SAI: A Python Package for Statistics for Adaptive Introgression

Adaptive introgression is an important evolutionary process, yet widely used summary statistics—such as the number of uniquely shared sites and the quantile of the derived allele frequencies in such sites—lack accessible implementations, limiting reproducibility and methodological clarity. Here, we present SAI, a Python package for computing these statistics, and apply it to three datasets. First, using the 1000 Genomes Project data, we replicated previously reported candidate regions and identified additional ones, including a region detected by studies using supervised deep learning. Second, reanalysis of a Lithuanian genome dataset revealed no candidates in the HLA region. Finally, we investigated bonobo introgression into central chimpanzees and identified a candidate region that overlaps a high-frequency Denisovan-introgressed haplotype block reported in modern Papuans—an intriguing co-occurrence across divergent lineages. Discrepancies with prior results highlight the importance of transparent and reproducible analysis workflows, especially as machine learning becomes increasingly prevalent in evolutionary genomics.