DOI: 10.5061/dryad.tb2rbp03n
title: Population cytogenetics of the spiny-tailed goanna

description: Chromosomal rearrangements are often associated with local adaptation and
 speciation because they suppress recombination, and as a result,
 rearrangements have been implicated in disrupting geneflow. Although there
 is strong evidence to suggest that chromosome rearrangements are a factor
 in genetic isolation of divergent populations, the underlying mechanism
 remains elusive. Here we applied an integrative cytogenetics and genomics
 approach testing whether chromosomal rearrangements are the initial
 process, or a consequence, of population divergence in the dwarf goanna,
 Varanus acanthurus. Specifically, we tested whether chromosome
 rearrangements are indicators of genetic barriers that can be used to
 identify divergent populations by looking at geneflow within and between
 populations with rearrangements. We found that geneflow was present
 between individuals with chromosome rearrangements within populations, but
 there was no geneflow between populations that had similar chromosome
 rearrangements. Moreover, we identified a correlation between reduced
 genetic variation in populations with a higher frequency of homozygous
 submetacentric individuals. These findings suggest that chromosomal
 rearrangements were widespread prior to divergence and because we found
 populations with higher frequencies of submetacentric chromosomes were
 associated with lower genetic diversity, this could indicate that
 polymorphisms within populations are early indicators of genetic drift.
description: <strong>SNP analysis</strong> We
 used Diversity Arrays Technology (DArT, Bruce, ACT, Australia) for
 genotyping 49 individuals including the 34 individuals that were
 karyotyped (Table 1). DArT is a genome-wide SNP typing technology that
 utilizes complexity reduction and Illumina sequencing (Kilian et al.
 2012). DNA samples were digested with restriction enzymes to ~ 500bp size
 fragments; these fragments were cloned, amplified and sequenced using
 Illumina. The target libraries were generated from all individuals and the
 allele differences for each sequence were characterized by 0, 1 or 2
 (homozygous, heterozygous, homozygous for opposite allele). These data
 were compared to the reference clone from Illumina Barcoding (Kilian et
 al. 2012). <strong>Data sorting and
 Analysis</strong> The data was analyzed using the
 dartR package, which was developed specifically for Diversity Arrays
 Technology output (Gruber et al. 2018). Briefly, the data were initially
 filtered for call rate by locus and individuals separately with a
 threshold of 0.8 and 0.75 respectively. Following call rate filtering, we
 filtered by reproducibility at a threshold of 0.99 and removed monomorphic
 loci from the whole dataset for initial analysis. We then performed a
 scree plot of eigenvalues that indicated informative axes (Unmack et al.
 2019) and performed principal coordinate analysis (PCA) (Pearson 1901;
 Hotelling 1933; Jollife 2002; Jollife and Cadima 2016) with no a priori
 population assignments (Table 4a and Table 4b). Fixed differences and Fst
 analysis were performed for each population to further characterize the
 distribution and frequencies of alleles between populations (Table 4). To
 analyze each population independently we filtered additional monomorphic
 loci that were population specific. We then used (PCA) to assess genetic
 distances for each individual within populations from each locality and
 assign the karyotypes to individual genotypes within the PCA to observe
 the distribution of karyotype morphologies with Euclidean genetic distance
 measures (Figure 3a, 3c, 3e and 3g). This allowed for within-population
 genetic distance measures between individuals with different karyotype
 morphologies. To determine geneflow between individuals
 with different karyotype morphologies within populations, we used both PCA
 and isolation by distance using a dissimilar measure of unshared alleles
 (1 – proportion of shared alleles). We assigned karyotypes to each
 individual and plotted the analyses (Figure 3b, 3d, 3f and 3h). In Figure
 3 we also used a neighbor-joining tree to demonstrate the relationships
 between populations based on SNP data and karyotypes.
 To investigate genetic differentiation and estimate geneflow
 between 221 populations, we performed an isolation-by-distance analysis
 based on the mantel test (Rousset 1997). First, we analyzed each
 population individually as defined by locality and PCA ordination (north,
 south, east and west) pairwise with unshared alleles (1 – proportion of
 shared alleles) for each individual versus distance (Figure 4c). Next, we
 analyzed these four populations pairwise for F<sub>st</sub>
 values (Figure 4d) versus distance. Following these analyses, we performed
 the same analysis with the karyotypes as populations to test for geneflow
 between karyotypes using both metrics, unshared alleles (1 – proportion of
 shared alleles) versus distance (Figure 4e) and Fst vs distance (Figure
 4f).
description: R Rstudio