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bioRxiv · 10.1101/2023.03.20.533554

Harnessing the power of regional baselines for broad-scale genetic stock identification: A multistage, integrated, and cost-effective approach

Hsu, B.·Habicht, C.
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Abstract

Genetic stock identification (GSI) estimates the contribution of each population to a mixture and these analyses are usually conducted regionally using genetic baselines specific to the stocks expected in that region. Often these regional baselines cannot be combined to produce broader geographical baselines. In cases where the mixture contains stocks spanning across a wide area a broad-scale baseline is created, but these baselines often are unable to resolve among regional stocks. Here, we introduce a new GSI method to harness the resolution capabilities of baselines developed for regional applications in the analyses of mixtures containing fish from a broad geographic range. This multistage process allows for disparate baselines to be used in a single integrated process that estimates the propagated errors from each stage. The baselines used by this model do not require any overlap in markers or in populations representing the broad-scale or regional baselines. The integrated multistage framework allows GSI of a wide geographic area without first developing a large scale, high resolution genetic baseline, or dividing a mixture sample into smaller regions beforehand. This approach is more cost-effective than updating range-wide baselines with all critical regionally important markers.

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Hsu, B., Habicht, C.. 2023-03-23. Harnessing the power of regional baselines for broad-scale genetic stock identification: A multistage, integrated, and cost-effective approach. https://doi.org/10.1101/2023.03.20.533554

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