Outbreak of cardiomyopathic syndrome (CMS) in farming sites has helped in uncovering genetic QTL markers for this disease. CMS field outbreak mortality from a Marine Harvest site indicates that genetic markers on two chromosomal regions might confer stronger survival and robustness to CMS.

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Reidun Lilleholt Kraugerud  

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This finding will be key in reducing mortalities due to CMS in Atlantic salmon farming. The late onset of the disease with high mortality when fish is around 3 to 5 kilos lead to high economic losses for the salmon industry, and was regarded as the most serious fish disease in aquaculture by fish health personnel, according to Fish health report 2017.


Analyses by Nofima and an industry partner (Marine Harvest) showed that there is a genetic variation (families differ) for CMS resistance. This means some families fight CMS better than the others. Selection of stronger and resistant individuals or families using genetic QTL markers would help to reduce mortalities caused by CMS (or due to underlying stress reasons during disease outbreak).

The results of this study are part of the “SalmoResist” research project that is funded by the Research Council of Norway and is led by Nofima. The project has two industrial collaborative partners: Marine Harvest and SalmoBreed. One of the goals of this project is to utilize data from natural field outbreaks of diseases for QTL detection, and to use this information for selection of broodstock.

The approach to utilizing field data

Scientists in Nofima and Marine Harvest set out to sample fish from field outbreaks due to viral diseases during the 2016/17 farming season. When a field outbreak of any disease was reported, veterinarians were asked to confirm the disease through clinical signs and symptoms as well as qPCR analysis. Once the specific cause of the outbreaks was determined, dead fish was collected over a short period to limit the potential of sampling fish that were not dying due to the disease of interest.

At the end of the production cycle, an equal number of survivors were sampled during slaughtering at the slaughter plant. A few of the sampled survivors are also investigated by veterinarians to confirm that they were potentially healthy. All fish were genotyped and analysed for QTL, which finally led to the detection of two QTL regions for CMS.

Manhattan plot of resistance to CMS with data from a field outbreak. The two high bars crossing the blue and red lines show where the QTLs are likely located. Illustration: Nofima.

The QTL analysis was undertaken by Solomon Boison then at Nofima, now Marine Harvest:

“The two QTL regions explained about 50% of the genetic variation of resistance to CMS and therefore will go a long way to help the industry select fish that will be resistant to the virus that cause CMS.”

Luqman Aslam, researcher of Nofima, is very pleased that the field outbreak results were further verified by challenge tests conducted by the industry partners at the end of 2017. He thinks that the QTL-findings which explain large proportions of genetic variance might allow breeding companies to opt for the efficient and cost-effective marker assisted selection.

“That will be a game changer in the fight against CMS”, he says.

Further validation of the QTL by the industry

Both Marine Harvest and SalmoBreed have validated the QTL identified in the SalmoResist project by performing challenge tests developed by Veso Vikan, Namsos, Norway. The results from the two breeding companies point to the same chromosomal region.

Matthew Baranski from Marine Harvest finds the approach used by the project exciting:

“Instead of waiting for challenge models to be developed in the laboratory for some of these diseases, field data could be the first starting point until laboratory models are fully developed, refined and validated.”

Borghild Hillestad from SalmoBreed says: “The findings are very important, especially since they make the breeding companies able to utilize the associated genetic markers with much higher confidence.”

Her colleague Hooman Moghadam adds: “In the future, however, to make the best out of such field outbreaks, we need to improve strategies to be able to better account for various sources of noise, error and unknown factors that are inherent part of these type of data collection”.

Nofima will seek to adopt the approach used in this study to help uncover QTLs for other traits instead of going through a challenge test model, especially for diseases with no challenge test models at all. However, care must be taken by confirming outbreaks by veterinarians before sampling.

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