<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>16(12)</volume><submitter>Chang SL</submitter><funding>Freshwater Fisheries Society of British Columbia</funding><funding>British Columbia Ministry of Forests, Lands, Natural Resource Operations and Rural Development</funding><pubmed_abstract>The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the freshwater resident form of sockeye salmon (Oncorhynchus nerka), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous O. nerka in some systems across their pan-Pacific distribution. Here, we developed a multi-purpose Genotyping-in-Thousands by sequencing (GT-seq) panel of 288 targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada. The GT-seq panel exhibited high self-assignment accuracy (93.3%) and perfect assignment of individuals not included in the baseline to their geographic basin, migratory form, and reproductive ecotype of origin. The GT-seq panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan® SNP genotyping assays, while improving resolution, extending a long-term time-series, and demonstrating the scalability of this approach for this system and others.</pubmed_abstract><journal>PloS one</journal><pagination>e0261966</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8699693</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>Genotyping-in-Thousands by sequencing panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales.</pubmed_title><pmcid>PMC8699693</pmcid><pubmed_authors>Ward HGM</pubmed_authors><pubmed_authors>Russello MA</pubmed_authors><pubmed_authors>Chang SL</pubmed_authors></additional><is_claimable>false</is_claimable><name>Genotyping-in-Thousands by sequencing panel development and application to inform kokanee salmon (Oncorhynchus nerka) fisheries management at multiple scales.</name><description>The ability to differentiate life history variants is vital for estimating fisheries management parameters, yet traditional survey methods can be inaccurate in mixed-stock fisheries. Such is the case for kokanee, the freshwater resident form of sockeye salmon (Oncorhynchus nerka), which exhibits various reproductive ecotypes (stream-, shore-, deep-spawning) that co-occur with each other and/or anadromous O. nerka in some systems across their pan-Pacific distribution. Here, we developed a multi-purpose Genotyping-in-Thousands by sequencing (GT-seq) panel of 288 targeted single nucleotide polymorphisms (SNPs) to enable accurate kokanee stock identification by geographic basin, migratory form, and reproductive ecotype across British Columbia, Canada. The GT-seq panel exhibited high self-assignment accuracy (93.3%) and perfect assignment of individuals not included in the baseline to their geographic basin, migratory form, and reproductive ecotype of origin. The GT-seq panel was subsequently applied to Wood Lake, a valuable mixed-stock fishery, revealing high concordance (>98%) with previous assignments to ecotype using microsatellites and TaqMan® SNP genotyping assays, while improving resolution, extending a long-term time-series, and demonstrating the scalability of this approach for this system and others.</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021</publication><modification>2024-02-15T22:17:20.792Z</modification><creation>2022-02-11T14:30:53.292Z</creation></dates><accession>S-EPMC8699693</accession><cross_references><pubmed>34941943</pubmed><doi>10.1371/journal.pone.0261966</doi></cross_references></HashMap>