codEvol

Scripts, supporting data, and figures from analysis of Atlantic cod genome sequences through time (Northeast Arctic and Canada), as published in

Pinsky, ML, AM Eikeset, C Helmerson, IR Bradbury, P Bentzen, C Morris, A Gondek, HT Baalsrud, MSO Brieuc, OS Kjesbu, JA Godiksen, JMI Barth, M Matschiner, NC Stenseth, KS Jakobsen, SJentoft, and B Star. Genomic stability through time despite decades of exploitation in cod on both sides of the Atlantic. PNAS.

This repository has been archived on Zenodo. Cite as ML Pinsky & B Star. (2021, February 21). pinskylab/codEvol: Publication (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.4554375

These scripts contain hard links and specific environment settings. They would require adaptation to run elsewhere and on other data. The scripts were run on a Linux scientific workstation (cod) and two Linux clusters with SLURM job management (abel and saga) at the University of Oslo, plus on a MacBook Pro running R 3.*.

Basic directory structure

• analysis/: Files output by scripts. Not track by Git.
• data/: Input data. Does not include the DNA sequence reads, which are at ENA with project #PRJEB41431
• figures/: Output figures
• output/: Output files that are tracked by Git (as opposed to analysis/)
• scripts/: Scripts to do analyses
• tables/: Tables output by scripts.
• tmp/: Temporary files output by scripts. Not tracked by git.

data/

1. kjesbu_fecundity/AFWG_Table3.11proportionmatureatage.csv: Proportion of Northeast Arctic cod mature at each age. From Table 3.11 in ICES AFWG Report 2016. ICES CM 2016/ACOM:06. Report of the Arctic Fisheries Working Group (AFWG).
2. kjesbu_fecundity/AFWG_Table3.16stocknumbersatage.csv: Numbers at age from the stock assessment. Table 3.16 in ICES AFWG Report 2016.
3. kjesbu_fecundity/AFWG_Table3.6catchnumbersatage.csv: Numbers at age from catch data. Table 3.16 in ICES AFWG Report 2016.
4. kjesbu_fecundity/Fekunditet Andenes 1986_2006 Excel_2.csv: Fecundity data from a local fishing port in northern Norway covering a period of 20 years. Use Main_series == Y (Yes), Oto_Age, whole body weight (in g) in Fish_Weight, liver weight (in g) in Fish_Liver, Oto_type == 3, 4 or 5 (meaning that we exclude Coastal cod type 1 and 2), fecundity (in mill.) in Fec_Mill. Mean oocyte diameter is in Ooc_Mean_Dia in micrometers; females with small oocytes tend to show a decrease in fecundity up to spawning due to atresia (reabsorption of developing oocytes).
5. kjesbu_fecundity/Leverdata.csv: Relative liver size, called the hepatosomatic index, from Kjesbu et al. 2014 ICES Journal of Marine Science
6. kjesbu_fecundity/Rollesfsen1953_Fig1.csv: Female age distribution from Fig. 1 in Rollefsen 1953 Observations on the cod and cod fisheries of Lofoten. Rapports et Proces-Verbaux des Reunions, Conseil lnternationale pour l'Exploration de la Mer 136: 40-47
7. phenotypes/AFWG_2019_3_Northeast Arctic_Cod_Table3.18.csv: Average fishing mortality (F) for ages 5-10 on the Northeast Arctic stock, from the stock assessment. From Table 3.18 in ICES AFWG Report 2019
8. phenotypes/AFWG_Table 3.11.csv: Proportion mature at each age, Northeast Arctic cod. From Table. 3.11 in ICES. 2020. Arctic Fisheries Working Group (AFWG). ICES Scientific Reports 2(52), 577 pp
9. phenotypes/Brattey et al_2018_Table18_proportion_mature.csv: Proportion mature at each age, Canadian population. From Table 18 in Brattey, J., Cadigan, N., Dwyer, K. S., Healey, B. P., Ings, D. W., Lee, E. M., Maddock Parsons, D., Morgan, M. J., Regular, P., Rideout, R. M. 2018. Assessment of the Northern Cod (Gadus morhua) stock in NAFO Divisions 2J3KL in 2016. DFO Can. Sci. Advis. Sec. Res. Doc. 2018/018. v + 107p
10. phenotypes/Brattey_etal_2018_CSAS_TableA2-5_NorthernCod_fishingmortality.csv: Fishing mortality (F) by age, from Brattey et al. 2018.
11. phenotypes/Eikeset_Age_and_length_at_maturation.csv: Age and length at maturity in Northeast Arctic cod, from Eikeset et al. 2016 PNAS
12. inversions.csv: Genome locations of the cod inversions. From Star et al. 2017 PNAS
13. lg_length.csv: Length in bp of each linkage group. From Tørresen et al 2019 BMC Genomics

scripts/

Some scripts make use of BEAGLE Utilities.

Phenotypes

1. phenotypes.r: Calculates the age at 50% mature in Canadian and Northeast Arctic populations. Needs data in data/phenotypes/. Writes output/age_50percmature.csv.

Bioinformatics

Aligning and handling sequence reads:

1. Paleomix_settings_historic_cod.yaml: Settings for handling raw historic reads in Paleomix (Adapter removal, Collapse of Paired End reads, BWA, and Duplicate Removal). Requires (paired) fasta.gz and reference genome. Outcome: BAM files and summary data
2. Paleomix_settings_modern_cod.yaml: Settings for handling raw modern reads in Paleomix (Adapter removal, Collapse of Paired End reads, BWA, and Duplicate Removal) Requires (paired) fasta.gz and reference genome Outcome: BAM files and summary data
3. Remove_clipped_reads.sh: Script to remove clipped reads from BAM file. Any clipped reads (including entire pairs for which one reads was clipped) will has been removed. Required: BAM files. Outcome: BAM file with clipped reads removed.

SNP calling

1. Haplotype_caller_GATK.sh: Running GATK's haplotype caller. Requires BAM files, reference genome (as used to create BAM) Outputs a g.vcf.gz file per individual for further analyses
2. Genotype_Caller.sh: Running GATK's genotype caller. Note we ran this per chromosome in parallel jointly on all .g.vcf files. Requires g.vcf.gz, list of chromosomes, reference genome. Outputs Unfiltered vcf.gz
3. ANGSD_snp_calling: SNP calling using ANGSD. We followed N. O. Therkildsen, et al., Contrasting genomic shifts underlie parallel phenotypic evolution in response to fishing. Science 365, 487–490 (2019), for which N.O.T. kindly shared a template script. Specifically, we identified SNP locations that can be used for faster handling later on (and we used a subset of these based on GATK filtered data). Requires BAM files and reference genome. Outputs a range of standard ANGSD output files for further analyses, including a location file. This file is edited slightly following N.O.T.'s examples.

Filtering of VCF files

1. Create_mappability_track.sh: We used the GEM mapper to create mappability tracks that can be uniquely mapped. K-mer size determines the size of the unique regions. Requires reference genome and choice of K-mer size. Outputs BED track with unique sections of the genome.
2. Filter_vcf_files.sh: Filter settings for GATK VCF file as described. Please note that the script cannot be run in a single iteration: various subscripts (such as the binomial test or variance calculations) have to be run at intermediate stages. These scripts generate lists of SNPs to be ex- or included. Requires Unfiltered VCF. Outputs filtered VCF.
3. allele_balance_calc.R: Script to calculate bias in the number of allelic variants in heterozygote genotypes. I.e. is there consistent bias for specific variants (expection is 50/50). Requires VCF GT and DP files. Wrapper script. Outputs list of SNPs to be filtered.
4. allele_balance_plot.R: Script to plot histograms of read-depth of alternate alleles for heterozygote genotypes. Requires VCF GT and DP files. Outputs a histogram.
5. filter_allele_balance.r: Script that performs a binomial test on heterozygote genotype calls. Reequires VCF GT and DP files. Outputs list of SNPs to be filtered.
6. filter_allele_balance.saga.sh: Wrapper script to submit the filter_allele_balance.r to the SAGA compute cluster. Requires VCFfile, allele_balance_calc.R. Outputs list of SNPs to be filtered at certain thresholds.
7. Calculate_variance2.sh: Script that calculates a measure of read-depth variation between individuals on a specfic site. Requires VCF file. Outputs list of SNPs with a measure of variation in read depth between individuals. Top 5% were filtered.

Basic ANGSD analyses

1. Resample_ANGSD_slurm: Not used. Random resampling of individuals. We resample to get bootstrap intervals of windowed Fst by randomly resampling the temporal comparisons. Requires BAM files, reference genome, list of individuals. Outputs population frequencies based on randomly resampled individuals.
2. ANGSD_pop_freq.sh: Calculating population specific frequencies. Requires BAM files, reference genome, list of individuals. Outputs population frequencies
3. ngsLD_bypop.sh: Calculate linkage disequilibrium using ngsLD. Needs list of high qualty SNPs (data_2020.05.07/GATK_filtered_SNP_no_dam2.tab) and beagle files. Outputs ld.*.gatk.gz
4. ngsLD_decay.r: Make a plot of the genome-wide average LD decay. Run after ngsLD_bypop.sh. Writes a figure.
5. ngsLD_find_blocks.r: Finds blocks of linked SNPs. Run after ngsLD_bypop.sh. Writes analysis/ld.blocks.gatk.nodam.csv.gz.
6. ngsLD_find_blocks.sh: Submits a job with ngsLD_find_blocks.r.
7. ngsLD_find_unlinked.r: Identifies unlinked loci for use in downstream analyses. Run after nsdLD_find_blocks.sh. Writes analysis/ld.unlinked.*.gatk.nodam.csv.gz.

Basic GATK analyses

1. ld_decay.r: Not used. Plot linkage disequilibrium decay using output from vcftools, e.g.,

vcftools --gzvcf data_2020.05.07/Historic_dataset_no_clip.vcf.gz_HF_GQ_HWE_MISS_IND_Kmer_VAR_Binom_No_Dam2.vcf.gz --geno-r2 --ld-window-bp 5000 --keep data_2020.05.07/popLof07.txt --minGQ 15 --minDP 3 --not-chr LG01 --not-chr LG02 --not-chr LG07 --not-chr LG12 --not-chr Unplaced --chrom-map data_2019_03_18/chrom_map --out analysis/LOF_07

PCA and Admixture

from ANGSD

1. PCAANGSD.sh: Runs PCA from ANGSD. Requires beagle files, reference, positions file. Outputs PCA.
2. Make_eigen_R_PCA_ANGSD.r: Script to rescale eigen values coming from PCAANGSD. Requires PCA ANGSD output. Outputs rescaled eigenvalues.
3. ANGSD_admixture: Runs ANSGD admixture for different values of K. Requires BAM files, reference genome, list of individuals. Outputs ADMIXTURE results.
4. Plot_admixture.R: Basic R script to plot admixture output. Requires ANGSD Admixture output. Outputs Rplot with admixture results.
5. angsd_pcangsd_pca.sh: Also runs PCAngsd and admixutre a few ways, including after dropping individual BM_115 and removing linked loci. Requires beagle files, output from ngsLD_find_unlinked.r. Outputs PCA files.
6. angsd_pcangsd_plot_pca.R: Plot output from angsd_pcangsd_pca.sh

from GATK

1. Filter_prune_admixture: Script to run ADMIXTURE for a number of K on SAGA using GATK generated VCF. Requires VCF file, chromosome names for plink conversion Outputs Admixture results.
2. Filter_Prune_PCA_LG12.sh: Script to run PCA using a GATK generated VCF. This is specifically run on an inversion location to determine haplotype status. Requires VCF file, chromosome names for plink conversion, inversion boundaries Outputs PCA showing inversion haplotypes.
3. Filter_Prune_PCA_LG07.sh: Script to run PCA using a GATK generated VCF. This is specifically run on an inversion location to determine haplotype status. Requires VCF file, chromosome names for plink conversion, inversion boundaries Outputs PCA showing inversion haplotypes.
4. Filter_Prune_PCA_LG02.sh: Script to run PCA using a GATK generated VCF. This is specifically run on an inversion location to determine haplotype status.Requires VCF file, chromosome names for plink conversion, inversion boundaries Outputs PCA showing inversion haplotypes.
5. Filter_Prune_PCA_LG01.sh: Script to run PCA using a GATK generated VCF. This is specifically run on an inversion location to determine haplotype status. Requires VCF file, chromosome names for plink conversion, inversion boundaries. Outputs PCA showing inversion haplotypes.
6. Filter_Prune_PCA.sh: Script to run PCA using a GATK generated VCF. Various filters are applied (E.g. a filter for LD, and removal of inversion chromosomes). Requires VCF file, chromosome names for plink conversion, inversion boundaries Outputs PCA showing inversion haplotypes.

Diversity and Ne from ANGSD

1. angsd_theta.sh: Prints the per-site pi and calculates windowed pi and Tajima's D by population. Needs *.thetas.idx and *.thetas.gz files from realSFS and thetaStat, e.g., realSFS theta/$1.saf.idx -fold 1 -P 14 > theta/$1.sfs realSFS saf2theta theta/$1.saf.idx -sfs theta/$1.sfs -outname theta_out/$1 thetaStat print theta_out/$1.thetas.idx 2>/dev/null | head thetaStat do_stat out.thetas.idx cat out.thetas.idx.pestPG thetaStat do_stat out.thetas.idx -win 50000 -step 10000 -outnames theta.thetasWindow.gz
2. angsd_theta_bypop.R: Calculate theta and Tajima's D for the whole genome and bootstrap across linkage groups. Run by angsd_theta_bypop.sh. Needs *.pestPG.gz files from angsd_theta.sh. Writes analysis/thetas.boot.cis.csv.
3. angsd_theta_bypop.sh: Submits angsd_theta_bypop.R to the SLURM system.
4. kjesbu_calcs.R: Analyses fecundity, abundance, maturity, and liver condition data to calculate generation time (average age of parents) for the Northeast Arctic population. Needs files in data\kjesbu_fecundity.
5. calcNe_ANGSD.r: Calculates effective population size (Ne) from ANGSD output using the Jorde-Ryman temporal method. Bootstraps across loci. Based on equations in the manual from NeEstimator 2.1.

Selection scans

Windowed FST

1. angsd_fst.sh: Calculate Fst from ANGSD output (global, by site, and windowed). Outputs *.fst.AB.gz, *.slide, *.fst.idx, *.ml, *fst.gz
2. angsd_fst_siteshuffle_null.r: Shuffles FST A and B values across SNP positions to calculate a null distribution of maximum FST values per genome. Run after angsd_fst.sh. Outputs analysis/*.*.gatk.nodam.fst.siteshuffle.csv.gz and analysis/*.*.gatk.nodam.ldtrim.fst.AB.csv.gz
3. angsd_fst_siteshuffle_null.sh: Submits angsd_fst_siteshuffle_null.r to SLURM.
4. angsd_fst_siteshuffle_null_stats.r: Examine the results from shuffling FST values across SNP positions and calculate a p-value per window. Run after angsd_fst_siteshuffle_null.r. Writes output/fst_siteshuffle.angsd.gatk.csv.gz.

Windowed Pi and Tajima's D

1. angsd_theta_siteshuffle_null.r: Shuffles per-locus theta value values across SNP positions to calculate a null distribution of maximum theta values per genome. Needs *.pestPG.gz files from angsd_theta.sh. Outputs analysis/theta.siteshuffle.*.csv.gz
2. angsd_theta_siteshuffle_null.sh: Submits angsd_theta_siteshuffle_null.r to SLURM.
3. angsd_theta_siteshuffle_null_stats.R: Examine the results from shuffling per-locus theta values across SNP positions and calculate a p-value per window. Run after angsd_theta_siteshuffle_null.sh/.r. Writes analysis/theta_siteshuffle.angsd.gatk.csv.gz.

Windowed linkage disequilibrium (LD)

1. ngsLD_region_change.r: Calculate changes in LD in windows. Needs output from ngsLD_bypop.sh. Writes analysis/ld_change_region_5e4_ngsLD.gatk.csv.gz.

Shared changes across populations

1. region_change_compare.r: Compare Fst, LD change, Tajima's D change, pi change across populations to find regions that change substantially in more than one. Needs output from ANGSD Fst, ngsLD, and ANGSD theta change calculations. Writes analysis/outlier_50kregions_shared_07-11-14_Can.csv.gz.

Wright-Fisher drift and sampling null model

Make the simulations

1. wfs_byf1samp.r: Function to do Wright-Fisher simulation with selection and a finite sample size using priors on Ne and s and specifying initial sample allele frequency. Samples at two time points. (i.e., Canada)
2. wfs_byf1samp_3samps.r: Function to do Wright-Fisher simulation with selection and a finite sample size using priors on Ne and s and specifying initial sample allele frequency. Samples at three time points (i.e., Northeast Arctic).
3. wfs_make_sims_null_function.r: Make simulations with the null model for a particular set of sample sizes. For two samples in time (like Canada). Uses starting sample allele frequencies that match the observed data. Uses the function in wfs_byf1samp.r. Used by wfs_make_sims_null_sbatch.sh.
4. wfs_make_sims_null.r: Superceded by wfs_make_sims_null_function.r. Make simulations with the null model for a particular set of sample sizes. For two samples in time (like Canada). Uses starting sample allele frequencies that match the observed data. Uses the function in wfs_byf1samp.r.
5. wfs_make_sims_null_3times.r: Make simulations with the null model for a particular set of sample sizes. For three samples in time (like Northeast Arctic). Uses starting sample allele frequencies that match the observed data. Uses the function in wfs_byf1samp_3samps.r.
6. wfs_make_sims_null_sbatch.sh: Submits a SLURM job to make simulations with wfs_make_sims_null_function.r.
7. wfs_make_sims_null_makebatchscript.r: Creates batch scripts to submit all the wfs_make_sims_null_sbatch.sh jobs needed to make the null model simulations for a particular temporal comparison.
8. wfs_test.r: Code to check that the simulations match theoretical expectations.
9. wfs_ts.r: Function to do Wright-Fisher forward simulations with drift and selection and save all the allele frequencies through time. Used by wfs_ts_examples.r.
10. wfs_ts_examples.r: Plot a null model and a selection simulation example. Uses wfs_ts.r.
11. wfs_nullmodel_compare_sims_and_obs.r: Not used. Code to compare the simulated and observed allele frequency changes and check that the simulations are appropriate.
12. wfs.r: Not used. Function to do Wright-Fisher forward simulations with drift and selection and save initial and final allele frequencies. Uses priors on Ne, s, and initial true allele frequency (as opposed to initial sample allele frequency in wfs_byf1samp.r).

Calculate p-values

1. wfs_nullmodel_function.r: Helper script that compares null model simulations to the observations and calculates a per-locus p-value. Runs in parallel across many loci at once.
2. wfs_nullmodel_sbatch.sh: Submits wfs_nullmodel_function.r to run for a set of loci in a population comparison with two time points.
3. wfs_nullmodel_sbatch_3times.sh: Submits wfs_nullmodel_function.r to run for a set of loci in a population comparison with three time points.
4. wfs_nullmodel_makesbatchscript.r: Creates batch scripts to submit all the wfs_nullmodel_sbatch.sh jobs for a particular temporal comparison. All of those scripts should then be run to calculate the per-locus p-values.
5. wfs_nullmodel_combine.r: Run after wfs_nullmodel_function.r to combine the results together. Writes analysis/wfs_nullmodel_pos&pvals_*.rds.
6. wfs_nullmodel_lowp_rerun.r: Script to make more null model simulations for loci with low p-values. Run after wfs_nullmodel_combine.r. Writes analysis/wfs_nullmodel_pos&pvals_*.rds.
7. wfs_nullmodel_analysis.r: Examines the frequencies at which the null model produces results as extreme as our observations. Run after wfs_nullmodel_function.r and wfs_nullmodel_combine.r/wfs_nullmodel_lowp_rerun.r. Writes analysis/wfs_nullmodel_padj.csv.gz.
8. wfs_nullmodel_analysis_Canada,1907-2011&2014.r: Not used. Messy code to examine the p-values.

PCAngsd

1. angsd_cut_beagle_bypop.sh: Divides the beagle genotype file by population comparison, to get ready for running PCAngsd. Needs beagle file from ANGSD trimmed to the GATK high quality SNPs (All_ind_beagle.GATK_no_dam.gz). Outputs beagle.gz files.
2. angsd_pcangsdoutlier.sh: Run the PCAngsd selection scan. Needs beagle files, output from ngsLD_find_unlinked.r. Outputs *.cov, *.selection.npy, *.sites.
3. angsd_pcangsd_plot_selection.R: Run after angsd_pcangsdoutlier.sh to plot the output.

Examine potential outliers

1. split_fasta.sh: Split the reference genome fasta file apart by linkage group. Output is used by annotate_outliers.r.
2. annotate_outliers.r: Compiles annotation and other information about potential outlier SNPs. Needs the VCF file (All_rerun_hist.vcf.gz_HF_GQ_HWE_MISS_0.6_IND_Norway.vcf.gz) and the reference genome FASTA split apart by linkage group (see split_fasta.sh), plus analysis/pcangsd_outlier.gatk.nodam.unlinked.csv.gz from running PCAngsd, analysis/wfs_nullmodel_padj.csv.gz from the WFS null model, output/fst_siteshuffle.angsd.gatk.csv.gz from the windowed FST outlier scan, analysis/theta_siteshuffle.angsd.gatk.csv.gz from the windowed pi and Tajima's D outlier scan, and analysis/outlier_50kregions_shared_07-11-14_Can.csv.gz from the shared outlier regions across populations. Writes tables/outlier_annotation.csv and output/outlierSNPs_in_outlierregions.csv

Power analyses with SLiM

1. slim_makeburnin.py: Helper script to make coalescent burn-ins for the SLiM simulations. Writes a vcf file.
2. slim_runburnins.sh: Set up and run the SLiM burn-ins by calling slim_makeburnin.py. Writes a set of vcf files.
3. slim_softsweep.slim: SLiM code to run a forward simulation and output vcf file samples before and after soft sweep.
4. slim_run.R: Set up and run the SLiM simulations by calling slim_softsweep.slim. Checks which simulation output already exists so that those are not run again. Uses burnin files from slim_makeburnin.py. Outputs vcf files.
5. fst_reynolds_fromvcf.R: Helper script. Calculates Reynolds FST from .vcf files and writes a table of per-locus A and B values. Useful for processing output from SLiM. ANGSD also uses Reynolds FST.
6. slim_calcfst_reynolds.sh: Calculate Reynolds FST components for slim output. Processes vcf files from SLiM and calls fst_reynolds_fromvcf.R.
7. slim_fst_reynolds_plot.R: Plot per-locus Reynolds Fst as output by slim_calcfst_reynolds.sh.
8. slim_calcfst.sh: Not used. Calculate sliding window FST using vcftools on SLiM vcf output. Writes slim_sim_n*_s*_f*_i*.windowed.weir.fst.
9. slim_fst_plot.R: Not used. Makes FST Manhattan plots from vcftools output to visualize the effect of the sweeps and make sure the sims worked well. Run after slim_calcfst.sh.
10. vcftobeagle.R: Helper script to convert a vcf file to beagle format so that PCAngsd can run. Used by slim_calcpcangsdoutlier.sh.
11. slim_calcpcangsdoutlier.sh: Run the PCAngsd outlier test on the vcf output from SLiM. Writes analysis/slim_sim/*.cov, *.selection.npy, and *.sites. 1 slim_pcangsdoutlier_plot.R: Plots the output from slim_calcpcangsdoutlier.sh.
12. slim_fst_siteshuffle_null.r: Conduct the windowed FST outlier test on the SLiM simulations. Needs output from slim_calcfst_reynolds.sh. Writes analysis/slim_sim/slim_sim_n30000_*.fst.siteshuffle.csv.gz.
13. slim_fst_siteshuffle_null.sh: Calls slim_fst_siteshuffle_null.r across all SLiM simulations.
14. slim_fst_siteshuffle_plot.R: Visualize some of the results from the windowed FST outlier test. Needs output from slim_fst_siteshuffle_null.sh.

Make figures

1. figures_for_paper.r: Makes the figures for the paper. Needs output from many of the previous analyses. Writes figures/*.png and .pdf

Unused helper files

1. vcf_to_trimmed_genepop.sh: Convert vcf file to a genepop format and trim out linked loci with plink. Uses PGDSpider.
2. vcf_to_trimmedplink.sh: Convert vcf file to a plink format and trim out linked loci with plink. Uses PGDSpider.

output/

1. age_50percmature.csv: Age at 50% mature in Canada and NE Arctic cod. From scripts/phenotypes.r
2. fst_siteshuffle.angsd.gatk.csv.gz: p-values from the windowed FST outlier test in angsd_fst_siteshuffle_null_stats.r.
3. outlierSNPs_in_outlierregions.csv: A list of SNPs with FST>0.2 in outlier regions. Produced by annotate_outliers.r.

figures/

The script-produced figures from the paper, plus a handful of others that were useful along the way.

tables/

1. outlier_annotation.csv: Annotated list of potential outlier SNPs and regions. Produced by annotate_outliers.r. Used by figures_for_paper.r
2. tableS5.csv: Nicely formated information on potential outlier SNPs and regions. Produced by figures_for_paper.r.