Faculty Collaboration Database

Discovery of Ongoing Selective Sweeps within Anopheles Mosquito Populations Using Deep Learning. Mol Biol Evol 2021 Mar 09;38(3):1168-1183

Date

10/07/2020

Pubmed ID

33022051

Pubmed Central ID

PMC7947845

DOI

10.1093/molbev/msaa259

Scopus ID

2-s2.0-85102911117 (requires institutional sign-in at Scopus site)   15 Citations

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.

Author List

Xue AT, Schrider DR, Kern AD, Ag1000g Consortium

Author

Michelle M. Riehle PhD Assistant Professor in the Microbiology and Immunology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Animals
Anopheles
Genome, Insect
Insecticide Resistance
Selection, Genetic