Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software

In this study, a total of 18 novel productive traits, three related to carcass [cNiT] and fifteen related to morphometric [mNiT]), were measured in gilthead seabream (Sparus aurata) using Non-invasive Technologies (NiT) as implemented in IMAFISH_ML (MatLab script). Their potential to be used in indu...

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Autores: León-Bernabeu, Sergi, Shin, Hyun Suk, Lorenzo-Felipe, Alvaro, García-Pérez, Cathaysa, Berbel, Concepción, Elalfy, Islam Said, Armero, Eva, Pérez-Sánchez, Jaume, Arizcun, Marta, Zamorano, Maria Jesús, Manchado, Manuel, Afonso, Juan Manuel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/313929
Acceso en línea:http://hdl.handle.net/10261/313929
Access Level:acceso abierto
Palabra clave:Centro Oceanográfico de Murcia
Giilthead seabream
Acuicultura
IMAFISH_ML
Non-invasive Technology (NiT)
Heritability
Genetic correlation
KET
fish
quality
aquaculture
Genetics
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oai_identifier_str oai:digital.csic.es:10261/313929
network_acronym_str ES
network_name_str España
repository_id_str
spelling Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML softwareLeón-Bernabeu, SergiShin, Hyun SukLorenzo-Felipe, AlvaroGarcía-Pérez, CathaysaBerbel, ConcepciónElalfy, Islam SaidArmero, EvaPérez-Sánchez, JaumeArizcun, MartaZamorano, Maria JesúsManchado, ManuelAfonso, Juan ManuelCentro Oceanográfico de MurciaGiilthead seabreamAcuiculturaIMAFISH_MLNon-invasive Technology (NiT)HeritabilityGenetic correlationKETfishqualityaquacultureGeneticsIn this study, a total of 18 novel productive traits, three related to carcass [cNiT] and fifteen related to morphometric [mNiT]), were measured in gilthead seabream (Sparus aurata) using Non-invasive Technologies (NiT) as implemented in IMAFISH_ML (MatLab script). Their potential to be used in industrial breeding programs were evaluated in 2348 offspring reared under different production systems (estuarine ponds, oceanic cage, inland tank) at harvest. All animals were photographed, and digitally measured and main genetic parameters were estimated. Heritability for growth traits was medium (0.25–0.37) whereas for NiT traits medium-high (0.24–0.61). In general, genetic correlations between mNiT, cNiT and growth and traits were high and positive. Image analysis artifacts such as fin unfold or shades, that may interfere in the precision of some digital measurements, were discarded as a major bias factor since heritability of NiT traits after correcting them were no significantly different from original ones. Indirect selection of growth traits through NiT traits produced a better predicted response than directly measuring Body Weight (13–23%), demonstrating that this methodological approach is highly cost-effective in terms of accuracy and data processing time.SI202320232021info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/313929reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésCentro Oceanográfico de Murciainfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3139292026-05-22T06:33:51Z
dc.title.none.fl_str_mv Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
title Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
spellingShingle Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
León-Bernabeu, Sergi
Centro Oceanográfico de Murcia
Giilthead seabream
Acuicultura
IMAFISH_ML
Non-invasive Technology (NiT)
Heritability
Genetic correlation
KET
fish
quality
aquaculture
Genetics
title_short Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
title_full Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
title_fullStr Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
title_full_unstemmed Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
title_sort Genetic parameter estimations of new traits of morphological quality on gilthead seabream (Sparus aurata) by using IMAFISH_ML software
dc.creator.none.fl_str_mv León-Bernabeu, Sergi
Shin, Hyun Suk
Lorenzo-Felipe, Alvaro
García-Pérez, Cathaysa
Berbel, Concepción
Elalfy, Islam Said
Armero, Eva
Pérez-Sánchez, Jaume
Arizcun, Marta
Zamorano, Maria Jesús
Manchado, Manuel
Afonso, Juan Manuel
author León-Bernabeu, Sergi
author_facet León-Bernabeu, Sergi
Shin, Hyun Suk
Lorenzo-Felipe, Alvaro
García-Pérez, Cathaysa
Berbel, Concepción
Elalfy, Islam Said
Armero, Eva
Pérez-Sánchez, Jaume
Arizcun, Marta
Zamorano, Maria Jesús
Manchado, Manuel
Afonso, Juan Manuel
author_role author
author2 Shin, Hyun Suk
Lorenzo-Felipe, Alvaro
García-Pérez, Cathaysa
Berbel, Concepción
Elalfy, Islam Said
Armero, Eva
Pérez-Sánchez, Jaume
Arizcun, Marta
Zamorano, Maria Jesús
Manchado, Manuel
Afonso, Juan Manuel
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Centro Oceanográfico de Murcia
Giilthead seabream
Acuicultura
IMAFISH_ML
Non-invasive Technology (NiT)
Heritability
Genetic correlation
KET
fish
quality
aquaculture
Genetics
topic Centro Oceanográfico de Murcia
Giilthead seabream
Acuicultura
IMAFISH_ML
Non-invasive Technology (NiT)
Heritability
Genetic correlation
KET
fish
quality
aquaculture
Genetics
description In this study, a total of 18 novel productive traits, three related to carcass [cNiT] and fifteen related to morphometric [mNiT]), were measured in gilthead seabream (Sparus aurata) using Non-invasive Technologies (NiT) as implemented in IMAFISH_ML (MatLab script). Their potential to be used in industrial breeding programs were evaluated in 2348 offspring reared under different production systems (estuarine ponds, oceanic cage, inland tank) at harvest. All animals were photographed, and digitally measured and main genetic parameters were estimated. Heritability for growth traits was medium (0.25–0.37) whereas for NiT traits medium-high (0.24–0.61). In general, genetic correlations between mNiT, cNiT and growth and traits were high and positive. Image analysis artifacts such as fin unfold or shades, that may interfere in the precision of some digital measurements, were discarded as a major bias factor since heritability of NiT traits after correcting them were no significantly different from original ones. Indirect selection of growth traits through NiT traits produced a better predicted response than directly measuring Body Weight (13–23%), demonstrating that this methodological approach is highly cost-effective in terms of accuracy and data processing time.
publishDate 2021
dc.date.none.fl_str_mv 2021
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/313929
url http://hdl.handle.net/10261/313929
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Centro Oceanográfico de Murcia
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422107834712064
score 15,811543

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