Inferring selection in partially sequenced regions

Jeffrey D. Jensen, Kevin R. Thornton, Charles F. Aquadro

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)438-446
Number of pages9
JournalMolecular biology and evolution
Issue number2
StatePublished - Feb 2008
Externally publishedYes


  • Composite likelihood
  • Natural selection
  • Recurrent selection
  • Selective sweeps

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Molecular Biology
  • Genetics


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