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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning
Molecular Biology and Evolution, Volume 38, No. 3, Year 2021
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Description
Identification of partial sweeps, which include both hard and soft sweeps that have not currently reached fixation, provides crucial information about ongoing evolutionary responses. To this end, we introduce partialS/HIC, a deep learning method to discover selective sweeps from population genomic data. partialS/HIC uses a convolutional neural network for image processing, which is trained with a large suite of summary statistics derived from coalescent simulations incorporating population-specific history, to distinguish between completed versus partial sweeps, hard versus soft sweeps, and regions directly affected by selection versus those merely linked to nearby selective sweeps. We perform several simulation experiments under various demographic scenarios to demonstrate partialS/HIC's performance, which exhibits excellent resolution for detecting partial sweeps. We also apply our classifier to whole genomes from eight mosquito populations sampled across sub-Saharan Africa by the Anopheles gambiae 1000 Genomes Consortium, elucidating both continent-wide patterns as well as sweeps unique to specific geographic regions. These populations have experienced intense insecticide exposure over the past two decades, and we observe a strong overrepresentation of sweeps at insecticide resistance loci. Our analysis thus provides a list of candidate adaptive loci that may be relevant to mosquito control efforts. More broadly, our supervised machine learning approach introduces a method to distinguish between completed and partial sweeps, as well as between hard and soft sweeps, under a variety of demographic scenarios. As whole-genome data rapidly accumulate for a greater diversity of organisms, partialS/HIC addresses an increasing demand for useful selection scan tools that can track in-progress evolutionary dynamics. © 2020 The Author(s) 2020. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.
Available Materials
https://efashare.b-cdn.net/share/pmc/articles/PMC7947845/bin/msaa259_supplementary_data.zip
Authors & Co-Authors
Schrider, Daniel R.
United States, Chapel Hill
The University of North Carolina at Chapel Hill
Kern, Andrew D.
United States, Eugene
University of Oregon
della Torre, Alessandra
Unknown Affiliation
Caputo, Beniamino
Unknown Affiliation
Kabula, Bilali I.
Unknown Affiliation
White, Bradley J.
Unknown Affiliation
Godfray, Charles H.J.
Unknown Affiliation
Edi, Constant V.A.
Unknown Affiliation
Wilding, Craig Stephen
Unknown Affiliation
Neafsey, Daniel E.
Unknown Affiliation
Conway, David J.
Unknown Affiliation
Weetman, David
Unknown Affiliation
Ayala, Diego
Unknown Affiliation
Kwiatkowski, Dominic P.
Unknown Affiliation
Sharakhov, Igor V.
Unknown Affiliation
Midega, Janet T.
Unknown Affiliation
Xu, Jiannong
Unknown Affiliation
Pinto, João
Unknown Affiliation
Essandoh, John
Unknown Affiliation
Matowo, Johnson J.
Unknown Affiliation
Vernick, Kenneth D.
Unknown Affiliation
Djogbenou, Salako Luc
Unknown Affiliation
Coulibaly, Mamadou B.
Unknown Affiliation
Lawniczak, Mara K.N.
Unknown Affiliation
Donnelly, Martin J.
Unknown Affiliation
Hahn, Matthew W.
Unknown Affiliation
Fontaine, Michaël C.
Unknown Affiliation
Riehle, Michelle M.
Unknown Affiliation
Besansky, Nora J.
Unknown Affiliation
Cornejo, Omar Eduardo
Unknown Affiliation
O'Loughlin, Samantha M.
Unknown Affiliation
Robert, Vincent
Unknown Affiliation
Miles, Alistair J.
Unknown Affiliation
Clarkson, Chris S.
Unknown Affiliation
Battey, Christopher J.
Unknown Affiliation
Champion, Cody J.
Unknown Affiliation
Labbé, Frédéric
Unknown Affiliation
Bottà, Giordano
Unknown Affiliation
Harding, Nicholas J.
Unknown Affiliation
Small, Scott T.
Unknown Affiliation
Redmond, Seth N.
Unknown Affiliation
Antão, Tiago Rodrigues
Unknown Affiliation
Statistics
Citations: 25
Authors: 42
Affiliations: 3
Identifiers
Doi:
10.1093/molbev/msaa259
ISSN:
07374038
Study Design
Cross Sectional Study