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 inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events like 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 address these challenges, we introduce a novel Bayesian approach for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to account for sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our method permits the reconstruction of the underlying mutant allele frequency trajectory of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and illustrate its utility with an application to the ancient horse samples genotyped at the loci for coat colouration.

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

“…To overcome a fundamental limitation of He et al (2022), which did not aim to model genetic interactions, we presented a novel Bayesian approach for inferring temporally variable selection from the data on aDNA sequences with the flexibility of modelling linkage and epistasis in this work. Our method was mainly built upon the two-layer HMM framework of He et al (2022), but we introduced a Wright-Fisher diffusion to describe the underlying evolutionary dynamics of two linked genes subject to phenotypic selection, which was modelled through the differential fitness of different phenotypic traits with a genotype-phenotype map. Such an HMM framework allows us to account for two-gene interactions and sample uncertainties resulting from the damage and fragmentation of aDNA molecules.…”

“…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…”

“…We reanalyse the aDNA data associated with horse base coat colours and pinto coat patterns from Wutke et al (2016) to show the applicability of our method on aDNA data, where base coat colours (bay, black and chestnut) are controlled by the ASIP and MC1R genes with epistatic interaction while pinto coat patterns (solid, sabino and tobiano) are determined by the KIT13 and KIT16 genes with tight linkage. We compare our results with those produced through the approach of He et al (2022) to demonstrate the necessity of modelling linkage and epistasis in the inference of selection. We test our approach with extensive simulations for each phenotypic trait to show that our procedure can deliver accurate selection inferences from genotype likelihoods.…”

“…To our knowledge, most existing methods tailored to aDNA rely on the diffusion approximation of the Wright-Fisher model. By working with the diffusion approximation, its HMM framework permits efficient integration over the probability distribution of the underlying population allele frequencies and therefore the calculation of the likelihood based on the observed sample allele frequencies can be completed within a reasonable amount of time (e.g., Bollback et al, 2008;Malaspinas et al, 2012;Steinrücken et al, 2014;Schraiber et al, 2016;Ferrer-Admetlla et al, 2016;He et al, 2020b,c;Lyu et al, 2022;He et al, 2022). These approaches have already been successfully used in aDNA studies; e.g., the method of Bollback et al (2008) was applied in Ludwig et al (2009) to analyse the aDNA data associated with horse coat colouration and illustrated that positive selection acted on the derived ASIP and MC1R alleles, suggesting that domestication and selective breeding contributed to changes in horse coat colouration.…”

“…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.…”