Adaptive introgression in malaria mosquitoes

Lead Research Organisation: Liverpool School of Tropical Medicine
Department Name: Vector Biology

Abstract

Major reductions in malaria morbidity have occurred across Africa; driven by the use of neurotoxic insecticides. Emergent insecticide resistance could rapidly reverse this trend. The goal of this proposal is to revolutionize the identification of molecular markers for insecticide resistance across Africa by using genome wide association studies based on whole genome sequencing to streamline the identification of resistance associated variants.
 
Description Anopheles gambiae 1000 genomes project 
Organisation The Wellcome Trust Sanger Institute
Country United Kingdom 
Sector Charity/Non Profit 
PI Contribution I have been involved with the quality control validation of the phase 3 data offered by the Anopheles gambiae 1000 Genome (Ag1000G) project. This validation of phase 3 data feeds into my research, as that data is uniquely available to me for my PhD research. The output of my PhD research will directly feed back into the Ag1000G project.
Collaborator Contribution Partners have provided me with training on the relevant computational technologies and analysis pathways relevant to my PhD research. For example, collaborators at The Welllome Trust Sanger Institute hosted me for 3 months to train me on Python and the Jupyter environment workflow, in addition to introducing me to the data generation pipeline. colleagues at the Big Data Institute at Oxford University spent time with me to develop an analysis plan for my PhD topic.
Impact At this point (March 19) the primary outcome of this collaboration is the generations of a panel of ancestry informative markers, relative to Anopheles gamiae and An. arabiensis. This output is currently being used to assist the overall introgression analysis plan, developed in collaboration with these partners.
Start Year 2018
 
Description Anopheles gambiae 1000 genomes project 
Organisation University of Oxford
Country United Kingdom 
Sector Academic/University 
PI Contribution I have been involved with the quality control validation of the phase 3 data offered by the Anopheles gambiae 1000 Genome (Ag1000G) project. This validation of phase 3 data feeds into my research, as that data is uniquely available to me for my PhD research. The output of my PhD research will directly feed back into the Ag1000G project.
Collaborator Contribution Partners have provided me with training on the relevant computational technologies and analysis pathways relevant to my PhD research. For example, collaborators at The Welllome Trust Sanger Institute hosted me for 3 months to train me on Python and the Jupyter environment workflow, in addition to introducing me to the data generation pipeline. colleagues at the Big Data Institute at Oxford University spent time with me to develop an analysis plan for my PhD topic.
Impact At this point (March 19) the primary outcome of this collaboration is the generations of a panel of ancestry informative markers, relative to Anopheles gamiae and An. arabiensis. This output is currently being used to assist the overall introgression analysis plan, developed in collaboration with these partners.
Start Year 2018
 
Title A shiny application to visualise data provided by the Malaria Atlas Project 
Description MAP-district-comparison is a Shiny web application that allows users to generate summary statistics and visualise surfaces from the Malaria Atlas Project without the need to interact with the R coding language. The application allows selection of up to one country, all available districts within that country and up to four rasters for summary statistics generation and surface visualisation. The application allows users to download a formatted R Markdown file for the generated statistics. The 'Application' page is comprised of two main sections: 'Inputs' where the user selects a single country, any number of districts within that country and up to four surfaces. 'Outputs' Where the rendered results appear under three tabs on the right-hand side of the application page. Generate Statistic Button When the generate statistics button is clicked, the application retrieves summary statistics and raster layer visualisations for the selected input and renders them in the three output tabs on the right-hand side of the application The application generates district-level summary statistics for a range of malaria indicators/malariometric data, as available by MAP. The aggregated district-level statistics enable the interpretation of disaggregated, high-spatial resolution trends (5 km x 5 km), at the administrative level. The application allows user interaction and creates interactive visualizations such as maps displaying mean values for each district selected by the user. 
Type Of Technology Webtool/Application 
Year Produced 2019 
Impact We present here a novel tool that bridges the gap between computer scientists and public health researchers. This application enables those non-versed in the R programming language to visualise, compare and aggregate data produced by MAP; ultimately, allowing the computation of informative summary statistics at the first-level administrative division within Africa, encouraging researches to continue looking into the data provided by MAP and the more advanced skills needed to interface with it. Throughout the design and development of the application, we approached global health researchers within the Liverpool School of Tropical Medicine for their thoughts on meaningful outputs, and have subsequently included both summary tables and plots within a formatted output report, based on variables of interest to a given user. Furthermore, feedback for this method of data sharing and interaction was noted to be highly desirable by many researchers and an avenue that ought to be explored for pre-existing datasets within the institute and associated research consortia. Additionally, the application facilitates the generation of administrative level reports for countries which currently do not have easily accessible indicator reports available through the DHS Program. This includes data for countries such as Algeria, Djibouti, Guinea-Bissau, Libya, Somalia, Swaziland and Western Sahara. The formatted output reports enable quick access to summaries and trends within the MAP data; such reports are vital for informing public health decisions. We reduce barriers between contemporary estimates, and digestible information through an application with a simple to use interface - one which can be dynamically updated as and when new outputs are produced by the MAP. Innovation of Approach Whilst the methods used for the application development (i.e. 'Shiny') are not novel, we feel that the presentation of summary statistics in an easy-to-use interface presents novel opportunities for public health professions to interact with this data resource, especially where a lack of R knowledge may have previously prevented individuals from recovering these statistics through the conventional workflow. Additionally, as MAP provides a vast array of malariometric and indicator variables, developing an application which hones in on a particular suite of variables enables ease of access to this wealth of data. For example, our application was developed to show summary statistics and plots for the most impactful variables, however, the framework we have developed is receptive to any of the X surfaces generated by MAP. By tailoring which surfaces are available within the app, we allow for a more streamline and easy-to-use interface which reduces the likelihood that users become overwhelmed by the vast amount of data available. As a proof of concept, the app was developed for the African continent, however, future work may expand the inclusion to additional countries with endemic malaria transmission. Finally, presenting publicly available data in such a way that it can be easily interacted with is a positive exercise from a public engagement perspective. More and more the importance of presenting work and finding to the general public is a key feature of many grant applications and a general expectation of researchers. 
URL https://github.com/SeanTomlinson30/SHINY-map-prize