Simulated patterns of mitochondrial diversity are consistent with partial population turnover in Bronze Age Central Europe.

Objectives: The analysis of ancient mitochondrial DNA from osteological remains has challenged previous conclusions drawn from the analysis of mitochondrial DNA from present populations, notably by revealing an absence of genetic continuity between the Neolithic and modern populations in Central Europe. Our study investigates how to reconcile these contradictions at the mitochondrial level using a modeling approach.

Materials And Methods: We used a spatially explicit computational framework to simulate ancient and modern DNA sequences under various evolutionary scenarios of post Neolithic demographic events and compared the genetic diversity of the simulated and observed mitochondrial sequences.

Bayesian estimation of partial population continuity using ancient DNA and spatially explicit simulations.

The retrieval of ancient DNA from osteological material provides direct evidence of human genetic diversity in the past. Ancient DNA samples are often used to investigate whether there was population continuity in the settlement history of an area. Methods based on the serial coalescent algorithm have been developed to test whether the population continuity hypothesis can be statistically rejected by analysing DNA samples from the same region but of different ages.

Investigating population continuity with ancient DNA under a spatially explicit simulation framework.

Background: Recent advances in sequencing technologies have allowed for the retrieval of ancient DNA data (aDNA) from skeletal remains, providing direct genetic snapshots from diverse periods of human prehistory. Comparing samples taken in the same region but at different times, hereafter called "serial samples", may indicate whether there is continuity in the peopling history of that area or whether an immigration of a genetically different population has occurred between the two sampling times. However, the exploration of genetic relationships between serial samples generally ignores their geographical locations and the spatiotemporal dynamics of populations.