Enhancing the methodological framework for inferring selection with ancient DNA: theoretical insights, improvements and comparison

Over the past decade, the emergence of ancient DNA has opened new opportunities for studying evolutionary processes. However, inferring signals of selection from such data remains a methodological challenge since controlling for population stratification, admixture, and dynamically changing demographic histories, among other confounding evolutionary processes, is difficult. In this context, ancient DNA time series data, which have proliferated, have led to the development of methods based on two main frameworks: Hidden Markov Models and Generalized Linear Mixed Models. In this work, we aim to clarify how these frameworks relate to the classical Wright–Fisher model, enabling targeted modeling improvements and producing more relevant comparisons across methods.