Genomic and machine learning-based screening of aquaculture-associated introgression into at-risk wild North American Atlantic salmon (Salmo salar) populations

Cameron M. Nugent, Tony Kess, Matthew K. Brachmann, Barbara L. Langille, Melissa K. Holborn, Samantha V. Beck, Nicole Smith, Steven J. Duffy, Sarah J. Lehnert, Brendan F. Wringe, Paul Bentzen, Ian R. Bradbury

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The negative genetic impacts of gene flow from domestic to wild populations can be dependent on the degree of domestication and exacerbated by the magnitude of pre-existing genetic differences between wild populations and the domestication source. Recent evidence of European ancestry within North American aquaculture Atlantic salmon (Salmo salar) has elevated the potential impact of escaped farmed salmon on often at-risk wild North American salmon populations. Here, we compare the ability of single nucleotide polymorphism (SNP) and microsatellite (SSR) marker panels of different sizes (7-SSR, 100-SSR and 220K-SNP) to detect introgression of European genetic information into North American wild and aquaculture populations. Linear regression comparing admixture predictions for a set of individuals common to the three datasets showed that the 100-SSR panel and 7-SSR panels replicated the full 220K-SNP-based admixture estimates with low accuracy (r2 of.64 and.49, respectively). Additional tests explored the effects of individual sample size and marker number, which revealed that ~300 randomly selected SNPs could replicate the 220K-SNP admixture predictions with greater than 95% fidelity. We designed a custom SNP panel (301-SNP) for European admixture detection in future monitoring work and then developed and tested a python package, salmoneuadmix (https://github.com/CNuge/SalmonEuAdmix), which uses a deep neural network to make de novo estimates of individuals' European admixture proportion without the need to conduct complete admixture analysis utilizing baseline samples. The results demonstrate the mobilization of targeted SNP panels and machine learning in support of at-risk species conservation and management.

Original languageEnglish
JournalMolecular Ecology Resources
Early online date28 May 2023
DOIs
Publication statusE-pub ahead of print - 28 May 2023

Keywords

  • admixture
  • aquaculture
  • Atlantic salmon
  • machine learning
  • management

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