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\begin{document}

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



\author[1]{Zhangyi He}%
\author[2]{Xiaoyang Dai}%
\author[3]{Wenyang Lyu}%
\author[3]{Mark Beaumont}%
\author[3]{Feng Yu}%
\affil[1]{Cancer Research UK Beatson Institute}%
\affil[2]{Queen Mary University of London Barts and The London School of Medicine and Dentistry}%
\affil[3]{University of Bristol}%


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  \date{May 17, 2024}


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\begin{abstract}
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 characterising
selection processes that shape the phenotypes and genotypes of
contemporary populations or species. However, utilising aDNA to study
past selection processes still involves considerable hurdles such as how
to eliminate the confounding effect of genetic interactions in the
inference of selection. To circumvent this challenge, in this work we
extend the method introduced by He et al. (2022) to infer temporally
variable selection from the data on aDNA sequences with the flexibility
of modelling linkage and epistasis. Our posterior computation is carried
out through a robust adaptive version of the particle marginal
Metropolis-Hastings algorithm with a coerced acceptance rate. Moreover,
our extension inherits their desirable features like modelling sample
uncertainties resulting from the damage and fragmentation of aDNA
molecules and reconstructing underlying gamete frequency trajectories of
the population. We assess the performance and show the utility of our
procedure with an application to ancient horse samples genotyped at the
loci encoding base coat colours and pinto coat patterns.%
\end{abstract}%



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