Modeling the spatiotemporal spread of beneficial alleles using ancient genomes

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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 high-light possible avenues of improvement for accurately tracing the spread of beneficial alleles in more complex scenarios.

OriginalsprogEngelsk
Artikelnummere73767
TidsskrifteLife
Vol/bind11
Antal sider33
ISSN2050-084X
DOI
StatusUdgivet - 2022

Bibliografisk note

Funding Information:
We thank Graham Gower, Evan Irving-Pease, Montgomery Slatkin, the members of the Racimo group, and two anonymous reviewers for helpful comments and advice. FR and RM were funded by a Villum Fonden Young Investigator award to FR (project no. 00025300). FR was also supported by a Novo Nordisk Fonden Ascending Investigator Award (NF22OC0076816) and a ERC Synergy grant (ID 951385). Additionally, MP and FR were supported by a Lundbeckfonden grant (R302-2018-2155) and a Novo Nordisk Fonden grant (NNF18SA0035006) to the GeoGenetics Centre. TSK was funded by a Carlsberg grant (CF19-0712). JN was funded by NIH grant R01 GM132383.

Publisher Copyright:
© Muktupavela et al.

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