Comparing Neanderthal introgression maps reveals core agreement but substantial heterogeneity.

Area of Science:

• Human evolutionary genomics
• Population genetics
• Bioinformatics

Background:

• Identifying Neanderthal ancestry in modern human genomes relies on statistical methods with varying assumptions.
• Current research often employs a single method, limiting understanding of comparative accuracy and sensitivity.

Purpose of the Study:

• To conduct a large-scale comparison of 12 different algorithms for detecting Neanderthal introgression.
• To assess the agreement and discrepancies among various genome-wide introgression maps.

Main Methods:

• Evaluated 12 Neanderthal introgression detection algorithms with diverse inputs (archaic/human genomes, simulated data).
• Methods included ArchaicSeeker2, CRF, DICAL-ADMIX, S*, Sprime, HMM, SARGE, ARGWeaver-D, IBDmix, and ArchIE.
• Generated and compared genome-wide introgression maps from each method.

Main Results:

• Identified a core set of Neanderthal-introgressed regions consistently predicted across most methods.
• Observed substantial heterogeneity in introgression maps generated by different algorithms.
• Downstream analyses yielded varying conclusions based on the specific introgression map utilized.

Conclusions:

• The choice of method significantly impacts the inferred Neanderthal introgression landscape.
• Recommends careful selection and the use of multiple introgression maps to ensure robust genetic ancestry findings.
• Integrated prediction sets are provided to aid further research on Neanderthal legacy in modern humans.