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Thesis

Estimation of Plasmodium falciparum allele and multi-SNP haplotype and genotype frequencies

Alternative title:
Estimation of malaria haplotype frequencies
Abstract:

Malaria kills hundreds of thousands of people each year, yet is entirely curable given prompt treatment. Malaria parasites evolve resistance to antimalarial drugs, hence routine surveillance of antimalarial resistance is vital. The surveillance of parasite genetic markers of resistance provides an economical adjunct to clinical efficacy trials, and has the potential to resolve drug specific resistance ahead of clinical failure. To monitor spatiotemporal changes using genetic markers, frequencies of alleles and/or haplotypes and genotypes spanning multiple single nucleotide polymorphisms (SNPs) are required. However, multiclonal infections complicate frequency estimation, especially in highly endemic regions.

With the aim of harnessing the full potential of genetic markers for the surveillance of antimalarial resistance, a statistical model to estimate frequencies is proposed. The model builds upon existing methods (reviewed in chapter 2), without reliance upon experimentally-derived estimates of the sample-wise multiplicities of infection (MOIs). Its ability to generate precise and accurate estimates within a Bayesian framework is documented in chapter 3. In chapter 4, the model is applied to data collected from a cohort of children enrolled in a longitudinal trial in Uganda, generating valuable insight into haplotype frequency trends. In chapter 5, the model is extended to investigate inter-child variability in the aforesaid cohort, revealing a small amount of inter-child variation. In chapter 6, the model is modified to enable the analysis of short-read sequencing data, with application to data from malaria patients in Northern Ghana, providing insight into the extent of within-host diversity and anti-folate resistance in the region.

In summary, this thesis documents the development, application, extension and modification of a model designed to estimate population-level frequencies of P. falciparum alleles and multi-SNP haplotypes and genotypes within a Bayesian framework. It is hoped that the model and its proposed framework will provide a practical tool for surveillance of antimalarial resistance, as well as a foundation on which to develop further methods.

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Institution:
University of Oxford
Division:
MPLS
Department:
Doctoral Training Centre - MPLS
Role:
Author

Contributors

Department:
Statistics
Role:
Supervisor
Department:
Tropical Medicine
Role:
Supervisor
Department:
School of Mathematical Sciences at Monash University
Role:
Supervisor
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Human Genetics Wt Centre
Role:
Examiner
Department:
Harvard T.H. Chan School of Public Health
Role:
Examiner


More from this funder
Grant:
Oxford Systems Biology Doctoral Training Centre


Type of award:
DPhil
Level of award:
Doctoral
Awarding institution:
University of Oxford


UUID:
uuid:c192e7cb-b6e0-4e23-a880-de46d668ef07
Deposit date:
2016-09-05

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