Identification of selective sweeps in closely related populations of the house mouse based on microsatellite scans

Genetics. 2008 Nov;180(3):1537-45. doi: 10.1534/genetics.108.090811. Epub 2008 Sep 14.

Abstract

Genome scans of polymorphisms promise to provide insights into the patterns and frequencies of positive selection under natural conditions. The use of microsatellites as markers has the potential to focus on very recent events, since in contrast to SNPs, their high mutation rates should remove signatures of older events. We assess this concept here in a large-scale study. We have analyzed two population pairs of the house mouse, one pair of the subspecies Mus musculus domesticus and the other of M. m. musculus. A total of 915 microsatellite loci chosen to cover the whole genome were assessed in a prescreening procedure, followed by individual typing of candidate loci. Schlötterer's ratio statistics (lnRH) were applied to detect loci with significant deviations from patterns of neutral expectation. For eight loci from each population pair we have determined the size of the potential sweep window and applied a second statistical procedure (linked locus statistics). For the two population pairs, we find five and four significant sweep loci, respectively, with an average estimated window size of 120 kb. On the basis of the analysis of individual allele frequencies, it is possible to identify the most recent sweep, for which we estimate an onset of 400-600 years ago. Given the known population history for the French-German population pair, we infer that the average frequency of selective sweeps in these populations is higher than 1 in 100 generations across the whole genome. We discuss the implications for adaptation processes in natural populations.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Chromosome Mapping*
  • Evolution, Molecular
  • Gene Frequency
  • Genetics, Population
  • Genome
  • Mice
  • Microsatellite Repeats / genetics*
  • Selection, Genetic*
  • Statistics as Topic / methods*