A narrow window for geographic cline analysis using genomic data:
effects of age, size, and migration on error rates
- Gaston Jofre,
- Gil Rosenthal
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 measured the effects of drift on
genetic differentiation, and on false positive and false negatives
detection using geographic clines. We modeled multiple stepping stone
hybrid zones with distinct age, deme sizes, and migration rates, and
evolving under different types of selection. We found that in young
hybrid zones, drift increases overall genomic divergence, distorts cline
shapes and increases both false positive and false negative rates. In
old hybrid zones, genomic divergence and cline distortion are higher.
Our results suggest that geographic clines are most useful for outlier
analysis in young hybrid zones with large populations of hybrid
individuals.