Data from: VolcanoFinder: genomic scans for adaptive introgression

Recent research shows that introgression between closely-related species
is an important source of adaptive alleles for a wide range of taxa.
Typically, detection of adaptive introgression from genomic data relies on
comparative analyses that require sequence data from both the recipient
and the donor species. However, in many cases, the donor is unknown or the
data is not currently available. Here, we introduce a genome-scan
method---VolcanoFinder---to detect recent events of adaptive introgression
using polymorphism data from the recipient species only. VolcanoFinder
detects adaptive introgression sweeps from the pattern of excess
intermediate-frequency polymorphism they produce in the flanking region of
the genome, a pattern which appears as a volcano-shape in pairwise genetic
diversity. Using coalescent theory, we derive analytical predictions for
these patterns. Based on these results, we develop a composite-likelihood
test to detect signatures of adaptive introgression relative to the
genomic background. Simulation results show that VolcanoFinder has high
statistical power to detect these signatures, even for older sweeps and
for soft sweeps initiated by multiple migrant haplotypes. Finally, we
implement VolcanoFinder to detect archaic introgression in European and
sub-Saharan African human populations, and uncovered interesting
candidates in both populations, such as TSHR in Europeans and TCHH-RPTN in
Africans. We discuss their biological implications and provide guidelines
for identifying and circumventing artifactual signals during empirical
applications of VolcanoFinder.