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Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning

Xue, AT, Schrider, DR, Kern, AD, Della Torre, A, Caputo, B, Kabula, B, White, B, Godfray, C, Edi, C, Wilding, C, Neafsey, D, Conway, D, Weetman, D, Ayala, D, Kwiatkowski, D, Sharakhov, I, Midega, J, Xu, J, Pinto, J, Essandoh, J , Matowo, J, Vernick, K, Djogbenou, LS, Coulibaly, M, Lawniczak, M, Donnelly, M, Hahn, M, Fontaine, M, Riehle, M, Besansky, N, Cornejo, O, Mccann, R, O'loughlin, S, Robert, V, Xue, A, Miles, A, Clarkson, C, Battey, CJ, Champion, C, Labbe, F, Bottà, G, Adrion, J, Nelson, J, Harding, N, Wang, R, Small, ST, Redmond, S and Antão, T (2020) Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning. Molecular Biology and Evolution, 38 (3). pp. 1168-1183. ISSN 0737-4038

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Open Access URL: https://doi.org/10.1093/molbev/msaa259 (Published version)


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.

Item Type: Article
Uncontrolled Keywords: Ag1000g Consortium; Animals; Anopheles; Insecticide Resistance; Genome, Insect; Selection, Genetic; Deep Learning; convolutional neural networks; machine learning; partial sweeps; population genomics; selective sweeps; Animals; Anopheles; Deep Learning; Genome, Insect; Insecticide Resistance; Selection, Genetic; 0601 Biochemistry and Cell Biology; 0603 Evolutionary Biology; 0604 Genetics; Evolutionary Biology
Subjects: Q Science > QH Natural history
Q Science > QH Natural history > QH301 Biology
Divisions: Biological & Environmental Sciences (from Sep 19)
Publisher: Oxford University Press (OUP)
SWORD Depositor: A Symplectic
Date Deposited: 06 Jan 2023 13:13
Last Modified: 06 Jan 2023 13:13
DOI or ID number: 10.1093/molbev/msaa259
Editors: Falush, Daniel
URI: https://researchonline.ljmu.ac.uk/id/eprint/18553
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