MBE Advance Access originally published online on November 24, 2008
Molecular Biology and Evolution 2009 26(3):513-526; doi:10.1093/molbev/msn270
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Research Articles |
Inferring the Strength of Selection in Drosophila under Complex Demographic Models
Department of Biology, Stanford University
E-mail: messer{at}stanford.edu.
Accepted for publication November 6, 2008.
Transposable elements (TEs) constitute a substantial fraction of the genomes of many species, and it is thus important to understand their population dynamics. The strength of natural selection against TEs is a key parameter in understanding these dynamics. In principle, the strength of selection can be inferred from the frequencies of a sample of TEs. However, complicated demographic histories, such as found in Drosophila melanogaster, could lead to a substantial distortion of the TE frequency distribution compared with that expected for a panmictic, constant-sized population. The current methodology for the estimation of selection intensity acting against TEs does not take into account demographic history and might generate erroneous estimates especially for TE families under weak selection. Here, we develop a flexible maximum likelihood methodology that explicitly accounts both for demographic history and for the ascertainment biases of identifying TEs. We apply this method to the newly generated frequency data of the BS family of non-long terminal repeat retrotransposons in D. melanogaster in concert with two recent models of the demographic history of the species to infer the intensity of selection against this family. We find the estimate to differ substantially compared with a prior estimate that was made assuming a model of constant population size. Further, we find there to be relatively little information about selection intensity present in the derived non-African frequency data and that the ancestral African subpopulation is much more informative in this respect. These findings highlight the importance of accounting for demographic history and bear on study design for the inference of selection coefficients generally.
Key Words: bottleneck • transposable element • maximum likelihood
1 These authors contributed equally and are listed in alphabetical order.
Marcy Uyenoyama, Associate Editor
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