The efficient analysis of hybrid designs [e.g., affected families, controls, and (optionally) independent cases] is attractive because it should have increased power to detect associations between genetic variants and disease. However, the computational complexity of such an analysis is not trivial, especially when the data contain pedigrees of arbitrary size and structure. To address this concern, we developed a pragmatic test of association that summarizes all of the available evidence in certain hybrid designs, irrespective of pedigree size or structure. Under the null hypothesis of no association, our proposed test statistic (POPFAM+) is the quadratic form of two correlated tests: a population-based test (e.g., wQLS), and a family-based test (e.g., PDT). We use the parametric bootstrap in conjunction with an estimate of the correlation to compute p-values, and we illustrate the potential for increased power when (1) the heritability of the trait is high; and, (2) the marker-specific association is driven by the over-representation of risk alleles in cases, and by the preferential transmission of risk alleles from heterozygous parents to their affected offspring. Based on simulation, we show that type I error is controlled, and that POPFAM+ is more powerful than wQLS or PDT alone. In a real data application, we used POPFAM+ to analyze 43 genes of a hybrid epilepsy study containing 85 affected families, 80 independent cases, 234 controls, and 118 reference samples from the International HapMap Project. The results of our analysis identified a promising epilepsy candidate gene for follow-up sequencing: malic enzyme 2 (ME2; min p < 0.0084).