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Area of Science:

  • Evolutionary biology
  • Population genetics
  • Epidemiology

Background:

  • Epidemic outbreaks have historically impacted populations, prompting research into their role in driving natural selection.
  • Evidence for strong, short-term natural selection on disease-resistance genes during epidemics is scarce, possibly due to underpowered detection methods.
  • Standing genetic variation may be key to host survival during rapid, severe outbreaks.

Purpose of the Study:

  • To develop and present a simulation-based framework for evaluating the power of selection scan methods (e.g., FST) to detect epidemic-driven selection.
  • To explore conditions under which selection scan methods can successfully identify genetic variants conferring disease resistance.
  • To guide the design of future, well-powered studies investigating the evolutionary impact of epidemics.

Main Methods:

  • Developed a simulation framework to model epidemic-driven selection.
  • Assessed the power of selection scan methods, including FST, under various scenarios.
  • Compared the efficacy of different sampling schemes, specifically contrasting survivor-vs-deceased comparisons with standard methods.

Main Results:

  • The simulation framework can identify circumstances where selection scan methods have power to detect epidemic-driven selection.
  • Comparing individuals who died from an outbreak to survivors significantly increases the power to detect selection compared to other sampling schemes.
  • Even severe outbreaks like the Black Death may result in only modest allele frequency shifts, necessitating large sample sizes for detection.

Conclusions:

  • The developed framework aids in designing effective studies to detect epidemic-driven selection.
  • Survivor-bias sampling strategies show promise for identifying disease-protective genetic variants.
  • Understanding the evolutionary role of epidemics requires robust methodologies and potentially large sample sizes to detect subtle genetic changes.