Archaic ancestry inference in imputed ancient human genomes

When modern humans expanded from Africa into Eurasia, they interbred with archaic hominins such as Neanderthals and Denisovans. This groundbreaking discovery, made in part possible through the genomic analysis of archaic remains, reshaped our understanding of human origins, and opened new research avenues to study the effects and consequences of archaic introgression. While significant progress has been made in identifying and quantifying archaic introgression, most studies have focused on contemporary individual genomic data. As a result, relatively little is known about the evolution of archaic variants within modern human populations shortly after interbreeding occurred. While analysis of ancient DNA from modern humans offers the potential to address this scientific gap, its poor quality has hindered its exploration. However, recent studies have shown that imputation using contemporary reference panels can accurately infer missing genotypes in ancient genomes. Here, we investigate the feasibility of using imputation to improve both global and local archaic ancestry inference in ancient genomes, by downsampling to different low-coverage values and imputing 20 high-coverage (>10X) ancient genomes, representing individuals from diverse temporal and geographical contexts.

We tested the reliability of detecting and quantifying archaic introgression using D-statistics and the f4-ratio . We identified consistent results from the imputed and the original ancient genomes, suggesting that genomic estimates to detect and quantify introgression work well in imputed genomes. Regarding Local Ancestry Inference we find that we can identify more introgressed segments in imputed genomes than in non-imputed genomes. Surprisingly, we find that imputation accuracy is even higher in regions of archaic ancestry compared to other regions of the genome, facilitating the detection of introgressed segments in imputed genomes. Imputation also allows detection of Denisovan segments in Siberian and Alaskan individuals. We show that segments identified even at 0.0625X coverage can be used to reconstruct the history of introgressed haplotypes. For example, comparisons with archaic segments in contemporary humans reveal that the oldest individual analyzed, Ust’Ishim, carried segments that are now found exclusively in either European or East Eurasian populations, indicating that these segments co-occurred in a shared ancestral population. By comparing only ancient individuals, we demonstrate that clustering based solely on the identified archaic segments effectively groups individuals into genetic clusters that correspond to populations defined by geography and time. This analysis shows a clear distinction between individuals with Denisovan introgression and those without, as well as a more detailed separation between Mesolithic and Neolithic Europeans, with the latter closely resembling Central Eurasian individuals. Furthermore, despite the small sample size in this study, we are able to reconstruct the origins of genes identified as candidates for adaptive introgression in contemporary populations, such as the BCN2 gene, which is an adaptively introgressed gene identified in contemporary West Eurasians. Additionally, we identify new candidates for adaptive introgression including LEMD2 and MLN in Europe. In this gene-region, imputation helps resolve the introgressed haplotype, which is closest to the Vindija Neanderthal haplotype.