Int. J. Mol. Sci. 2013, 14(3), 5694-5711; doi:10.3390/ijms14035694

Article
Development and Validation of Single Nucleotide Polymorphisms (SNPs) Markers from Two Transcriptome 454-Runs of Turbot (Scophthalmus maximus) Using High-Throughput Genotyping
Manuel Vera 1,2,*, Jose-Antonio Alvarez-Dios 3, Carlos Fernandez 2, Carmen Bouza 2, Roman Vilas 2 and Paulino Martinez 2
1
Laboratory of Genetics Ichthyology, Department of Biology, Faculty of Sciences, University of Girona, Campus of Montilivi s/n, Girona 17071, Spain
2
Department of Genetics, Faculty of Veterinary, University of Santiago de Compostela, Campus of Lugo, Lugo 27002, Spain; E-Mails: carlos.fernandez.lopez@usc.es (C.F.); mcarmen.bouza@usc.es (C.B.); roman.vilas@usc.es (R.V.); paulino.martinez@usc.es (P.M.)
3
Department of Applied Mathematics, Faculty of Mathematics, University of Santiago de Compostela, Santiago de Compostela 15782, Spain; E-Mail: joseantonio.alvarez.dios@usc.es
*
Author to whom correspondence should be addressed; E-Mail: manuel.verar@udg.edu; Tel.: +34-972-418-168; Fax: +34-972-418-277.
Received: 3 December 2012; in revised form: 17 February 2013 / Accepted: 22 February 2013 /
Published: 12 March 2013

Abstract

: The turbot (Scophthalmus maximus) is a commercially valuable flatfish and one of the most promising aquaculture species in Europe. Two transcriptome 454-pyrosequencing runs were used in order to detect Single Nucleotide Polymorphisms (SNPs) in genes related to immune response and gonad differentiation. A total of 866 true SNPs were detected in 140 different contigs representing 262,093 bp as a whole. Only one true SNP was analyzed in each contig. One hundred and thirteen SNPs out of the 140 analyzed were feasible (genotyped), while III were polymorphic in a wild population. Transition/transversion ratio (1.354) was similar to that observed in other fish studies. Unbiased gene diversity (He) estimates ranged from 0.060 to 0.510 (mean = 0.351), minimum allele frequency (MAF) from 0.030 to 0.500 (mean = 0.259) and all loci were in Hardy-Weinberg equilibrium after Bonferroni correction. A large number of SNPs (49) were located in the coding region, 33 representing synonymous and 16 non-synonymous changes. Most SNP-containing genes were related to immune response and gonad differentiation processes, and could be candidates for functional changes leading to phenotypic changes. These markers will be useful for population screening to look for adaptive variation in wild and domestic turbot.
Keywords:
turbot; Scophthalmus maximus; SNP validation; EST database; non-synonymous substitution; high-throughput genotyping

1. Introduction

The turbot (Scophthalmus maximus; Scophthalmidae, Pleuronectiformes) is a commercially valuable flatfish that has been intensively cultured since the 1980s. Its production has steadily increased up to the present figure of 8549 tons in 2011 (91.2% European production from Spain; [1]) and it appears to be one of the most promising aquaculture species in Europe. In response to turbot industry demands, genetic markers have been developed in this species in order to evaluate genetic resources in both wild and hatchery populations and perform parentage analysis to support genetic breeding programs [24]. These markers have also been applied to develop genomic tools to identify genomic regions associated with productive characters [57] and to detect selection footprints in wild populations [8]. Increasing growth rate, controlling sex ratio (females largely outgrow males) and enhancing disease resistance currently constitute the main goals of genetic breeding programs in this species.

The necessity of understanding the immune response to pathogens of industrial relevance and to identify genes involved in the sex differentiation pathway led us to increase genomic resources in turbot. As a consequence thereof, an Expressed Sequence Tag (EST) database from cDNA libraries of the main immune tissues was constructed using Sanger sequencing [9]. Recently, this database has been amplified with two 454 FLX runs [10,11] (454-Life Sciences, Brandford, CT, USA; for 454-technique methodology see [12,13]). Next Generation Sequencing (NGS) technologies offer the ability to produce an enormous volume of data with a very low sequencing cost per base [12]. Thus, this turbot EST database is currently composed of ~70,000 unique sequences (~20,000 contigs and ~50,000 singletons). ESTs are essential to ascertain the gene [14,15], but also to identify polymorphic gene-associated markers, such as microsatellites and single nucleotide polymorphisms (SNPs) (type I markers; [9,1618]). Type I markers are very useful for constructing genetic or physical maps, and for comparative mapping [7,19,20].

SNPs have several advantages over other markers when it comes to mapping genes or inferring population structure [21]. They can be easily evaluated in silico off public databases and their genotypes quickly assessed by mini-sequencing reactions [9,22] or by high-throughput technologies [23,24]. SNP alleles are almost exclusively identical-by-descent (IBD) and thus they prevent scoring errors associated to homoplasy [25]. They are extremely stable, due to low mutation rates [26], and occur more often in the genome than other markers. In the human genome, for instance, there is on average 1 SNP per 300 bp [27], and their frequency in non-model species has been estimated at ~1 in 200–500 bases for non-coding DNA and ~1 in 500–1000 bases for coding DNA [28]. In turbot, Vera et al.[29] estimated 1 true SNP every ~100 bp from the EST database composed only of Sanger sequences, suggesting the existence of large SNP resources in this species. During the last decade, SNP discovery pipelines have been developed for aquaculture species including fish [18,3035], shellfish [3638] and crustaceans [39,40]. In turbot, a SNP calling tool was included in the turbot database [9] and it has been refined in the updated version [11]. In this study, we screened genomic resources available in an updated version of the turbot EST database using contigs containing NGS 454-sequences to identify and characterize SNPs associated to immune- and reproduction-related genes. These markers will be used for further structural genomic analysis focused on quantitative trait loci (QTLs) linked to productive traits, as well as for population screening to look for adaptive variation in wild and domestic turbot.

2. Results and Discussion

2.1. Database Exploitation and SNP Detection

The main characteristics of the turbot 454-transcriptome sequencing runs have been described in previous studies [10,11]. The used database (version 4.0 September 2011) was constituted by 71,033 unique sequences, 18,880 contigs and 52,153 singletons including 454-sequences and Sanger sequences [9] with a total length of 52,402,177 base pairs (bp, ~52 Mb). However, in order to avoid duplicates with the previous SNPs developed from sequences obtained with Sanger methodology [29], and since we were mainly interested in validating SNPs at new immune- and reproduction-related genes, only contigs composed exclusively of at least four 454-sequences were used for SNP detection. Thus, 140 contigs from the turbot database, which met these requirements, were taken into account for the SNP development. The total length analyzed was 262,093 bp and contig length ranged from 728 bp to 4885 bp, with a mean length value of 1872.09 ± 746.69 bp. The total number of true SNPs detected using the program QualitySNP (for true SNP definition see the experimental section) was 866, SNP number per contig ranged from 1 to 58, with a mean value of 6.18 ± 8.34. Thus, the expected frequency of SNP appearance in the analyzed sequences would be 1 SNP every 302 bp. This value is lower than that previously reported in S. maximus (1 SNP each ~100 bp; [29]), but similar to those described in non-model species [28]. The success of any genotyping method is reflected in what is referred to as the conversion rate and the global success rate. The former only considers the polymorphic markers, whereas the latter considers all the markers (monomorphic and polymorphic) that were successfully typed within the analyzed samples [41]. Of the 140 true SNPs tested, 27 (19.3%) could not be genotyped, and thus they were considered to be genotyping failures due to technical and/or genotyping problems. Only 2 out of the 113 feasible SNPs (see definition in the experimental section) were monomorphic. Therefore, the global success rate and conversion rate were 80.7% and 79.3%, respectively. Global success rate was very similar to that previously described in the species (78.4%), but conversion rate was much higher than previously reported using sequences from cDNA libraries (37.7%; see Vera et al.[29]), likely due to the different library construction methods and bioinformatic pipeline approaches followed in 454 and Sanger contigs (see experimental section).

2.2. SNP Performance

A total of 65 transitions (A/G and C/T) and 48 transversions (A/C, A/T, C/G and G/T) were detected among feasible SNPs, A/G being the most common (35) and A/C the least common (6) substitutions observed (Figure 1). This represented a transition/transversion (ts/tv) ratio of 1.354. This ratio was lower than that observed by Vera et al.[29] (1.885) and in silico (1.456) by Pardo et al.[9], but it was very similar to that described for common carp (Cyprinus carpio) (1.310) [42] and gilthead seabream (Sparus aurata) (1.375) [31]. Also, the most frequent transitions and transversions differed from previous reports: C/T and G/T, respectively [29], and A/G and A/C [9]. These discrepancies could be due to the opposite sequencing directions, as all sequences by Vera et al.[29] and Pardo et al.[9] were obtained from the 3′ end using cDNA libraries, while those from the 454-run were randomly obtained by fragmentation of the whole cDNA according to the cDNA rapid library preparation method (Roche Farma, S. A. [43]). Moreover, the longer coding region portion analyzed in 454-runs regarding Sanger sequencing in our study may determine differences because of the different selective constraints of UTR regarding coding regions. No differences were detected among distribution of the variants between tested SNPs and feasible SNPs (χ2 = 0.3115; p = 0.9974). All polymorphic SNP loci showed two alleles and all of them agreed with those expected from the database information.

2.3. SNP Diversity

Only two loci among the 113 feasible SNPs were monomorphic (SmaSNP_287 and SmaSNP_334). Among polymorphic SNPs, unbiased gene diversity (He) estimates ranged from 0.060 at SmaSNP_237, SmaSNP_245 and SmaSNP_305 to 0.510 at SmaSNP_225 with a mean value of 0.344 ± 0.149. The minimum allele frequency (MAF) in the polymorphic markers ranged from 0.030 (SmaSNP_237, SmaSNP_245 and SmaSNP_305) to 0.500 in SmaSNP_249 with a mean value of 0.259 ± 0.140. Departures from Hardy-Weinberg equilibrium (HWE) were detected in five markers (SmaSNP_253, SmaSNP_271, SmaSNP279, SmaSNP_289, SmaSNP_326; Table 1), although all markers were at equilibrium after Bonferroni correction (p = 0.0004). The samples from the Cantabrian turbot population were globally in accordance with HWE expectations when tested simultaneously for all loci (p = 0.9999). These polymorphic values were in the range to those previously described in the species [29], and they were also similar to those reported in other fish species [42,44]. No Linkage disequilibrium (LD) was detected among the 6328 loci pairs after Bonferroni correction (p = 0.0004).

2.4. SNP Position within Genes: Synonymous vs. Non-Synonymous Substitutions

Consensus sequences of contigs containing polymorphic SNPs were compared using NCBI BLAST with public databases, namely UniRef90, NCBI’s nr, KEGG, COG, PFAM, LSU and SSU. The subsequent BLAST output was then parsed with Auto FACT [45]. All contigs containing feasible SNPs were annotated (except SmaSNP_320, Table 1). The informative strand, reading frame, and stop codon at each contig were recorded using homology with the highest homologous annotated sequence in public databases. Nine feasible SNPs (8.0%) could not be positioned, because no consistent reading frames were detected (indicated as “unknown” location on Table 2). Fifty-five SNPs (48.7%) were located in untranslated regions (UTR), either in the 5′ UTR (17, 15.0%) or 3′ UTR (38, 33.6%), which is in accordance with the approximately double length of 3′ compared to 5′ UTR [9]. On the other hand, 49 SNPs (43.4%) were localized in the coding region (Table 2), a percentage of SNPs higher than previously reported in the species (24.7%, [29]) and in other aquaculture fish species (e.g., Atlantic salmon 24%, [32]; Atlantic cod 17.4%, [34]). All these studies followed a 3′ UTR Sanger sequencing strategy, and therefore the coding region was less represented than in the case of the 454 Roche runs after a cDNA rapid library preparation protocol, which accounts for the differences observed. This result shows the utility of the NGS methodologies for SNP detection in the coding region. Thirty-three (29.2%) of these 49 SNPs were synonymous, and 16 (14.2%) were non-synonymous. On the other hand, the relationship between synonymous vs. non-synonymous changes (2:1) was lower than in other species [46,47]. Evolutionary constraints should preferentially eliminate non-synonymous variation because it is usually associated with deleterious mutations [35].

Non-synonymous SNPs in coding regions represent alternative allelic variants of a gene, which can determine functional changes in the corresponding proteins and lead to phenotypic changes. Among these genes there can be found a retinol dehydrogenase (SmaSNP_264), three zona pellucida proteins (SmaSNP_212, SmaSNP_217, SmaSNP_282) related to reproduction processes, and a lipocalin (SmaSNP_325) involved in tear secretion (Table 2).

In the present study, we used sequences obtained from two transcriptome 454-pyrosequencing runs, one related to immune system [10] and another one from the hypothalamic pituitary-gonad axis [11]. Thus, GO terms were mainly related to immune response and reproduction processes (Table 2). The non-synonymous variation was associated with genes involving either immune response or sex differentiation processes. A large number of SNP linked to annotated genes were identified and validated. This set of markers are being used for population genomic studies and turbot genetic map enrichment, both approaches providing useful information for evolutionary and turbot industry applied studies.

3. Experimental Section

3.1. EST Database, SNP Detection and Annotation

Sequences were obtained from two transcriptome 454-pyrosequencing runs of turbot cDNA libraries, one belonging to the immune transcriptome [10] and another one from the hypothalamic pituitary-gonad axis [11]. A brief description of both runs is shown in Table 3. All the 454-reads were assembled with MIRA [48], and they make up the 454-sequences incorporated into the turbot database. In order to create contigs and detect SNPs, these 454-sequences were assembled alongside Sanger sequences available [9] in the database with CAP3 [49] using default parameters. This is a common strategy when dealing with hybrid Sanger-454 assemblies [50]. The resulting ACE format assembly file was fed into QualitySNP [51] in conformity with the bioinformatic pipeline described by Vera et al.[29]. Briefly, QualitySNP uses three filters for the identification of reliable SNPs: Filter 1 screens for all potential SNPs with the requirement that every allele is represented in more than one sequence (each contig has to have at least a depth of 4 sequences); filter 2 uses a haplotype-based strategy to detect reliable SNPs after reconstructing confident haplotypes with an algorithm that minimizes false haplotypes due to the occurrence of sequencing errors; and filter three screen SNPs by calculating a confidence score based on sequence redundancy and quality (only sequences with PHRED >20 were used). SNPs that pass filters 1 and 2 are called real SNPs and those passing all filters are called true SNPs [51]. Resulting files were processed with our own custom Perl programs to extract relevant information. The obtained data were imported into a mySQL server [52]. A user-friendly web access interface was designed so that contig graphs are clickable and the output visually refined with color-coded nucleotide views [53]. A graphical interface allowing for SNP database search by alleles, contig depth, and annotation was set up. EST annotation of these contigs was performed using BLASTx, which searches proteins using a translated nucleotide query [54]. Only E-values lower than 10−5 were considered for gene annotation (Table 1, Table S1).

3.2. SNP Genotyping and Validation

DNA of all individuals analyzed was extracted from a piece of caudal fin using standard phenol-chloroform procedures [55].

SNPs identified were validated and genotyped with the MassARRAY platform (Sequenom, San Diego, CA, USA) following the protocols and recommendations provided by the manufacturer. Briefly, the technique consists of an initial locus-specific polymerase chain reaction (PCR), followed by single-base extension using mass-modified dideoxynucleotide terminators of an oligonucleotide primer that anneals immediately upstream of the polymorphic site (SNP) of interest (see [56,57] for more technical information). The distinct mass of the extended primer identifies the SNP allele. Primer sequences, SNP position, expected variants and annotation for the 140 tested SNPs are shown on Supplementary Table 1. MALDI-TOF mass spectrometry analysis in an Autoflex spectrometer was used for allele scoring.

Assays were designed for 140 true SNPs always located in different sequences and were combined in 7 multiplex reactions including 24 SNPs each except for multiplex 5 (23 SNPs), 6 (18 SNPs) and 7 (3 SNPs) (see Supplementary Table 1 for multiplex information). SNP multiplexes were designed in silico and tested on a panel of 8 turbot individuals from a wild Cantabrian (northern Spain) population. SNPs were classified based on manual inspection as “failed assays” (in case that the majority of genotypes could not be scored and/or the samples did not cluster well according to genotype), and feasible SNPs (markers with proper and reliable genotypes), these being either monomorphic or polymorphic.

3.3. Gene Diversity and Population Analysis

In order to estimate genetic diversity parameters, all SNPs were genotyped for polymorphism evaluation in a sample of 33 individuals (including the 8 individuals used for marker performance) from the wild Cantabrian population previously used.

Estimates of genetic diversity (unbiased expected heterozygosity (He) and minimum allele frequency (MAF)) were estimated using FSTAT 2.9.3 [58]. The conformance to Hardy-Weinberg (HW) and genotypic equilibria were obtained using GENEPOP 4.0 [59,60]. Conformance to HWE was checked using the complete enumeration method [61] because only two alleles were detected at each locus. Bonferroni correction was applied when multiple tests were performed [62].

3.4. Detection of Synonymous/Non-Synonymous SNPs

All the six possible reading frames of the consensus sequence of each containing SNP functionally annotated contig were obtained using ORF (Open Reading Frame) Finder application [63]. The best candidate frame (usually the longest one) was compared against the NCBI protein database using BLASTp and BLASTx, and the protein with highest E-value was downloaded and aligned with the selected frame for SNP location using Clustal W [64] implemented in BioEdit v. 7.1. [65]. This approach enabled us to locate SNPs by looking at the coding region. For those SNPs in the coding region, the resulting amino acid sequences of both variants were translated to determine whether SNP variants were synonymous or non-synonymous. Gene onthology (GO) terms were searched using QUICKGO [66] and AmiGO [67] utilities.

4. Conclusions

A total of 140 contigs (total length 262,093 bp) formed exclusively by 454-pyrosequencing reads were used to identify new putative SNPs in S. maximus. One hundred and thirteen SNPs of the 140 tested were amplified and genotyped, 111 being polymorphic in a wild Cantabrian population, showing the utility of the new NGS techniques for true SNP detection (conversion rate = 79.3%). Diversity levels at the population were similar to previous studies [29,42,44] and were in accordance with HWE expectations. An important number of these polymorphic SNPs were located in the coding region and 16 of them (14.4%) represented non-synonymous changes at genes related to immune response and gonad differentiation processes as shown by the detected GO terms. Therefore, these SNPs are valuable resources for future population genetics, high-resolution genetic maps, quantitative trait loci (QTL) identification, association studies and marker assisted selection (MAS) breeding in turbot.

Acknowledgments

We thank Susana Sánchez-Darriba and Sonia Gómez for their technical assistance. We are indebted to M. Torres for her support on Sequenom methodology. Genotyping was performed in the USC node of the Spanish National Centre of Genotyping (CeGen ISCIII). This study was supported by the Consolider Ingenio Aquagenomics (CSD200700002), the Science and Education Spanish Ministry (AGL2009-11782) and the Xunta de Galicia (09MMA011261PR) projects.

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Ijms 14 05694f1 200
Figure 1. Distribution of SNP variants analyzed in this study (a) using all SNPs tested; (b) using only feasible SNPs. Transitions (ts) and transversions (tv) are indicated in black and grey colour, respectively.

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Figure 1. Distribution of SNP variants analyzed in this study (a) using all SNPs tested; (b) using only feasible SNPs. Transitions (ts) and transversions (tv) are indicated in black and grey colour, respectively.
Ijms 14 05694f1 1024
Table 1. Annotation, variants and diversity values of the 113 technically feasible SNPs in the Cantabric turbot population (33 individuals) used in this study.

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Table 1. Annotation, variants and diversity values of the 113 technically feasible SNPs in the Cantabric turbot population (33 individuals) used in this study.
SNP NameAnnotationVariantsMAFP (HW)HeFis
SmaSNP_211Cyclin-dependent kinase 2 interacting proteinA/TA = 0.1520.13070.2620.307
SmaSNP_212Zona pellucida sperm-binding protein 3A/GA = 0.1520.41980.2650.179
SmaSNP_215Mitotic specific cyclin-B1C/TT = 0.2120.29480.338−0.255
SmaSNP_216Pre-mRNA branch site protein p14A/TT = 0.3480.70030.460−0.119
SmaSNP_217Zona pellucida protein C1A/GG = 0.2580.16160.3900.301
SmaSNP_218Mitochondrial ribosomal protein S18AG/TT = 0.3331.00000.4520.061
SmaSNP_219U3 small nucleolar ribonucleoprotein protein IMP3G/TG = 0.4091.00000.491−0.050
SmaSNP_220Coatomer subunit epsilon isoform 1C/TT = 0.1970.57500.3220.153
SmaSNP_222Signal recognition particle 14 kDa proteinG/TG = 0.2031.00000.329−0.046
SmaSNP_223Epithelial cell adhesion proteinA/TT = 0.3331.00000.4520.061
SmaSNP_224Transcription initiation factor TFIID subunit D11C/GG = 0.1821.00000.302−0.003
SmaSNP_225Acidic ribosomal protein P1A/GG = 0.4801.00000.5100.059
SmaSNP_226Alcohol dehydrogenase Class-3C/TT = 0.2880.69130.416−0.093
SmaSNP_227Thioredoxin protein 4AA/GA = 0.2421.00000.3730.025
SmaSNP_228Novel protein similar to vertebrate THAP domain containing 4 (THAP4)A/GG = 0.2120.60680.3400.109
SmaSNP_229Tumor suppressor candidate 2A/GA = 0.0311.00000.061−0.016
SmaSNP_230Optic atrophy 3 proteinC/TT = 0.2660.64770.3970.135
SmaSNP_231RNA 3′-terminal phosphate cyclaseA/CC = 0.2660.64750.3970.135
SmaSNP_232RAD1 homologA/GA = 0.4380.49210.5010.127
SmaSNP_233Ubiquitin carrier proteinG/TT = 0.4091.00000.491−0.050
SmaSNP_234chromatin accessibility complex protein 1A/GG = 0.0470.05040.0920.659
SmaSNP_235Nucleolar protein 16A/GG = 0.2580.40230.3890.144
SmaSNP_236Isopentenyl-diphosphate delta-isomerase 1C/GG = 0.1410.47630.2460.111
SmaSNP_237Ran-specific GTPase-activating proteinG/TT = 0.0301.00000.060−0.016
SmaSNP_238Forkhead box H1A/GA = 0.4531.00000.503−0.056
SmaSNP_239StathminC/TC = 0.2580.64360.387−0.174
SmaSNP_240Ubiquinol-cytochrome c reductase core I proteinC/TC = 0.1520.55210.2610.072
SmaSNP_241BolA-like protein 3C/GG = 0.3130.43710.4380.143
SmaSNP_243ce ceroid-lipofuscinosis neuronal protein 5G/TG = 0.4551.00000.5040.038
SmaSNP_244SSU rRNA; Psetta maxima (turbot)C/TC = 0.0611.00000.116−0.049
SmaSNP_245Chromobox protein homolog 3G/TT = 0.0301.00000.060−0.016
SmaSNP_246Transmembrane protein 208A/CA = 0.4690.71980.505−0.114
SmaSNP_247Ribosomal protein L18aA/CA = 0.2341.00000.3650.058
SmaSNP_248Pre-mRNA-processing factor 19C/TC = 0.3181.00000.440−0.032
SmaSNP_249Alpha-l-fucosidaseA/GA = 0.5000.72750.5090.106
SmaSNP_250Protein phosphatase 2 (Formerly 2A)A/GG = 0.4060.05980.4930.366
SmaSNP_252LON peptidase N-terminal domain and RING finger protein 1G/TT = 0.1671.00000.2820.034
SmaSNP_253IK cytokineA/GA = 0.4390.03480.497−0.402
SmaSNP_256Ribonuclease UK114C/TC = 0.2320.60380.3640.116
SmaSNP_257Inner centromere proteinA/GG = 0.3030.42390.4300.154
SmaSNP_259Beta-galactoside-binding lectinC/TC = 0.3790.72420.477−0.079
SmaSNP_260Enoyl-Coenzyme A hydrataseA/TA = 0.2730.38190.402−0.208
SmaSNP_261Sept2 proteinA/GG = 0.1971.00000.321−0.038
SmaSNP_262DNA-directed RNA polymerase I subunit RPA34A/GA = 0.0781.00000.146−0.069
SmaSNP_263Epithelial membrane protein 2A/GG = 0.3790.13360.4800.306
SmaSNP_264Retinol dehydrogenase 3C/GG = 0.4091.00000.491−0.050
SmaSNP_265WD repeat-containing protein 54A/GA = 0.0761.00000.142−0.067
SmaSNP_266tRNA pseudouridine synthase 3C/TC = 0.1360.46370.2400.115
SmaSNP_267Transmembrane protein 167 precursorG/TG = 0.2580.64630.387−0.174
SmaSNP_270Flotillin-1C/GG = 0.4380.16940.5020.253
SmaSNP_271NAD(P)H dehydrogenase quinone 1A/GG = 0.3590.04880.4710.403
SmaSNP_273Ubiquitin protein ligase E3 componentC/TC = 0.4840.73530.5080.077
SmaSNP_274K13213 matrin 3C/GG = 0.1060.29830.1930.216
SmaSNP_275Dolichol-phosphate mannosyltransferaseA/GA = 0.0910.22090.1690.281
SmaSNP_276DNA-directed RNA polymerases i II and III subunit rpabc1A/GA = 0.1970.57280.3220.153
SmaSNP_277Syndecan 2A/CA = 0.4291.00000.505−0.130
SmaSNP_278Peptide methionine sulfoxide reductaseC/GC = 0.0781.00000.146−0.069
SmaSNP_279Methyltransferase-like protein 21DG/TG = 0.4700.01290.5090.465
SmaSNP_281Phosphatidylinositol transfer protein beta isoform-like isoform 2C/TT = 0.3180.43330.439−0.172
SmaSNP_282Zona pellucida protein CA/TT = 0.1211.00000.216−0.123
SmaSNP_283AP-2 complex subunit alpha-2-likeG/TG = 0.3330.26690.450−0.213
SmaSNP_284Apoptosis regulator BAXA/GG = 0.4090.07800.4930.324
SmaSNP_285BorealinG/TT = 0.0321.00000.063−0.017
SmaSNP_286Brain protein 44C/TC = 0.3940.26910.483−0.255
SmaSNP_287Exosome component 8A/GG =1.000-0.000NA
SmaSNP_288Atrophin-1 domain containing proteinG/TT = 0.4391.00000.500−0.030
SmaSNP_289similar to connectin/titinA/TA = 0.3030.00180.4330.580
SmaSNP_290Ubiquitin carboxyl-terminal hydrolase L5C/TT = 0.4691.00000.5060.012
SmaSNP_292Histone deacetylase complex subunit SAP18C/TC = 0.1880.55680.308−0.216
SmaSNP_293Replication protein A 14 kDa subunitC/GG = 0.1821.00000.302−0.003
SmaSNP_296Carbonic anhydraseG/TG = 0.0761.00000.142−0.067
SmaSNP_297UPF0414 transmembrane proteinC/TC = 0.2120.60800.3400.109
SmaSNP_298Queuine tRNA-ribosyltransferaseC/TT = 0.0611.00000.116−0.049
SmaSNP_299NHP2-like protein 1C/GC = 0.3790.13580.4800.306
SmaSNP_304Microsomal glutathione S-transferase 3A/GA = 0.0911.00000.168−0.085
SmaSNP_305Actin related protein 2/3 complex subunit 4C/TT = 0.0301.00000.060−0.016
SmaSNP_306Cyclophilin BC/GC = 0.0611.00000.116−0.049
SmaSNP_307Dynein light chain Tctex-type 3C/GC = 0.0611.00000.116−0.049
SmaSNP_308Ependymin-1A/GA = 0.2340.31350.3660.231
SmaSNP_309C-4 methylsterol oxidaseA/GA = 0.2971.00000.4240.043
SmaSNP_310Dynein light chain LC8-typeG/TT = 0.0451.00000.088−0.032
SmaSNP_311Rho-related GTP-binding protein RhoFA/TT = 0.3940.26690.483−0.255
SmaSNP_312Golgi SNAP receptor complex member 1A/TA = 0.1880.55870.308−0.216
SmaSNP_314Ribosomal L1 domain-containing protein 1A/GA = 0.2031.00000.329−0.046
SmaSNP_315N-alpha-acetyltransferase 50A/TA = 0.2421.00000.3730.025
SmaSNP_316Oncogene DJ-1 isoform 1C/TC = 0.4531.00000.503−0.056
SmaSNP_317Wu:fj40d12 protein n = 7 Tax = Euteleostomi RepID = A3KP21_DANREA/GA = 0.4381.00000.5000.000
SmaSNP_318Mucin multi-domain proteinC/GC = 0.1670.56170.281−0.185
SmaSNP_319Adenosine kinaseA/GA = 0.1820.55750.301−0.208
SmaSNP_320No homology foundA/GA = 0.3940.49010.4860.127
SmaSNP_321Zymogen granule membrane protein 16A/GG = 0.3331.00000.451−0.076
SmaSNP_3226-Pyruvoyl tetrahydrobiopterin synthaseC/TC = 0.0311.00000.061−0.016
SmaSNP_323Proteasome subunit betaC/TT = 0.1251.00000.222−0.127
SmaSNP_324RING finger protein 4A/GA = 0.3940.06520.4880.379
SmaSNP_325LipocalinC/GC = 0.1361.00000.239−0.143
SmaSNP_326Choline transporter-like protein 2A/GG = 0.4550.03110.5070.402
SmaSNP_328RNA-binding proteins (RRM domain)C/TC = 0.1061.00000.192−0.103
SmaSNP_329Type II keratinC/GG = 0.0611.00000.116−0.049
SmaSNP_330Novel protein similar to vertebrate thyroid hormone receptor interactor 12 (TRIP12)C/TT = 0.0941.00000.172−0.088
SmaSNP_332Ribosomal protein S6 kinaseA/CA = 0.4700.72870.5070.103
SmaSNP_333Transmembrane 6 superfamily member 2A/TT = 0.2880.07960.4190.348
SmaSNP_334PREDICTED: hypothetical protein LOC100712283 [Oreochromis niloticus]C/TT =1.000-0.000NA
SmaSNP_3371-Alkyl-2-acetylglycerophosphocholine esteraseC/TC = 0.2341.00000.3650.058
SmaSNP_338CD151 antigenC/TT = 0.2660.39090.395−0.186
SmaSNP_339Arsenite methyltransferase 1A/TA = 0.3131.00000.436−0.002
SmaSNP_340Receptor expression-enhancing protein 5C/TT = 0.2340.65070.364−0.116
SmaSNP_341Cathepsin SC/GG = 0.3330.11190.4540.332
SmaSNP_342Trans-1,2-dihydrobenzene-1,2-diol dehydrogenaseA/GA = 0.4240.28180.494−0.226
SmaSNP_343High mobility group protein 2G/TG = 0.4700.29800.5080.224
SmaSNP_346ATP-binding cassette, sub-family A (ABC1)C/TT = 0.2881.00000.4170.055
SmaSNP_347Myomesin 1a (skelemin)C/TT = 0.0911.00000.168−0.085
SmaSNP_348Retinoic acid receptor responder protein 3A/GG = 0.4391.00000.500−0.030
SmaSNP_349Nucleophosmin 1A/CA = 0.2580.64660.387−0.174
Table 2. Predicted position, SNP location within genes and their correspondent synonymous vs. non-synonymous variants of the 113 technically feasible SNPs.

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Table 2. Predicted position, SNP location within genes and their correspondent synonymous vs. non-synonymous variants of the 113 technically feasible SNPs.
SNP NameSNP location/effectGO term
SmaSNP_2113′ UTRphosphorylation (GO:0016310)
SmaSNP_212Non synonymousreproduction (GO:0000003)
SmaSNP_215Synonymousmitotic cell cycle (GO:0000278)
SmaSNP_2163′ UTRprotein localization to cell division site (GO:0072741)
SmaSNP_217Non synonymousbinding of sperm to zona pellucida ( GO:0007339)
SmaSNP_218Non synonymousprotein import into mitochondrial matrix (GO:0030150)
SmaSNP_2193′ UTRribonucleoprotein complex biogenesis (GO:0022613)
SmaSNP_220Synonymousribosomal large subunit assembly (GO:0000027)
SmaSNP_2225′ UTRregulation of peptidoglycan recognition protein signaling pathway (GO:0061058)
SmaSNP_223Synonymouscell adhesion (GO:0007155)
SmaSNP_224SynonymousDNA-dependent transcription, initiation (GO:0006352)
SmaSNP_2253′ UTRribosomal large subunit assembly (GO:0000027)
SmaSNP_226Synonymouscellular alcohol metabolic process (GO:0044107)
SmaSNP_227Synonymousthioredoxin biosynthetic process (GO:0042964)
SmaSNP_2285′ UTRregulation of nucleotide-binding oligomerization domain containing signaling pathway (GO:0070424 )
SmaSNP_2293′ UTRimmune response to tumor cell (GO:0002418)
SmaSNP_2303′ UTRreproduction (GO:0000003)
SmaSNP_231Non synonymousphosphorylation of RNA polymerase II C-terminal domain (GO:0070816)
SmaSNP_232Synonymousresolution of meiotic recombination intermediates (GO:0000712)
SmaSNP_233Synonymousubiquitin-dependent protein catabolic process (GO:0006511)
SmaSNP_2343′ UTRregulation of macrophage inflammatory protein 1 alpha production (GO:0071640)
SmaSNP_235Synonymousprotein localization to nucleolar rDNA repeats (GO:0034503)
SmaSNP_236SynonymousT-helper 1 cell activation (GO:0035711)
SmaSNP_2375′ UTRtermination of G-protein coupled receptor signaling pathway (GO:0038032)
SmaSNP_238Non synonymoustranscription initiation from RNA polymerase III type 2 promoter (GO:0001023)
SmaSNP_2395′ UTRNot found
SmaSNP_240SynonymousMHC class I protein complex assembly (GO:0002397)
SmaSNP_2413′ UTRreproduction (GO:0000003)
SmaSNP_243Non synonymousneuronal stem cell maintenance (GO:0097150)
SmaSNP_244UnknownNot found
SmaSNP_2455′ UTRreproduction (GO:0000003)
SmaSNP_246Synonymousintracellular protein transmembrane transport (GO:0065002)
SmaSNP_247Non synonymousribosomal protein import into nucleus (GO:0006610)
SmaSNP_2483′ UTRregulation of mitotic recombination (0000019)
SmaSNP_2493′ UTRalpha-l-fucosidase activity (GO:0004560)
SmaSNP_2503′ UTRmodulation by virus of host protein serine/threonine phosphatase activity (GO:0039517)
SmaSNP_2523′ UTRregulation of macrophage inflammatory protein 1 alpha production (GO:0071640)
SmaSNP_253Synonymousregulation of cytokinesis (GO:0032465)
SmaSNP_256Synonymousregulation of ribonuclease activity (GO:0060700)
SmaSNP_257Synonymouscentromere complex assembly (GO:0034508)
SmaSNP_2593′ UTRcomplement activation, lectin pathway (GO:0001867)
SmaSNP_2603′ UTRamitosis (GO:0051337)
SmaSNP_2613′ UTRprotein processing (GO:0016485)
SmaSNP_262SynonymousRNA polymerase I transcriptional preinitiation complex assembly (GO:0001188)
SmaSNP_263Synonymousmembrane protein proteolysis (GO:0033619)
SmaSNP_264Non synonymousreproduction (GO:0000003)
SmaSNP_2655′ UTRribosomal subunit export from nucleus (GO:0000054)
SmaSNP_266Synonymousreproduction (GO:0000003)
SmaSNP_2673′ UTRsmoothened signaling pathway involved in regulation of cerebellar granule cell precursor cell proliferation (GO:0021938)
SmaSNP_2703′ UTRflotillin complex (GO:0016600)
SmaSNP_271SynonymousNAD(P)H dehydrogenase complex assembly (GO:0010275)
SmaSNP_2733′ UTRregulation of ubiquitin-protein ligase activity involved in mitotic cell cycle (GO:0051439)
SmaSNP_274Unknownreproduction (GO:0000003)
SmaSNP_275Synonymousdolichyl-phosphate beta-d-mannosyltransferase activity (GO:0004582)
SmaSNP_276Synonymoustranscription from RNA polymerase III type 2 promoter (GO:0001009)
SmaSNP_2773′ UTRT-helper 2 cell activation (GO:0035712)
SmaSNP_2783′ UTRcellular response to methionine (GO:0061431)
SmaSNP_279Non synonymousprotein import (GO:0017038)
SmaSNP_2815′ UTRregulation of beta 2 integrin biosynthetic process (GO:0045115)
SmaSNP_282Non synonymousregulation of binding of sperm to zona pellucida (GO:2000359)
SmaSNP_2833′ UTRcellular macromolecular complex subunit organization (GO:0034621)
SmaSNP_284Non synonymousregulation of apoptotic process (GO:0042981)
SmaSNP_285Synonymouschromosome passenger complex localization to kinetochore (GO:0072356)
SmaSNP_2865′ UTRbrain development (GO:0007420)
SmaSNP_287Non synonymousextracellular vesicular exosome assembly (GO:0071971)
SmaSNP_288UnknownNot found
SmaSNP_289UnknownNot found
SmaSNP_290Synonymousregulation of ubiquitin-specific protease activity (GO:2000152)
SmaSNP_2923′ UTRsuppression by virus of host TAP complex (GO:0039589)
SmaSNP_2933′ UTRDNA replication preinitiation complex assembly (GO:0071163)
SmaSNP_296Synonymouscarbon utilization (GO:0015976)
SmaSNP_2973′ UTRmembrane protein proteolysis (GO:0033619)
SmaSNP_298Non synonymousqueuine tRNA-ribosyltransferase activity (GO:0008479)
SmaSNP_299SynonymousNot found
SmaSNP_304Synonymousreproduction (GO:0000003)
SmaSNP_3053′ UTRprotein-DNA complex subunit organization (GO:0071824)
SmaSNP_3063′ UTRbehavioral response to stimulus (GO:0007610)
SmaSNP_307Synonymousreproduction (GO:0000003)
SmaSNP_308Non synonymousNot found
SmaSNP_309Synonymoustestosterone secretion (GO:0035936)
SmaSNP_3103′ UTRdynein-driven meiotic oscillatory nuclear movement (GO:0030989)
SmaSNP_3113′ UTRsuppression by virus of host tapasin activity (GO:0039591)
SmaSNP_3125′ UTRNot found
SmaSNP_314Synonymousregulation of macrophage inflammatory protein 1 alpha production (GO:0071640)
SmaSNP_3153′ UTRmenopause (GO:0042697)
SmaSNP_3163′ UTRT-helper 1 cell activation (GO:0035711)
SmaSNP_3175′ UTRNot found
SNP NameSNP location/effectGO term
SmaSNP_318UnknownNot found
SmaSNP_3195′ UTRphosphorylation (GO:0016310)
SmaSNP_320UnknownNot found
SmaSNP_3213′ UTRGolgi to plasma membrane protein transport (GO:0043001)
SmaSNP_3223′ UTRregulation of ATP citrate synthase activity (GO:2000983)
SmaSNP_3233′ UTRregulation of G-protein beta subunit-mediated signal transduction in response to host (GO:0075162)
SmaSNP_324Non synonymouscytokinesis, actomyosin contractile ring assembly (GO:0000915)
SmaSNP_325Non synonymoustear secretion (GO:0070075)
SmaSNP_3265′ UTRNot found
SmaSNP_328UnknownNot found
SmaSNP_3295′ UTRregulation of type II hypersensitivity (GO:0002892)
SmaSNP_330UnknownNot found
SmaSNP_3325′ UTRphosphorylation (GO:0016310)
SmaSNP_3335′ UTRNot found
SmaSNP_3345′ UTRNot found
SmaSNP_3373′ UTRjuvenile-hormone esterase activity (GO:0004453)
SmaSNP_3383′ UTRinflammatory response to antigenic stimulus (GO:0002437)
SmaSNP_339SynonymousT-helper 1 cell activation (GO:0035711)
SmaSNP_340Synonymousregulation of G-protein coupled receptor protein signaling pathway (GO:0008277)
SmaSNP_341Synonymoussperm entry (GO:0035037)
SmaSNP_342UnknownNot found
SmaSNP_343Synonymouscollagen metabolic process (GO:0032963)
SmaSNP_3463′ UTRchromatin silencing at silent mating-type cassette (GO:0030466)
SmaSNP_3473′ UTRnucleoside oxidase activity (GO:0033715)
SmaSNP_3485′ UTRretinoic acid receptor signaling pathway (GO:0048384)
SmaSNP_3493′ UTRT-helper 1 cell activation (GO:0035711)
Table 3. Description of two transcriptome 454-pyrosequencing runs of turbot.

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Table 3. Description of two transcriptome 454-pyrosequencing runs of turbot.
Inmune 1Hypothalamic pituitary-gonad axis 2
Samples
Number of individuals5230
OriginCommercial fish farmCommercial fish farm
Data
Number of reads915,7821,191,866
Total megabases (Mb)291.04341.20
Average read length317.8286.0
Assembly
Number of contigs55,50465,472
Mean length (bp)671.3625.9
Average contig coverage4.44.6

1From Pereiro et al.[10];2From Rivas et al.[11].

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