Population structure: genetic differentiation and disparity
To visualize population clustering, we used principal component analyses
(PCA), starting from genotype-frequency-based covariance matrices
(pcangsd v.0.99, Meisner & Albrechtsen 2018). We further visualized
coancestry between individuals for each species using heatmaps (GPLOTS
v.3.0.1.1; Warnes et al. 2020).
We used two measures of genetic distance to test whether populations
were significantly different from each other. First, we converted
pcangsd-derived covariance matrices to distance matrices
(dist.from.cov , rwc v.1.11;
Hanks 2018). Second, we calculated
pairwise genetic distances between all individuals of each species
(ngsDist). We then tested for significant differences in genetic
diversity between populations of the same species (adonis , VEGAN;
Oksanen et al. 2019). We used pairwise.adonis (with corrected
p-value estimation) as post-hoc test to assess which populations were
significantly different. In order to test whether certain populations
were more disparate (genetically variable) than others, we usedbetadisper and TukeyHSD as post-hoc test (results presented in
SI).
In order to assess population genetic differentiation, we calculatedFST values on genotype likelihoods starting from
unfolded, pairwise SFS (core Merianieae) or folded SFS (Ad.
adscendens ) using realSFS in ANGSD.
We further calculated the admixture coefficient for each individual by
estimating the likelihood of genetic clustering in the data (NGSadmix,
Skotte et al. 2013). We randomly selected only one SNP per locus and
used ten random initializations to estimate admixture from K = 1 to K =
n+1 ancestral populations, n being the total number of sampled
localities in each species. We compared the rate of change in the
log-likelihood of different successive K-values
(Evanno 2005) to select the
K-value best describing clustering in the data and used bar plots for
visualization.