Signatures of soft selective sweeps predominate in the yellow fever mosquito Aedes aegypti

Abstract

The Aedes aegypti mosquito is a vector for human arboviruses and zoonotic diseases, such as yellow fever, dengue, Zika, and chikungunya, and as such poses a serious threat to public health. Understanding how Ae. aegypti adapts to environmental pressures-such as insecticides-is critical for developing effective mitigation strategies. However, most traditional methods for detecting recent positive selection search for signatures of classic "hard" selective sweeps, and to date no studies have examined soft sweeps in Ae. aegypti. This represents a significant limitation as this is vital information for understanding the pace at which an organism can adapt-populations that are able to immediately respond to new selective pressures are expected to adapt more often via standing variation or recurrent adaptive mutations (both of which may produce soft sweeps) than via de novo mutations (which produces hard sweeps). To this end, we used a machine learning method capable of detecting hard and soft sweeps to investigate positive selection in Ae. aegypti population samples from Africa and the Americas. Our results reveal that soft sweep signatures are significantly more common than hard sweeps in all population samples, including those that have experienced population bottlenecks, which may imply that this species can respond quickly to environmental stressors. This is a particularly concerning finding for vector control methods that aim to eradicate Ae. aegypti through the use of insecticides. We highlight genes under selection that include both well-characterized and putatively novel insecticide resistance genes. These findings underscore the importance of using methods capable of detecting and distinguishing hard and soft sweeps, implicate soft sweeps as a major selective mode in Ae. aegypti, and highlight genes that may aid in the control of Ae. aegypti populations.

Figure 1.

ROC curves and precision-recall curves summarizing the performance of each population samples’ classifier. A) ROC curves showing the true and false positive rates for the binary classification task of distinguishing between selective sweeps (hard and soft) vs unselected regions (sweep-linked and neutral) with varying threshold cutoffs highlighted with different shapes. B) Precision-recall curves showing the classifiers performance at the same task (sweep vs unselected) with varying threshold cutoffs highlighted with different shapes. The precision is defined by the fraction of regions classified as sweeps that truly were sweeps and the recall is defined by the true positive rate. For the Cali classifier, there were no windows classified as a sweep with probability ≥0.99, so no marker is included on Cali’s precision-recall curve for that threshold.

Figure 2.

Upset plot showing the intersection of selective sweep locations across the four population samples in this study based on the posterior probability cutoff of a sweep of 0.95.

Figure 3.

A soft sweep in Senegal at three cytochrome P450 genes: Cyp6a14, Cyp6a8, and Cyp6a13. The diploS/HIC classification track shows the class with the highest posterior probability, with soft sweeps as dark blue, soft sweep-linked regions as light blue, hard sweeps as red, hard sweep-linked as light red, and neutrally evolving regions in black. Above the diploS/HIC classifications are a subset of the summary statistics used by the classifier.

Figure 4.

A soft sweep in Kenya at the sodium/potassium/calcium exchanger NCKX30C. The diploS/HIC classification track shows the class with the highest posterior probability, with soft sweeps as dark blue, soft sweep-linked regions as light blue, hard sweeps as red, hard sweep-linked as light red, and neutrally evolving regions in black. Above the diploS/HIC classifications are a subset of the summary statistics used by the classifier.