Genomic assignment tests can provide important diagnostic biological characteristics, such as population of origin or ecotype. Yet, assignment tests often rely on moderate- to high-coverage sequence data that can be difficult to obtain for fields such as molecular ecology and ancient DNA. We have developed a novel approach that efficiently assigns biologically relevant information (i.e., population identity or structural variants such as inversions) in extremely low-coverage sequence data. First, we generate databases from existing reference data using a subset of diagnostic single nucleotide polymorphisms (SNPs) associated with a biological characteristic. Low-coverage alignment files are subsequently compared to these databases to ascertain allelic state, yielding a joint probability for each association. To assess the efficacy of this approach, we assigned haplotypes and population identity in Heliconius butterflies, Atlantic herring, and Atlantic cod using chromosomal inversion sites and whole-genome data. We scored both modern and ancient specimens, including the first whole-genome sequence data recovered from ancient Atlantic herring bones. The method accurately assigns biological characteristics, including population membership, using extremely low-coverage data (as low as 0.0001x) based on genome-wide SNPs. This approach will therefore increase the number of samples in evolutionary, ecological and archaeological research for which relevant biological information can be obtained.