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.
Competing Interest Statement
The authors have declared no competing interest.
