Efficient population representation with more genetic markers increases performance of a steelhead (Oncorhynchus mykiss) genetic stock identification baseline

Hargrove, John S.; Delomas, Thomas A.; Powell, John H.; Hess, Jon E.; Narum, Shawn R.; Campbell, Matthew R.

doi:10.1111/eva.13610

Title: Efficient population representation with more genetic markers increases performance of a steelhead (Oncorhynchus mykiss) genetic stock identification baseline

Journal Article · 26 December 2023 · Evolutionary Applications

DOI: https://doi.org/10.1111/eva.13610 · OSTI ID:2263375

^[1]; Delomas, Thomas A. ^[2];

^[3];

^[4];

^[5]; Campbell, Matthew R. ^[3]

Pacific States Marine Fisheries Commission Eagle Idaho USA
U.S. Department of Agriculture Agricultural Research Service National Cold Water Marine Aquaculture Center Kingston Rhode Island USA
Idaho Department of Fish and Game Eagle Idaho USA
Columbia River Inter‐Tribal Fish Commission Portland Oregon USA
Columbia River Inter‐Tribal Fish Commission Hagerman Idaho USA

Abstract Genetic stock identification (GSI) is an important fisheries management tool to identify the origin of fish harvested in mixed stock fisheries. Periodic updates of genetic baselines can improve performance via the addition of unsampled or under‐sampled populations and the inclusion of more informative markers. We used a combination of baselines to evaluate how population representation, marker number, and marker type affected the performance and accuracy of genetic stock assignments (self‐assignment, bias, and holdout group tests) for steelhead ( Oncorhynchus mykiss ) in the Snake River basin. First, we compared the performance of an existing genetic baseline with a newly developed one which had a reduced number of individuals from more populations using the same set of markers. Self‐assignment rates were significantly higher ( p < 0.001; +5.4%) for the older, larger baseline, bias did not differ significantly between the two, but there was a significant improvement in performance for the new baseline in holdout results ( p < 0.001; mean increase of 25.0%). Second, we compared the performance of the new baseline with increased numbers of genetic markers (~2x increase of single‐nucleotide polymorphisms; SNPs) for the same set of baseline individuals. In this comparison, results produced significantly higher rates of self‐assignment ( p < 0.001; +9.7%) but neither bias nor leave‐one‐out were significantly affected. Third, we compared 334 SNPs versus opportunistically discovered microhaplotypes from the same amplicons for the new baseline, and showed the latter produced significantly higher rates of self‐assignment ( p < 0.01; +2.6%), similar bias, but slightly lower holdout performance (−0.1%). Combined, we show the performance of genetic baselines can be improved via representative and efficient sampling, that increased marker number consistently improved performance over the original baseline, and that opportunistic discovery of microhaplotypes can lead to small improvements in GSI performance.

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Sponsoring Organization:: USDOE

OSTI ID:: 2263375

Alternate ID(s):: OSTI ID: 2263376

Journal Information:: Evolutionary Applications, Journal Name: Evolutionary Applications Vol. 17 Journal Issue: 2; ISSN 1752-4571

Publisher:: Wiley-BlackwellCopyright Statement

Country of Publication:: United Kingdom

Language:: English

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