DOI: 10.5061/dryad.nk98sf817
title: Species-informative GT-seq markers for Columbia River salmonid fishes: Genotypic data and computing resources

description: Genetic monitoring of Pacific salmon in the Columbia River basin provides
 crucial information to fisheries managers that is otherwise challenging to
 obtain using traditional methods. Monitoring programs such as genetic
 stock identification (GSI) and parentage-based tagging (PBT) involve
 genotyping tens of thousands of individuals annually. Although rare, these
 large sample collections inevitably include misidentified species, which
 exhibit low genotyping success on species-specific Genotyping-in-Thousands
 by sequencing (GT-seq) panels. For laboratories involved in large-scale
 genotyping efforts, diagnosing non-target species and reassigning them to
 the appropriate monitoring program can be costly and time-consuming. To
 address this problem, we identified 19 primer pairs that exhibit
 consistent cross-species amplification among salmonids and contain 51
 species-informative variants. These genetic markers reliably discriminate
 among 11 salmonid species and two subspecies of Cutthroat Trout and have
 been included in species-specific GT-seq panels for Chinook Salmon, Coho
 Salmon, Sockeye Salmon, and Rainbow Trout, commonly used for Pacific
 salmon genetic monitoring. The majority of species-informative amplicons
 (16) were newly identified from the four existing GT-seq panels, thus
 demonstrating a low-cost approach to species-identification when using
 targeted sequencing methods. A species-calling script was developed that
 is tailored for routine GT-seq genotyping pipelines and automates the
 identification of non-target species. Following extensive testing with
 empirical and simulated data, we demonstrated that the genetic markers and
 accompanying script accurately identified species and are robust to
 missing genotypic data and low-frequency, shared polymorphisms among
 species. Finally, we used these tools to identify Coho Salmon incidentally
 caught in the Columbia River Chinook Salmon sport fishery and used PBT to
 determine their hatchery of origin. These molecular and computing
 resources provide a valuable tool for Pacific salmon conservation in the
 Columbia River basin and demonstrate a cost-effective approach to species
 identification for genetic monitoring programs.
description: Details on data collection and processing can be found in the
 corresponding manuscript (Robinson et al. 2024; DOI: 10.1111/eva.13680).
 Briefly, known-species samples of Columbia River salmonid fishes were
 obtained by various state, federal, and tribal agencies. All samples were
 processed using the GT-seq library preparation protocol and genotyping
 pipeline described in Campbell et al. (2015).  Individual multi-locus
 genotypes from species-informative loci and collection metadata are
 provided. We also provide the genotyping pipelines and computing resources
 used to make genotypic and species calls.
description: # Species-informative GT-seq markers for Columbia River salmonid fishes:
 Genotypic data and computing resources
 [https://doi.org/10.5061/dryad.nk98sf817](https://doi.org/10.5061/dryad.nk98sf817) Multi-locus species-informative genotypes used in Robinson et al. 2024. ## Description of the data and file structure: ###### 96610466EVA-2023-186-OA.R2_CRITFC_ValidationSamples.csv: 1. Multi-locus genotypes for Columbia River Salmonid samples processed by the Hagerman Genetics Lab, CRITFC. 2. Contains individual information regarding field-identified species, genetic species identification, sample year, sample location, and species-informative genotypes. ###### 96610466EVA-2023-186-OA.R2_IDFG_ValidationSamples.csv: 1. Multi-locus genotypes for Columbia River Salmonid samples processed by the Eagle Fish Genetics Lab, IDFG. 2. Contains individual information regarding field-identified species, genetic species identification, sample year, sample location, and species-informative genotypes. ###### 96610466EVA-2023-186-OA.R2_OtsSportFishery.csv : 1. multi-locus genotypes used to identify non-target species in a Chinook Salmon sport fishery sample collection in Washington and Oregon, USA, from 2021 to 2022. 2. Contains individual information regarding genetic species identification, sample year, and species-informative genotypes. ## Code/Software Genotyping and species calling scripts are provided: ##### GTseq-pipeline A slightly modified version of the Perl-based GTseq genotyping pipeline presented in Campbell et al. (2015) Originally authored by Nate Campbell and later modified by Kim Vertacnik and Zak Robinson. ###### Bug Fixes and Modifications 1. GTseq_Genotyper_v5.pl now allows for multiple snps for amplicons. At present, each SNP is called independently, and phased haplotypes are not provided. 2. A typo resulting in asymmetrical heterozygote calling based on the ratio of allelic counts has been corrected. 3. An additional column, termed a "probeSeq flag," has been added to probeSeq files. These flags allow for accurate counting of on-target reads in GTseq_GenoCompile_v5.1.pl and to omit certain loci from genotyping success (%GT) filters. If the probeSeq flag is omitted from the file, all loci assume a value of 1. | ProbeSeq Flag | Explanation | | ------------- | --------------------------------------------------- | | 0 | Sex marker with stand-alone script | | 1 | Flagship SNP of amplicon | | 2 | Secondary SNP | | 3 | Flagship SNP of amplicon. Not subject to %GT filter | | 4 | Secondary SNP. Not subject to %GT filter | ###### Example Usage On A Linux System: A Chinook Salmon GT-seq Library ###### Notes on generating a raw FASTQ file: *GTseq_Demultiplex.py* operates on a non-demultiplexed FASTQ file from an Illumina sequencing run. The provided example file <i>Undetermined_S0_R1_001.fastq.gz</i> was generated using the following command using a generic sample sheet: bcl-convert --output-directory --force --bcl-input-directory --sample-sheet SampleSheetV2_GTseq.csv --strict-mode true --no-lane-splitting true ###### Using GTseq_Demultiplex_V1.1.py Using the provided example files: ``` ./GTseq_Demultiplex_V1.1.py --barcodeFile NS2K_85-bc.csv --fastq Undetermined_S0_R1_001.fastq.gz --i5rc Y --seqType GTseq --gzipOutput N ``` This should result in three fastq files i202_03_TOSS2145_Ots_Harvest1.fastq, i202_09_TOSS2145_Ots_Harvest2.fastq, and i211_21_TOSS2154_Ots_Harvest3.fastq Now GZIP and move them to a new directory: ``` gzip i* mkdir READS mv i* ./READS cd READS ``` ###### Genotype with *GTseq_Genotyper_v5.pl* With one FASTQ: ``` ./GTseq_Genotyper_v5.pl OtsGTseqV9.2_363-probeSeqs.csv i202_03_TOSS2145_Ots_Harvest1.fastq.gz &gt; i202_03_TOSS2145_Ots_Harvest1.genos ``` Parallelized example: ``` ls | grep fastq | while read -r LINE; do genos_out=$(echo $LINE | sed -e s/fastq.gz/genos/g); echo "../../GTseq_Genotyper_v5.pl ../OtsGTseqV9.2_363-probeSeqs.csv $LINE &gt; $genos_out" &gt;&gt; GenotypeCommands.cmds; done parallel -j 3 &lt; GenotypeCommands.cmds &gt; Genotyper.log 2&gt;&1 # Execute sex marker script in directory with FASTQ and .genos files OtsSEX_v3.1.pl --jobs=3 --ctrl_ratio=0.005 ``` Move '.genos' files to their own directory ``` mkdir ../GENOS mv *genos ../GENOS cd ../GENOS ``` ###### Compile Genotypes into wide-format CSV file using GTseq_GenoCompile_v5.1.pl ``` ./GTseq_GenoCompile_v5.1.pl . --name=index --output=genotype --filter=0 &gt; NS2K_85-GenosNF.csv ./GTseq_GenoCompile_v5.1.pl . --name=sample --output=genotype --filter=90 &gt; NS2K_85-ProgGenos90.csv ``` ### *Optional Analyses* Look for off-target species among these putative chinook: ``` ./CallSpecies.py --SpeciesSeq SpeciesSeq.csv --inGENO NS2K_85-ProgGenos90.csv --outGENO NS2K_85-ProgGenos90_CALLED.csv ``` Refer to MANUAL_CALLSPECIES.pdf (Zenodo link in the Related Works section) for a detailed description.