Bayesian methods for inferring selection and demographic from historical and contemporary DNA sequences

  • Xiaoyang Dai

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)


In this thesis, I propose Bayesian methods to infer selection coefficients and allele age using time-series data and uncover the demographic history given contemporary whole-genome data.

Approximate Bayesian computation and Markov chain Monte Carlo method are widely used in solving population genetics problems. Time-series allele frequency problems often are modeled by the Hidden Markov Model, which is complex to make accurate inferences from. Here I employ a particle marginal Metropolis-Hastings method to make co-estimates of selection coefficients
and allele age based on the single-locus Wright-Fisher model and the two-locus Wright-Fisher model. In addition, I also propose an EP method with the ABC algorithm to extract demographic information from whole-genome contemporary data.

For each method, I make simulation studies to present the accuracy of the method and apply the method to re-analysis of published data to show the method can achieve effective and accurate estimates for genetic parameters of interests.
Date of Award12 May 2020
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorMark A Beaumont (Supervisor)

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