How Family Trees And DNA Fingerprinting Are Helping European Abalone Aquaculture

Credit: Wikimedia Commons

The way that people source their seafood has changed dramatically in recent years. In 1980, more than 90% of all the fish and shellfish that humans ate were caught in the wild. Yet in the last four decades, aquaculture, the farming of aquatic species, has increased so rapidly that today it accounts for more than half of all fish and shellfish consumed [1]. This production is a vital source of nutrition for hundreds of millions of people, and developments in aquaculture play a key role in meeting the global populations’ food needs without depleting stocks of wild fisheries.

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After fish, mollusks are the second most cultivated group of aquatic animals, and mollusk farming may be more sustainable than fish farming in the long term, due to reduced ecological impacts and fewer question marks about animal welfare. Although oysters and mussels are the most important mollusks in terms of production, interest in farming other mollusks, such as abalone, is growing. Wild populations of these gastropod mollusks have been fished commercially since the 19th century, but overexploitation led to the closure of many fisheries in the second half of the 20th century. Now farming of this animal has risen to meet the continuing demand, with aquaculture of Pacific species taking off in China and South Korea [2].

In Europe, the native European Abalone (Haliotis tuberculata) continues to be fished on a small scale, although here too, declining stocks have led to the introduction of quotas. But following the demonstration of successful abalone aquaculture in the Pacific region, there is increasing interest in farming of H. tuberculata.

Currently, the European abalone grows slower than many of its Pacific cousins. Abalone farmers are therefore interested in reducing its growing time (and therefore reducing production costs) through selective breeding programs. Such artificial selection has been carried out by humans for millennia, resulting in the domestication of many plants and animals. Nowadays, selective breeding programs can rapidly select for advantageous traits by mating many males and females with one another and combining information about which offspring have the most desirable traits (such as fast growth), with the relationships of the family tree (known as a pedigree).

The application of such breeding programs presents both opportunities and challenges with many aquatic species, which breed by releasing millions of tiny eggs and sperm into the water in a process known as broadcast spawning. This form of reproduction allows many potential parent combinations to be produced simply by mixing their eggs and sperm, but it also makes it very difficult to identify which offspring came from which parent [3]. This information is vital not only for identifying the genetic basis of desirable traits but also for controlling inbreeding, which can lead to reduced genetic diversity in the population and the appearance of genetic disorders.

One way to identify parent-offspring relationships is by using genetic markers to assign parentage, much in the same way that DNA fingerprinting is used to test parentage in humans and provide DNA evidence in forensic criminal investigations. In this study, we used this technique with a test population of 985 farmed abalone (16 adult females, 24 adult males and 945 of their offspring) reared at France Haliotis, an aquaculture facility in Brittany, France. The genetic markers were used to identify parent-offspring relationships, to assess if inbreeding could be a problem, and to provide tools for the improved aquaculture of H. tuberculata.

We used hundreds of genetic markers known as SNPs (single nucleotide polymorphisms) in this study. Generally, SNPs are found by directly sequencing DNA from some of the individuals being studied. However, we examined whether SNPs could be found in pre-existing RNA sequences. This approach was a little speculative, as finding a sufficient number of SNPs in RNA sequences is more difficult than in DNA sequences. Furthermore, SNPs in the pre-existing sequences may not be present in the individuals being studied. However, for many organisms, RNA sequences are more readily available than DNA sequences, and so we hoped to demonstrate an easy and cost-effective way of identifying genetic markers for new aquaculture species.

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Fortunately, the pre-existing RNA sequences contained plenty of potential SNPs. We tested for the presence of 500 SNPs in all 985 animals and found 298 SNPs that were present in at least 90% of individuals. From this group of 298, we picked a subset of 123 highly variable SNPs to reconstruct the pedigree.

These 123 SNPs allowed 99% of the offspring to be matched to both mother and father. Interestingly, certain combinations of parents were not present in the population, and certain parents contributed far more offspring to the next generation than others. When adults were mated, eggs from all 16 females were fertilized with sperm from all 24 males, which could have produced up to 384 offspring families. Yet we found just 189 (49%) families and three particularly prolific females were mothers of 63% of all the offspring tested. Strong variation in reproductive success is common in broadcast spawning organisms, but having so many members of the population descended from the same parents could lead to inbreeding.

We used the results of the parentage assignment to estimate a genetic parameter known as effective population size, which when compared to similar results in related species [4] suggested that the population of animals in this study was not large enough to support a selective breeding program, and that inbreeding could arise if genetic diversity was not increased.

These results provide a guide for how H. tuberculata farms can improve their selective-breeding programs: more precise mixing of eggs and sperm during spawning could increase the chance that all parent combinations are represented in the following generation, and provide the broadest range of genetic variation for artificial selection. The SNPs that we identified could also be used to reduce inbreeding risks by finding genetically varied adults for future selective breeding programs. These markers can also be used in a broader context, assessing the health of the wild population and ensuring that it does not suffer from over-exploitation.

Image 1: Adult abalone at the France Haliotis aquaculture facility (image © France Haliotis)
Image 2: Juvenile abalone at the France Haliotis aquaculture facility (image © France Haliotis)

These findings are described in the article entitled Transcriptome based SNP discovery and validation for parentage assignment in hatchery progeny of the European abalone Haliotis tuberculata, recently published in the journal Aquaculture. The work was conducted by Ewan Harney and Sabine Roussel from the University of Brest, Sebastien Lachambre and Sylvain Huchette from France Haliotis, Florian Enez, Romain Morvezen, and Pierrick Haffray from the Syndicat des Sélectionneurs Avicoles et Aquacoles Français, and Pierre Boudry from Ifremer.

References: 

  1. Food and Agriculture Organization of the United Nations. (2016) The State Of World Fisheries And Aquaculture 2016. Rome.
  2. Cook, P.A. (2016) Recent Trends in Worldwide Abalone Production. Journal of Shellfish Research, 35, 581–583.
  3. Vandeputte, M. & Haffray, P. (2014) Parentage assignment with genomic markers: A major advance for understanding and exploiting genetic variation of quantitative traits in farmed aquatic animals. Frontiers in Genetics, 5, 432.
  4. Rhode, C., Maduna, S.N., Roodt-Wilding, R. & Bester-Van Der Merwe, A.E. (2014) Comparison of population genetic estimates amongst wild, F1 and F2 cultured abalone (Haliotis midae). Animal Genetics, 45, 456–459.
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Cite this article as:
Ewan Harney. How Family Trees And DNA Fingerprinting Are Helping European Abalone Aquaculture, Science Trends, 2018. Available at:
http://doi.org/10.31988/SciTrends.16315
*Note, DOIs are registered Friday weekly and therefore may not work until then.

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