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

Authors: HARGROVE, JOHN - National Marine Fisheries; Delomas, Thomas; POWELL, JOHN - Idaho Department Of Fish & Game; HESS, JON - Columbia River Intertribal Fish Commission; NARUM, SHAWN - Columbia River Intertribal Fish Commission; CAMPBELL, MATTHEW - Idaho Department Of Fish & Game

Submitted to: Evolutionary Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/16/2023
Publication Date: 12/26/2023
Citation: Hargrove, J.S., Delomas, T.A., Powell, J.H., Hess, J.E., Narum, S.R., Campbell, M.R. 2023. Efficient population representation with more genetic markers increases performance of a steelhead (Oncorhynchus mykiss) genetic stock identification baseline. Evolutionary Applications. 17;e13610. https://doi.org/10.1111%2Feva.13610.
DOI: https://doi.org/10.1111%2Feva.13610

Interpretive Summary: Knowing the genetic origin of plants and animals is often informative for managing agricultural and wild populations. One technique to obtain this information is called "genetic stock assignment" and entails comparing genetic data from an individual of unknown origin to genetic data from known-origin individuals, termed a "baseline". One of the barriers to use of this technique is the number of samples needed to build a well-performing baseline. We tested several different ways of building a baseline that can reduce the number of samples needed. We show that with more statistically powerful genetic panels, accuracy can be preserved or improved compared to a baseline with more samples but a less powerful genetic panel.

Technical 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, self-assignment 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.