Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations
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...
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Online Access: | http://dx.doi.org/10.1111/1755-0998.13811 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.13811 |
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crwiley:10.1111/1755-0998.13811 2024-06-02T08:03:37+00:00 Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations Nugent, Cameron M. Kess, Tony Brachmann, Matthew K. Langille, Barbara L. Holborn, Melissa K. Beck, Samantha V. Smith, Nicole Duffy, Steven J. Lehnert, Sarah J. Wringe, Brendan F. Bentzen, Paul Bradbury, Ian R. 2023 http://dx.doi.org/10.1111/1755-0998.13811 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.13811 en eng Wiley http://onlinelibrary.wiley.com/termsAndConditions#vor Molecular Ecology Resources ISSN 1755-098X 1755-0998 journal-article 2023 crwiley https://doi.org/10.1111/1755-0998.13811 2024-05-03T11:47:38Z 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 2 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. Article in Journal/Newspaper Atlantic salmon Salmo salar Wiley Online Library Molecular Ecology Resources |
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Wiley Online Library |
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language |
English |
description |
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 2 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. |
format |
Article in Journal/Newspaper |
author |
Nugent, Cameron M. Kess, Tony Brachmann, Matthew K. Langille, Barbara L. Holborn, Melissa K. Beck, Samantha V. Smith, Nicole Duffy, Steven J. Lehnert, Sarah J. Wringe, Brendan F. Bentzen, Paul Bradbury, Ian R. |
spellingShingle |
Nugent, Cameron M. Kess, Tony Brachmann, Matthew K. Langille, Barbara L. Holborn, Melissa K. Beck, Samantha V. Smith, Nicole Duffy, Steven J. Lehnert, Sarah J. Wringe, Brendan F. Bentzen, Paul Bradbury, Ian R. Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations |
author_facet |
Nugent, Cameron M. Kess, Tony Brachmann, Matthew K. Langille, Barbara L. Holborn, Melissa K. Beck, Samantha V. Smith, Nicole Duffy, Steven J. Lehnert, Sarah J. Wringe, Brendan F. Bentzen, Paul Bradbury, Ian R. |
author_sort |
Nugent, Cameron M. |
title |
Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations |
title_short |
Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations |
title_full |
Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations |
title_fullStr |
Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations |
title_full_unstemmed |
Genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild North American Atlantic salmon ( Salmo salar) populations |
title_sort |
genomic and machine learning‐based screening of aquaculture‐associated introgression into at‐risk wild north american atlantic salmon ( salmo salar) populations |
publisher |
Wiley |
publishDate |
2023 |
url |
http://dx.doi.org/10.1111/1755-0998.13811 https://onlinelibrary.wiley.com/doi/pdf/10.1111/1755-0998.13811 |
genre |
Atlantic salmon Salmo salar |
genre_facet |
Atlantic salmon Salmo salar |
op_source |
Molecular Ecology Resources ISSN 1755-098X 1755-0998 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/1755-0998.13811 |
container_title |
Molecular Ecology Resources |
_version_ |
1800748200200503296 |