Natural populations of switchgrass (Panicum virgatum L.), a warm-season (C4) perennial grass and emerging bioenery crop, are found in a variety of habitats that vary widely in vegetation, soil type, temperature, and moisture regime. Characterizing patterns of genetic variation in switchgrass can identify centers of diversity and genetically unique varieties. Furthermore, quantifying population structure is essential to selecting appropriate panels for association mapping studies for the improvement of lines for bioenergy production. However, switchgrass is polyploid and wind pollinated, making studies of genetic variation more difficult, particularly when using traditional genotyping techniques. Therefore, we adapted a next-generation sequencing approach, Genotyping-By-Sequencing (GBS), to genotype switchgrass at thousands of loci in order to augment and further resolve patterns of genetic variation. We genotyped 119 switchgrass individuals from across the native range, generating 17.3 billion bp of genotyping data and discovering more than 10,000 candidate SNPs. We used these data to measure isolation-by-distance and population structure, and because polymorphisms are identified and called in the same step when using GBS, ascertainment bias is essentially eliminated from our results. These results confirm previous range-wide estimates of population structure in switchgrass while providing much greater genome-level detail of the genetic variation that exists throughout the species.