Diseases transmitted by blood-feeding insects including malaria, dengue fever and filariasis are still major human health issues. The declining success of pesticides and concerns over global climate change augment the urgency of developing novel vector control approaches. Interestingly, out of the 500 Anopheline species that were sequenced, only a few transmitted human malaria, pointing towards an underlying genetic basis for this observed heterogeneity. The EU-funded ANOCAP (Comparative evolutionary and functional genomics of disease-vector anopheles mosquitoes) project has set out to develop computational strategies for identifying genetic patterns of natural selection in multiple mosquito genomes. To understand what defines an effective malaria vector and develop successful control strategies, researchers need to dissect the genetic determinants responsible for behavioural and physiological responses through evolution. In this context, ANOCAP will build multiple whole genome alignments to identify functional genomic elements. They are taking into consideration genome size and the evolutionary distances between the studied mosquito species. The generated alignments were viewed using a web-based tool and through a computational pipeline they were screened for protein-coding regions. Significant effort has been devoted to the identification of evolutionary constraints across these alignments, which is synonymous with functional importance. Preliminary results already highlight the translation capacity of genomic data into improved biological understanding of disease vectors. The combination of comparative evolutionary genomics and functional validation should significantly advance disease control strategies. This will also contribute to the development of innovative approaches to tackle global health issues.
Genome, vector, malaria, anopheles, mosquito, computational, evolution