Scalable ARG-free Detection of Denisovan-mediated Superarchaic Introgression Reveals Heterogeneous Patterns across Populations

Abstract

Ghost introgression from unsampled hominin lineages has emerged as an increasingly important component of human evolutionary history. Recent studies suggest that deeply divergent hominin lineages may have contributed ancestry either directly to modern humans or indirectly through Denisovan introgression, while inference remains difficult due to few reference genomes, weak signal, and uncertainty in reconstructing deep genealogies. Here we show analytically and through simulations that Denisovan-mediated superarchaic introgression produces predictable shifts in local coalescent depth that can be approximated by scalable summary statistics, particularly pairwise sequence divergence, suggesting that substantial information regarding deeply divergent ancestry is preserved in sequence variations without explicit reconstruction of genealogies. Leveraging this insight, we develop DEEP (Deep ancestry Estimation through Efficient Proxies), an ARG-free neural-network framework for identifying candidate regions of superarchaic ancestry. DEEP retains detectable power at low false positive rates across a broad range of demographic parameter space, remains scalable and recovers signals from small sample sizes. Applying DEEP to Oceanians, Tibetans, and Han Chinese, we identify approximately 0.4-0.6% of genomic windows with evidence of superarchaic ancestry. Candidate regions show both substantial overlap and notable heterogeneity across populations, with repeated enrichment near the HLA locus across all populations, suggesting immune-related regions recurrently retain deeply divergent ancestry.