Estimating Temporally Variable Selection Intensity from Ancient DNA Data

Abstract

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies, and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat colouration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

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

“…As Wutke et al (2016) only provided called genotypes for each gene (including missing calls), we use the same scheme as in He et al (2022) to convert to corresponding genotype likelihoods.…”

“…For aDNA data with high degrees of uncertainty, working on genotype likelihoods rather than called genotypes has been illustrated to be beneficial in the inference of selection (He et al, 2022).…”

“…Figure 1: Graphical representation of the two-layer HMM framework extended from He et al (2022) for the data on ancient DNA sequences. and population gamete frequency trajectories can be expressed as…”

“…As in He et al (2022), our procedure can allow the selection coefficients s ij,i ′ j ′ to change over time (piecewise constant), e.g., let the selection coefficients…”

“…However, utilising 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 (2022) 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.…”