Generating PSMC input files from simulated data

The file contains a function written in R. This function randomly samples RAD loci from chromosomes simulated by ms (Hudson 2002), and transforms the RAD data into a ".psmcfa" file for PSMC analysis

Plot PSMC results

The file contains functions written in R. The functions are used to visualize the results from PSMC

Generating PSMC input files from empirical RAD data

The files contains a script written in R. The script creates input files for PSMC from empirical RAD data using tag files and allele files. Tag files and allele files are generated through pstacks in Stacks v1.13

empirical data(psmcfa files)

The zip file contains the input files for PSMC from the empirical data of threespine sticklebacks in the study. Seventeen of them are from RAD data, and one of them is from whole genome sequencing data

Data_Sheet_1_Analysis of Molecular Variance (AMOVA) for Autopolyploids.ZIP

<p>Autopolyploids present several challenges to researchers studying population genetics, since almost all population genetics theory, and the expectations derived from this theory, has been developed for haploids and diploids. Also many statistical tools for the analysis of genetic data, such as AMOVA and genome scans, are available only for haploids and diploids. In this paper, we show how the Analysis of Molecular Variance (AMOVA) framework can be extended to include autopolyploid data, which will allow calculating several genetic summary statistics for estimating the strength of genetic differentiation among autopolyploid populations (F_ST, φ_ST, or R_ST). We show how this can be done by adjusting the equations for calculating the Sums of Squares, degrees of freedom and covariance components. The method can be applied to a dataset containing a single ploidy level, but also to datasets with a mixture of ploidy levels. In addition, we show how AMOVA can be used to estimate the summary statistic ρ, which was developed especially for polyploid data, but unfortunately has seen very little use. The ρ-statistic can be calculated in an AMOVA by first calculating a matrix of squared Euclidean distances for all pairs of individuals, based on the within-individual allele frequencies. The ρ-statistic is well suited for polyploid data since its expected value is independent of the ploidy level, the rate of double reduction, the frequency of polysomic inheritance, and the mating system. We tested the method using data simulated under a hierarchical island model: the results of the analyses of the simulated data closely matched the values derived from theoretical expectations. The problem of missing dosage information cannot be taken into account directly into the analysis, but can be remedied effectively by imputation of the allele frequencies. We hope that the development of AMOVA for autopolyploids will help to narrow the gap in availability of statistical tools for diploids and polyploids. We also hope that this research will increase the adoption of the ploidy-independent ρ-statistic, which has many qualities that makes it better suited for comparisons among species than the standard F_ST, both for diploids and for polyploids.</p

mtDNA_alignments

The zip file contains the three mtDNA alignments used in this study. "mitogenomes.fas" is the alignment of the mitogenomes of the 81 individuals included in this study together with an outgroup. Control Region was removed from the alignment. Based on it, two other alignments, "alignment_with_1994-cytb.fas" and "alignment_with_2008-cytb.fas", were created by incorporating the data from Orti et al. (1994) and Makinen & Merila (2008), respectively

SNPs_subset

A VCF file containing a random subset (1%) of all SNPs discovered in this study. This file was used for conducting PCA and calculating nucleotide diversity

Map showing sites at which blood samples were collected from tree sparrows in Beijing and outlying rural sites, China.

<p>Map showing sites at which blood samples were collected from tree sparrows in Beijing and outlying rural sites, China.</p

Genetic variability at seven microsatellite loci in tree sparrows from 19 different sites in urban and rural Beijing, China.

*<p>heterozygosity values significantly different from those expected under the Hardy–Weinberg equilibrium (<i>P</i><0.05).</p

Genetic variance components and hierarchical F statistics for tree sparrows from Beijing.

<p>A, B and C are three different grouping methods used in AMOVA; Method A treated samples from one site as a group, Method B divided all samples into urban and rural groups and Method C treated samples on the same side of a highway as a group. The subscripts of F refer to the hierarchical levels being compared; GT, groups to total population; IG, individual to group; individual to total population.</p