A workflow used to design low density SNP panels for parentage assignment and traceability in aquaculture species and its validation in Atlantic salmon
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Accurate parentage assignment is key for the development of a successful breeding program, allowing pedigree reconstruction from mixed families and control of inbreeding. In the present study we developed a workflow for the design of an efficient single nucleotide polymorphism (SNP) panel for paternity assignment and validated it in Atlantic salmon (Salmo salar L.). A total of 86,468 SNPs were identified from Restriction Site Associated DNA Sequencing (RAD-seq) libraries, and reduced to 1517 following the application of quality control filters and stringent selection criteria. A subsample of SNPs were chosen for the design of high-throughput SNP assays and a training set of known parents and offspring was then used to achieve further filtering. A panel comprising 94 SNPs balanced across the salmon genome were identified, providing 100% assignment accuracy in known pedigrees. Additionally, the panel was able to assign individuals to one of three farmed salmon populations used in this study with 100% accuracy. We conclude that the workflow described is suitable for the design of cost effective parentage assignment and traceability tools for aquaculture species.
Holman , L E , Garcia de la Serrana , D , Onoufriou , A , Hillestad , B & Johnston , I A 2017 , ' A workflow used to design low density SNP panels for parentage assignment and traceability in aquaculture species and its validation in Atlantic salmon ' Aquaculture , vol. 476 , pp. 59-64 . https://doi.org/10.1016/j.aquaculture.2017.04.001
© 2017 Elsevier Ltd. All rights reserved. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.aquaculture.2017.04.001
DescriptionThis project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 654008.
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