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Microsatellite length mutations are often modeled using the generalized stepwise mutation process, which is a type of random walk. If this model is sufficiently accurate, one can estimate the coalescence time between alleles of a locus after a mathematical transformation of the allele lengths. When large-scale microsatellite genotyping first became possible, there was substantial interest in using this approach to make inferences about time and demography, but that interest has waned because it has not been possible to empirically validate the clock by comparing it with data in which the mutation process is well understood. We analyzed data from 783 microsatellite loci in human populations and 292 loci in chimpanzee populations, and compared them with up to one gigabase of aligned sequence data, where the molecular clock based upon nucleotide substitutions is believed to be reliable. We empirically demonstrate a remarkable linearity (r(2) &gt; 0.95) between the microsatellite average square distance statistic and sequence divergence. We demonstrate that microsatellites are accurate molecular clocks for coalescent times of at least 2 million years (My). We apply this insight to confirm that the African populations San, Biaka Pygmy, and Mbuti Pygmy have the deepest coalescent times among populations in the Human Genome Diversity Project. Furthermore, we show that microsatellites support unbiased estimates of population differentiation (F(ST)) that are less subject to ascertainment bias than single nucleotide polymorphism (SNP) F(ST). These results raise the prospect of using microsatellite data sets to determine parameters of population history. When genotyped along with SNPs, microsatellite data can also be used to correct for SNP ascertainment bias.

Microsatellites Are Molecular Clocks That Support Accurate Inferences about History