Evidence map›Paper›PMID 41818228›Full record

ArticleMolecular biology and evolution2026

Soft Selective Sweeps Predominate in the Yellow Fever Mosquito Aedes aegypti.

Remi N Ketchum, Daniel R Matute, Daniel R Schrider

Abstract read
In one paragraph

Article in Molecular biology and evolution, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Remi N KetchumDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-0818-2908
Daniel R MatuteDepartment of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0002-7597-602X
Daniel R SchriderDepartment of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.ORCID 0000-0001-5249-4151

Funding

Deep learning for population geneticsR01HG010774 · NHGRI · UNIVERSITY OF OREGON · PI ANDREW D KERN · 2020 to 2026
$3.2M
Advancing evolutionary genetics through deep learningR35GM138286 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI DANIEL R SCHRIDER · 2020 to 2026
$2.8M
Drivers and consequences of introgression in evolutionR35GM148244 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI Daniel Matute · 2023 to 2026
$2.2M
NHGRI NIH HHS R01 HG010774NIGMS NIH HHS R35 GM138286NIGMS NIH HHS R35 GM148244NIH HHS R01HG010774NIH HHS R35GM138286NIH HHS R35GM148244
6 · The paper itself

Abstract

The Aedes aegypti mosquito is a vector for human arboviruses and zoonotic diseases and therefore 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 is a significant limitation as this is vital information for understanding the pace of adaptation-populations that can 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 produce 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 sweeps are significantly more common than hard sweeps, 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 using 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.

Indexed as

AedesSelection, GeneticAnimalsInsecticide ResistanceMachine LearningMosquito VectorsYellow FeverAedes aegyptiinsecticide resistancemachine learningrapid adaptationsoft selective sweeps

Identifiers

PMID41818228
PMCPMC13042252

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.