Measuring the sensitivity of single-locus "neutrality tests" using a direct perturbation approach.
A large number of statistical tests have been proposed to detect natural selection based on a sample of variation at a single genetic locus. These tests measure the deviation of the allelic frequency distribution observed within populations from the distribution expected under a set of assumptions that includes both neutral evolution and equilibrium population demography. The present study considers a new way to assess the statistical properties of these tests of selection, by their behavior in response to direct perturbations of the steady-state allelic frequency distribution, unconstrained by any particular nonequilibrium demographic scenario. Results from Monte Carlo computer simulations indicate that most tests of selection are more sensitive to perturbations of the allele frequency distribution that increase the variance in allele frequencies than to perturbations that decrease the variance. Simulations also demonstrate that it requires, on average, 4N generations (N is the diploid effective population size) for tests of selection to relax to their theoretical, steady-state distributions following different perturbations of the allele frequency distribution to its extremes. This relatively long relaxation time highlights the fact that these tests are not robust to violations of the other assumptions of the null model besides neutrality. Lastly, genetic variation arising under an example of a regularly cycling demographic scenario is simulated. Tests of selection performed on this last set of simulated data confirm the confounding nature of these tests for the inference of natural selection, under a demographic scenario that likely holds for many species. The utility of using empirical, genomic distributions of test statistics, instead of the theoretical steady-state distribution, is discussed as an alternative for improving the statistical inference of natural selection.
Garrigan
Daniel
D
Department of Organismic and Evolutionary Biology, Harvard University, Boston, MA, USA. daniel.garrigan@rochester.edu
Lewontin
Richard
R
Wakeley
John
J
D000483:Alleles; D000818:Animals; D056426:Genetic Loci; D006801:Humans; D008957:Models, Genetic; D009010:Monte Carlo Method; D011110:Polymorphism, Genetic; D012641:Selection, Genetic; D017422:Sequence Analysis, DNA; D049490:Systems Biology / Q000379:methods*