ArticleBMC evolutionary biology2014
Distinguishing migration from isolation using genes with intragenic recombination: detecting introgression in the Drosophila simulans species complex.
Article in BMC evolutionary biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Hybrid Sterility, Genetic Conflict and Complex Speciation: Lessons From theFrontiers in genetics · 2021Review
- Supervised machine learning reveals introgressed loci in the genomes of Drosophila simulans and D. sechellia.PLoS genetics · 2018Article
- Shedding Light on the Grey Zone of Speciation along a Continuum of Genomic Divergence.PLoS biology · 2016Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDetermining the presence or absence of gene flow between populations is the target of some statistical methods in population genetics. Until recently, these methods either avoided the use of recombining genes, or treated recombination as a nuisance parameter. However, genes with recombination contribute additional information for the detection of gene flow (i.e. through linkage disequilibrium).
methodsWe present three summary statistics based on the spatial arrangement of fixed differences, and shared and exclusive polymorphisms that are sensitive to the presence and direction of gene flow. Power and false positive rate for tests based on these statistics are studied by simulation.
resultsThe application of these tests to populations from the Drosophila simulans species complex yielded results consistent with migration between D. simulans and its two endemic sister species D. mauritiana and D. sechellia, and between populations D. mauritiana on the islands of the Mauritius and Rodrigues.
conclusionsWe demonstrate the sensitivity of the developed statistics to the presence and direction of gene flow, and characterize their power as a function of differentiation level and recombination rate. The properties of these statistics make them especially suitable for analyzing high-throughput sequencing data or for their integration within the approximate Bayesian computation framework.
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