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Sampling strategy optimization to increase statistical power in landscape genomics: A simulation-based approach.

Oliver Selmoni1, Elia Vajana1, Annie Guillaume1

  • 1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Molecular Ecology Resources
|September 25, 2019
PubMed
Summary
This summary is machine-generated.

Optimizing sampling strategies is crucial for landscape genomics. This study reveals that sample size and site selection significantly impact detecting local adaptation, offering key guidelines for researchers.

Keywords:
false discovery ratelandscape genomicssample sizesampling strategystatistical power

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Area of Science:

  • Ecology
  • Evolutionary Biology
  • Genetics

Background:

  • Landscape genomics is increasingly used to study local adaptation in various populations.
  • Effective sampling strategies are vital but often overlooked, considering environmental complexity and logistical constraints.

Purpose of the Study:

  • To evaluate the impact of different sampling strategies on the statistical performance of landscape genomics experiments.
  • To provide guidelines for optimizing sampling design based on simulated genomic data and environmental variables.

Main Methods:

  • Simulated genomic datasets were analyzed against real environmental data.
  • Distinct sampling strategies were tested, varying in design approach, number of locations, and sample sizes.
  • Statistical performance (power and false discoveries) was assessed under different demographic scenarios.

Main Results:

  • Appropriate sample size is critical and depends on population demography: >200 for limited dispersal species, >400 for random mating populations.
  • A design maximizing environmental and geographical representativeness outperformed random or regular sampling.
  • 20 sampling sites can yield results comparable to 40-50 sites, balancing power and discovery rates.

Conclusions:

  • This study offers valuable guidelines for optimizing sampling strategies in landscape genomics.
  • The findings emphasize tailoring sample size and site selection to specific population characteristics for robust local adaptation studies.