Original Research ARTICLE
Genetic Diversity and Connectivity in Maurolicus muelleri in the Bay of Biscay Inferred from Thousands of SNP Markers
- 1Marine Research Division, AZTI, Sukarrieta, Spain
- 2Marine Research Division, AZTI, Pasaia, Spain
Mesopelagic fish are largely abundant poorly studied fish that are still intact, but which, due to their potentially great added value, will be imminently exploited by humans. Therefore, studies that provide information to anticipate the anthropogenic impact on this important resource are urgently needed. In particular, knowledge about their connectivity, potential adaptation and resilience are needed. This information can be obtained through the analysis of genome-wide markers which are now relatively easily and cost-efficiently discovered thanks to high-throughput sequencing technologies. Here, we have generated thousands of SNP markers in Maurolicus muelleri, based on the restriction-site associated DNA sequencing method, and preformed population connectivity and genetic diversity analyses in a subset of samples collected from the Bay of Biscay. Our study proves the method valid for obtaining genome-wide markers in this species and provides the first insights into the population genomics of M. muelleri. Importantly, the genomic resources developed here are made available for future studies and set the basics for additional endeavors on this issue.
The lack of physical barriers in the ocean and large populations sizes generally results in low genetic differentiation in marine fishes (Ward et al., 1994; Bradbury et al., 2008), which renders the task of inferring demographic patterns in this environment particularly difficult. Yet, over the last few years, an increasing number of studies based on high-throughput genetic data have provided evidences of fine scale population differentiation (e.g., Hess et al., 2013; Benestan et al., 2015; Rodríguez-Ezpeleta et al., 2016), challenging previous assumptions based on traditional methods (Hauser and Carvalho, 2008). In particular, the advent of the restriction site associated DNA sequencing (RAD-seq) (Baird et al., 2008) and related methods (Davey et al., 2011) has revolutionized the field of marine conservation genomics (Narum et al., 2013). This approach consists on subsampling putative homologous regions from the genome in several individuals with the aim of identifying and genotyping single nucleotide polymorphisms (SNPs). Interestingly, the method can be applied to organisms for which no prior genomic resources are available, and it is suitable to study both, neutral population structure and local adaptation (Allendorf et al., 2010). In the context of marine management, RAD-seq is particularly relevant for poorly studied widely distributed species, as it can provide quick estimates of genetic diversity, population connectivity and adaptation more cost-effectively than relying on genome or transcriptome sequencing, or on non-sequencing based microsatellite or SNP typing.
Among the poorly studied marine organisms are mesopelagic fish, i.e., those inhabiting the deep scattering layer, that represent the largest fish biomass in the ocean (Kaartvedt et al., 2012; Irigoien et al., 2014) and play important roles in the marine ecosystem and global biogeochemical cycles (Klevjer et al., 2016). The large abundance of these fish make them particularly attractive for exploitation (St. John et al., 2016). Yet, the mesopelagic ecosystem is still largely unknown (Sutton, 2013), and the potential effects of newly introduced anthropogenic pressures in this realm should be anticipated so that sustainable management strategies for this valuable resource can be developed (St. John et al., 2016). Thus, there is an urgent need for establishing focused scientific surveys, developing appropriate sampling gear and generating additional biological data to booster knowledge in these species. In particular, developing genomic tools is foremost, as they can provide insights to understand population persistence, productivity and response to exploitation (Palumbi, 2003; Cowen et al., 2006).
Several studies have focused on deciphering species boundaries and population connectivity within mesopelagic fishes using genetic variants, but they are all based on allozymes (Gunawickrama and Naevdal, 2001; Kristoffersen and Salvanes, 2009), single mitochondrial markers (Kojima et al., 2009; Habib et al., 2012; Gordeeva, 2014) or a few microsatellites (Gordeeva, 2011; Van de Putte et al., 2012). To our knowledge, no studies based on genomic-wide SNP data have been published. Yet, simulated and empirical data based evidences support that high-throughput SNP data analyses provide more accurate population structure inferences than single or a few polymorphic marker based analyses (Haasl and Payseur, 2011; Rašić et al., 2014; Benestan et al., 2015; Szulkin et al., 2016). Here, in a first attempt to introduce genome-wide SNP discovery and genotyping to study mesopelagic fishes, we have focused on the Mueller’s pearlside, Maurolicus muelleri.
Although initially thought to be distributed worldwide, previous reports of M. muelleri in the Northeast Atlantic (Gjøsætr and Kawaguchi, 1980), the South Atlantic (Hulley and Prosch, 1987), the Southeast Pacific (Robertson, 1976), and the coasts of Japan (Okiyama, 1971) are now believed to belong to different species of the genus Maurolicus. The genus was split into fifteen species based on morphological characters and geographic distribution (Parin and Kobyliansky, 1993), but recent studies have suggested an overestimate of species diversity in Maurolicus (Kim et al., 2008; Rees et al., 2017), illustrating a need for more in-depth phylogeographical analyses. In response to these needs, we have discovered and genotyped thousands of SNP markers in M. muelleri, which constitute the first genomic resource of a mesopelagic fish.
Materials and Methods
Tissue Sampling and DNA Extraction
Specimens of M. muelleri were collected at several coastal locations of southern Bay of Biscay (Figure 1) through pelagic trawling on board the R/V Emma Bardán and Ramón Margalef in September 2016. Catches were collected between 106 and 212 m and during daytime. Fish where frozen at -20°C until DNA extraction. Genomic DNA was extracted from about 20 mg of tissue using the Wizard® Genomic DNA Purification kit (Promega, Fitchburg, WI, United States) following manufacturer’s instructions for “Isolating Genomic DNA from Tissue Culture Cells and Animal Tissue.” Extracted DNA was suspended in Milli-Q water and concentration was determined with the Quant-iT dsDNA HS assay kit using a Qubit® 2.0 Fluorometer (Life Technologies). DNA integrity was assessed by electrophoresis, migrating about 100 ng of GelRedTM-stained DNA on an agarose 1.0% gel.
FIGURE 1. Map depicting the location where samples used for this study were obtained. Color indicates depth at which samples were caught, and boxplots depict size distribution of each catch.
RAD-Seq Library Preparation and Data Analysis
Restriction-site-associated DNA libraries of 94 individuals (10 to 20 per catch) were prepared following the methods of Etter et al. (2011). Briefly, starting DNA (ranging from 300 to 500 ng) was digested with the SbfI restriction enzyme and ligated to modified Illumina P1 adapters containing 5 bp unique barcodes. Pools of 30 or 32 individuals were sheared using the Covaris® M220 Focused-ultrasonicatorTM Instrument (Life Technologies) and size selected to 300–500 pb by cutting agarose migrated DNA. After Illumina P2 adaptor (including 5 pb index) ligation, each library was amplified using 14 PCR cycles. The three pools, each with one unique Illumina index, were combined and paired-end sequenced (100 pb) on an Illumina HiSeq2000. Generated RAD-tags were analyzed using Stacks version 1.44 (Catchen et al., 2013b). Quality filtering and demultiplexing was performed with process_radtags with default parameters and removing the last 10 bases of the end of the reads. Putative orthologous tags (stacks) per individual were assembled using ustacks with a minimum depth of coverage required to create a stack (m) of 3 and a maximum nucleotide mismatches (M) allowed between stacks of 2, 4, or 6. Catalogs of RAD loci were assembled using cstacks with number of mismatches allowed between sample tags when generating the catalog (n) of 3, 6, and 9 for M-values of 2, 4, and 6, respectively. Matches of individual RAD loci to the catalog were searched using sstacks. RAD loci found in at least 75% of the individuals were selected using populations and used to calculate (nucleotide diversity – π, minor allele frequency – MAF, expected heterozygosity – He, expected homozygosity – Ho, and inbreeding coefficient – FIS). Using PLINK version 1.07 (Purcell et al., 2007), SNPs with MAF larger than 0.05 and a genotyping rate larger than 0.9 were selected for population structure analyses. The obtained genotype dataset was exported to Structure and Genepop formats using PGDSpider version 126.96.36.199 (Lischer and Excoffier, 2012).
Genetic Diversity and Population Structure
FST values per group pairs were calculated following the Weir and Cockerham (1984) formulation as implemented in Genepop 4.3 (Rousset, 2008). Principal component analyses (PCA) were performed with the R package adegenet (Jombart and Ahmed, 2011) without any a priori grouping assumption. The percentage of appurtenance of each individual to each of the K (ranging from 1 to 4) hypothetical ancestral was calculated with the Bayesian clustering approach implemented in STRUCTURE (Pritchard et al., 2000) without any prior population assignment, based on the admixture model and a burn-in period of 100,000 iterations followed by 300,000 iterations from which estimates were obtained. Ten replicates for each K-value were performed and analyzed with CLUMPP (Jakobsson and Rosenberg, 2007) to identify common modes. Results were plotted using DISTRUCT (Rosenberg, 2004), and best K was identified according to the Evanno method (Evanno et al., 2005) as implemented in StructureHarvester (Earl and vonHoldt, 2012).
The number of RAD-seq reads passing quality filters per individual included in the final analyses ranges from 326 824 to 2 116 073 with an average of 974 008. The number of RAD-tags obtained per individual ranges from about 30K to about 110K, with an average of 67 592, 66 359, and 65 852 for M-values of 2, 4, and 6, respectively (Figure 2A). Average coverage per individual ranges from 6 to 20× (with an average of 11×). Number of RAD-tags per individual increases with number of reads used for stack formation and does not reach a maximum value even when more than 100K reads are used (Figure 2B). This places the number of SbfI cut sites in M. muelleri above 32 000, which is larger than in other teleost species (Catchen et al., 2013a; Diaz-Arce et al., 2016; Rodríguez-Ezpeleta et al., 2016).
FIGURE 2. Boxplot depicting first and third quartile, median and standard deviation of number of RAD-tags per individual (A) and relationship among number of RAD-tags and used reads (B) when the M parameter is set to 2 (red), 4 (green) or 6 (blue).
Above twenty thousand RAD-tags are present in at least 75% of the individuals, and comprise more than two million sites of which more than a thousand are variable (Table 1). The number of tags present in more than 75% of the individuals increases with increasing M because more common loci can be found when these are composed by more alleles. Consequently, larger M-values results in more alleles and thus more variable positions (Catchen et al., 2013b; Rodríguez-Ezpeleta et al., 2016). Selecting only those positions with MAF larger than 0.05 and applying a stringent filtering of a minimum of 90% of the individuals being genotyped per SNP, results in three genotype datasets of 1 625, 2 350, and 2 409 SNPs for M-values 2, 4, and 6, respectively.
TABLE 1. Number of tags present in at least 75% of the individuals, as well as number of positions remaining after each filtering step for each value of M used.
The number of polymorphic sites contained in the tags present in at least 75% off the individuals is 6.5, 8.3, and 9.4% for M-values of 2, 4, and 6, respectively. When calculated considering all individuals a single group, overall nucleotide diversity (π), inbreeding coefficient (FIS), and expected (He) and observed heterozygosity (Ho) values are congruent across the three parameter sets used. Expectedly, using only variable sites or selecting those with at least 0.05 MAF increases nucleotide diversity and both, expected and observed heterozygosity (Table 2). For all set of parameters and position subsets used, observed heterozygosity is lower than expected both overall and when calculated per catch. Per catch, values of π and He are similar, whereas values of Ho and FIS differ (Figure 3). In general, catches with low observed heterozygosity are also those with highest inbreeding coefficient (9018, 9204, 9201) and vice versa (hauls 9007, 9219, 9234), although this does not hold for catches 9012 and 9029 with present large Ho, but medium FIs values with respect to the others. Similar to other studies (Rodríguez-Ezpeleta et al., 2016), we find that, for the four variables, absolute values depend on the set of parameters used for SNP discovery, but that relative values among groups are maintained.
TABLE 2. Average nucleotide diversity (π), inbreeding coefficient (FIS), and observed (Ho) and expected (He) heterozygosity for each value of M when considering all positions included in tags present in at least 75% of the individuals (All), only variable positions within these tags (Variable) or only selected SNPs (Selected).
FIGURE 3. Radar plots showing average nucleotide diversity (π), inbreeding coefficient (FIS), and expected (He) and observed (Ho) heterozygosity for each catch (colored lines) considering each value of M (2, 4, or 6) and for either, all positions included in tags present in at least 75% of the individuals (All), only variable positions within these tags (Variable) or only selected SNPs (Selected).
In the Bayesian population structure analyses (Figure 4), all individuals display admixed representation of each of the hypothetical ancestral population, suggesting genetic connectivity within the area of study. Interestingly, catch 9018 shows an ancestry pattern that differs from the rest of the catches, yet, this difference is not visible in the PCA plots (Figure 5), where no differentiation among groups can be observed. In accordance with the hypothesis of high connectivity within the area of study, FST values between pairs of populations are low (Table 3), although, consistent with the pattern observed in the structure analyses, pairs including catch 9018 are those with highest FST values.
FIGURE 4. Graphical representation of the Bayesian clustering approach for the best K-value obtained for M-values 2, 4, or 6; each bar represents an individual and each color, its inferred membership to each K potential ancestral populations. Pie charts represent per catch averaged proportion of assignment to each potential ancestral population. For each M-value, only the best value of K is shown.
FIGURE 5. Principal component analysis (PCA) of allele frequencies. Each plot shows the first two principal components of the PCA obtained from datasets built using M-values of 2, 4, or 6 (top to bottom). Each dot represents one sample and is colored according to the area of origin. Ovals represent 95% inertia ellipses.
Restriction-site associated DNA sequencing constitutes an unprecedented opportunity for performing demographic inferences in species for which no prior genetic resources are available (Corander et al., 2013; Hess et al., 2013). Here, we have discovered and genotyped thousands of RAD-seq derived SNP markers in M. muelleri in a first attempt to use genome wide data to study diversity and connectivity in this mesopelagic species, which is particularly relevant in view of the imminent exploitation of this till now pristine marine resource.
We have demonstrated that the Sbf I restriction enzyme is as a good candidate for RAD-sequencing based SNP discovery in M. muelleri. Interestingly, we found that, although the average number of RAD-tags per individual is similar to that found in other species (Amores et al., 2011; Catchen et al., 2013b; Puebla et al., 2014; Diaz-Arce et al., 2016; Rodríguez-Ezpeleta et al., 2016), the number of RAD-tags obtained per individual increases with the number of sequencing reads produced; this suggests that most likely the genome of M. muelleri could have as many as 50 000 SbfI cut sites, which is higher than most fish species studied so far. The presence of a large number of cut sites, and therefore of loci, could suggest lower coverage per locus. Yet, although lower coverage than in other species is obtained, all individuals had more than 6× coverage and average was of 11×. Multiplexing less individuals per lane or sequencing the same pool in more than one lane would increase coverage accordingly. Yet, the large number of loci shared among 75% of the individuals and the above one thousand SNP markers obtained after filtering suggest that this increased sequencing would not be necessary.
In our analyses, observed heterozygosity is lower than expected. This could be indicator of (i) population differentiation within the area of study, referred to as the Wahlund effect (Wahlund, 1928), (ii) preferential mating with close relatives or inbreeding (Wright, 1921) or (iii) non-random sampling of members from a limited number of families (Robertson, 1965). Due to the large biomass of mesopelagic fish (Irigoien et al., 2014), it would seem unlikely that our samples, composed by a few individuals per location, contain relatives. Similarly, preferential mating with close relatives or inbreeding would be difficult also to explain. Thus, the natural explanation for the low observed heterozygosity obtained would be population differentiation within the area of study. Yet, we do not find clear evidences of population stratification except for catch 9018, which appears genetically differentiated in the STRUCTURE plots, but not in the PCA.
The fact that when separating individuals into catches, observed heterozygosity is also lower than expected, suggests that one of the three possible explanations should be also acting at the catch level. In this sense, the possibilities of inbreeding and non-random sampling members of the same family should also be considered. Indeed, the fact that size distributions are quite homogeneous within catches (Figure 1), supports previous observations that individuals are grouped according to their age (Staby et al., 2011), and claims for more studies linking the reproductive behavior of the species and the genetic diversity results obtained here, which should be further confirmed with additional data. Similarly, no explanation to the different genetic diversity estimates obtained per catch could be found. These differences could be simply due to a low sample size per catch or have a more biologically meaningful information, and thus, more analyses are needed to confirm either one.
Overall, we have validated the RAD-seq based SNP discovery method using the SbfI restriction enzyme for M. muelleri, which has resulted in the first genomic resources for this species that are moreover made available for future studies (raw sequences are available at NCBI SRA Bioproject PRJNA417219). This data will contribute to shed light on phylogeographic patterns within the species (Rodríguez-Ezpeleta et al., 2016), detect genetic adaptation (Van Wyngaarden et al., 2017), and to resolve the phylogeny within the genus for species delimitation (Diaz-Arce et al., 2016).
NR-E designed the study, performed the analyses, interpreted the data, and wrote the manuscript. PÁ collected the samples, contributed to the interpretation of the data and revised the manuscript. XI contributed to the interpretation of the data and revised the manuscript.
This project was supported by the Department of Agriculture, Fisheries and Food of the Basque Country.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors wish to thank Naroa Aldanondo and Carlota Pérez for fish sampling and Iñaki Mendibil for preparing the RAD-seq libraries. This manuscript is contribution 833 from the Marine Research Division of AZTI.
Amores, A., Catchen, J., Ferrara, A., Fontenot, Q., and Postlethwait, J. H. (2011). Genome evolution and meiotic maps by massively parallel DNA sequencing: spotted gar, an outgroup for the teleost genome duplication. Genetics 188, 799–808. doi: 10.1534/genetics.111.127324
Baird, N. A., Etter, P. D., Atwood, T. S., Currey, M. C., Shiver, A. L., Lewis, Z. A., et al. (2008). Rapid SNP discovery and genetic mapping using sequenced RAD markers. PLOS ONE 3:e3376. doi: 10.1371/journal.pone.0003376
Benestan, L., Gosselin, T., Perrier, C., Sainte-Marie, B., Rochette, R., and Bernatchez, L. (2015). RAD genotyping reveals fine-scale genetic structuring and provides powerful population assignment in a widely distributed marine species, the American lobster (Homarus americanus). Mol. Ecol. 24, 3299–3315. doi: 10.1111/mec.13245
Bradbury, I. R., Laurel, B., Snelgrove, P. V., Bentzen, P., and Campana, S. E. (2008). Global patterns in marine dispersal estimates: the influence of geography, taxonomic category and life history. Proc. Biol. Sci. 275, 1803–1809. doi: 10.1098/rspb.2008.0216
Catchen, J., Bassham, S., Wilson, T., Currey, M., O’Brien, C., Yeates, Q., et al. (2013a). The population structure and recent colonization history of Oregon threespine stickleback determined using restriction-site associated DNA-sequencing. Mol. Ecol. 22, 2864–2883. doi: 10.1111/mec.12330
Corander, J., Majander, K. K., Cheng, L., and Merila, J. (2013). High degree of cryptic population differentiation in the Baltic Sea herring Clupea harengus. Mol. Ecol. 22, 2931–2940. doi: 10.1111/mec.12174
Davey, J. W., Hohenlohe, P. A., Etter, P. D., Boone, J. Q., Catchen, J. M., and Blaxter, M. L. (2011). Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nat. Rev. Genet. 12, 499–510. doi: 10.1038/nrg3012
Diaz-Arce, N., Arrizabalaga, H., Murua, H., Irigoien, X., and Rodriguez-Ezpeleta, N. (2016). RAD-seq derived genome-wide nuclear markers resolve the phylogeny of tunas. Mol. Phylogenet. Evol. 102, 202–207. doi: 10.1016/j.ympev.2016.06.002
Earl, D. A., and vonHoldt, B. M. (2012). STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. doi: 10.1007/s12686-011-9548-7
Etter, P. D., Bassham, S., Hohenlohe, P. A., Johnson, E. A., and Cresko, W. A. (2011). SNP discovery and genotyping for evolutionary genetics using RAD sequencing. Methods Mol. Biol. 772, 157–178. doi: 10.1007/978-1-61779-228-1_9
Evanno, G., Regnaut, S., and Goudet, J. (2005). Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. doi: 10.1111/j.1365-294X.2005.02553.x
Gordeeva, N. V. (2011). On structure of species in pelagic fish: the results of populational-genetic analysis of four species of lanternfish (Myctophidae) from the southern Atlantic. J. Ichthyol. 51, 152. doi: 10.1134/s0032945211020032
Gunawickrama, K. B. S., and Naevdal, G. (2001). Genetic and morphological stock structure of the pearlside, Maurolicus muelleri (Pisces, Sternoptychidae), among Norwegian fjords and offshore area. Sarsia 86, 191–201. doi: 10.1080/00364827.2001.10420475
Haasl, R. J., and Payseur, B. A. (2011). Multi-locus inference of population structure: a comparison between single nucleotide polymorphisms and microsatellites. Heredity 106, 158–171. doi: 10.1038/hdy.2010.21
Habib, K. A., Oh, J., Kim, S., and Lee, Y.-H. (2012). Divergence and gene flow between the East Sea and the Southeast Atlantic populations of North Pacific light fish Maurolicus japonicus Ishikawa. Genes Genomics 34, 609–618. doi: 10.1007/s13258-012-0059-z
Hess, J. E., Campbell, N. R., Close, D. A., Docker, M. F., and Narum, S. R. (2013). Population genomics of Pacific lamprey: adaptive variation in a highly dispersive species. Mol. Ecol. 22, 2898–2916. doi: 10.1111/mec.12150
Irigoien, X., Klevjer, T. A., Røstad, A., Martinez, U., Boyra, G., Acuña, J. L., et al. (2014). Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Nat. Commun. 5:3271. doi: 10.1038/ncomms4271
Jakobsson, M., and Rosenberg, N. A. (2007). CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806. doi: 10.1093/bioinformatics/btm233
Kaartvedt, S., Staby, A., and Aksnes, D. L. (2012). Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass. Mar. Ecol. Prog. Ser. 456, 1–6. doi: 10.3354/Meps09785
Kim, S., Kim, C., Oh, J., Kim, B., Seo, H., Kim, W., et al. (2008). Genetic similarity between the South Atlantic and the western North Pacific Maurolicus (Stomiiformes: Actinopterygii) taxa, M-walvisensis Parin & Kobyliansky and M-japonicus Ishikawa: evidence for synonymy? J. Fish Biol. 72, 1202–1214. doi: 10.1111/j.1095-8649.2007.01786.x
Klevjer, T., Irigoien, X., Røstad, A., Fraile-Nuez, E., Benítez-Barrios, V., and Kaartvedt, S. (2016). Large scale patterns in vertical distribution and behaviour of mesopelagic scattering layers. Sci. Rep. 6:19873. doi: 10.1038/srep19873
Kojima, S., Moku, M., and Kawaguchi, K. (2009). Genetic diversity and population structure of three dominant myctophid fishes (Diaphus theta, Stenobrachius leucopsarus, and S. nannochir) in the North Pacific Ocean. J. Oceanogr. 65, 187–193. doi: 10.1007/s10872-009-0018-8
Kristoffersen, J. B., and Salvanes, A. G. V. (2009). Distribution, growth, and population genetics of the glacier lanternfish (Benthosema glaciale) in Norwegian waters: contrasting patterns in fjords and the ocean. Mar. Biol. Res. 5, 596–604. doi: 10.1080/17451000903042479
Lischer, H. E., and Excoffier, L. (2012). PGDSpider: an automated data conversion tool for connecting population genetics and genomics programs. Bioinformatics 28, 298–299. doi: 10.1093/bioinformatics/btr642
Narum, S. R., Buerkle, C. A., Davey, J. W., Miller, M. R., and Hohenlohe, P. A. (2013). Genotyping-by-sequencing in ecological and conservation genomics. Mol. Ecol. 22, 2841–2847. doi: 10.1111/mec.12350
Parin, N. V., and Kobyliansky, S. G. (1993). Review for the genus Maurolicus (Sternoptychidae, Stomiiformes) with re-establishing validity of five species considered junior synonyms of M. Mueller and descriptions of nine species. Biol. Ocean Fish. Squids 128, 69–101.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A., Bender, D., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575. doi: 10.1086/519795
Rašić, G., Filipović, I., Weeks, A. R., and Hoffmann, A. A. (2014). Genome-wide SNPs lead to strong signals of geographic structure and relatedness patterns in the major arbovirus vector. Aedes aegypti. BMC Genomics 15:275. doi: 10.1186/1471-2164-15-275
Rees, D. J., Byrkjedal, I., and Sutton, T. T. (2017). Pruning the pearlsides: reconciling morphology and molecules in mesopelagic fishes (Maurolicus: Sternoptychidae). Deep Sea Res. II Top. Stud. Oceanogr. 137(Suppl. C), 246–257. doi: 10.1016/j.dsr2.2016.04.024
Rodríguez-Ezpeleta, N., Bradbury, I. R., Mendibil, I., Álvarez, P., Cotano, U., and Irigoien, X. (2016). Population structure of Atlantic mackerel inferred from RAD-seq-derived SNP markers: effects of sequence clustering parameters and hierarchical SNP selection. Mol. Ecol. Resour. 16, 991–1001. doi: 10.1111/1755-0998.12518
St. John, M. A., Borja, A., Chust, G., Heath, M., Grigorov, I., Mariani, P., et al. (2016). A dark hole in our understanding of marine ecosystems and their services: perspectives from the mesopelagic community. Front. Mar. Sci. 3:31. doi: 10.3389/fmars.2016.00031
Staby, A., Røstad, A., and Kaartvedt, S. (2011). Long-term acoustical observations of the mesopelagic fish Maurolicus muelleri reveal novel and varied vertical migration patterns. Mar. Ecol. Prog. Ser. 441, 241–255. doi: 10.3354/meps09363
Szulkin, M., Gagnaire, P. A., Bierne, N., and Charmantier, A. (2016). Population genomic footprints of fine-scale differentiation between habitats in Mediterranean blue tits. Mol. Ecol. 25, 542–558. doi: 10.1111/mec.13486
Van de Putte, A. P., Van Houdt, J. K. J., Maes, G. E., Hellemans, B., Collins, M. A., and Volckaert, F. A. M. (2012). High genetic diversity and connectivity in a common mesopelagic fish of the Southern Ocean: the myctophid Electrona antarctica. Deep Sea Res. II Top. Stud. Oceanogr. 59(Suppl. C), 199–207. doi: 10.1016/j.dsr2.2011.05.011
Van Wyngaarden, M., Snelgrove, P. V., DiBacco, C., Hamilton, L. C., Rodriguez-Ezpeleta, N., Jeffery, N. W., et al. (2017). Identifying patterns of dispersal, connectivity and selection in the sea scallop, Placopecten magellanicus, using RADseq-derived SNPs. Evol. Appl. 10, 102–117. doi: 10.1111/eva.12432
Ward, R. D., Woodmark, M., and Skibinski, D. (1994). A comparison of genetic diversity levels in marine, freshwater and anadromous fishes. J. Fish Biol. 44, 213–232. doi: 10.1111/j.1095-8649.1994.tb01200.x
Keywords: mesopelagic fish, high-throughput sequencing, RAD-seq, population connectivity, genetic diversity, Maurolicus muelleri
Citation: Rodriguez-Ezpeleta N, Álvarez P and Irigoien X (2017) Genetic Diversity and Connectivity in Maurolicus muelleri in the Bay of Biscay Inferred from Thousands of SNP Markers. Front. Genet. 8:195. doi: 10.3389/fgene.2017.00195
Received: 09 October 2017; Accepted: 16 November 2017;
Published: 28 November 2017.
Edited by:Miguel Arenas, University of Vigo, Spain
Reviewed by:Baocheng Guo, Institute of Zoology (CAS), China
David John Rees, University of Bergen, Norway
Copyright © 2017 Rodriguez-Ezpeleta, Álvarez and Irigoien. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Naiara Rodriguez-Ezpeleta, email@example.com