Profiling the Dead: Generating Microsatellite Data from Fossil Bones of Extinct Megafauna—Protocols, Problems, and Prospects

Abstract

We present the first set of microsatellite markers developed exclusively for an extinct taxon. Microsatellite data have been analysed in thousands of genetic studies on extant species but the technology can be problematic when applied to low copy number (LCN) DNA. It is therefore rarely used on substrates more than a few decades old. Now, with the primers and protocols presented here, microsatellite markers are available to study the extinct New Zealand moa (Aves: Dinornithiformes) and, as with single nucleotide polymorphism (SNP) technology, the markers represent a means by which the field of ancient DNA can (preservation allowing) move on from its reliance on mitochondrial DNA. Candidate markers were identified using high throughput sequencing technology (GS-FLX) on DNA extracted from fossil moa bone and eggshell. From the ‘shotgun’ reads, >60 primer pairs were designed and tested on DNA from bones of the South Island giant moa ( Dinornis robustus). Six polymorphic loci were characterised and used to assess measures of genetic diversity. Because of low template numbers, typical of ancient DNA, allelic dropout was observed in 36–70% of the PCR reactions at each microsatellite marker. However, a comprehensive survey of allelic dropout, combined with supporting quantitative PCR data, allowed us to establish a set of criteria that maximised data fidelity. Finally, we demonstrated the viability of the primers and the protocols, by compiling a full Dinornis microsatellite dataset representing fossils of c. 600–5000 years of age. A multi-locus genotype was obtained from 74 individuals (84% success rate), and the data showed no signs of being compromised by allelic dropout. The methodology presented here provides a framework by which to generate and evaluate microsatellite data from samples of much greater antiquity than attempted before, and opens new opportunities for ancient DNA research.