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MBE Advance Access published online on December 28, 2007
Molecular Biology and Evolution, doi:10.1093/molbev/msm273
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© The Author 2007. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Research Article

Inferring Selection in Partially Sequenced Regions

Jeffrey D. Jensen1, Kevin R. Thornton2 and Charles F. Aquadro

Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York
1 current address: Department of Ecology, Behavior and Evolution, University of California, San Diego
2 current address: Department of Ecology and Evolution, University of California, Irvine

Corresponding author: Jeffrey D. Jensen, Department of Ecology, Behavior and Evolution, AP&M Annex, 4th Floor MC0115, University of California, San Diego, San Diego, CA 92037, Voice: (607) 351-7999, Email: jjensen{at}ucsd.edu

Received for publication November 15, 2007. Revision received November 28, 2007. Accepted for publication December 1, 2007.

A common approach for identifying loci influenced by positive selection involves scanning large portions of the genome for regions that are inconsistent with the neutral equilibrium model or represent outliers relative to the empirical distribution of some aspect of the data. Once identified, partial sequence is generated spanning this more localized region in order to quantify the site frequency spectrum and evaluate the data with tests of neutrality and selection. This method is widely used as partial sequencing is less expensive with regard to both time and money. Here, we demonstrate that this approach can lead to biased maximum likelihood estimates of selection parameters and reduced rejection rates, with some parameter combinations resulting in clearly misleading results. Most significantly, for a commonly used sample size in Drosophila population genetics (i.e., n = 12), the estimate of the target of selection has a large mean-square error and the strength of selection is severely under estimated when the true selected site has not been sampled. We propose sequencing approaches that are much more likely to accurately localize the target and estimate the strength of selection. Additionally, we examine the performance of a commonly used test of selection under a variety of recurrent and single sweep models

Key Words: selective sweeps • natural selection • composite likelihood • recurrent selection


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