Zhivotovsky et al. [11] examined a large autosomal microsatellite dataset in 52 worldwide populations and concluded that African farmers, but not hunter-gatherers, exhibit the signal of population growth.
For instance, a common type of genotyping error in microsatellite datasets are null alleles, which are often the result of a mutation in the primer annealing site.
It is well known that genotyping errors are present in most microsatellite datasets [53] and the theoretical 6.6% genotyping error rate we report here is not higher than what is expected, or observed, for 'modern' data [53] [55].
Genetic variation in both mitochondrial and microsatellite datasets was diagnostic of major regions across the native range of M. stigmatizans, with strong concordance between an individual's mtDNA lineage and the cluster to which the majority of its microsatellite genotype was assigned (χ2 = 449.8, d.f. = 21, P ≈ 0).
In DAPC, the optimal number of genetic clusters was one (k = 1) for both nuclear SNPs and microsatellite datasets, as indicated by the lowest BIC value in the successive k-means procedure.
No deviations from Hardy-Weinberg and linkage equilibrium were observed in the microsatellite dataset, as tested in FSTAT [54], using 15,000 randomizations.
The alignment part of WAMI can also be used to compare parent/son microsatellite datasets, provided that the microsatellite units are mapped to symbols, in analogy to the unit typing step of minisatellites.
We strongly advocate the exclusion of tetra-nucleotides from future population genetics analysis of this dataset, and we argue that other microsatellite datasets should be investigated for the presence of bias using the approach outlined in this article.
This index finds its origins on the concept of geographical segregation[6], often related to social studies, to infer the evolutionary pathways from microsatellite datasets.
While there was large congruence between these mtDNA and microsatellite datasets, it was not strict.
