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Towards the development of genomic tools to optimise selection in the rainbow trout

INRA scientists have shown that it is possible to exploit existing genomic resources in the rainbow trout in order to identify SNP genetic markers, which are of considerable value to analysing different traits.  These markers will enable the development of essential selection tools for the trout-rearing industry so that it can achieve gains in competitiveness and sustainability.

Rainbow trout. © INRA, MARIE Didier

The usefulness of genomic selection

Technological advances in the field of high-throughput genomics have enabled sequencing of the full genome of several livestock species (cattle, pigs, horses and chickens).  These sequencing projects have also led to the development of several hundreds of thousands of SNPs (Single Nucleotide Polymorphisms, or point mutations)..  With the advent of new genomic tools such as DNA arrays, there is now a tendency to select breeding animals that display valuable zootechnical performance based only on the sequence polymorphism of their DNA rather than on the performance of their progeny (see the article entitled "A new DNA chip to enable less costly genomic selection in dairy cattle").  These genomic data enable significant savings in time, are cost effective and facilitate the selection of traits that are difficult to observe or measure (for example, disease resistance).

The rainbow trout: from mRNA to SNP markers

As the most common farmed fish species, rainbow trout is of economic importance in both France and Europe. In addition to its commercial interest, rainbow trout is also a model species for a wide range of genome-related research activities.  It occupies a unique taxonomic position among fish, belonging to a family that is specific for having undergone recent whole genome duplication (WGD)..  This evolutionary history offers an opportunity, at different stages of divergence, to study the modes of evolution and rediploidisation in a recently duplicated genome, but partial duplication of the remaining genome seriously complicates standard molecular genetic approaches.  In particular, it may lead to confusion regarding variations between duplicated genomic regions (false SNPs) and variations within a given region (true SNPs).
Major efforts have enabled the development of genomic resources for this species, and sequencing of the complete genome is currently under way.  However,  sequencing of the rainbow trout genome has been performed using a doubled haploid homozygous DNA sample which hinders the identification of new SNPs. Therefore, the identification of SNP markers requires a specific approach.  The INRA scientists thus explored mutations detected during the sequencing of trout mRNA (genomic regions coding genes) using EST (Expressed Sequence Tag) databanks that had already been constituted and were available at a low cost.

A first step towards genomic tools in the trout

In the context of a research project funded by INRA, the scientists tried to identify and then validate SNPs from trout EST sequences available at the INRA bioinformatics platform, SIGENAE (see box).  A preliminary bioinformatics study performed by SIGENAE enabled the identification of approximately 40,000 putative SNPs, but it was anticipated that a proportion of these SNPs might be linked to artefacts, particularly because of the complex structure of the trout genome.  

To estimate the proportion of markers that could actually be exploited, it was essential to test the SNPs on a certain number of individuals.  After applying a succession of qualitative filters to enable pre-selection of the most probable SNPs, the scientists chose a "test" sample of 1152 SNPs.  They then verified the presence of these SNPs in the trout genome.  To achieve this, each of these SNPs was determined by genotyping a population of 480 trout from different genetic origins at the INRA genomics GET PlaGe core facility  using Illumina GoldenGate  Assay ®,.  A fraction of these SNPs could not be amplified (poor quality of the DNA fragments generated).  Approximately 608 genomic sites out of the remaining 958 proved to be non-informative, either because they were not polymorphic (identical sequence in all individuals) or because they corresponded to sequences arising from duplicated regions in the genome.  The remaining 350 genomic sites were confirmed as containing a validated SNP.

Scientific contact(s):

Associated Division(s):
Animal Genetics
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The SIGENAE Platform

The SIGENAE platform centralizes data harvested during the partial or total sequencing of transcriptomes from six livestock species (cattle, pigs, rabbit, sheep, chicken, trout).  This platform was set up in the context of the AGENAE Scientific Interest Group (Groupement d'Intérêt Scientifique AGENAE - Analyse du GENome des Animaux d'Elevage, or Analysis of the Genome of Farmed Livestock). The GIS AGENAE brings together researchers from CIRAD and INRA as well as representatives from the different sectors concerned.  Created in 2002 for a period of 5 years (and renewed in 2008), this GIS has enabled some major discoveries, notably in genomics, in species of importance to livestock farming in France.
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The Get-PlaGe core facility

Get-PlaGe is a core facility that provides both the private and public scientific community with state-of-the-art technologies and expertise in next generation sequencing, genotyping and transcriptomics. This facility was set up by the INRA Animal Genetics Division and the Toulouse Midi-Pyrénées Genopole (http://www.genotoul.fr/). Get-PlaGe forms part of the national genomic platform infrastructure “France Genomique”, supported by a French “Investissements d’Avenir” (Investments for the Future) grant, created to optimize and strengthen French capacity in the fields of high-throughput genomics and related bioinformatics.
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for further information

  • Boussaha Mekki, Guyomard René, Cabau Cédric, Esquerré Diane, Quillet Edwige. Development and characterisation of an expressed sequence tags (EST)-derived single nucleotide polymorphisms (SNPs) resource in rainbow trout". 2012  BMC Genomics. 13, 238