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

Malaria prevalence in Sub-Saharan Africa (SSA) has been reduced by 50% since 2000, primarily due to insecticide-based mosquito control measures. However, progress has stagnated in recent years, and has even reversed in some areas, due in part to the rise of insecticide resistance in mosquitoes. High-throughput genetics offers the prospect of accurate and reliable methods for large-scale characterisation of resistance in mosquito populations using routine collections, avoiding the laborious work of phenotypically testing mosquito resistance. The scientific community is currently at the advent of an exciting era in genomics where whole-genome sequencing can be performed at a rapidly increasing scale. These very large genomic datasets require novel analytical methods to deal with challenges of auto-correlation and over-fitting. Machine learning is a promising approach that has been used extensively to produce powerful classifiers in big data analyses, but is has yet to be applied to the field of vector-borne disease control because the necessary genomic data have been lacking. Through our involvement and leadership of the Vector Observatory and Genomics for African Anopheles Resistance Diagnostics projects, we will be whole-genome sequencing thousands of mosquitoes with known insecticide-resistance phenotypes from across SSA. This study will experiment with different machine learning approaches and explore the possibility of combining a range of different information, for example combining gene expression and genomic data in a single analysis. It will also work with collaborators in SSA to design use-case scenarios for the implementation of our genetic screens in malaria-endemic areas.

Project details

Drug Resistance
Enabling Technologies & Assays
Information Systems

Dec 2019 — Aug 2023

$658,064

United Kingdom