Evidence map›Paper›PMID 34815428›Full record

ArticleScientific reports2021

Genotyping-in-Thousands by sequencing of archival fish scales reveals maintenance of genetic variation following a severe demographic contraction in kokanee salmon.

Christopher Setzke, Carmen Wong, Michael A Russello

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In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Christopher SetzkeDepartment of Biology, University of British Columbia, Okanagan Campus, 3247 University Way, Kelowna, BC, V1V 1V7, Canada.
Carmen WongParks Canada Yukon Field Unit, Suite 205 - 300 Main St, Whitehorse, YT, Y1A 2B5, Canada.
Michael A RusselloDepartment of Biology, University of British Columbia, Okanagan Campus, 3247 University Way, Kelowna, BC, V1V 1V7, Canada. michael.russello@ubc.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Historical DNA analysis of archival samples has added new dimensions to population genetic studies, enabling spatiotemporal approaches for reconstructing population history and informing conservation management. Here we tested the efficacy of Genotyping-in-Thousands by sequencing (GT-seq) for collecting targeted single nucleotide polymorphism genotypic data from archival scale samples, and applied this approach to a study of kokanee salmon (Oncorhynchus nerka) in Kluane National Park and Reserve (KNPR; Yukon, Canada) that underwent a severe 12-year population decline followed by a rapid rebound. We genotyped archival scales sampled pre-crash and contemporary fin clips collected post-crash, revealing high coverage (> 90% average genotyping across all individuals) and low genotyping error (< 0.01% within-libraries, 0.60% among-libraries) despite the relatively poor quality of recovered DNA. We observed slight decreases in expected heterozygosity, allelic diversity, and effective population size post-crash, but none were significant, suggesting genetic diversity was retained despite the severe demographic contraction. Genotypic data also revealed the genetic distinctiveness of a now extirpated population just outside of KNPR, revealing biodiversity loss at the northern edge of the species distribution. More broadly, we demonstrated GT-seq as a valuable tool for collecting genome-wide data from archival samples to address basic questions in ecology and evolution, and inform applied research in wildlife conservation and fisheries management.

Indexed as

BiodiversityBiological EvolutionDemographyEcologyPolymorphism, Single NucleotideAnimalsDNA, ArchaealGenotypeSalmonDNA, Archaeal

Identifiers

PMID34815428
PMCPMC8611073

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.