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Genomic introgression events in the Anopheles gambiae complex

Tomlinson, Sean (2021) Genomic introgression events in the Anopheles gambiae complex, Thesis (Doctoral), Liverpool School of Tropical Medicine.

S Tomlinson - Thesis Final.pdf - Accepted Version

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Malaria is a disease caused by the Plasmodium parasite and is transmitted by the infectious bites of Anopheles mosquitoes. An estimated 409,000 deaths were attributed to the disease in 2019, with approximately 229 million cases worldwide. Long-lasting insecticidal nets and indoor residual spraying are two applications that have been the most effective in controlling transmission. However, the effectiveness of these public health insecticides has long been threatened by insecticide resistance in the key vector species which transmit the parasite. The Anopheles gambiae 1000 genomes (Ag1000G) project is an international consortium that aims to create a databank of whole-genome sequenced Anopheles samples from across Africa. The third phase of the Ag1000G increases the number of specimens to ~3000 from 18 countries, these samples are to include An. arabiensis from Uganda, Malawi, Kenya and Tanzania. These data provide a unique opportunity to investigate the An. arabiensis genome. This is of specific interest due to the growing importance of An. arabiensis as it displaces An. gambiae populations in some regions. In this thesis, I develop and explore three key investigations with the An. arabiensis genome as the focus, these are: development of ancestry informative markers, discovery of copy number variation and signals of introgression.
A panel of ancestry informative markers (AIMs), which can resolve between An. gambiae and An. arabiensis is a useful tool for many bioinformatic analyses. Here, we develop this panel and use them to inform the discovery of genome regions which may be introgressed between the two species. Our results show that the application of AIMs in this study was unable to satisfactorily identify regions of introgression between the two species. However, the panel has a broader application in other analyses which seeks to identify SNPs which are segregated between the species. Recent findings show that detoxification genes are significantly enriched in the CNVs discovered in An. gambiae mosquitoes. We used the An. arabiensis whole genome sequence data from the Ag1000G project to apply a minimally modified version of the previously published method for CNV discovery. Though we identified many fewer CNVs in the An. arabiensis dataset, we did discover enrichment of CNVs which contain genes associated with insecticide detoxification. Finally, we developed an analyses pipeline which calculates Patterson’s D statistic across the genome. This pipeline is generalised and applicable to future analyses. We calculated Patterson’s D statistic for all combinations of populations and outgroups that could be informative about introgression between An. gambiae and An. arabiensis. Many significant signals of introgression were observed that were both ubiquitous across all populations and in regions that contain known insecticide resistance loci. We discuss here the implications and limitations of the analyses and contextualise the importance of continuing to characterise the role of introgression in both the general evolution and adaptation to anthropogenic pressures from insecticides. We ultimately demonstrate and provide a basis for continued analyses into the role of introgression in insecticide resistance.

Item Type: Thesis (Doctoral)
Subjects: QU Biochemistry > Genetics > QU 460 Genomics. Proteomics
QX Parasitology > QX 20 Research (General)
QX Parasitology > Insects. Other Parasites > QX 515 Anopheles
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Open source 3D printable replacement parts for the WHO insecticide susceptibility bioassay system.
Malaria Data by District: An open-source web application for increasing access to malaria information
Faculty: Department: Biological Sciences > Vector Biology Department
Depositing User: Lynn Roberts-Maloney
Date Deposited: 06 Apr 2022 10:11
Last Modified: 06 Jul 2022 01:02


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