Assessing the performance of historical skins and bones for museomics using wolf specimens as a case study

Advances in the field of museomics have promoted a high sampling demand for natural history collections (NHCs), eventually resulting in damage to invaluable resources to understand historical biodiversity. It is thus essential to achieve a consensus about which historical tissues present the best sources of DNA. In this study, we evaluated the performance of different historical tissues from Iberian wolf NHCs in genome-wide assessments. We targeted three tissues—bone (jaw and femur), maxilloturbinal bone, and skin—that have been favored by traditional taxidermy practices for mammalian carnivores. Specifically, we performed shotgun sequencing and target capture enrichment for 100,000 single nucleotide polymorphisms (SNPs) selected from the commercial Canine HD BeadChip across 103 specimens from 1912 to 2005. The performance of the different tissues was assessed using metrics based on endogenous DNA content, uniquely high-quality mapped reads after capture, and enrichment proportions. All samples succeeded as DNA sources, regardless of their collection year or sample type. Skin samples yielded significantly higher amounts of endogenous DNA compared to both bone types, which yielded equivalent amounts. There was no evidence for a direct effect of tissue type on capture efficiency; however, the number of genotyped SNPs was strictly associated with the starting amount of endogenous DNA. Evaluation of genotyping accuracy for distinct minimum read depths across tissue types showed a consistent overall low genotyping error rate (<7%), even at low (3x) coverage. We recommend the use of skins as reliable and minimally destructive sources of endogenous DNA for whole-genome and target enrichment approaches in mammalian carnivores. In addition, we provide a new 100,000 SNP capture array validated for historical DNA (hDNA) compatible to the Canine HD BeadChip for high-quality DNA. The increasing demand for NHCs as DNA sources should encourage the generation of genomic datasets comparable among studies.