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.
Highlights
Malaria represents an enormous burden on human health, with an estimated 214 million cases and 438,000 deaths in 2015 (WHO 2015)
Individual simulations were converted into 2D matrices, or feature vector images, built from 89 rows corresponding to different summary statistics, and 11 columns corresponding to adjacent subwindows
The 89 statistics include, along with 14 that were previously implemented in S/HIC and/or diploS/HIC, 3 genomic region variants of the single nucleotide polymorphism (SNP)-specific integrated haplotype scores (iHS) statistic (Voight et al 2006) and 72 derivatives of the SAFE score (Akbari et al 2018). partialS/HIC is trained to classify genomic segments into one of nine states: unaffected by selection; containing a completed hard, completed soft, partial hard, or partial soft sweep, respectively; or linked to a completed hard, completed soft, partial hard, or partial soft sweep, respectively
Summary
Malaria represents an enormous burden on human health, with an estimated 214 million cases and 438,000 deaths in 2015 (WHO 2015). As mosquitos of the Anopheles gambiae species complex are the major vector for Plasmodium parasites, roughly 70% of global malaria relief budgets have been focused on mosquito control, including insecticide-treated bed-nets, indoor residual spraying, and larva control through the direct modification of habitats as well as the application of larvicide. These vector control efforts have successfully produced major reductions of malaria transmission rates over the past 15 years (WHO 2015), there has been an alarming increase in mosquitos resistant to insecticides, pyrethroids, observed across most areas of the world covered by anti-malarial efforts (Hemingway et al 2016).
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