Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing
Migration traits are presumed to be complex and to involve interaction among multiple genes. We used both univariate analyses and a multivariate random forest (RF) machine learning algorithm to conduct association mapping of 15 239 single nucleotide polymorphisms (SNPs) for adult migration-timing ph...
Published in: | Proceedings of the Royal Society B: Biological Sciences |
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crroyalsociety:10.1098/rspb.2015.3064 2024-09-15T18:33:03+00:00 Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing Hess, Jon E. Zendt, Joseph S. Matala, Amanda R. Narum, Shawn R. Bonneville Power Administration 2016 http://dx.doi.org/10.1098/rspb.2015.3064 https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2015.3064 https://royalsocietypublishing.org/doi/full-xml/10.1098/rspb.2015.3064 en eng The Royal Society https://royalsociety.org/journals/ethics-policies/data-sharing-mining/ Proceedings of the Royal Society B: Biological Sciences volume 283, issue 1830, page 20153064 ISSN 0962-8452 1471-2954 journal-article 2016 crroyalsociety https://doi.org/10.1098/rspb.2015.3064 2024-08-05T04:35:26Z Migration traits are presumed to be complex and to involve interaction among multiple genes. We used both univariate analyses and a multivariate random forest (RF) machine learning algorithm to conduct association mapping of 15 239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead ( Oncorhynchus mykiss ). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration timing ( p < 0.000005) that explained 46% of trait variation. Alignment to the annotated Salmo salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an oestrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these three SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration timing of steelhead to facilitate conservation management of this species, and this study demonstrates the benefit of multivariate analyses for association studies. Article in Journal/Newspaper Salmo salar The Royal Society Proceedings of the Royal Society B: Biological Sciences 283 1830 20153064 |
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The Royal Society |
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crroyalsociety |
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English |
description |
Migration traits are presumed to be complex and to involve interaction among multiple genes. We used both univariate analyses and a multivariate random forest (RF) machine learning algorithm to conduct association mapping of 15 239 single nucleotide polymorphisms (SNPs) for adult migration-timing phenotype in steelhead ( Oncorhynchus mykiss ). Our study focused on a model natural population of steelhead that exhibits two distinct migration-timing life histories with high levels of admixture in nature. Neutral divergence was limited between fish exhibiting summer- and winter-run migration owing to high levels of interbreeding, but a univariate mixed linear model found three SNPs from a major effect gene to be significantly associated with migration timing ( p < 0.000005) that explained 46% of trait variation. Alignment to the annotated Salmo salar genome provided evidence that all three SNPs localize within a 46 kb region overlapping GREB1-like (an oestrogen target gene) on chromosome Ssa03. Additionally, multivariate analyses with RF identified that these three SNPs plus 15 additional SNPs explained up to 60% of trait variation. These candidate SNPs may provide the ability to predict adult migration timing of steelhead to facilitate conservation management of this species, and this study demonstrates the benefit of multivariate analyses for association studies. |
author2 |
Bonneville Power Administration |
format |
Article in Journal/Newspaper |
author |
Hess, Jon E. Zendt, Joseph S. Matala, Amanda R. Narum, Shawn R. |
spellingShingle |
Hess, Jon E. Zendt, Joseph S. Matala, Amanda R. Narum, Shawn R. Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
author_facet |
Hess, Jon E. Zendt, Joseph S. Matala, Amanda R. Narum, Shawn R. |
author_sort |
Hess, Jon E. |
title |
Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
title_short |
Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
title_full |
Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
title_fullStr |
Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
title_full_unstemmed |
Genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
title_sort |
genetic basis of adult migration timing in anadromous steelhead discovered through multivariate association testing |
publisher |
The Royal Society |
publishDate |
2016 |
url |
http://dx.doi.org/10.1098/rspb.2015.3064 https://royalsocietypublishing.org/doi/pdf/10.1098/rspb.2015.3064 https://royalsocietypublishing.org/doi/full-xml/10.1098/rspb.2015.3064 |
genre |
Salmo salar |
genre_facet |
Salmo salar |
op_source |
Proceedings of the Royal Society B: Biological Sciences volume 283, issue 1830, page 20153064 ISSN 0962-8452 1471-2954 |
op_rights |
https://royalsociety.org/journals/ethics-policies/data-sharing-mining/ |
op_doi |
https://doi.org/10.1098/rspb.2015.3064 |
container_title |
Proceedings of the Royal Society B: Biological Sciences |
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283 |
container_issue |
1830 |
container_start_page |
20153064 |
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1810474809314770944 |