ArticleMolecular ecology resources2021
A narrow window for geographic cline analysis using genomic data: Effects of age, drift, and migration on error rates.
Article in Molecular ecology resources, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A spatial approach to jointly estimate Wright's neighborhood size and long-term effective population size.Genetics · 2024Article
- Genetic admixture drives climate adaptation in the bank vole.Communications biology · 2024Article
- A narrow window for geographic cline analysis using genomic data: Effects of age, drift, and migration on error rates.Molecular ecology resources · 2021Article
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2 authors.
Funding
Abstract
The use of genomic and phenotypic data to scan for outliers is a mainstay for studies of hybridization and speciation. Geographic cline analysis of natural hybrid zones is widely used to identify putative signatures of selection by detecting deviations from baseline patterns of introgression. As with other outlier-based approaches, demographic histories can make neutral regions appear to be under selection and vice versa. In this study, we use a forward-time individual-based simulation approach to evaluate the robustness of geographic cline analysis under different evolutionary scenarios. We modelled multiple stepping-stone hybrid zones with distinct age, deme sizes, and migration rates, and evolving under different types of selection. We found that drift distorts cline shapes and increases false positive rates for signatures of selection. This effect increases with hybrid zone age, particularly if migration between demes is low. Drift can also distort the signature of deleterious effects of hybridization, with genetic incompatibilities and particularly underdominance prone to spurious typing as adaptive introgression. Our results suggest that geographic clines are most useful for outlier analysis in young hybrid zones with large populations of hybrid individuals. Current approaches may overestimate adaptive introgression and underestimate selection against maladaptive genotypes.
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