DOI: 10.5061/dryad.jwstqjqjk
title: Genotyping-in-Thousands by sequencing (GT-seq) genotyped data used for panel validation in GSI

description: Single Nucleotide Polymorphism (SNP) panels are powerful tools for
 assessing genetic population structure and dispersal of fishes and can
 enhance management practices for commercial, recreational, and subsistence
 mixed-stock fisheries. Arctic Char (Salvelinus alpinus), Brook Trout
 (Salvelinus fontinalis), and Lake Whitefish (Coregonus clupeaformis) are
 amongst the most harvested and consumed fish species in northern
 Indigenous communities in Canada, contributing significantly to food
 security, culture, tradition, and economy. However, genetic resources
 supporting Indigenous fisheries have not been widely accessible to
 northern communities (e.g., Inuit, Cree, and Dene). Here, we developed
 Genotyping-in-Thousands by sequencing (GT-seq) panels for population
 assignment and mixed-stock analyses of three salmonids, to support
 fisheries stewardship or co-management in northern Canada. Using
 low-coverage Whole Genome Sequencing data from 943 individuals across
 source populations in Cambridge Bay (Nunavut), Great Slave Lake (Northwest
 Territories), James Bay (Québec), and Mistassini Lake (Québec), we
 developed a bioinformatic SNP filtering workflow to select informative SNP
 markers from genotype likelihoods. These markers were then used to design
 GT-seq panels, thus enabling high-throughput genotyping for these species.
 The three GT-seq panels yielded an average of 413 autosomal loci and were
 validated with an average assignment accuracy of 83.03%. Thus, these
 GT-seq panels are powerful tools for assessing population structure and
 quantifying the relative contributions of populations/stocks in mixed
 stock fisheries across multiple regions. Interweaving these
 genomic-derived tools with Traditional Ecological Knowledge will ensure
 the sustainable harvest of three culturally important salmonids in
 Indigenous communities, contributing to food security programs and the
 economy in northern Canada.
description: 793 fish samples were genotyped using GT-seq panel developed in
 this study. After filtering (SNP with &gt;50% missing data; samples
 with&gt;30% missing data) the final datasets were converted
 in <em>rubias</em> format for each of the five (5)
 species/region datasets used in our paper for estimating assignment
 accuracies for GSI studies.
description: # GT-seq genotyped data used for panel validation in GSI.
 [https://doi.org/10.5061/dryad.jwstqjqjk](https://doi.org/10.5061/dryad.jwstqjqjk) ## Description of the data and file structure Genotyping data in *rubias* format for each of the five (5) species/region datasets used in our paper for estimating assignment accuracies for GSI studies. See manuscript for further details.  * Arctic Charr in Cambridge Bay (Dryad_GTseqRubias_Set1_ARCH-CBAY.txt):  * Five pops, 95 samples for the training set (TS) and 196 samples for the holdout set (HS) all genotyped at 398 SNPs.  * Brook Trout in James Bay (Dryad_GTseqRubias_Set3_BKT-JB.txt): * Four pops, 56 samples for the training set (TS), 64 samples for the holdout set (HS) and 41 samples for the independent set (IS) all genotyped at 328 SNPs * Brook Trout in Mistassini Lake (Dryad_GTseqRubias_Set2_BKT-MSL.txt): * Three pops, 38 samples for the training set (TS), 10 samples for the holdout set (HS) and 50 samples for the independent set (IS) all genotyped at 393 SNPs  * Lake Whitefish in James Bay (Dryad_GTseqRubias_Set5_LKWF-JB.txt): * Three pops, 37 samples for the training set (TS), 81 samples for the holdout set (HS) and 29 samples for the independent set (IS) all genotyped at 345 SNPs * Lake Whitefish in Great Slave Lake (Dryad_GTseqRubias_Set4_LKWF-GSL.txt):  * Two pops, 42 samples for the training set (TS), 54 samples for the holdout set (HS) all genotyped at 354 SNPs ### Files and variables **Description:** Each txt file is a dataset used for running *infer_mixture* and *self_assign* functions in the R package *[rubias](https://cran.r-project.org/web/packages/rubias/vignettes/rubias-overview.html#self-assigning-fish-from-the-reference) * ##### Variables Each file is structured as follows: * sample_type: mixture or reference - repunit: for reference samples a population ID is given otherwise, for "mixture" samples reporting unit is "NA" * collection: for all samples popID  - indiv: an individual ID * set: samples are denoted as part of the TS (Training set), HS (Holdout set ) or IS (Independent set) accordingly  - Loci columns: Two columns per locus. Alleles are expressed as characters (nucleotides: A,C,T,G) and missing data at a locus is expressed with NA values for each gene copy (column) at the locus ## Code/software All analyses for validating the power of the panels for GSI were performed in R, using the package rubias. Generally, we follow recommendations provided by Eric Anderson, the developer, found in this [link](https://cran.r-project.org/web/packages/rubias/vignettes/rubias-overview.html#self-assigning-fish-from-the-reference).  A general code to run all five genotype datasets included in the publication is provided as a separate file on Dryad "GTseqValidation_Rubias_GeneralCode.R"