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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
1Human Genetics Institute of New Jersey and Department of Genetics, Rutgers University, 604 Allison Road, Piscataway, NJ 08854
2Simons 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
3Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC 27514
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Andrew D. Kern
1Human Genetics Institute of New Jersey and Department of Genetics, Rutgers University, 604 Allison Road, Piscataway, NJ 08854
4Institute 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 a deep learning approach that uses a convolutional neural network for image processing, which is trained with coalescent simulations incorporating population-specific history, to discover selective sweeps from population genomic data. This approach distinguishes 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 the performance of our deep learning classifier partialS/HIC, which exhibits unprecedented resolution for detecting partial sweeps. We also apply our method 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 catalog of candidate adaptive loci that may aid mosquito control efforts. More broadly, the success of our supervised machine learning approach introduces a powerful 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.

Author Summary Recent successful efforts to reduce malaria transmission are in danger of collapse due to evolving insecticide resistance in the mosquito vector Anopheles gambiae. We aim to understand the genetic basis of current adaptation to vector control efforts by deploying a novel method that can classify multiple categories of selective sweeps from population genomic data. In recent years, there has been great progress made in the identification of completed selective sweeps through the use of supervised machine learning (SML), but SML techniques have rarely been applied to partial or ongoing selective sweeps. Partial sweeps represent an important facet of evolution as they reflect present-day selection and thus may give insight into future dynamics. However, the genomic impact left by partial sweeps is more subtle than that left by completed sweeps, making such signatures more difficult to detect. To this end, we extend a recent SML method to partial sweep inference and apply it to elucidate ongoing selective sweeps from Anopheles population genomic samples.

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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 March 28, 2019.
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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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