Optimising genomic breeding of farmed salmon ...

The relatively recent domestication of aquaculture species, such as Atlantic salmon, produces both significant challenges and substantial opportunities for the use of genomic tools in animal breeding which could improve both production and welfare. The recent development of single-nucleotide polymor...

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Main Author: Kokkinias, Panagiotis
Format: Article in Journal/Newspaper
Language:unknown
Published: The University of Edinburgh 2022
Subjects:
Online Access:https://dx.doi.org/10.7488/era/2064
https://era.ed.ac.uk/handle/1842/38810
id ftdatacite:10.7488/era/2064
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spelling ftdatacite:10.7488/era/2064 2023-06-11T04:10:19+02:00 Optimising genomic breeding of farmed salmon ... Kokkinias, Panagiotis 2022 https://dx.doi.org/10.7488/era/2064 https://era.ed.ac.uk/handle/1842/38810 unknown The University of Edinburgh article CreativeWork 2022 ftdatacite https://doi.org/10.7488/era/2064 2023-06-01T11:04:49Z The relatively recent domestication of aquaculture species, such as Atlantic salmon, produces both significant challenges and substantial opportunities for the use of genomic tools in animal breeding which could improve both production and welfare. The recent development of single-nucleotide polymorphism (SNP) genotyping arrays raises the possibility of substantially increasing rates of genetic improvement using genomic selection approaches. Genomic selection utilises computational analysis to combine genome-wide SNP data with trait information to identify individuals carrying the best of the naturally occurring genetic variation for desirable characteristics and hence select the best fish to breed future generations. The project is a collaboration with Landcatch (Hendrix Genetics), a salmon breeding company in Scotland with an extensive experience in genetic breeding programmes, which provide data from several different populations of salmon that have been recorded for traits such as body weight per year, ... Article in Journal/Newspaper Atlantic salmon DataCite Metadata Store (German National Library of Science and Technology)
institution Open Polar
collection DataCite Metadata Store (German National Library of Science and Technology)
op_collection_id ftdatacite
language unknown
description The relatively recent domestication of aquaculture species, such as Atlantic salmon, produces both significant challenges and substantial opportunities for the use of genomic tools in animal breeding which could improve both production and welfare. The recent development of single-nucleotide polymorphism (SNP) genotyping arrays raises the possibility of substantially increasing rates of genetic improvement using genomic selection approaches. Genomic selection utilises computational analysis to combine genome-wide SNP data with trait information to identify individuals carrying the best of the naturally occurring genetic variation for desirable characteristics and hence select the best fish to breed future generations. The project is a collaboration with Landcatch (Hendrix Genetics), a salmon breeding company in Scotland with an extensive experience in genetic breeding programmes, which provide data from several different populations of salmon that have been recorded for traits such as body weight per year, ...
format Article in Journal/Newspaper
author Kokkinias, Panagiotis
spellingShingle Kokkinias, Panagiotis
Optimising genomic breeding of farmed salmon ...
author_facet Kokkinias, Panagiotis
author_sort Kokkinias, Panagiotis
title Optimising genomic breeding of farmed salmon ...
title_short Optimising genomic breeding of farmed salmon ...
title_full Optimising genomic breeding of farmed salmon ...
title_fullStr Optimising genomic breeding of farmed salmon ...
title_full_unstemmed Optimising genomic breeding of farmed salmon ...
title_sort optimising genomic breeding of farmed salmon ...
publisher The University of Edinburgh
publishDate 2022
url https://dx.doi.org/10.7488/era/2064
https://era.ed.ac.uk/handle/1842/38810
genre Atlantic salmon
genre_facet Atlantic salmon
op_doi https://doi.org/10.7488/era/2064
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