COMPARISON OF NON-GAUSSIAN QUANTITATIVE GENETIC MODELS FOR MIGRATION AND STABILIZING SELECTION
Version of Record online: 27 JUN 2012
© 2012 The Author(s). Evolution© 2012 The Society for the Study of Evolution.
Volume 66, Issue 11, pages 3444–3461, November 2012
How to Cite
Huisman, J. and Tufto, J. (2012), COMPARISON OF NON-GAUSSIAN QUANTITATIVE GENETIC MODELS FOR MIGRATION AND STABILIZING SELECTION. Evolution, 66: 3444–3461. doi: 10.1111/j.1558-5646.2012.01707.x
- Issue online: 25 OCT 2012
- Version of Record online: 27 JUN 2012
- Accepted manuscript online: 1 JUN 2012 02:33PM EST
- Received September 14, 2011 Accepted May 25, 2012 Data Archived Dryad: doi:10.5061/dryad.s808f
- infinitesimal model;
- linkage disequilibrium;
- migration–selection balance;
The balance between stabilizing selection and migration of maladapted individuals has formerly been modeled using a variety of quantitative genetic models of increasing complexity, including models based on a constant expressed genetic variance and models based on normality. The infinitesimal model can accommodate nonnormality and a nonconstant genetic variance as a result of linkage disequilibrium. It can be seen as a parsimonious one-parameter model that approximates the underlying genetic details well when a large number of loci are involved. Here, the performance of this model is compared to several more realistic explicit multilocus models, with either two, several or a large number of alleles per locus with unequal effect sizes. Predictions for the deviation of the population mean from the optimum are highly similar across the different models, so that the non-Gaussian infinitesimal model forms a good approximation. It does, however, generally estimate a higher genetic variance than the multilocus models, with the difference decreasing with an increasing number of loci. The difference between multilocus models depends more strongly on the effective number of loci, accounting for relative contributions of loci to the variance, than on the number of alleles per locus.