Genomic Selection in Aquaculture Species

François Allal; Nguyen Hong Nguyen

doi:10.1007/978-1-0716-2205-6_17

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Genomic Selection in Aquaculture Species

Book chapter

Peer reviewed

Genomic Selection in Aquaculture Species

François Allal and Nguyen Hong Nguyen

Complex Trait Prediction: Methods and Protocols, pp.469-491

Methods in Molecular Biology, 2467, Humana New York

2022

DOI: https://doi.org/10.1007/978-1-0716-2205-6_17

PMID: 35451787

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Abstract

Genotype-by-environment

Accuracy

Crustaceans

Molluscs

Finfish

Genomic selection

Aquaculture

To date, genomic prediction has been conducted in about 20 aquaculture species, with a preference for intra-family genomic selection (GS). For every trait under GS, the increase in accuracy obtained by genomic estimated breeding values instead of classical pedigree-based estimation of breeding values is very important in aquaculture species ranging from 15% to 89% for growth traits, and from 0% to 567% for disease resistance. Although the implementation of GS in aquaculture is of little additional investment in breeding programs already implementing sib testing on pedigree, the deployment of GS remains sparse, but could be boosted by adaptation of cost-effective imputation from low-density panels. Moreover, GS could help to anticipate the effect of climate change by improving sustainability-related traits such as production yield (e.g., carcass or fillet yields), feed efficiency or disease resistance, and by improving resistance to environmental variation (tolerance to temperature or salinity variation). This chapter synthesized the literature in applications of GS in finfish, crustaceans and molluscs aquaculture in the present and future breeding programs.

Details

Title: Genomic Selection in Aquaculture Species
Authors: François Allal (Author) - University of Montpellier
Nguyen Hong Nguyen (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
Contributors: Nourollah Ahmadi (Editor) - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Jérôme Bartholomé (Editor) - Centre de Coopération Internationale en Recherche Agronomique pour le Développement
Publication details: Complex Trait Prediction: Methods and Protocols, pp.469-491
Series: Methods in Molecular Biology; 2467
Publisher: Humana New York
DOI: 10.1007/978-1-0716-2205-6_17; 10.1007/978-1-0716-2205-6
ISSN: 1940-6029
ISBN: 9781071622056
PMID: 35451787
Organisation Unit: Centre for Bioinnovation; School of Science, Technology and Engineering; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99632441002621
Output Type: Book chapter

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