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

View ORCID ProfileOliver Selmoni, View ORCID ProfileElia Vajana, View ORCID ProfileAnnie Guillaume, View ORCID ProfileEstelle Rochat, View ORCID ProfileStéphane Joost
doi: https://doi.org/10.1101/603829
Oliver Selmoni
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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  • For correspondence: oliver.selmoni@epfl.ch
Elia Vajana
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Annie Guillaume
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Estelle Rochat
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Stéphane Joost
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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Abstract

An increasing number of studies are using landscape genomics to investigate local adaptation in wild and domestic populations. The implementation of this approach requires the sampling phase to consider the complexity of environmental settings and the burden of logistic constraints. These important aspects are often underestimated in the literature dedicated to sampling strategies.

In this study, we computed simulated genomic datasets to run against actual environmental data in order to trial landscape genomics experiments under distinct sampling strategies. These strategies differed by design approach (to enhance environmental and/or geographic representativeness at study sites), number of sampling locations and sample sizes. We then evaluated how these elements affected statistical performances (power and false discoveries) under two antithetical demographic scenarios.

Our results highlight the importance of selecting an appropriate sample size, which should be modified based on the demographic characteristics of the studied population. For species with limited dispersal, sample sizes above 200 units are generally sufficient to detect most adaptive signals, while in random mating populations this threshold should be increased to 400 units. Furthermore, we describe a design approach that maximizes both environmental and geographical representativeness of sampling sites and show how it systematically outperforms random or regular sampling schemes. Finally, we show that although having more sampling locations (between 40 and 50 sites) increase statistical power and reduce false discovery rate, similar results can be achieved with a moderate number of sites (20 sites). Overall, this study provides valuable guidelines for optimizing sampling strategies for landscape genomics experiments.

Footnotes

  • Methods improved with comparison with CDPOP simultions.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted August 05, 2019.
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Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach
Oliver Selmoni, Elia Vajana, Annie Guillaume, Estelle Rochat, Stéphane Joost
bioRxiv 603829; doi: https://doi.org/10.1101/603829
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Sampling strategy optimization to increase statistical power in landscape genomics: a simulation-based approach
Oliver Selmoni, Elia Vajana, Annie Guillaume, Estelle Rochat, Stéphane Joost
bioRxiv 603829; doi: https://doi.org/10.1101/603829

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