View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

Despite decades of research, identifying selective sweeps, the genomic footprints of positive selection, remains a core problem in population genetics. Of the myriad methods that have been made towards this goal, few have are designed to take advantage of the potential of genomic time-series data. This is because in most population genetic analyses only a single, brief period of time can be sampled for a study. Recent advancements in sequencing technology, including improvements in extracting and sequencing ancient DNA, have made repeated samplings of a population possible, allowing for serialized genomic datasets. With these advances in mind, here we present Timesweeper, a fast convolutional neural network-based tool for identifying selective sweeps in data consisting of multiple genomic samplings of a population over time. Timesweeper utilizes this serialized sampling of a population by first simulating data under a demographic model appropriate for the data of interest, training a 1D Convolutional Neural Network on said model, and inferring which SNPs in this serialized dataset were the direct target of a completed or ongoing selective sweep. We show that Timesweeper is powerful under multiple simulated demographic and sampling scenarios, works with low coverage anthropological data, identifies selected variants with impressive resolution, and has high tolerance for demographic model misspecification. In sum, we show that dramatically more accurate inferences about natural selection are possible when genomic time-series data are available, as will increasingly be the case in coming years due to both the sequencing of ancient samples and repeated samplings of extant populations with faster generation times. Methods like Timesweeper thus have the potential to help resolve the controversy over the role of positive selection in the genome. We provide Timesweeper both as a Python package and Snakemake workflow for use by the community.

Timesweeper: Detecting positive selection using genomic time series