Back to Search
Start Over
Genomic and machine learning-based screening of aquaculture-associated introgression into at-risk wild North American Atlantic salmon (Salmo salar) populations.
- Source :
-
Molecular ecology resources [Mol Ecol Resour] 2023 May 28. Date of Electronic Publication: 2023 May 28. - Publication Year :
- 2023
- Publisher :
- Ahead of Print
-
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 (r <superscript>2</superscript> 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.<br /> (©2023 His Majesty the King in Right of Canada and The Authors. Molecular Ecology Resources © 2023 John Wiley & Sons Ltd. Reproduced with the permission of the Minister of Fisheries and Oceans Canada.)
Details
- Language :
- English
- ISSN :
- 1755-0998
- Database :
- MEDLINE
- Journal :
- Molecular ecology resources
- Publication Type :
- Academic Journal
- Accession number :
- 37246351
- Full Text :
- https://doi.org/10.1111/1755-0998.13811