Genetic architecture and genomic selection of female reproduction traits in rainbow trout.


Journal

BMC genomics
ISSN: 1471-2164
Titre abrégé: BMC Genomics
Pays: England
ID NLM: 100965258

Informations de publication

Date de publication:
14 Aug 2020
Historique:
received: 04 04 2020
accepted: 27 07 2020
entrez: 16 8 2020
pubmed: 17 8 2020
medline: 15 5 2021
Statut: epublish

Résumé

Rainbow trout is a significant fish farming species under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits. A pedigreed population of 1343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits. Our genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

Sections du résumé

BACKGROUND BACKGROUND
Rainbow trout is a significant fish farming species under temperate climates. Female reproduction traits play an important role in the economy of breeding companies with the sale of fertilized eggs. The objectives of this study are threefold: to estimate the genetic parameters of female reproduction traits, to determine the genetic architecture of these traits by the identification of quantitative trait loci (QTL), and to assess the expected efficiency of a pedigree-based selection (BLUP) or genomic selection for these traits.
RESULTS RESULTS
A pedigreed population of 1343 trout were genotyped for 57,000 SNP markers and phenotyped for seven traits at 2 years of age: spawning date, female body weight before and after spawning, the spawn weight and the egg number of the spawn, the egg average weight and average diameter. Genetic parameters were estimated in multi-trait linear animal models. Heritability estimates were moderate, varying from 0.27 to 0.44. The female body weight was not genetically correlated to any of the reproduction traits. Spawn weight showed strong and favourable genetic correlation with the number of eggs in the spawn and individual egg size traits, but the egg number was uncorrelated to the egg size traits. The genome-wide association studies showed that all traits were very polygenic since less than 10% of the genetic variance was explained by the cumulative effects of the QTLs: for any trait, only 2 to 4 QTLs were detected that explained in-between 1 and 3% of the genetic variance. Genomic selection based on a reference population of only one thousand individuals related to candidates would improve the efficiency of BLUP selection from 16 to 37% depending on traits.
CONCLUSIONS CONCLUSIONS
Our genetic parameter estimates made unlikely the hypothesis that selection for growth could induce any indirect improvement for female reproduction traits. It is thus important to consider direct selection for spawn weight for improving egg production traits in rainbow trout breeding programs. Due to the low proportion of genetic variance explained by the few QTLs detected for each reproduction traits, marker assisted selection cannot be effective. However genomic selection would allow significant gains of accuracy compared to pedigree-based selection.

Identifiants

pubmed: 32795250
doi: 10.1186/s12864-020-06955-7
pii: 10.1186/s12864-020-06955-7
pmc: PMC7430828
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

558

Subventions

Organisme : ANRT
ID : N°2017/0239
Organisme : European Maritime and Fisheries Fund
ID : RFEA47 0016 FA 1000016
Organisme : FranceAgrimer
ID : 2017-0239

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Auteurs

J D'Ambrosio (J)

Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.
SYSAAF, Station INRAE-LPGP, Campus de Beaulieu, 35042, Rennes cedex, France.

R Morvezen (R)

SYSAAF, Station INRAE-LPGP, Campus de Beaulieu, 35042, Rennes cedex, France.

S Brard-Fudulea (S)

SYSAAF, Section Avicole, Centre INRAE Val de Loire, 37380, Nouzilly, France.

A Bestin (A)

SYSAAF, Station INRAE-LPGP, Campus de Beaulieu, 35042, Rennes cedex, France.

A Acin Perez (A)

Viviers de Sarrance, Pisciculture Labedan, 64490, Sarrance, France.

D Guéméné (D)

SYSAAF, Section Avicole, Centre INRAE Val de Loire, 37380, Nouzilly, France.

C Poncet (C)

Université Clermont-Auvergne, INRAE, GDEC, 63039, Clermont-Ferrand, France.

P Haffray (P)

SYSAAF, Station INRAE-LPGP, Campus de Beaulieu, 35042, Rennes cedex, France.

M Dupont-Nivet (M)

Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France.

F Phocas (F)

Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France. florence.phocas@inrae.fr.

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Classifications MeSH