DOI: 10.5061/dryad.s1rn8pkdv
title: Data from: Evaluating genotyping-in-thousands by sequencing as a genetic monitoring tool for a climate sentinel mammal using non-invasive and archival samples

description: Genetic tools for wildlife monitoring can provide valuable information on
 spatiotemporal population trends and connectivity, particularly in systems
 experiencing rapid environmental change. Though many DNA sequencing
 approaches still require high quality and quantity of DNA obtained from
 traditional sources (e.g. blood and tissue), rapid genotyping tools such
 as Genotyping-in-Thousands by sequencing (GT-seq) have improved our
 ability to make use of degraded and less concentrated DNA commonly
 obtained from non-invasive and archival samples. Here, we developed a
 multi-purpose GT-seq panel (307 single nucleotide polymorphisms) for a
 climate sentinel mammal (the American pika, Ochotona princeps) for use as
 a genetic tool for monitoring populations in the Canadian Rocky Mountains.
 We optimized the panel using contemporary tissue samples (n = 77) and
 subsequently applied it to archival tissue (n = 17) and contemporary fecal
 pellet samples (n = 129) to evaluate its effectiveness at identifying
 individuals and sex, estimating relatedness, and inferring population
 structure. The panel demonstrated high efficacy with contemporary and
 archival tissue samples (94.7% and 90.5% genotyping success, respectively)
 and negligible genotyping error (0.001% and 0.0%, respectively). Despite
 relatively high genotyping success for fecal pellet samples (79.7%), high
 genotyping error (28.4%) limited its power as a monitoring tool to assess
 genetic variation using non-invasive samples and highlighted the need for
 further optimization around sample and data collection.
description: Samples were genotyped using Genotyping-in-Thousands by
 sequencing (GTseq; Campbell et al. 2015) panel as modified in Burgess et
 al. (2022; https://doi.org/10.1002/ece3.8993). Raw data were processed using the GTseq workflow.
description: NA
description: # Data from: Evaluating genotyping-in-thousands by sequencing as a genetic
 monitoring tool for a climate sentinel mammal using non-invasive and
 archival samples --- This dataset contains genotypic data of American pika
 (*Ochotona princeps*) scat (n=143), archival skin tissue (n=18), and liver
 tissue (n=85) samples at 307 single nucleotide polymorphisms (SNPs),
 genotyped using Genotyping-in-Thousands by sequencing (described by
 Campbell et al. (2015), as modified in Burgess et al. (2022)). Tissue
 samples were collected across North America in 2004; scat and archival
 skin samples were collected in the Canadian Rocky Mountain National Parks
 (specifically, Banff, Kootenay, Yoho, and Jasper National Parks) in
 2021-2022 and 1930/1945, respectively. ## Description of the data and file
 structure Ocp_liver_genotypes_307snps.vcf - this is a VCF file containing
 SNP and genotypic information collected using for 85 liver tissue samples
 (including 8 technical replicates) at 307 SNPs
 Ocp_scat_archival_genotypes_307snps.vcf - this is a VCF file containing
 SNP and genotypic information collected using for 143 scat (fecal pellet)
 samples (including 14 technical replicates) and 18 archival skin tissue
 samples (including 1 technical replicate) at 307 SNPs sample_metadata.csv
 - this CSV file describes relevant sample metadata for all samples
 included in both Ocp_liver_genotypes_307snps.vcf and
 Ocp_scat_archival_genotypes_307snps.vcf, including: sex, collection date
 and location, and whether or not the sample was retained in downstream
 analyses following preliminary quality control measures. snp_metadata.csv
 - this CSV file describes any relevant information about the SNPs
 genotyped in all scat, skin, and liver samples, including chromosomal
 position and criteria for inclusion in the GT-seq panel. Three primary
 categories of SNPs were included in this dataset: 1 sex-associated SNP, 42
 putatively adaptive SNPs, and 264 putatively neutral SNPs. The putatively
 adaptive SNPs were either elevation-associated (see Schmidt et al., 2021),
 associated with positively selected genes (PSGs; see Sjodin and Russello,
 2022), or robust outlier loci (Schmidt et al., in prep).