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Genetic diversity within and among Atlantic cod (Gadus morhua) farmed in marine cages: a proof-of-concept study for the identification of escapees


K. A. Glover, Institute of Marine Research, PO Box 1870, Nordnes, N-5817 Bergen, Norway.
E-mail: kevin.glover@imr.no


This study presents a molecular genetic characterization of Atlantic cod reared in commercial marine farms. Samples consisted of approximately 47 fish collected from nine cages located on four farms throughout Norway. In addition, 28 farmed escapees were recaptured in the sea (443 fish in total). Nine microsatellite loci and the Pan I gene were analysed, revealing a total of 181 alleles. Each sample contained 43–63% of total allelic variation. Comparing variation with published data for wild cod indicates that lower genetic variation exists within single cages than in wild populations. Significant linkage disequilibrium was observed amongst pairs of loci in all samples, suggesting a low number of contributing parental fish. Global FST was 0.049, and the highest pairwise FST value (pooled loci) was 0.085. For single loci, the Pan I gene was the most diagnostic, displaying a global FST of 0.203. Simulations amongst the samples collected on farms revealed an overall correct self-assignment percentage of 75%, demonstrating a high probability of identifying individuals to their farm of origin. Identification of the 28 escapees revealed a single cage as the most likely source of origin for half of the escapees, whilst the remaining fish were assigned to a mixture of samples, suggesting more than one source of escapees.


The Atlantic cod (Gadus morhua L.) represents one of the most economically important fish in the north Atlantic. However, serious decline in abundance has been observed in many coastal stocks, a situation which has stimulated interest for cod farming. In Norway, which is one of the primary producing countries, current aquaculture production of cod is approximately 10 000 tonnes/year. This industry has considerable potential to expand, and the Norwegian government has issued sufficient farming licences to enable a commercial production of approximately 300 000 tonnes/year.

Heritability of commercially important production traits in cod has been studied (e.g. Gjerde et al. 2004; Kolstad et al. 2006), and breeding programmes have been established. Despite this, the industry may be regarded as being in its infancy, and some production is still based upon the spawning of wild captured adults. Larvae and fry production include the application of both extensive and intensive technology, including mass-spawning tanks. Breeding success is often skewed in mass-spawning tanks (Herlin et al. 2008 and references therein), potentially leading to reduced genetic variation.

Cod have been a subject to a large number of molecular genetic studies to delineate population structure (e.g. Frydenberg et al. 1965; Pampoulie et al. 2006a; Jorde et al. 2007; O′Leary et al. 2007; Westgaard & Fevolden 2007). However, with the exception of broodstock screening in connection with stock enhancement programmes (Jørstad 1986; Jørstad et al.1994), and analysis of experimental farmed strains (Pampoulie et al. 2006b), there is no molecular genetic characterization of cod in commercial production. This contrasts with the situation in Atlantic salmon (Salmo salar L.), where a number of studies have been conducted, generally reporting reduced genetic variation and increased genetic differentiation in and among farmed strains compared with wild fish populations (e.g. Mjølnerød et al. 1997; Norris et al. 1999; Skaala et al. 2004).

A major challenge for aquaculture is containment. In Norway, the reported numbers of farmed escaped cod have ranged from 20 000 to 290 000 yearly in the period 2004–2008, although the real figure is probably higher because of under-reporting (Baarøy et al. 2004). There is universal concern over the potential for negative genetic interactions between farmed escaped fish and their wild counterparts (Genimpact 2006). Recently, Glover et al. (2008) used genetic assignment to successfully identify the farm of origin for escaped Atlantic salmon recaptured in a Norwegian fjord. These authors have developed the approach for identification of escapees in further studies (Glover 2008; Glover et al. 2009), which involves matching the multi-locus genetic profile of individual escapees with groups of fish sampled in commercial production cages. However, sufficient among-farm genetic variation is a pre-requisite for the robust identification of escapees to their farm of origin, and this needs to be quantified to enable evaluating whether it is possible to apply a similar technique to other aquaculture species, such as cod.

This study was designed to investigate the level of genetic variation observed in groups of cod reared in commercial production cages, and to quantify the level of genetic differentiation amongst cages and farms. This was conducted with the primary intention of evaluating the potential of using genetic assignment to identify the cage and farm of origin for escapees, and also to perform a genetic characterization of farmed Atlantic cod in Norway.

Materials and methods

Biological samples

A total of nine samples were collected from four commercial grow-out farms in the period January–March 2009 (Table 1). Each sample consisted of approximately 50 fin clips taken from fish reared in a single marine cage. For farms that reared more than one strain of cod, i.e. fish of a different genetic background or delivered by more than one juvenile producer (i.e. a hatchery producer of small fish ready for grow-out), a cage representing each of these genetic groups was sampled. These are henceforth referred to as the baseline samples and represent potential sources of fish escape to be considered in the assignment simulations conducted herein. In addition, 28 farmed escaped cod were captured by a commercial gill-net fisherman close to farm E in March 2009. Tissue samples were secured from these fish.

Table 1.   Origin and background of nine samples of caged Atlantic cod collected on four farms (E, G, H and S), and a group of escapees (RF) recaptured in the vicinity of farm E.
SampleFry producerGenetic strainMean weight (kg)Sampling dateDate placed in cage


DNA was isolated in 96 well format using the Qiagen DNAeasy extraction kit at the Institute of Marine Research (IMR). In addition to the Pan I locus (Fevolden & Pogson 1997), nine microsatellite loci were analysed: Gmo 3, Gmo 8, Gmo 34, Gmo 35 and Gmo 37 (Miller et al. 2000); Gmo 2 and Gmo132 (Brooker et al. 1994); and Tch 11 and Tch 13 (O’Reilly et al. 2000). The protocol of Westgaard & Fevolden (2007) was slightly modified to allow for a 2.5 μl reaction volume in the PCR. The amplified alleles were separated using an ABI 3130 XL sequence analyser (Applied Biosystems) and scored with the software Genemapper 4.0 (Applied Biosystems). The Pan I locus was genotyped according to Stenvik et al. (2006) using one unlabelled forward primer and two different reverse primers; one Pan IA specific primer labelled with 6-FAM, and one Pan IB specific primer labelled with PET. The PCR products were run on an ABI3730 sequence analyser and scored with GeneMapper v4.0.

Statistical analysis

The samples were characterized by a range of standard population genetic tests and parameters conducted in various programmes. The program msa (Dieringer & Schlötterer 2003) was used to compute summary and F-statistics. Genepop V3.3 (Raymond & Rousset 1995) was used to test for deviation from Hardy–Weinberg equilibrium (HWE), and to test for linkage disequilibrium (LD), both using a Fisher’s exact test (demorization 10 000; 100 batches; 5000 iterations). LD was tested for all pairs of loci in all samples. fstat (Goudet 2001) was used to compute allelic richness. A matrix of pairwise FST values was used in the program mega (Tamura et al. 2007) to produce a phylogenetic tree using the UPGMA method (Sneath & Sokal 1973). The tree was linearized, assuming equal evolutionary rates in all lineages, according to Takezaki et al. (2004).

Bayesian clustering analysis implemented in STRUCTURE 2.2 (Pritchard et al. 2000; Falush et al. 2003) was used for estimating the number of populations/groups (k) represented by all the sampled individuals (including escapees) and for assigning individuals to these groups without using prior information about their origin. Runs were conducted at = 1–10, each with five iterations. Correlated allele frequencies and an admixture model were assumed. Each run consisted of a burn-in of 50 000 MCMC steps, followed by 200 000 steps. STRUCTURE was also used to assign escapees to the cage-samples using prior information about sample of origin for the individuals sampled in cages; no prior was used for the escapees (k set at 9).

GeneClass2 (Piry et al. 2004) was used to perform self-assignment simulations with the samples taken from cages using the leave one out procedure and the Rannala & Mountain (1997) method of computation. This program was also used to perform direct assignment of the escapees and exclusion (α = 0.05) with a variety of methods (see Results). Although a range of assignment programs are available (see Hansen et al. 2001; Manel et al. 2005), GeneClass2 was used for assignment in this study because it permits the calculation of exclusion (i.e. rejection of unknown individuals from baseline samples at different significance levels), which is important in a forensic application. This is also important to counteract the possibility of false assignment in the case of a potentially incomplete baseline.


Genetic variation within samples

A total of 181 alleles were observed in the entire data set consisting of 443 fish genotyped at ten loci (Table 2). GMO 8 (41 alleles) and GMO 132 (30 alleles) represented the most polymorphic loci, whilst the Pan I locus (2 alleles) was the least polymorphic. Individual samples displayed 78–114 alleles across loci, equating to 43–63% of the variation observed in the entire data set.

Table 2.   Genetic variation observed in cod sampled from nine cages on commercial farms (G3-S), and a group of farmed escapees (RF), genotyped at nine microsatellite loci and the Pan I gene.
  1. N, number of individuals genotyped per sample; AT, total number of alleles; AM, mean number of alleles; AR, allelic richness; NG, locus not genotyped for sample.

  2. 1Significant deviation from Hardy–Weinberg equilibrium (HWE) (α = 0.05)

  3. 2Significant deviation from HWE following correction (α = 0.005).

G347 8 724172110136216211411.491.70.67
G447 8 8 16116215111341621989.878.10.64
G647 7 92 1611211 65252132868.667.00.65
E147 71017162211441131110510.581.70.65
E239 8 72015191146116110710.789.70.68
E547 710211414 8226171210110.182.40.66
E947 6 91 111111101627142787.862.70.64
H447 6 81 162102112535122787.863.30.64
S47 8 9151718735152999.978.70.67
RF28 9NG 1621414110315162899.987.30.66
Total4431114412030178830218118.1 0.67
Global FST  0.039 0.0460.0460.0450.0350.0700.0300.023 0.0370.204    
Gene diversity  0.80 0.810.880.890.890.760.160.44 0.880.20    

From 99 tests, 26 of the population by locus combinations deviated from HWE (α = 0.05) (Table 2). Deviations were mostly associated with positive Fis values (data not presented). Following adjustment for ten tests per sample (new α = 0.005), the total number of significant departures dropped to 11, and these were distributed unevenly amongst the samples, with G6 and H4 each displaying three departures (Table 2). However, none of the loci were implicated in more than two significant departures from HWE (post-correction). A total of 123 locus pairs displayed significant LD of 414 tests which could be computed (a total of 450 pairwise tests could be computed but because of some monomorphic loci in some samples only 414 tests were computed). Following sequential Bonferroni correction, 30 of these remained significant. LD was observed for a variety of different locus pairs; however, distribution of LD amongst samples was less even, ranging from none in the sample of escapees (RF) to 9 significant pairs in one of the cage-samples (G4).

Genetic variation amongst samples

Global FST was computed at 0.049 (0.043 excluding Pan I). For individual microsatellite loci, global FST ranged from a low of 0.023 for GMO 34, to 0.07 for GMO 2. In contrast, the Pan I locus displayed greater among-sample variation, with a global FST of 0.204, and a highest pairwise FST of 0.45. The high FST values displayed by Pan I reflect the fact that it was monomorphic in three of the samples, but displayed Pan IB allele frequencies of 0.46, 0.37, 0.24 and 0.16 in samples S, G6, E9 and RF respectively.

Pooling loci, highly variable FST values were observed pairwise between samples, ranging from a low of 0.008 between G3 and E2, to 0.085 between E1 and G6. Only six of 46 pairwise FST values were below 0.02. Significant genetic differentiation was also observed between cages located on the same farm. For example, samples E2 and E9 displayed a pairwise FST value of 0.069 across all loci.

Three major genetic clusters were identified; however, sample S changed cluster when Pan I was included (Fig. 1). This is likely to be a result of its exhibiting a very high frequency of the Pan IB allele. In both UPGMA diagrams, the sample consisting of 28 escapees was located in a cluster with E9 and H4. Bayesian clustering of the data revealed significant structure (Fig. 2), which displayed concordance with the three major lineages identified by the UPGMA diagram that included data from all loci (Fig. 1). Increasing from k 3 to k 5 led to an increase in noise rather than distinct structure; however, the sample of escapees (RF) remained clustered with samples E9 and H4 at all k examined.

Figure 1.

FST-based UPGMA diagrams illustrating genetic relationships amongst nine samples of cod taken from marine cages and a group of 28 escapees. Data are based upon nine microsatellite loci (top), or nine microsatellite loci and the Pan I gene (bottom).

Figure 2.

 Bayesian clustering of cod sampled from nine marine cages and 28 recaptured escapees. Each vertical bar denotes an individual, whilst colours denote inferred clusters. Note that colours are not universal between = 2 and 5.

Self-assignment simulations

An overall correct self-assignment percentage of 75% was observed amongst the nine samples collected on farms. This remained almost unchanged when the locus Pan I was excluded from this analysis (74%), and only minor differences in patterns of incorrect assignment were observed (data not shown). Using a genetic distance-based method, DA (Nei et al. 1983), and all loci, overall correct self-assignment was also high (73%).

Correct self-assignment ranged from 51 to 89% for individual samples, and the pattern of incorrect assignment varied (Table 3). For example, sample H4 only incorrectly assigned to samples E9 and S, whereas sample E5 was assigned incorrectly to all but two of the samples. Incorrect assignments amongst samples tended to reflect genetic similarity. For example, samples G3 and E2 displayed the greatest similarity of any pairwise comparison (Fig. 2), and clearly incorrect assignment was greatest between these two samples.

Table 3.   A matrix of self-assignment amongst nine cage-samples of cod using the program GeneClass2. Numbers in bold represent individuals correctly assigned to sample. Overall self-assignment = 75%.
 G3G4G6E1E2E5E9H4SN% correct
  1. N, number of individuals genotyped per sample.

G32461 111  44751
G4135 28   14774
G62 421  1 14789
E14  3652   4777
E272 5214   3954
E511 233415 4772
E9 2    423 4789
H4      44124787
S323 1  1374779

Assignment of escapees

All genetic assignment methods implemented identified sample E9 as the most likely origin for 13–15 of the 28 escapees (Table 4). The remaining escapees were directly assigned to a mixture of the baseline samples, with a maximum of four escapees being directly assigned to any one alternative sample.

Table 4.   Assignment of 28 farmed escaped cod to nine samples from caged cod using different statistical methods implemented in the programs GeneClass2 and STRUCTURE.
StatisticBaseline sample
  1. 1Rannala & Mountain (1997).

  2. 2Nei et al.’s (1983)DA.

  3. 3A total of 4 and 3 cod were excluded from all samples using methods (1) and (2) respectively.

Direct assignment
 Geneclass21 all loci3200241331
 Geneclass21 no Pan I2100331432
Exclusion3 (α = 0.05)

At the chosen level of probability (α = 0.05), samples G3, E2, E5 and E9 could not be excluded as potential sources for 12–16 of the escapees, whilst samples G4, G6, E1, H4 and S could be excluded as potential sources for 20–25 of the 28 escapees. At the same time, 3–4 of the escapees (depending upon method) were excluded from all baseline samples at this probability.


This study has demonstrated that highly significant genetic differentiation exists among groups of cod reared in production cages, both within and between farms. These differences in allele frequencies are driven by a mixture of genetic drift and founder effects acting both within and amongst the commercial strains. Through a combination of genetic analyses, in addition to real-life assignment of 28 unknown escapees, it has been shown that genetic assignment may be used to identify the source of escaped farmed cod, as has previously been described for Atlantic salmon (Glover et al. 2008, 2009) and rainbow trout (Glover 2008). Consequently, this proof-of-concept study provides management authorities and commercial producers with an identification tool that will enable greater control over management practices in the industry.

In this study, pairwise FST values were as high as 0.085, and the majority were over 0.02. Even when the highly informative Pan I locus was excluded, pairwise FST values as high as 0.075 were observed. The FST values reported in this study are high compared with samples collected from wild cod within regions. For example, in a study of wild cod from southern coast of Norway, Knutsen et al. (2003) obtained a global FST of 0.0023, with maximum of 0.0051 for a single locus. Pampoulie et al. (2006a) observed a mean FST of 0.003 amongst cod sampled in Iceland, and Ruzzante et al. (2001) observed FST values of 0.0039 to 0.0053 in cod sampled at Newfoundland. However, over larger geographical distances, genetic differentiation is more similar to the levels observed here. For example, Nielsen et al. (2009) reported a single pairwise value of 0.062 between a sample in the North sea and Northeast arctic cod, and O′Leary et al. (2007) reported a pairwise FST value of 0.11 amongst samples from the Scotian shelf and the Baltic sea.

In a study of Northeast Arctic and Norwegian coastal cod on the Lofoten spawning grounds in northern Norway, Wennevik et al. (2008) reported numbers of alleles observed per sample for six microsatellite markers overlapping with those used in this study. Although sample sizes were smaller in this study, tentative comparison of allelic variation at mutual loci suggests that the level of genetic variation observed in a single production cage is less than a sample of spawning cod taken from the wild. To illustrate, Wennevik et al. (2008) reported the following range in number of alleles per sample across 12 samples, and total number of alleles in the entire data set (in brackets) for the following loci: GMO 2 = 10–17 (21), GMO 3 = 3–8 (10), GMO 132 = 8–22 (33), TCH 11 = 17–23 (28), GMO 35 = 7–10 (12), GMO 34 = 3–8 (8). Both ranges and total numbers of alleles were lower for all of these markers in this study (Table 2). In addition, a large number of pairs of loci were found to display significant linkage disequilibrium here, although this has not been observed for these loci in wild populations (Westgaard & Fevolden 2007). Taken together, these data indicate that a low number of parental fish contributed to the groups of cod reared in the cages sampled in this study. Significant linkage disequilibrium is routinely observed in cage-samples of Atlantic salmon (Glover K. A., personal observation), and this has been reported in hatchery and commercial strains of Atlantic salmon (Withler et al. 2005; Innes & Elliot 2006). Genetic drift is higher when few parents contribute to each generation. This elevates the levels of FST between domesticated populations (as reported in this study), thus increasing the chance of assigning escapees to the farm of origin.

Loss of genetic variation in farmed strains compared with their wild counterparts has been documented for the Atlantic salmon (Mjølnerød et al. 1997; Norris et al. 1999; Skaala et al. 2004), in experimental farmed cod strains (Pampoulie et al. 2006b), and other marine species (Tanaguchi 2004; Bert et al. 2007). Here, whilst reduced genetic variation compared with wild populations was detected, none of the samples displayed exceptionally low levels of genetic variation. This is surprising, as cod are often mass-spawned in large tanks, which tends to result in highly skewed recruitment (e.g. Herlin et al. 2008). No clear evidence suggesting such an effect was observed in this study. However, the degree to which any such skewed mass-spawning contribution may have been counteracted, by juvenile producers mixing cod from a number of mass-spawning tanks, remains unclear.

It was not the specific intention of this study to identify markers that would distinguish wild and farmed cod. The principle of tracing escapees back to cage and farm of origin circumvents this particular challenge by using morphological characteristics for identification. Nevertheless, several of the samples analysed in this study contained moderate to high frequencies of the Pan IB allele, which is rare in Norwegian coastal cod populations (Fevolden & Pogson 1997; Sarvas & Fevolden 2005). Therefore, where cod strains displaying a high frequency of this allele are farmed in regions where this allele is rare in wild fish, it may serve as a ‘diagnostic’ genetic marker. Farmed cod may be able to impact on wild populations, even without escaping, by spawning in net pens (Jørstad et al. 2008). Therefore, regulatory authorities should evaluate the merits of selective breeding programmes for the Pan IB allele, or alternatively, genetic incorporation of a rare enzyme allele (Jørstad et al. 1991) into farmed cod strains, to actively monitor escapement and potential impacts on wild populations.

In this study, sample E9 was identified as the most likely source of origin for approximately half of the 28 escapees. However, our analyses suggest more than one source for the escapees, which is supported by the fact that sample RF displayed the second highest allelic richness, much higher than sample E9, where approximately half of them were assigned. Only a small fraction of incorrect assignment from E9 to H4 was expected (Table 3), and, consequently, assignments of escapees to samples E2 and E5 indicate that fish have also escaped from these cages. None of the ‘alternative’ baseline samples (G3–G6, H4 or S) were considered as ‘real’ potential sources of the escapees as a result of the fact that they were not located in the same region as farm E. However, it is possible that these fish were from another cod farm located closer to farm E that was not included in this study.

In conclusion, this proof-of-concept study has demonstrated that cod from different cages and/or farms may be genetically divergent, to a degree that enables the identification of escapees back to source without prior knowledge of the pedigree. Atlantic cod aquaculture is a growing industry, and it is suggested that continued genetic monitoring of the domestication process will be important to produce well-informed decisions that will enable the sustainable co-existence of wild and cultured populations.


We would like to acknowledge Anne-Grete Søvik Eide for assisting with DNA extraction and Pan I analyses, Tor-Arne Helle and the Norwegian Directorate of Fisheries for sampling, and the farming companies for permitting access. This study was conducted with financial support from the Institute of Marine Research, the Norwegian Ministry of Fisheries and the Norwegian research council.