Aykanat_et_al_Dryad_files

FILES IN THE BUNDLE: mainparams: parameter file for the structure runs. NOTE that k and n are "number of populations" and "number of replicate", respectively and appropriate number should be used instead of these letters when running the software. teno_MS = The GenABEL format main file. The file includes genotype (n= 2874 SNPs), phenotype as well as location information from 662 individuals, which is sufficient to replicate the main results. Structure raw files.zip = Includes raw results from the structure analysis which is sufficient to replicate all structure related results

Bayescan_Fdist2.tar.gz

This archive contains files used by Pritchard et al. (2016) for Fst outlier analyses: Index.txt - index of SNPs. Bayescan*.in - Bayescan input files, SNPs in index order. Fdist*.in -Fdist2 input files, SNPs in index order. datacal.c; fdist2.c; Original_README_fdist2 - Fdist2 source code & information. R_code_for_analysing_Fdist2_output.txt - modified R code from Lotterhos & Whitlock (2014). See README file in archive for further information

Genotyping_allelotyping.tar.gz

This archive contains the following data files for Pritchard et al. (2016): B_allele_freq_Naatamo_pools.csv: Uncorrected B allele frequencies for the Naatamo pools. B_allele_freq_New_Teno_pools.csv: Uncorrected B allele frequencies for the New Teno mainstem pools. B_allele_freq_Teno_Finnmark_pools.csv: Uncorrected B allele frequencies for the old Teno and Finnmark pools. B_allele_freqs_all_pops.csv: Corrected B allele frequencies for all populations. Individuals_and_pooling.csv: Details of genotyped individuals. Pritchardetal_genotypes.map; Pritchardetal_genotypes.ped: Individual genotypes in PLINK format. See README file in archive for further information

Hybrid_class_assignment.tar.gz

This archive contains code used for hybrid class assignments, and hybrid class assignment results for Pritchard et al. (2016): Code_for_hybrid_class_assigments.txt: code used to generate NewHybrids and Structure input files and parse results. Folder "Extra_required_files" contains text files required by the above code. Folder "Results" contains hybrid class assignment results. See README file in archive for further information

PPC_correction.tar.gz

This archive (700MB) contains the following data files for Pritchard et al. (2016), used for calculating and applying the PPC correction: R_code for_PPC_correction.txt: Annotated R code for calculating PPC correction coefficients from data in CALL.txt and INT.txt. CALL.txt: File of genotype calls. INT2.txt: File of A and B allele probe intensities. PPC_correction_coefficients.csv: PPC correction coefficients returned for each SNP. See README file in archive for further information

Code_and_data_for_figures.tar.gz

This archive contains the ggplot2 code and data files required for making all the figures in Pritchard et al. (2016): Code_for_ggplot.txt contains annotated ggplot code for making figures. See README file in archive for further information

Simupop_code.tar.gz

This archive contains Python code for simulating populations using the package simuPOP, plus additional data files, used in Pritchard et al. (2016): *.py: code for simulating test or reference individuals. Estimated_allele_frequencies.csv: estimated frequencies of the 200 discriminatory alleles, from Table S3. Allele_frequencies_for_simulations.csv: adjusted allele frequencies used for simulations, from Table S3. See README file in archive for further information

Data from: Genome-wide SNP analysis reveals a genetic basis for sea-age variation in a wild population of Atlantic salmon (Salmo salar)

Data from: Genome-wide SNP analysis reveals a genetic basis for sea-age variation in a wild population of Atlantic salmon (Salmo salar

Workflow diagram indicating the main steps of the analyses.

<p>Workflow diagram indicating the main steps of the analyses.</p

SNP overlap among different ranking approaches and population datasets.

<p>(A) Venn diagrams showing the extent of overlap among four approaches (global F_ST, pairwise F_ST, <i>Delta</i> and outlier) for three population datasets. (B) Venn diagrams showing the extent of overlap among three population datasets for four ranking approaches. For all SNP ranking methods the top 100 SNPs are presented, except for the outlier approach where 95 and 35 SNPs were identified as being under selection for dataset II and III, respectively.</p