Bayescan_Fdist2.tar.gzThis 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.gzThis 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.Simupop_code.tar.gzThis 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.Hybrid_class_assignment.tar.gzThis 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.Code_and_data_for_figures.tar.gzThis 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.PPC_correction.tar.gzThis 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.

Many wild Atlantic salmon (Salmo salar) populations are threatened by introgressive hybridization from domesticated fish that have escaped from aquaculture facilities. A detailed understanding of the hybridization dynamics between wild salmon and aquaculture escapees requires discrimination of different hybrid classes, however markers currently available to discriminate the two types of parental genome have limited power to do this. Using a high-density Atlantic salmon SNP array in combination with pooled-sample allelotyping and an Fst outlier approach, we identified 200 SNPs that differentiated an important Atlantic salmon stock from the escapees potentially hybridizing with it. By simulating multiple generations of wild-escapee hybridization involving wild populations in two major phylogeographic lineages, and a genetically diverse set of escapees, we showed that both the complete set of SNPs and smaller sub-sets could reliably assign individuals to different hybrid classes up to the third hybrid (F3) generation. This set of markers will be a useful tool for investigating the genetic interactions between native wild fish and aquaculture escapes in many Atlantic salmon populations.