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Inferring selection in partially sequenced regions.

Jeffrey D Jensen1, Kevin R Thornton, Charles F Aquadro

  • 1Department of Molecular Biology and Genetics, Cornell University, USA. jjensen@ucsd.edu

Molecular Biology and Evolution
|January 1, 2008
PubMed
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Current methods for detecting positive selection in genomes can produce biased estimates of selection strength and location, especially in population genetics studies. New sequencing strategies are proposed to improve accuracy in identifying targets of selection.

Area of Science:

  • Population Genetics
  • Evolutionary Biology
  • Genomics

Background:

  • Detecting positive selection often involves genome scans followed by partial sequencing.
  • This approach is cost-effective but may lead to inaccurate results.

Purpose of the Study:

  • To evaluate the accuracy of common methods for identifying positive selection.
  • To propose improved sequencing strategies for precise estimation of selection parameters.

Main Methods:

  • Simulations were used to assess maximum likelihood estimates of selection parameters.
  • Performance of a common selection test was evaluated under various sweep models.
  • Analysis focused on Drosophila population genetics sample sizes (n=12).

Main Results:

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  • The common approach can yield biased estimates of selection parameters and reduced power to detect selection.
  • When the selected site is not sampled, estimates of selection strength are severely underestimated.
  • Mean square error for estimating the target of selection is large.

Conclusions:

  • Current partial sequencing methods for positive selection detection can be misleading.
  • Proposed sequencing approaches are more likely to accurately localize selection targets and estimate selection strength.
  • The performance of selection tests under different evolutionary models requires careful consideration.