Genomic "scans" to identify loci that contribute to local adaptation are becoming increasingly common. Many methods used for such studies have assumed that local adaptation is created by loci experiencing antagonistic pleiotropy (AP) and that the selected locus itself is assayed, and few consider how signals of selection change through time. However, most empirical data sets have marker density too low to assume that a selected locus itself is assayed, researchers seldom know when selection was first imposed, and many locally adapted loci likely experience not AP but conditional neutrality (CN). We simulated data to evaluate how these factors affect the performance of tests for genotype-environment association (GEA). We found that 3 types of regression-based analyses (linear models, mixed linear models, and latent factor mixed models) and an implementation of BayEnv all performed well, with high rates of true positives and low rates of false positives, when the selected locus experienced AP, and when the selected locus was assayed directly. However, all tests had reduced power to detect loci experiencing CN, and the probability of detecting associations was sharply reduced when physically linked rather than causative loci were sampled. AP also maintained detectable GEAs much longer than CN. Our analyses suggest that if local adaptation is often driven by loci experiencing CN, genome-scan methods will have limited capacity to find loci responsible for local adaptation.
Bibliographical noteFunding Information:
This work was funded by the National Science Foundation (1237993 to Nevin D. Young and P.T.). J.B.Y. is currently supported as a postdoctoral fellow with the CoAdapTree Project (Genome Canada to Sally Aitken, Sam Yeaman, and Richard Hamelin).
- antagonistic pleiotropy
- conditional neutrality
- genotype-environment association
- latent-factor mixed models
- selective sweeps