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Title: A bioinformatic analysis of malaria host and pathogen genomics
Author: Ravenhall, M.
ISNI:       0000 0004 7964 7412
Awarding Body: London School of Hygiene & Tropical Medicine
Current Institution: London School of Hygiene and Tropical Medicine (University of London)
Date of Award: 2019
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Malaria is a significant global disease caused by infection with parasites of the Plasmodium genus, which resulted in an estimated 216 million cases and 445,000 deaths in 2016 alone. Co-evolution of Plasmodium parasites and their human hosts has shaped both genomes for thousands of years. In this thesis I describe my work identifying and characterising novel genomic variants and selection signals associated with host-pathogen interactions in malaria. For the parasite, analysis of the impact of sustained sulfadoxine/pyrimethamine (SP) use on the Malawian parasite population (n=220) lead to the identification of selection signals associated with SP resistance factors and a novel 436 bp gch1 promoter region duplication at near-fixation. Next a global approach to copy number variation discovery (n=3,110), based on short read sequencing, identified several novel and geographically specific variants including large 22.9 kbp duplications of crt in West Africa. Finally, an inversion discovery pipeline was developed for a long read based approach to inversion detection (n=17). This led to the identification of a novel 'sandwich inversion' of pi4k in a sample of GB4, similar to inversion-duplication of gch1 in Dd2. For human genetics within the context of malaria, I conducted a GWAS with a Tanzanian dataset (n=914) and identified novel protective associations, such as for IL-23R and IL-12RBR2. I also identified novel structural variants (SV) with a short-read sequencing based dataset of Tanzanian parent-child trios (n=234) identifying several novel SVs associated with blood antigen systems. Near-fixation deletions in SEC22B and BET1L were also identified, suggesting an impact on intracellular transportation. Genomic variation associated with host-pathogen interactions is diverse, and SVs represent one overlooked aspect that requires further investigation. Bioinformatic approaches can help identify novel variants but depend upon novel software development, such as those described within this thesis (e.g. SV-Pop).
Supervisor: Clark, T. ; Campino, S. Sponsor: Biotechnology and Biological Sciences Research Council
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral