Optimization of an amplicon sequencing-based microsatellite panel and protocol for stock identification and kinship inference of lake trout ( Salvelinus namaycush ).

Ecology and evolution(2023)

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摘要
Kinship-based methods of population assessment such as close-kin mark-recapture require accurate and efficient genotyping methods capable of resolving complex relationships among kin. Inference of such relationships can be difficult using biallelic loci due to the large number of markers required to obtain the necessary power. Sequencing-based microsatellite panels offer an efficient alternative, combining high polymorphism with efficient next-generation methods. Here we construct, optimize, and test one such panel for lake trout () using a combination of previously-published loci adapted for sequencing and de novo loci mined from a genome assembly. We performed three rounds of primer optimization, yielding a final panel of 131 loci, followed by testing with two different levels of PCR multiplexing (all primers in one or two groups) and two different reaction volumes (5 and 10 μL). Our results showed that the use of the largest multiplex and smallest reaction volume did not substantially change results, allowing significant cost and time savings. To test panel accuracy, we used both a set of 153 known-origin samples from origins of management interest and a series of hatchery crosses representing nine families with parent-offspring, half-sibling, and largely-unrelated pairs. Our results indicate that sequencing-based microsatellite panels can efficiently and accurately provide the information required for a population genetics analyses including population assignment, calculation of between-population , and kinship-based population estimation techniques. Such techniques are seeing increasing applications for a wide range of taxa; our findings should provide insight and guidance for the development of the necessary molecular resources.
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关键词
half‐sibling,lake charr,mixed‐stock assignment,parent‐offspring,population genetics
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