Sampling and genotyping of Baltic cod

We genotyped 603 cod individuals from the southern Baltic Sea (ICES SD 22–26, Table 1 and Fig 1) obtained from survey, commercial and recreational catches in 2015 and 2016 with the minimum diagnostic panel of 23 SNPs and an extended marker set of 48 SNPs (see S1 File and S1–S4 Tables for details on whole-genome re-sequencing of cod samples, variant calling, SNP identification and power analysis of the selected SNPs). Spawning individuals (maturity stage 5 = pre-spawning and 6 = spawning [26]) were sampled in SD 22 and 25, assuming that reference specimens for the western and eastern Baltic cod stock were caught during their respective spawning season. In the Arkona Basin (SD 24), cod were collected during the whole year from active and passive catches and from individuals with various maturity stages. Pieces from gill or muscle tissue were stored in pure ethanol at -20°C until processing. DNA was isolated using the Invisorb Spin DNA Extraction Kit (Stratec Molecular, Germany) according to the manufacturer’s instructions. For genotyping cod with the 48-SNP panel, custom allele-specific SNPtype assays were designed for use on a Fluidigm BioMark HD System (S5 Table). Prior to genotyping, specific target amplifications were run to optimize DNA-concentrations for each assay. We used 2.86 μL of diluted PCR products (1:100) and prepared 96.96 IFC dynamic arrays with 96 samples against a 48-SNP panel in accordance with the manufacturer’ protocol and applied the suggested PCR conditions. Genotypes were first auto-called by SNP GENOTYPING ANALYSIS (Fluidigm) with a confidence threshold set at 95. All auto-calls were checked manually and overall data quality, locus and sample performances were evaluated. In total, ten loci were excluded from downstream-analyses (three diagnostic SNPs, seven biologically informative SNPs): all sex loci, monomorphic loci and loci with more than 50% of missing genotype data (see S5 Table). SNPs that did not cluster well were also removed from the dataset. Low-performing individuals with < 50% genotype data were removed. Thus, three out of 23 diagnostic SNPs were excluded from the panel. For convenience, the panel comprising only the resulting diagnostic SNP panel is hereafter referred to as minimum panel which contains 20 SNPs. The panel comprising the full chip, i.e. diagnostic SNPs, plus SNPs in inversions and candidate genes, is referred to as extended panel (38 SNPs).

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Table 1. Summary of samples used for genotyping (N = 603).

ICES subdivision, approximate water depth, ICES rectangle, sampling location and number of sampled individuals (N) are given together with sampling year and month, length information and standard deviation (SD). The proportion of spawning individuals was calculated from individual maturity stages, which were categorized based on their gonadal state of maturation (maturity stages V and IV, following [26]).

https://doi.org/10.1371/journal.pone.0218127.t001

Data analysis

For each locus, observed heterozygosity, expected heterozygosity and F_IS were calculated with R v3.4.1 [27] and the package HIERFSTAT v0.04 [28]. Classical χ²-tests based on the expected genotype frequencies calculated from the allelic frequencies were performed to test deviations from Hardy-Weinberg equilibrium (HWE) based on 10,000 permutations with the package PEGAS v0.1.0 [29]. Tests were run per sampling location (SD) and pooled over all sampling locations. Fixation indices were calculated according to [30] and levels of significance were assessed by 10,000 bootstrap permutations implemented in the package MMOD v1.3.3 [31]. For loci within chromosomal rearrangements (see S5 Table), population-wise bootstrapping of individual genotypes was used to infer the probability of a significant over- or underrepresentation of the presumed collinear allele by resampling 10,000 replicates [32] and sequential Bonferroni correction was applied to correct for multiple tests [33].

Pairwise fixation indices (F_ST) between six sampling locations were calculated for the minimum and the extended panel using the package HIERFSTAT v0.04 [28,34]. Confidence intervals were estimated based on 10,000 permutation replicates. For samples obtained within the same subdivision but from different years, we calculated F_ST-values to test for signs of temporal genetic variation. Since we found no significant genetic differentiation for samples from SD 26 between years, they were pooled for further analyses.

Individual assignment of Baltic cod was performed on the minimum and the extended panel based on a Bayesian clustering algorithm implemented in STRUCTURE v2.3.4 [35]. The number of genetic clusters that best fit the data was estimated using the Evanno method implemented in STRUCTURE HARVESTER [36]. The most likely number of populations (k) was two populations, thus 10 replicates assuming k = 2 were run to assign individuals to one of the two populations without use of prior information as to their geographic origin. The admixture model was run to estimate individual admixture proportions, where the estimated proportion of an individual’s genotype (q) is allowed to be a mixture of both parental populations. Each run consisted of a burnin period of 10,000 followed by 100,000 Markov chain Monte Carlo (MCMC) steps. To average cluster memberships CLUMPP v1.1.1 was used applying the full-search algorithm [37].

We also used a second, computationally faster approach to assign individuals to their population of origin based on Principal Component Analysis (PCA) implemented in the EIGENSOFT v5 software (smartpca) [38]. Here, the genotypes were normalized so that each SNP had a mean genotype value of 0 and normalized variances. From these normalized data, the covariance matrix among individuals was approximated. Smartpca runs a PCA on this matrix and outputs principal components (eigenvectors) and eigenvalues. We applied the option “lsqproject” to improve handling of missing genotype data. The parameter “poplistname” was set to infer eigenvectors using only individuals from a subset of all sampled populations, and then project individuals to be assigned onto those eigenvectors. Spawning individuals from SD 22 and SD 25 (Table 1) were used as reference samples. Tracy-Widom statistics were used to test the correlation of eigenvectors with the “true” axes of variation [38].

Establishment of new SNP panels and population structure of cod in the Baltic

We observed strong genetic differentiation between western and eastern locations, confirming the general validity of stock designations. At the same time we also confirm considerable mixing of the two populations in SD 24.

So far, methods for discriminating western and eastern Baltic cod were mainly based on phenotypic differences [39] and molecular techniques [17,18,40,41] with variable levels of accuracy. More recently, specimens were genetically characterized as originating from either of the two Baltic cod stocks by utilizing a high-graded 39 SNP-array [14,19]. In contrast to previous approaches for generating diagnostic marker panels for Baltic cod populations [7,14,19], we have included whole-genome information on genetic variation between cod populations sampled in the study area. We used more than 68,000 SNPs (see S1 File) to harvest our SNPs from, thus better representing the entire genome, compared to approximately 1,200 SNPs used for generating the existing high-graded 39 SNP-array. Consequently, the resolving power of our diagnostic marker set is increased, while at the same time the number of markers needed is reduced. Furthermore, by referring to a high-quality reference genome for Atlantic cod [20] we were able to select our SNP loci based on their genomic position, avoiding the integration of physically linked SNPs. Hence, the new minimum panel for discriminating between Baltic cod populations is a major improvement over existing SNP panels, because it accounts for the species’ specific genome architecture [21–23,25]. Diagnostic markers from highly linked genomic regions could thus be excluded from the panel a priori, to avoid potential biases in population assignment. The minimum panel introduced in the current study allows a more cost-efficient throughput of a large number of samples by minimizing the need for consumables and reagents, while maintaining a high level of confidence at identifying the individual’s source population. Moreover, our minimum panel can easily be extended by additional loci (this study, [7,42]) to gain extra biological information or to modify the spatial resolution of population structure, thereby providing the flexibility to address a wide suite of biological and management questions.

Applications to questions of stock discrimination and mixing of Baltic cod populations in SD 24

The use of our markers confirmed the co-occurrence of both western and eastern Baltic cod stocks in the Arkona Sea [14,19], but mixing may not be restricted to this area.

Results of a multi-year otolith analysis of stock mixing suggested that eastern Baltic cod have been abundant in the Arkona Basin during the last two decades with a substantial increase from 30% to around 70% in the late 2000s [14]. Nevertheless, there are still uncertainties whether the increase of eastern Baltic cod abundance in the Arkona Basin is an autonomous production from SD 24 or related to a spill-over of cod originating from SD 25 or to shifts in environmental conditions [14,43]. There is a clear need to assign individuals correctly to their native stock, so as to allow the monitoring of both populations as well as the identification of stock-specific or trait-specific (e.g. length, sex, age) movement patterns within the southern Baltic, and to resolve catch compositions from the different nations fishing in the Arkona Sea. The molecular approach presented here has proven useful for differentiating between individual western and eastern Baltic cod.

We recorded one spawning female eastern Baltic cod in the western Baltic (SD 22), which is consistent with observations that have been made also in previous studies [19,44]. Such occurrences suggest that mixing might also affect areas beyond the Arkona Basin. Hence, future work on mixing dynamics of cod in the southern Baltic should also consider adjacent areas to improve our understanding of cod migrations.

A moderate number of individuals could not unequivocally be determined as originating from the western or the eastern Baltic cod population. Methodological inaccuracies cannot be fully avoided due to factors hampering the determination of the different source populations. In our case, most of these samples that failed an assignment were characterized by high levels of missing data across all loci. The non-assigned samples originated from the total study area with a slight overrepresentation of the ones caught in SD 24 (26 out of 49). But compared to the total number of sampled individuals per ICES subdivision, only 7% of all samples caught in SD 24 failed an assignment, but 24% of all samples from SD 25. However, some of the individuals that failed to be assigned might be hybrids of mixed ancestry. There is an ongoing debate on the possibility of hybridization between western and eastern Baltic cod [17,19,40], but it is presently assumed that differences in the timing of spawning seasons and geographically segregated spawning grounds with different environmental conditions (i.e. with eastern Baltic cod spawning later in the season, further east, in deeper waters) provide effective barriers restricting gene flow between the two Baltic cod populations and stimulating reproductive isolation [9]. Interestingly, hybridization between the western and the eastern Baltic cod stock seems to be rare in the mixing zone as suggested by the low individual admixture proportions in our analyses and previous findings support a scenario of mechanical mixing as being the predominant form of interaction between the two stocks and hybridization as being only occasional [19]. The reproductive activity of western and eastern Baltic cod spatially and temporarily overlaps at least to some extent, which raises the question about mechanisms of reproductive isolation preventing natural hybridization.

Applications to questions of local adaptation of cod to the Baltic environment

In the Baltic Sea, the steep gradient from high salinity waters in the Kattegat to regions of lower salinity in the eastern- and northernmost parts limits the spatial distribution of marine organisms, and in the case of the Baltic cod, successful reproduction [45–48]. The Baltic Sea therefore constitutes an ideal model system to assess questions pertaining to local adaptations along environmental gradients [49]. Previous results from high density SNP-arrays indicated that in Baltic cod loci under directional selection were strongly correlated with habitat differences in salinity, oxygen and temperature, and that a substantial amount of divergence was driven by adaptation to low salinity [9]. By focusing on genes related to environmental conditions that are characteristic for a life in the eastern Baltic Sea, we found the most pronounced genetic differences at the hemoglobin and the prolactin loci. The hemoglobin polymorphism in Atlantic cod has been extensively assessed in the past [8,40]. The polygenic nature of hemoglobin enables cod to cope with chronic hypoxia or long-term changes in temperature by altering gene expression levels between hemoglobin isoforms [8]. Western Baltic cod live in the western, shallower areas with higher salinities; thermal convection results in full vertical mixing each winter and low oxygen conditions only occur in summer and autumn. In contrast, bathymetry and stratified hydrography in the deeper basins of the eastern Baltic Sea regularly lead to challenging conditions for cod. As a result, eastern Baltic cod live at the edge of the species‘ physiological limits and have to cope with low-oxygen conditions by expressing a hemoglobin variant with high oxygen-affinity [50–53]. Similarly important, prolactin is essential for the acclimatization to a hypo-osmotic medium by changing cellular water and ion permeability [54]. The variation in allele frequencies found between both populations thus may represent adaptive genotypic responses to habitat differences of the two Baltic cod populations.

In Atlantic cod, four large chromosomal inversions (e.g. on LG 1, 2, 7 and 12) have been identified to discriminate between cod populations throughout its geographical distribution, i.e. dominating the observed genomic divergence by large allele frequency shifts [22,23,55], whereas the rest of the genome displays low levels of genomic differentiation. These patterns strongly suggest that these inversions are maintained by selection, and thus play a major role in local adaptation to environmental conditions linked to oxygen and salinity [9] as well as migratory behaviour [21,23,56]. The occurrence of three distinct chromosomal inversions have recently been shown to provide a genomic basis for fine-scale local adaptation in spite of the species’ general potential for panmixia in its southern distribution [25,57]. For our extended panel, we chose SNPs from each of the three linkage groups (LG 2, 7 and 12) to investigate the distribution of alleles within the Baltic Sea. We found that allele frequencies of the collinear and the inverted allele mirror the population structure results of Baltic cod. However, for LG 7 we observed a more pronounced signal of divergence compared to LG 2 and LG 12, which might be due to reduced recombination or selection pressure.

The application of the new panels introduced here in genetic monitoring and in studies of the genetic signatures of local adaptation, holds strong potential to improve the sustainable management of western and eastern Baltic cod populations and to enhance our understanding of the evolutionary importance of genes related to local adaptation.