Comparing Neanderthal introgression maps reveals core agreement but substantial heterogeneity.

Statistical methods to identify Neanderthal ancestry in modern human genomes rest on varying assumptions and inputs. Nonetheless, most studies of introgression use only a single method to define Neanderthal ancestry. Due to a lack of "ground truth," we have a limited understanding of the accuracy, comparative strengths and weaknesses, and the sensitivity of downstream conclusions for these methods. Here, we performed large-scale comparisons of 14 genome-wide introgression maps computed by 11 representative Neanderthal introgression detection algorithms: admixfrog, ArchaicSeeker2, ArchIE, ARGWeaver-D, CRF, DICAL-ADMIX, hmmix, IBDmix, SARGE, Sprime, and S*. These algorithms span statistical approaches based on summary statistics, probabilistic modeling, and machine learning, and vary in their use of archaic, modern, and simulated genomes as input. Our results highlight a core set of regions predicted by nearly all methods, as well as substantial heterogeneity in commonly used Neanderthal introgression maps, especially at the individual genome level. Furthermore, we find that downstream analyses may result in different conclusions depending on the map used. Thus, we recommend careful consideration of map(s) chosen for downstream analysis and support the use of multiple maps to ensure robustness of conclusions. We make integrated prediction sets available, enabling further understanding of Neanderthal introgression's legacy on modern humans.