Inferences from DNA data: Population histories, evolutionary processes and forensic match probabilities

Abstract

We develop a flexible class of Metropolis-Hastings algorithms for drawing inferences about population histories and mutation rates from deoxyrlbonucleic acid (DNA) sequence data. Match probabilities for use in forensic identification are also obtained, which is particularly useful for mitochondrial DNA profiles. Our data augmentation approach, in which the ancestral DNA data are Inferred at each node of the genealogical tree, simplifies likelihood calculations and permits a wide class of mutation models to be employed, so that many different types of DNA sequence data can be analysed within our framework. Moreover, simpler likelihood calculations imply greater freedom for generating tree pro..