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A composite-likelihood approach for detecting directional selection from DNA sequence data.

Lan Zhu1, Carlos D Bustamante

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853, USA.

Genetics
|May 10, 2005
PubMed
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This study introduces a new composite-likelihood-ratio test (CLRT) to detect natural selection. The test effectively identifies genes under negative selection and shows moderate power for positive selection.

Area of Science:

  • Evolutionary genetics
  • Population genetics
  • Genomic analysis

Background:

  • Detecting natural selection is crucial for understanding evolution.
  • Existing methods may be limited in power or robustness to demographic factors.

Purpose of the Study:

  • Introduce a novel composite-likelihood-ratio test (CLRT) for detecting recurrent natural selection (positive or negative).
  • Evaluate the CLRT's performance, robustness, and discriminatory power under various evolutionary and demographic scenarios.

Main Methods:

  • Utilized Hartl et al.'s (1994) likelihood functions within a Wright-Fisher genic selection model.
  • Employed coalescent simulations with recombination to correct for site non-independence.
  • Characterized the CLRT statistic (Lambda) distribution relative to population recombination rate (R).

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Main Results:

  • The CLRT demonstrates excellent power for detecting weak negative selection and moderate power for positive selection.
  • The test is robust to local recombination rate estimation bias but sensitive to population growth or bottlenecks.
  • Maximum composite-likelihood estimation (MCLE) of the selection coefficient shows minimal bias for negative selection but downward bias for positive selection.

Conclusions:

  • The developed CLRT is a powerful tool for identifying genes under recurrent natural selection.
  • Researchers must consider demographic factors when applying the test, as it can be influenced by population growth or bottlenecks.
  • The MCLE provides reliable estimates for negative selection but requires caution for positively selected mutations.