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SAI: A Python Package for Statistics for Adaptive Introgression
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DOI：10.1093/molbev/msaf295.png)
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Abstract
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Adaptive introgression is an important evolutionary process, which can be identified with widely used summary statistics, such as the number of uniquely shared sites and the quantile of the derived allele frequencies in such sites. However, these as well as more recently developed statistics such as D+ and Danc, still lack accessible implementations. Here, we present SAI, a Python package for computing these statistics along with a new statistic, DD, and demonstrate its application on 2 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, we investigated bonobo introgression into central chimpanzees and identified candidate genes, finding one region that overlaps a high-frequency Denisovan-introgressed haplotype block reported in modern Papuans. This is an intriguing co-occurrence across divergent lineages, underscoring the role of adaptive introgression in evolution.
Keywords:
adaptive introgression
population genetics
statistical inference
Python
reproducibility
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