Gerbault, P; (2013) Modeling Demographic and Evolutionary History: Integrating Genetic and Archaeological Data. Doctoral thesis , UCL (University College London).

Abstract

In recent years, population genetic data has been used increasingly to make inferences on population history – particularly concerning the human past. However, any observed genetic data is only one outcome amongst a very large number of possibilities that can arise under the same population history; and reversely, multiple different histories can give rise to the same data. For this reason direct interpretation of patterns in genetic data to recover evolutionary history is highly problematic, and inferring evolutionary histories – including demographic and natural selection-related parameters – in a secure statistical framework, requires the exploration of a range of explicit models. Such models are better informed when conditioned on multiple data sources rather than purely genetic data, such as archaeological, environmental, and behavioural or cultural data. This PhD aims at integrating such data into a single framework in order to examine how well various population history hypotheses can explain observed patterns in various data. The approach I have used is simulation modeling coupled with approximate Bayesian computation (ABC) techniques to investigate three population histories. I have used a forward simulation model coupled with ABC to investigate demographic and evolutionary parameters of (i) the evolution of the ectodysplasin-A receptor (EDAR) derived allele in Southeast Asia, and (ii) the gene-culture co-evolution of lactase persistence (LP) and dairying in Europe. In both cases, the model simulates the allele frequency and the underlying population demography, conditioned on archaeological data for when and where farming starts. Natural selection is inferred to have driven both alleles to high frequencies in their respective regions. However, the reasons why these alleles would have been favored are still unclear. I therefore further apply the simulation / ABC framework to explore various selective hypotheses conditioned on key environmental factors. (iii) I have used a coalescent approach to assess how domestication has affected goat mitochondrial DNA (mtDNA) diversity. I have used the coalescent to simulate genealogies under demographic models informed by archaeological data. I then applied an ABC approach to determine which of those models best explains the observed patterns of mtDNA diversity in goats and to estimate demographic parameters from those models.

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