Population differentiation and genetic structure
To infer genetic differentiation parameters, haplotypes grouped by
continent or sub-region were considered to comprise distinct geographic
populations. We assessed the genetic differentiation among geographic
populations by computing the gene differentiation statistic developed by
Nei and Chesser (1983), an allele
frequency-based approach that relies on estimates of genetic
differentiation among geographic sub-populations. We further used
Hudson et al. (1992)’s statistical test,
a simple non-parametric method based on Monte Carlos permutations. That
method, compared to the traditional Chi-square analysis of genetic
differentiation estimates, helped understand whether the geographical
populations are genetically different from one another. In addition,
genetic differentiation among populations was estimated by computing a
distance matrix based on the number of mutational steps between
haplotypes (Nst) and by using haplotype frequencies (Gst).
Phylogeographical structure was tested based on the difference between
GST and NST using PERMUT 2.0
(Pons & Petit, 1996;
Chiu et al., 2013) with 1000 permutations.
In contrast to Gst, Nst considers sequence differences between the
haplotypes. Thus, Nst > Gst indicates that closely related
haplotypes are observed more often in a given geographical area than
would be expected by chance (Pons &
Petit, 1996; Burban et al., 1999;
Grivet, 2002;
Guicking et al., 2011;
Chiu et al., 2013;
Chávez-Pesqueira & Núñez-Farfán, 2016;
Sun et al., 2019). Following
Templeton (1996), we tested the null
hypothesis of homogeneity of nucleotide mutations using Fisher’s exact
test to investigate haplotypic differentiation within the overall
population. We also performed Fu’s Fs(Fu, 1997) to analyze the expansion level
of the population under the hypothesis of selective neutrality and
population equilibrium. Tajima’s D test also was implemented for
comparison with the Fu’s Fs .