Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.809864
Title: Bayesian methods for inferring selection and demographic from historical and contemporary DNA sequences
Author: Dai, Xiaoyang
ISNI:       0000 0004 9346 8867
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2020
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Abstract:
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.
Supervisor: Beaumont, Mark Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.809864  DOI: Not available
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