Asserting which allele is ancestral or derived, known as polarisation, is a prerequisite of many population and quantitative genetic methods. One important application is the inference of the unfolded site-frequency spectrum (uSFS). The most widely used approaches are based on outgroup data. However, for studies on species with only distantly related outgroups, large divergence between the ingroup and outgroup can result in alignment difficulties and substantial missing data, causing many sites of interest to be lost. Here, we present PolarBEAR (Polarisation By Estimation of the Ancestral Recombination graph), a method that uses the local genealogies from the ancestral recombination graph (ARG) to infer ancestral states. We show that PolarBEAR reaches high accuracy in polarisation and uSFS estimation using simulations under several scenarios. This accuracy, however, heavily depends on the ARG reconstruction method employed. We also applied our method to human population data and compared it with the outgroup-based method est-sfs. Although PolarBEAR could not infer the ancestral state with high confidence at certain positions, it obtained results for positions that est-sfs could not polarise due to missing outgroup data. The polarisation results of the two methods were highly consistent at positions inferred by both methods. The two methods inferred similar uSFS, with PolarBEAR estimating slightly fewer high-frequency derived alleles. Furthermore, we demonstrate that PolarBEAR is robust across different mutation models in our simulations, while est-sfs exhibits a bias in the presence of heterogeneous base composition. PolarBEAR can complement outgroup-based methods, or replace them when no appropriate outgroup sequence is available.
Liang et al. (Sun,) studied this question.