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A New Method to Scan Genomes for Introgression in a Secondary Contact Model
Secondary contact between divergent populations or incipient species may result in the exchange and introgression of genomic material. We develop a simple DNA sequence measure, called G (min), which is designed to identify genomic regions experiencing introgression in a secondary contact model. G (m...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4396994/ https://www.ncbi.nlm.nih.gov/pubmed/25874895 http://dx.doi.org/10.1371/journal.pone.0118621 |
Sumario: | Secondary contact between divergent populations or incipient species may result in the exchange and introgression of genomic material. We develop a simple DNA sequence measure, called G (min), which is designed to identify genomic regions experiencing introgression in a secondary contact model. G (min) is defined as the ratio of the minimum between-population number of nucleotide differences in a genomic window to the average number of between-population differences. Although it is conceptually simple, one advantage of G (min) is that it is computationally inexpensive relative to model-based methods for detecting gene flow and it scales easily to the level of whole-genome analysis. We compare the sensitivity and specificity of G (min) to those of the widely used index of population differentiation, F (ST), and suggest a simple statistical test for identifying genomic outliers. Extensive computer simulations demonstrate that G (min) has both greater sensitivity and specificity for detecting recent introgression than does F (ST). Furthermore, we find that the sensitivity of G (min) is robust with respect to both the population mutation and recombination rates. Finally, a scan of G (min) across the X chromosome of Drosophila melanogaster identifies candidate regions of introgression between sub-Saharan African and cosmopolitan populations that were previously missed by other methods. These results show that G (min) is a biologically straightforward, yet powerful, alternative to F (ST), as well as to more computationally intensive model-based methods for detecting gene flow. |
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