Genetic basis of latitudinal adaptation in Chinook salmon

Scripts This folder contains all the codes necessary to run the genomic analysis of the article "Genetic basis of latitudinal adaptation in Chinook salmon". This includes the pipeline to go from the raw fastq files "XXX_R1.fastq.gz" and "XXX_R2.fastq.gz" available here: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA694998 to the final results of the analysis. Script_00.script.K.Christensen.pipeline.txt describes the pipeline to align and filter the reads and generate the vcf file FirstFilter.GATK.gt3.vcftools.biallele.mm0.9.maf0.05.meanDP5-200 used for further analyses.Using the reference genome here: https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/018/296/145/GCF_018296145.1_Otsh_v2.0/GCF_018296145.1_Otsh_v2.0_genomic.gff.gz Script_01.script.make.snp.matrices.and.run.bglmer.r explains how to make SNP matrices from the vcf and run the BGLMER analysis. Script_02.script.PCA.and.figures.r explains how to run PCA analyses and produce the figures. Script_03.script.admixture.analysis.r explains how to run the ADMIXTURE analyses. Script_04.script.BAYPASS.r describes how to run the BAYPASS analyses. Script_05.simplify.gff.r produces a simplified gff to merge information from the gff and the BAYPASS/BGLMER analyses. Script_06.script.compute.mean.results.per.gene.and.sliding.window.r computes the mean -log10pvalue and Bayes Factor per gene and per sliding window of 50 SNP. Script_07.script.analyses.results.BAYPASS.and.BGLMER.r script computing the results found in the main text testing whether each region fall in the top 5%. Script_08.Liftover analysis.merging.our.137indiv.with.160indiv.thompson.txt merge the two VCFs from our analysis and Thompson et al. 2020 analysis. Script_09.Comparison.ALASKA.and.CALIFORNIA.after.liftover.txt describes how to merge datasets and run analyses in order to analyse the spring-run alleles (using california samples from Thompson et al.) across the latitudinal gradient. Script_10.script timing run alaska.txt analyses the data from different fish counts in order to assess what are the main dates of chinook run in Alaska, Washington state and California. Supplementary Files File S1. FirstFilter.GATK.gt3.vcftools.biallele.mm0.9.maf0.05.meanDP5-200.recode.noYY.vcf.recode.vcf.gz is a VCF file comprising 137 individuals from our study. File S2. Data counts from 10 rivers in Alaska in 2024 (Anchor, Ayakulik, Deshka, Gulkana, Kenai, Lake, Little, Nelson, Ninilchik, Nushagak) obtained on March 3, 2025 from the Columbia Basin Research website (https://www.cbr.washington.edu/). The table contains the information: year of sampling, count date, fish count, species ID, count location, count location ID. File S3. Data counts from the Columbia river in Washington state in 2024 obtained on March 3, 2025 from the Columbia Basin Research website (https://www.cbr.washington.edu/). The table contains the information: Area sampled (Project), Date, Chinook Run, number of Chinook (Chin), number of juvenile Chinook (JChin), number of Stealhead (Stlhd, WStlhd), Number of Sockeye (Sock), Number of Coho (Coho), Number of juvenile Coho (JCoho), Number of Shad (Shad), Number of Lampreys (Lmpry), number of brown trout (BTrout), Number of Chum salmon (Chum), Number of Pink salmon (Pink), Temperature in Celsius (TempC). File S4. Cal-Chinook-160.vcf.gz VCF file including the whole genome of 160 individuals of Chinook salmon from spring, winter, fall and late-fall runs in California (provided by E. Anderson, from Thompson et al. 2020). File S5. Information concerning the 160 individuals sequenced in Thompson et al. 2020 including population information. The original table was modified including dates of sampling and the number of days since the 1st of January for each year. File S6. VCF from the liftover analysis including 297 individuals from our study and Thompson et al. 2020. File S7. Input table with the sample information: fish name, river, site of sampling, state, latitude of the river mouth, longitude and potential run.