title: Population genetic inference using a fixed number of segregating sites: a reassessment.
abstract: Coalescent theory is commonly used to perform population genetic inference at the nucleotide level. Here, we examine the procedure that fixes the number of segregating sites (henceforth the FS procedure). In this approach a fixed number of segregating sites (S) are placed on a coalescent tree (independently of the total and internode lengths of the tree). Thus, although widely used, the FS procedure does not strictly follow the assumptions of coalescent theory and must be considered an approximation of (i) the standard procedure that uses a fixed population mutation parameter theta, and (ii) procedures that condition on the number of segregating sites. We study the differences in the false positive rate for nine statistics by comparing the FS procedure with the procedures (i) and (ii), using several evolutionary models with single-locus and multilocus data. Our results indicate that for single-locus data the FS procedure is accurate for the equilibrium neutral model, but problems arise under the alternative models studied; furthermore, for multilocus data, the FS procedure becomes inaccurate even for the standard neutral model. Therefore, we recommend a procedure that fixes the theta value (or alternatively, procedures that condition on S and take into account the uncertainty of theta) for analysing evolutionary models with multilocus data. With single-locus data, the FS procedure should not be employed for models other than the standard neutral model.
authors.affiliation: Max Planck Institute of Chemical Ecology, Jena, Germany. sramosonsins@ub.edu
authors.forename: Sebastián E
authors.initials: SE
authors.lastname: Ramos-Onsins
authors.forename: Sylvain
authors.initials: S
authors.lastname: Mousset
authors.forename: Thomas
authors.initials: T
authors.lastname: Mitchell-Olds
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Stephan
mesh_terms: D005075:Biological Evolution; D003198:Computer Simulation; D005828:Genetics, Population; D008957:Models, Genetic; D009010:Monte Carlo Method; D009154:Mutation / Q000235:genetics