Molecular and quantitative genetic divergence among populations of house mice with known evolutionary histories
- PMID: 15741999
- DOI: 10.1038/sj.hdy.6800652
Molecular and quantitative genetic divergence among populations of house mice with known evolutionary histories
Abstract
Evolutionary biologists have long been interested in the processes influencing population differentiation, but separating the effects of neutral and adaptive evolution has been an obstacle for studies of population subdivision. A recently developed method allows tests of whether disruptive (ie, spatially variable) or stabilizing (ie, spatially uniform) selection is influencing phenotypic differentiation among subpopulations. This method, referred to as the F(ST) vs Q(ST) comparison, separates the total additive genetic variance into within- and among-population components and evaluates this level of differentiation against a neutral hypothesis. Thus, levels of neutral molecular (F(ST)) and quantitative genetic (Q(ST)) divergence are compared to evaluate the effects of selection and genetic drift on phenotypic differentiation. Although the utility of such comparisons appears great, its accuracy has not yet been evaluated in populations with known evolutionary histories. In this study, F(ST) vs Q(ST) comparisons were evaluated using laboratory populations of house mice with known evolutionary histories. In this model system, the F(ST) vs Q(ST) comparisons between the selection groups should reveal quantitative trait differentiation consistent with disruptive selection, while the F(ST) vs Q(ST) comparisons among lines within the selection groups should suggest quantitative trait differentiation in agreement with drift. We find that F(ST) vs Q(ST) comparisons generally produce the correct evolutionary inference at each level in the population hierarchy. Additionally, we demonstrate that when strong selection is applied between populations Q(ST) increases relative to Q(ST) among populations diverging by drift. Finally, we show that the statistical properties of Q(ST), a variance component ratio, need further investigation.
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