期刊
MOLECULAR ECOLOGY
卷 24, 期 5, 页码 1031-1046出版社
WILEY
DOI: 10.1111/mec.13100
关键词
adaptation; genome scans; landscape genetics; range expansion; sampling design; spatial statistics
资金
- Genome Canada
- Genome BC
- Alberta Innovates Bio Solutions
- Forest Genetics Council of British Columbia
- BC Ministry of Forests, Lands and Natural Resources Operations
- Virginia Tech
- University of British Columbia
- University of California, Davis
- National Sciences and Engineering Research Council (Canada)
Although genome scans have become a popular approach towards understanding the genetic basis of local adaptation, the field still does not have a firm grasp on how sampling design and demographic history affect the performance of genome scans on complex landscapes. To explore these issues, we compared 20 different sampling designs in equilibrium (i.e. island model and isolation by distance) and nonequilibrium (i.e. range expansion from one or two refugia) demographic histories in spatially heterogeneous environments. We simulated spatially complex landscapes, which allowed us to exploit local maxima and minima in the environment in pair' and transect' sampling strategies. We compared F-ST outlier and genetic-environment association (GEA) methods for each of two approaches that control for population structure: with a covariance matrix or with latent factors. We show that while the relative power of two methods in the same category (F-ST or GEA) depended largely on the number of individuals sampled, overall GEA tests had higher power in the island model and F-ST had higher power under isolation by distance. In the refugia models, however, these methods varied in their power to detect local adaptation at weakly selected loci. At weakly selected loci, paired sampling designs had equal or higher power than transect or random designs to detect local adaptation. Our results can inform sampling designs for studies of local adaptation and have important implications for the interpretation of genome scans based on landscape data.
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