Individual assignment using microsatellite DNA reveals unambiguous breed identification in the domestic dog.

Abstract

Modern individual clustering methods utilising hypervariable nuclear microsatellite DNA polymorphisms are being increasingly applied in the field of population genetics. This study explores the efficiency of the clustering methods in identifying the breeds of origin of 250 domestic dog (Canis familiaris) individuals based on 10 microsatellite loci. An allele sharing distance (DAS) matrix and the corresponding neighbour-joining tree of individuals revealed monophyletic assemblages that corresponded perfectly with the breeds of origin of the dogs. Individual assignment tests using a Bayesian statistical approach, an allele frequency based method, and a DCE genetic distance based method were all extremely powerful. Most strikingly, the Bayesian method provided 100% assignment success of individuals into their correct breeds of origin and 100% exclusion success of individuals from all alternate reference populations with a high level of statistical confidence (P < 0.0001). A Bayesian Markov Chain Monte Carlo clustering approach revealed clear distinction of individuals into groups according to their breeds of origin, with a near-zero level of 'genetic admixture' among breeds. The results demonstrate that an FST of 0.18, mean expected gene diversity of 0.6 across 10 loci, and approximately 50 individuals per reference population suffice to provide maximum individual assignment success in C. familiaris. This refutes the traditional view that DNA based dog breed identification is not feasible at the individual level of resolution.