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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
Elia Vajana
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Annie Guillaume
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Estelle Rochat
1Laboratory of Geographic Information Systems (LASIG), School of Architecture, Civil and Environmental Engineering (ENAC), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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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Posted August 05, 2019.
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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