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Journal Articles eLife Year : 2022

Modeling the spatiotemporal spread of beneficial alleles using ancient genomes

Rasa Muktupavela
Martin Petr
John Novembre
Fernando Racimo


Ancient genome sequencing technologies now provide the opportunity to study natural selection in unprecedented detail. Rather than making inferences from indirect footprints left by selection in present-day genomes, we can directly observe whether a given allele was present or absent in a particular region of the world at almost any period of human history within the last 10,000 years. Methods for studying selection using ancient genomes often rely on partitioning individuals into discrete time periods or regions of the world. However, a complete understanding of natural selection requires more nuanced statistical methods which can explicitly model allele frequency changes in a continuum across space and time. Here we introduce a method for inferring the spread of a beneficial allele across a landscape using two-dimensional partial differential equations. Unlike previous approaches, our framework can handle time-stamped ancient samples, as well as genotype likelihoods and pseudohaploid sequences from low-coverage genomes. We apply the method to a panel of published ancient West Eurasian genomes to produce dynamic maps showcasing the inferred spread of candidate beneficial alleles over time and space. We also provide estimates for the strength of selection and diffusion rate for each of these alleles. Finally, we highlight possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.
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Dates and versions

hal-04234529 , version 1 (10-10-2023)



Rasa Muktupavela, Martin Petr, Laure Ségurel, Thorfinn Korneliussen, John Novembre, et al.. Modeling the spatiotemporal spread of beneficial alleles using ancient genomes. eLife, 2022, 11, ⟨10.7554/eLife.73767⟩. ⟨hal-04234529⟩
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