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Detecting Introgression in Anopheles Mosquito Genomes Using a Reconciliation-Based Approach

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Comparative Genomics (RECOMB-CG 2018)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 11183))

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Abstract

Introgression is an important evolutionary mechanism in insects and animals evolution. Current methods for detecting introgression rely on the analysis of phylogenetic incongruence, using either statistical tests based on expected phylogenetic patterns in small phylogenies or probabilistic modeling in a phylogenetic network context. Introgression leaves a phylogenetic signal similar to horizontal gene transfer, and it has been suggested that its detection can also be approached through the gene tree/species tree reconciliation framework, which accounts jointly for other evolutionary mechanisms such as gene duplication and gene loss. However so far the use of a reconciliation-based approach to detect introgression has not been investigated in large datasets. In this work, we apply this principle to a large dataset of Anopheles mosquito genomes. Our reconciliation-based approach recovers the extensive introgression that occurs in the gambiae complex, although with some variations compared to previous reports. Our analysis also suggests a possible ancient introgression event involving the ancestor of An. christyi.

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Notes

  1. 1.

    As mentioned previously, introgression and HGT are different evolutionary mechanisms; however, for expository reasons, we refer to the transfer of genetic material between two Anopheles species as an HGT.

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Acknowledgments

CC is supported by Natural Science and Engineering Research Council of Canada (NSERC) Discovery Grant RGPIN-2017-03986. Most computations were done on the Cedar system of ComputeCanada through a resource allocation to CC. We thank Luay Nakhleh for useful feedback on an early draft of this work.

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Correspondence to Cedric Chauve .

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Chauve, C., Feng, J., Wang, L. (2018). Detecting Introgression in Anopheles Mosquito Genomes Using a Reconciliation-Based Approach. In: Blanchette, M., Ouangraoua, A. (eds) Comparative Genomics. RECOMB-CG 2018. Lecture Notes in Computer Science(), vol 11183. Springer, Cham. https://doi.org/10.1007/978-3-030-00834-5_9

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  • DOI: https://doi.org/10.1007/978-3-030-00834-5_9

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