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Related Experiment Videos

Perspective: detecting adaptive molecular polymorphism: lessons from the MHC.

Daniel Garrigan1, Philip W Hedrick

  • 1Department of Biology, Arizona State University, Tempe, Arizona 85287-1501, USA. garrigan@email.arizona.edu

Evolution; International Journal of Organic Evolution
|September 25, 2003
PubMed
Summary

Balancing selection maintains high molecular variation, but tests struggle to detect it consistently in the vertebrate major histocompatibility complex (MHC). Signals of selection may persist longer than expected, requiring cautious interpretation.

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Area of Science:

  • Population genetics
  • Molecular evolution
  • Immunogenetics

Background:

  • Balancing selection was an early hypothesis for high molecular variation.
  • The neutral theory of molecular evolution provides a baseline for detecting selection.
  • The vertebrate major histocompatibility complex (MHC) is a key system for studying genetic variation.

Purpose of the Study:

  • To review evidence for balancing selection on the vertebrate MHC.
  • To assess the power of neutrality tests using MHC data.
  • To identify potential inference problems with current neutrality tests.

Main Methods:

  • Applying widely used neutrality tests to MHC data.
  • Categorizing tests by the timescale of selection detection (current generation, population history, species history).

Related Experiment Videos

  • Analyzing the persistence of selection signals over evolutionary time.

    • Main Results:

    • Selection on the MHC is not consistently detectable across all time scales, populations, or lineages.
    • Current neutrality tests may lack sufficient power or face inference challenges.
    • Selection signals can be generated quickly but erased slowly, especially for the nonsynonymous/synonymous substitution ratio.

    Conclusions:

    • Balancing selection on the MHC is likely heterogeneous or difficult to detect consistently.
    • Caution is needed when inferring selection events from current neutrality tests.
    • Combining tests across different timescales may improve the inference of selection.