high across-family variance. Reductions in marker density (70-80%) and training population size (25-50%) did not cause significant loss of prediction accuracy. Addition of fixed marker effects from genome-wide association had minimal impact on prediction accuracy in the full training population but improved accuracy in reduced training populations. Within-family prediction for traits highly influenced by family structure was improved by adding half-sibs to the training population. Connected half-sib training populations could be useful for new and established breeding programs looking to implement genomic selection due to benefits of family structure on prediction accuracy, genotyping, genetic diversity, and genetic mapping.