New advances in insecticide resistance genomics: using Machine Learning to predict resistance phenotype from large-scale genomic data.

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Malaria is a parasitic tropical disease which kills hundreds of thousands of people every year, predominantly children in Sub-Saharan Africa (SSA). The disease is transmitted by mosquitoes who acquire the parasites after taking a blood meal from an infected person. Elimination of malaria therefore relies on effectively reducing mosquito numbers to break the cycle of transmission. This is primarily achieved through the application of insecticides, either by spraying the walls of houses in which mosquitoes bite or by protecting humans with insecticide-treated bed nets. The documented evolution of resistance to insecticides in mosquitoes that carry malaria is therefore of great concern, and the continued effectiveness of control programmes requires knowledge of the insecticides to which a mosquito population is susceptible. Currently, this is achieved by experimentally exposing mosquitoes to insecticides to directly measure their resistance, but this process is slow and laborious, and not a good indicator of impact. Ideally, it should be possible to screen a mosquito population for key genes involved resistance, but our understanding of the genetics of insecticide resistance is still limited to a handful of genes. The scientific community is currently at the advent of an exciting era in genomics where modern genome sequencing capacity is rapidly increasing the scale at which genomic data can be produced. What have been lacking are analytical techniques that can utilise the huge scale of data and integrate all of the information it contains to predict resistance phenotypes. Machine learning is an approach that allows computers to use existing data to "learn" how to analyse new data and use it to make predictions. For example, given a sufficiently large dataset of mosquitoes whose genetics and resistance characteristics are known, machine learning tools can find associations between genetics and resistance, which can then be used to measure resistance using only genetics. Machine learning tools have yet to be applied to the field of insecticide resistance because they require large amounts of data from which to "learn", and the necessary genomic data have been lacking.We and our collaborators are currently amassing the largest collection of any species to date combining both genome-wide sequencing data and measures of insecticide resistance, producing unprecedented amounts of resistance-associated genomic data. We will leverage these data, using machine learning to improve our ability to estimate the insecticide resistance profile of a mosquito using genomic data. Most importantly, this project will help improve our ability to screen mosquito populations for insecticide resistance and will inform malaria control policy as a result. In collaboration with our partners in SSA who are closely involved with the mosquito control programmes, we will identify areas where our method can be most effectively applied to help improve the control of malaria.