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Discovery of ongoing selective sweeps within Anopheles mosquito populations using deep learning

View ORCID ProfileAlexander T. Xue, View ORCID ProfileDaniel R. Schrider, View ORCID ProfileAndrew D. Kern, Ag1000G Consortium
doi: https://doi.org/10.1101/589069
Alexander T. Xue
1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, One Bungtown Road, Cold Spring Harbor, NY 11724
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  • For correspondence: xanderxue@gmail.com
Daniel R. Schrider
2Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC 27514
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Andrew D. Kern
3Institute of Ecology and Evolution, 5289 University of Oregon, Onyx Bridge 272, Eugene, OR 97403
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Abstract

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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • We have included additional comparisons to competing methods as well as additional misspecification experiments to further demonstrate the performance and robustness of our novel machine learning tool, partialS/HIC. Moreover, we have clarified the description of our conceptual design earlier in the paper, improved the presentation of figures, and streamlined the writing.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted June 06, 2020.
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Discovery of ongoing selective sweeps within Anopheles mosquito populations using deep learning
Alexander T. Xue, Daniel R. Schrider, Andrew D. Kern, Ag1000G Consortium
bioRxiv 589069; doi: https://doi.org/10.1101/589069
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Discovery of ongoing selective sweeps within Anopheles mosquito populations using deep learning
Alexander T. Xue, Daniel R. Schrider, Andrew D. Kern, Ag1000G Consortium
bioRxiv 589069; doi: https://doi.org/10.1101/589069

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