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Abstract Phylogenomics has revealed the remarkable frequency with which introgression occurs across the tree of life. These discoveries have been enabled by the rapid growth of methods designed to detect and characterize introgression from whole-genome sequencing data. A large class of phylogenomic methods makes use of data across species to infer and characterize introgression based on expectations from the multispecies coalescent. These methods range from simple tests, such as the D-statistic, to model-based approaches for inferring phylogenetic networks. Here, we provide a detailed overview of the various signals that different modes of introgression are expected leave in the genome, and how current methods are designed to detect them. We discuss the strengths and pitfalls of these approaches and identify areas for future development, highlighting the different signals of introgression, and the power of each method to detect them. We conclude with a discussion of current challenges in inferring introgression and how they could potentially be addressed.
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Leveraging shared ancestral variation to detect local introgression
Introgression is a common evolutionary phenomenon that results in shared genetic material across non-sister taxa. Existing statistical methods such as Patterson’sDstatistic can detect introgression by measuring an excess of shared derived alleles between populations. TheDstatistic is effective to detect genome-wide patterns of introgression but can give spurious inferences of introgression when applied to local regions. We propose a new statistic,D+, that leverages both shared ancestral and derived alleles to infer local introgressed regions. Incorporating both shared derived and ancestral alleles increases the number of informative sites per region, improving our ability to identify local introgression. We use a coalescent framework to derive the expected value of this statistic as a function of different demographic parameters under an instantaneous admixture model and use coalescent simulations to compute the power and precision ofD+. While the power ofDandD+is comparable,D+has better precision thanD. We applyD+to empirical data from the 1000 Genome Project andHeliconiusbutterflies to infer local targets of introgression in humans and in butterflies.
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- PAR ID:
- 10505700
- Editor(s):
- Zhu, Xiaofeng
- Publisher / Repository:
- PLoS
- Date Published:
- Journal Name:
- PLOS Genetics
- Volume:
- 20
- Issue:
- 1
- ISSN:
- 1553-7404
- Page Range / eLocation ID:
- e1010155
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pgen.1010155
