FISHBOOST January Newsletter

Following the last General Assembly where Labogena have introduced you to their idea to create new tools to implement the results of the FISHBOOST project. They are enthusiastic to inform you that they plan to create a new “Fishboost DNA SNP-CHIP”, with the following species:Sea Bream : 76 SNPs
Carp : 219 SNPs
Rainbow Trout : 1048 SNPsThis chip will be used for the Fishboost project purposes, and Labogena will be glad to discuss with you if you have any interest for any other use, e.g. add Sea-Bass SNPs or other species if there is any interest. For further discussion, please contact Julien Pradelles.

The role of FISHBOOST in research-oriented European education process

The benefits that salmon farmers obtain by sourcing their stocks from cutting-edge breeding programmes are chronically undervalued, according to a new study. The study, ‘Cost-benefit analysis of a breeding program for Atlantic salmon’, estimated the economic impacts of genetic improvement, focusing on traits such as improved growth rates, lower FCRs, better fillet yields and improved resistance to sea lice. Results were obtained by simulation of a breeding programme that was based on the breeding programme used by SalmoBreed prior to 2016.

The results of the project were presented to delegates at Aqua2018 in Montpellier by Kasper Janssen. “The aim is to quantify the benefits that farmers gain from genetic improvement every generation – both by traditional family breeding programmes and by the use of genomic selection – as the economic benefits that producers get from the breeding companies hasn’t been effectively calculated before,” explained Kasper Janssen, from Wageningen University, who presented the research at Aqua2018 in Montpellier last week.

The first part of the study, which calculated the economic impacts of a generation produced by family selection where the emphasis on breeding goal traits is at its economic optimum, found that the combined effect of improving growth and FCR would increase profit by €245 per tonne of salmon produced per generation, and a saving in terms of lice treatments of €8 per tonne of salmon produced, by reducing the average treatments per cycle from 3.7 to 3.6. Overall, according to the study, using a strain produced by family selection led to a small increase in production volumes as well as cost savings, resulting in a profit increase of €275/tonne per generation.

The study forecasts that the annual genetic gain should equate to €50-80 per tonne of salmon produced per year. The second part of the study looked at the economic impact of a generation produced by genomic selection and the results suggest an even higher increase in profit for the producers, with growth rate up an additional 4 percent, FCR down 8 percent more, sea lice resistance up an extra 9 percent and fillet yield up 4 percent – leading to a €291/tonne benefit for the farmer per generation (every 3.7 years).

Moreover, the study forecasts that the annual genetic gain should equate to €50-80 per tonne per year. And, given that each egg leads to an average yield of 3.8 kg, the genetic potential of an egg would thereby increase by €0.20-0.30 per year, despite the sales price of each egg being less than €0.20 and typically increasing by only ~€0.01 per year Whether the rate of economic improvement per generation will be linear remains to be seen.

As Janssen reflects: “I expect that such results can be sustained over generations, although they become more uncertain towards the future. As long as genetic diversity is maintained by controlling the rate of inbreeding (which all breeding companies should), trait levels will be improved by selection. Their effect on farm profit may however be non-linear, such that future economic gains may change. On the other hand, gains may even increase with improvements in genomic techniques.”

He also adds the caveat that his study was not able to take all variables into account. Despite this, however, he feels that the underlying conclusions of the research are both striking and sound.

The importance of ensuring genetic variability when establishing aquaculture breeding programmes
María Saura (INIA, Spain)

Designing base populations with appropriate levels of genetic variability is fundamental to ensure the viability of aquaculture breeding programmes. Using genomic data obtained within FISHBOOST, INIA has determined the genetic status of commercial populations of turbot, gilthead seabream, European seabass and common carp, and shown the need of broadening the genetic composition of base populations from which selection programmes start and the application of strategies for maintaining genetic diversity once the programmes are in operation.

Importance of optimising the creation of base populations
The success of any breeding programme critically depends on the way in which the base population of breeders is built, as the genetic variability initially available will determine the genetic progress achieved. This is particularly important in aquaculture since given the high fecundity of fish, base populations can be created from very few individuals, leading to large losses in genetic variability and increased risk of inbreeding depression (i.e. reduction of performance due to inbreeding) that can compromise the viability of the programmes.

Genetic variability and effective population size
The rate of loss of genetic variability is directly related to the rate at which inbreeding increases and inversely related with the effective population size, a population parameter that can be estimated from genomic information. Using data obtained within FISHBOOST, INIA has estimated current and ancestral effective population size in populations of turbot, gilthead seabream, European seabass and common carp. Data used came from different challenge tests carried out in the project and corresponded to the broodstock used for creating the families challenged. These broodstock were sampled from different commercial European breeding programmes. Turbot data were obtained from the broodstock maintained at CETGA (Aquaculture Cluster of Galicia, Spain), a population that is representative of the main European breeding programmes that have the same Atlantic origin. Seabream data came from two different breeding programmes carried out at Ferme Marine du Douhet (FMD, France) and the Andromeda Group (Greece). Carp data came from the broodstock of the Amur Mirror Carp strain, recently created at the University of South Bohemia (Czech Republic) from crosses between two different strains. Fish were genotyped for thousands of Single Nucleotide Polymorphisms (SNPs) obtained using genotyping-by-sequencing (RAD-sequencing), and estimates of current and ancestral effective population size from linkage disequilibrium measures were obtained.

Genetic status of turbot, seabream, seabass and carp commercial populations
Results obtained from the analysis of genomic information revealed that current effective population size for all populations analysed was lower than the critical value of 50 individuals that is recommended to avoid inbreeding depression and retain fitness in the short-term. Specifically, estimates for turbot, seabream Andromeda, seabream FMD, seabass and carp were 28, 40, 41, 38 and 22 fish, respectively. These low estimates could be due to the way in which base populations were established (low number of breeding individuals and/or strong relationships between them) or/and to a suboptimal inbreeding control. Estimates of effective population size across time evidenced bottlenecks 4 – 11 generations ago. This point in time coincides with the number of generations for which turbot, seabream and seabass have been domesticated or for which selection programmes have been practised. The recent creation of the carp strain analysed also coincides with the observed bottleneck.

In aquaculture breeding, genomic selection is mainly used for traits that are not measured on the selection candidates, because they are invasive, but instead they are measured on sibs of the selection candidates. Examples of these are disease resistance and fillet quality, and they often constitute a large group of the traits in the breeding goals of well-developed breeding programs. Phenotypic and genomic information must be obtained from the training set of individuals, i.e. the sibs of the candidates. The effects of the SNPs estimated in this dataset are thereafter used to predict genomic breeding values of the selection candidates. With large numbers of traits and individuals per trait (15-50 per family), the total number of individuals to genotype becomes large. Methods that reduce genotyping costs for genomic selection are therefore important for genomic selection to be widely implemented in aquaculture breeding schemes.

Equal amounts of DNA from individuals can be pooled, and thereafter genotyped, resulting in genotyping of much fewer samples than individual genotyping. In a large data set of ~7000 Atlantic salmon that had been challenge tested for Pancreas Disease (PD) and genotyped individually with a 55 kSNP chip, the accuracy of genomic selection was 0.718. We did ‘in silico’ DNA pooling of the phenotypic extremes into 200 pools of SURVIVORS (that survived the whole challenge test) and 200 pools of MORTALITIES (that died earliest in the test), which thus would result in 400 genotypes in total. This dataset resulted in an accuracy of selection of 0.693. In a smaller dataset, also on PD, the reduction in accuracy of selection was larger. Hence, the dataset must be designed to get enough phenotypic variation for the DNA pooling to work well. Here we studied a trait, where we could get large phenotypic differences (a binary trait with heritability of ~0.3), which has probably influenced the positive results.

In a smaller dataset of Atlantic salmon, also tested for PD, and individually genotyped with a SNP chip, the selection accuracy was 0.737. DNA from these same individuals were in vivo pooled into two replicates of SURVIVORS and MORTALITIES pools, each with ~200 individuals from ~30 families. When these pools were sequenced 40x, selection accuracy was 0.716. When these pools were genotyped with a SNP chip and relative light intensities were used to estimate SNP alleles frequencies in the pools, the accuracy of selection was 0.690.

In conclusion, genotyping costs of genomic selection can be dramatically reduced when performing DNA pooling of extreme phenotypes of the training population. Selection accuracies were slightly reduced compared to individual genotyping, but this reduction became smaller when the number of pools increased. The methodology worked best in large populations.

This work was done in collaboration between SalmoBreed AS, Marine Harvest, Nofima, Norwegian University of Life Sciences. Please, contactanna.sonesson@nofima.no if you have questions.
Photo credits to Frank Gregersen and Nofima.

 

This years’ European Aquaculture Society meeting was co-organised with the World Aquaculture Society and was held in Montpellier, France, in the end of August. FISHBOOST had two very well-visited sessions at this conference.

One session was part of the Industry Forum, where industry relevance is in focus. Overviews of the FISHBOOST results so far were presented. The covered topics were improvement of disease and production efficiency traits, genetic gains and profitability of breeding programs, management of inbreeding and genomic selection. At the end of that session, Dr. Nikos Zampoukas from the European Commission was invited to present the new Framework Program, Horizon Europe.

 

This was followed up by a round table discussion on the future research needs within the aquaculture breeding field. The results from that discussion will be summarized and sent as input for future calls to the European Commission via the Farm Animal Breeding and Reproduction Technology Platform (FABRE TP). You can read more about the FABRE TP here:  http://www.fabretp.eu/

I’m contributing to the disease resistance workpackage (WP1). Specifically, we are looking at how the genetics of individuals affects disease spread and survival in fish populations. In addition to the conventional trait ‘disease resistance’, we consider new traits  (disease phenotypes), such as tolerance and infectivity. We know from theory that all of these traits also have a strong influence on disease spread and resulting mortality rates.

However, at present, very little is known about the genetic regulation of these traits, and whether it is possible to breed fish that are not only genetically less prone to becoming infected, but also less likely to transmit infection once infected, and possibly also more likely to survive or recover from infections. If this was possible, genetic selection would be a powerful tool to prevent disease outbreaks – potentially much more powerful than selection on survival, which is the current practice. In FISHBOOST we develop the methods and know-how needed to estimate genetic effects for the different disease traits.

The Roslin Institute is one of the world leading research institute in livestock genomics and breeding, including aquaculture. Together with CETGA and INIA, we have designed a large scale disease transmission experiment for an important disease in Turbot (Scuticociliatosis) to generate data that allow us to simultaneously estimate genetic effects for resistance, tolerance in infectivity. This is the first experiment in the world that dissects the black box ‘mortality’ as indicator for disease resistance into its underlying components. If you are selecting fish that are genetically more prone to survive infection without knowing what underlies survival, you could accidentally select fish that are more tolerant to infection and act as source of infection to others. In FISHBOOST we are developing the experiment design and also the mathematical models and computational algorithms needed to estimate genetic effects for the new traits infectivity and tolerance, in addition to susceptibility. We apply them to the experimental data generated within FISHBOOST. We hope to apply these to other diseases in aquaculture species in the future.

I’m very impressed about the scope of this project and the immense dedication of the project leaders and partners to collaboratively carry out relevant research that will have important practical implications to the aquaculture sector. This project generates a number of important technological and statistical tools and know-how to move aquaculture production in Europe a significant step forward. I’m very grateful to be part of it.

We could show that it is possible to conduct single transmission experiments that allows detection of genetic variation not only in mortality following a disease challenge, but also in all three important underlying host traits resistance, infectivity and tolerance. The data from the Turbot transmission experiments revealed that there is significant genetic variation in all of these traits, meaning that it would be possible in principle to include all three disease traits into a breeding programme. For Scuticocciliatosis in Turbot, resistance and tolerance appear to be genetically different traits (i.e. low genetic correlations), but resistance seems to be highly genetically correlated with mortality. Genome-wide association analyses carried out by our INIA collaborators also detected QTLs controlling tolerance and mortality. Although no significant associations were found for resistance, the pattern of association was the same as for mortality. The transmission experiment also provided the first empirical evidence that individuals not only differ genetically in their risk of becoming infected and how they cope with infection, but also in their ability to transmit infection (i.e. their infectivity). So far we could demonstrate that the genetic differences in infectivity not only affect disease spread but also survival rates. Analyses to estimate the relationship between infectivity and the other disease traits are currently under way.

The results and tools generated in FISHBOOST could help breeders to develop more effective breeding programmes that can exploit genetic variation in several key host traits underlying disease spread and survival.

CETGA is involved in FISHBOOST concerning culturing all the families used for turbot challenges in all the Work Packages. 

As Research Centre, we developed most of the trials regarding turbot: detecting genetic parameters to improve feed efficiency, performing different disease challenges and recording mortalities, time-to-death and growth. The cooperation among all the participants allow us to obtain accurate data about the genetic breeding selection, establishing for the first time a correlation between resilience, tolerance and infectivity that will allow to improve the breeding selection process.