Estimating temporally variable selection intensity from ancient DNA data with the flexibility of modelling linkage and epistasis

Feng Yu, Zhangyi He*, Xiaoyang Dai, Wenyang Lyu, Mark A Beaumont

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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Innovations in ancient DNA (aDNA) preparation and sequencing technologies have exponentially increased the quality and quantity of aDNA data extracted from ancient biological materials. The additional temporal component from the incoming aDNA data can provide improved power to address fundamental evolutionary questions like characterizing selection processes that shape the phenotypes and genotypes of contemporary populations or species. However, utilizing aDNA to study past selection processes still involves considerable hurdles like how to eliminate the confounding factor of genetic interactions in the inference of selection. To address this issue, we extend the approach of He et al., 2023 to infer temporally variable selection from the aDNA data in the form of genotype likelihoods with the flexibility of modelling linkage and epistasis in this work. Our posterior computation is carried out by a robust adaptive version of the particle marginal Metropolis-Hastings algorithm with a coerced acceptance rate. Our extension inherits the desirable features of He et al., 2023 such as modelling sample uncertainty resulting from the damage and fragmentation of aDNA molecules and reconstructing underlying gamete frequency trajectories of the population. We evaluate its performance through extensive simulations and show its utility with an application to the aDNA data from pigmentation loci in horses.

Original languageEnglish
Pages (from-to)1226-1240
Number of pages15
JournalMolecular Ecology Resources
Issue number6
Early online date30 Mar 2023
Publication statusPublished - 1 Aug 2023

Bibliographical note

Funding Information:
We thank the anonymous reviewers and the editor for their helpful comments on the earlier version of this work. This work was carried out using the computational facilities of the Advanced Computing Research Centre, University of Bristol—

Publisher Copyright:
© 2023 John Wiley & Sons Ltd.


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