VolcanoFinder: Genomic scans for adaptive introgression

Derek Setter, Sylvain Mousset, Xiaoheng Cheng, Rasmus Nielsen, Michael Degiorgio, Joachim Hermisson

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 TCHHRPTN in Africans. We discuss their biological implications and provide guidelines for identifying and circumventing artifactual signals during empirical applications of VolcanoFinder
Original languageEnglish
Article numbere1008867
JournalPLoS Genetics
Volume16
Issue number6
DOIs
Publication statusPublished - 18 Jun 2020

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