Data from: Evaluating genotyping-in-thousands by sequencing as a genetic monitoring tool for a climate sentinel mammal using non-invasive and archival samples
Data files
Mar 01, 2024 version files 404.06 KB
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Ocp_liver_genotypes_307snps.vcf
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Ocp_scat_archival_genotypes_307snps.vcf
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README.md
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sample_metadata.csv
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snp_metadata.csv
Abstract
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.
README: 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).
Methods
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.
Usage notes
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