In this thesis, I study the role of genetic population structure in the evolution and demography of Plasmodium falciparum by focusing on the recent onset of artemisinin resistance in Southeast Asia, an alarming event for global public health. I describe the population structure of Plasmodium falciparum in the Thai-Cambodian border region, characterizing sympatric but differentiated subpopulations associated with artemisinin resistance. I show evidence that they are the product of recent founder events and seem the primary force spreading resistance. Next, I study a superset of the kelch13 mutations associated with artemisinin resistance, assessing their relationship with population structure and recent founder effects. Each resistant subpopulation possesses a distinct kelch13 allele that, in conjunction with a particular genetic background, seem to have driven recent founder effects. I examine the demography of these resistance alleles using patterns of haplotype sharing and show that the primary mode of spread consists of independent mutational events, with limited gene flow within countries in East Southeast Asia. Subsequently, I assess the origin of kelch13 mutations observed in African isolates, concluding that they are indigenous and have originated independently. These observations undermine localized resistance containment as a strategy for malaria control and suggest that population structure and founder effects may predate and facilitate the emergence of resistance. Therefore, monitoring these phenomena could warn about the development of resistance before phenotypic evidence materializes. Next, given the importance of demographic inference to inform malaria control programs and the advent of large genomic datasets, I develop a fast and scalable method to build the ancestral haplotype graph. I show that this data structure, composed of a collection of local haplotype trees, is informative about the recent genealogical history of the sequences and can be used to summarize and study shared haplotype patterns along the genome. I describe a set of algorithms with quasilinear time complexity as a first step in the development of scalable demographic inferential methods that can be applied to several thousands of sequences. I also evaluate how mixed infections affect the analysis of deep sequencing data and review the F<sub>WS</sub> statistic, a relative measure of inbreeding and complexity of infection. In doing so, I show that the original F<sub>WS</sub> estimator discards the diversity encoded by rare variants and provide an alternative estimator without such bias that is simpler, more intuitive and has a better resolution.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:757673 |
| Date | January 2016 |
| Creators | Almagro-Garcia, Jacob |
| Contributors | McVean, Gilean ; Kwiatkowski, Dominic |
| Publisher | University of Oxford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://ora.ox.ac.uk/objects/uuid:665fd1aa-bcdd-4b05-8db0-868cf0a6572b |
Page generated in 0.002 seconds