The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
The relative power of genome scans to detect local adaptation depends on sampling design and statistical method
Abstract
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 FST 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 (FST or GEA) depended largely on the number of individuals sampled, overall GEA tests had higher power in the island model and FST 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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Cited by
Finding the genomic basis of local adaptation: pitfalls, practical solutions, and future directions
Hoban, S., Kelley, J. L., Lotterhos, K. E., Antolin, M. F., Bradburd, G., Lowry, D. B., Poss, M. L., Reed, L. K., Storfer, A., & Whitlock, M. C. (2016) · The American Naturalist · doi:10.1086/688018 · Cited by 876
“Having a high proportion of selected loci relative to neutral loci in the data set can affect the results (e.g., 1 10% in Lotterhos and Whitlock 2014), but this needs to be more thoroughly investigated.”
InfluentialSelf-citationLFMM 2: Fast and Accurate Inference of Gene-Environment Associations in Genome-Wide Studies
Caye, K., Jumentier, B., Lepeule, J., & François, O. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz008 · Cited by 404
A decade of seascape genetics: contributions to basic and applied marine connectivity
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Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes
Forester, B. R., Jones, M. R., Joost, S., Landguth, E. L., & Lasky, J. R. (2016) · Molecular Ecology · doi:10.1111/mec.13476 · Cited by 258
Conservation and the Genomics of Populations
Allendorf, F. W., Funk, W. C., Aitken, S. N., Byrne, M., Luikart, G., & Antunes, A. (2022) · doi:10.1093/oso/9780198856566.001.0001 · Cited by 207
Metadata
- Authors
- Lotterhos, K. E., & Whitlock, M. C.
- Venue
- Molecular Ecology
- Publisher
- Wiley
- Published
- 2015-02-18
- Volume
- 24
- Issue
- 5
- Pages
- 1031-1046
- ISSN
- 0962-1083 (ISSN-L)
- 1365-294X
- OpenAlex
- W2099269327
- Cited by
- 566
- References
- 42
- FWCI
- 32.69
- Type
- journal article
- Language
- en
- Open access
- closed
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