Accepted Article

Received Date : 07-Dec-2012 Revised Date

: 09-May-2014

Accepted Date : 09-May-2014 Article type

: Resource Article

Discovery and validation of genic single nucleotide polymorphisms in the Pacific oyster Crassostrea gigas

Jiafeng Wang1, 2 #, Haigang Qi1 #, Li Li1*, Huayong Que1, Di Wang1, Guofan Zhang1* 1

National & Local Joint Engineering Laboratory of Ecological Mariculture，Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China. 2

Affiliated Hospital of Guangdong Medical College, Guangdong 524001, China.

Keywords: Crassostrea gigas, transcriptome, Illumina sequencing, genic SNPs, high-resolution melting

# Equal contribution * Correspondence: Guofan Zhang & Li Li Fax: +86 0532 8289 8701 E-mail: [email protected]; [email protected]

Running title: SNP development of Crassostrea gigas

This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1111/1755-0998.12278 This article is protected by copyright. All rights reserved.

Accepted Article

Discovery and validation of genic single nucleotide polymorphisms in the Pacific oyster Crassostrea gigas Jiafeng Wang1, 2 #, Haigang Qi1 #, Li Li1*, Huayong Que1, Di Wang1, Guofan Zhang1* 1

National & Local Joint Engineering Laboratory of Ecological Mariculture，Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China. 2

Affiliated Hospital of Guangdong Medical College, Guangdong 524001, China.

Abstract The economic and ecological importance of the oyster necessitates further research on the molecular mechanisms that both regulate the commercially important traits of the oyster and help it to survive in the variable marine environment. Single nucleotide polymorphisms (SNPs) have been widely used to assess genetic variation and identify genes underlying target traits. In addition, high-resolution melting (HRM) analysis is a potentially powerful method for validating candidate SNPs. In this study, we adopted a rapid and efficient pipeline for the screening and validation of SNPs in the genic region of Crassostrea gigas based on transcriptome sequencing and HRM analysis. Transcriptomes of three wild oyster populations were sequenced using Illumina sequencing technology. In total, 50–60 million short reads, corresponding to 4.5–5.4 Gbp, from each population were aligned to the oyster genome, and 5.8 ×105 SNPs were putatively identified, resulting in a predicted SNP every 47 nucleotides on average. The putative SNPs were unevenly distributed in the genome and high-density (≥ 2%), non-synonymous coding SNPs were enriched in genes related to apoptosis and responses to biotic stimuli. Subsequently, 1,671 loci were detected by HRM analysis, accounting for 64.7% of the total selected candidate primers, and finally, 1,301 polymorphic SNP markers were developed based on HRM analysis. All of the validated SNPs were distributed into 897 genes and located in 672 scaffolds, and 275 of these genes were stress inducible under unfavorable salinity, temperature, and exposure to air and heavy metals. The validated SNPs in this study provide valuable molecular markers for genetic mapping and characterization of important traits in oysters. Keywords: Crassostrea gigas, transcriptome, Illumina sequencing, genic SNPs, high-resolution melting

Introduction Oysters are marine bivalves (Bivalvia, Mollusca) that are distributed worldwide and play important roles in coastal and estuarine ecological systems. They are also important for fisheries and aquaculture. The worldwide production of oysters is approximately 4.7 million tons/year, which is the highest production of all of the aquaculture marine animals (FAO, 2011). The Pacific oyster (Crassostrea gigas), which primarily originates from northeastern Asia, is a commercially important species and has been introduced to many countries all over the world because of its acclimation ability, rapid growth and high production. The Pacific oyster is now one of the most important economic bivalves produced throughout the world.

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Despite considerable progress in the oyster industry over the past few decades, oysters are still at an early stage of domestication (Hubert et al. 2004). The sustainable development of the oyster industry calls for the production of high-quality oysters with rapid growth and high disease- and stress-resistance. Genetic and genomic studies in the oyster industry have mainly focused on growth rate and yield (Rico-Villa et al. 2009; Meyer et al. 2010; Alunno et al. 2011; Guo et al. 2012; Kong et al. 2013; Olivier et al. 2013), summer mortality (Cotter et al. 2010; Dégremont et al. 2010; Fleury et al. 2010; Sauvage et al. 2010; Fleury et al. 2012), germplasm diversity (Plough et al. 2011; Miller et al. 2012), and viral infection (Pepin et al. 2010; Sauvage et al. 2010; William et al. 2011; Roque et al. 2012; Dégremont et al. 2013). Genetic mapping of important traits, gene characterization and molecular breeding require genetic markers (Aavey, et al, 2011). In the past few years, several studies have discovered traditional DNA markers for C. gigas (Sekino et al. 2003; Li et al. 2003; Yu et al. 2007; Yu et al. 2008; Bai et al. 2009; Qi et al. 2009; Sauvage et al. 2009; Yu et al. 2010; Li et al. 2011; Zhong et al. 2013), and linkage maps with different purposes have been constructed (Hubert et al. 2004; Li et al. 2004; Sauvage et al. 2010; Plough et al. 2011; Guo et al. 2012). Additionally, the whole genome sequencing of C. gigas was completed, deepening our understanding of the mechanisms of stress adaptation and shell formation of oysters (Zhang et al. 2012). The fine mapping of important traits is rare, mainly because of the lack of high-resolution genetic maps and association studies of gene functions. Therefore, more novel and useful DNA markers are needed to provide useful tools for estimating gene diversity, mapping quantitative traits and facilitating oyster molecular breeding (Beuzen et al. 2000; Liu et al. 2004). Single nucleotide polymorphisms (SNPs) are widespread nucleotide variations among individuals of a population and constitute the most abundant type of molecular marker in the genomes of plants and animals. Their high abundance, co-dominant mode of inheritance and ease of high-throughput detection have resulted in widespread use of SNPs in biological research (Brookes et al. 1999; Park et al. 2009; Level et al. 2011; Brandon et al. 2012). As next-generation high-throughput sequencing technologies have become more accessible (Feldmeyer et al. 2011; Davey et al. 2011; Gavery et al. 2012), it is now feasible to obtain genome and transcriptome sequences faster and at a lower cost (Pareek et al. 2011). In addition to assessing mRNA expression levels, transcriptome sequencing, including RNA-seq data, can also be used to discover SNPs (Novaes et al. 2008; Trick et al. 2009; Nielsen et al. 2010; Roberts et al. 2012; Helyar et al. 2012). The primary challenge in mining SNPs from transcript sequences is the assembly of millions of short reads (Feldmeyer et al. 2011; Yang et al. 2011); this difficulty can be addressed by reference-assisted transcriptome assembly and the improvement of de novo transcriptome assemblers or analysis workflows (Feldmeyer et al. 2011; Helyar et al. 2012; Roberts et al. 2012). The complete sequencing of the Pacific oyster genome (Zhang et al. 2012) has introduced a new research paradigm for C. gigas and its closest congeners, and will provide the basis for marker development based on transcriptome sequencing or resequencing. A high density of SNPs has been found in expressed sequence tags of C. gigas (Curole et al. 2005; Sauvage et al. 2007a, b), whereas no large-scale exploitation or evaluation of gene-targeted SNPs has been reported. Various methods are used for SNP genotyping. High-resolution melting (HRM) is a closed-tube method for mutation screening and genotyping that does not require fluorescently labeled probes. In contrast to conventional PCR product melting analysis, HRM characterizes saturating-dye-stained DNA samples based on their melting behavior, allowing even single-base changes to be readily

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identified (Ye et al. 2010). HRM-based SNP genotyping methods, including small-amplicon HRM (Smith et al. 2010; Ujino-Ihara et al. 2010) and unlabeled-probe HRM (Zhou et al. 2004; Erali et al. 2008; Jiang et al. 2011; Li et al. 2013; Zhong et al. 2013), have been widely used for SNP genotyping. In this study, transcriptome sequencing was conducted in three wild Pacific oyster populations of China, and the short read data were mapped to the reference oyster genome (Zhang et al. 2012). Genic SNPs were predicted, and more than 1,000 robust SNP markers were obtained using an improved small-amplicon HRM platform, which could be potentially useful for genetic, genomic and ecologic studies of the oyster. Materials and Methods Animal materials collection Wild oysters were collected from natural populations on the coasts of Qingdao (QD), Qinhuangdao (QH) and Dalian (DL), China. Twenty oysters were randomly selected from each site (G1 in Table 1), and the organs, including the gill, mantle and adductor muscle, of each oyster were excised, mixed and stored in liquid nitrogen for future RNA extraction. Another eight oysters, including three from QD, two from QH and three from DL (G2 in Table 1), were used to validate all of the selected SNPs. In total, 48 additional wild oysters from QD (G3 in Table 1) were used to characterize 40 SNPs that were initially validated with the eight oysters. The adductor muscle of each oyster was excised and stored in 75% ethanol for genomic DNA extraction. RNA extraction and transcriptome sequencing The frozen tissues (G1 in Table 1) were triturated, and total RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. DNA contaminants were removed by incubation with RQ1 RNase-Free DNase (Promega, Madison, WI, USA). Equal amounts of total RNA from 20 oysters within each population (G1 in Table 1) were mixed, and poly-A RNA was purified using an OligoTex mRNA mini kit (Qiagen, Hilden, Germany). After fragmentation into small pieces by ultrasound waves, the purified mRNA was used for first-strand cDNA synthesis using random hexamers and Superscript II reverse transcriptase (Invitrogen). The second strand was synthesized using Escherichia coli DNA PolI (Invitrogen), and the double-stranded cDNA was purified using a Qiaquick PCR purification kit (Qiagen). After end repair and addition of a 3’ dA overhang, the cDNA was ligated to the Illumina PE adapter oligo mix, and cDNA bands in the 200 ± 20 bp range were selected by agarose gel electrophoresis. After purification of the cDNA templates, 15 PCR cycles were performed to enrich the cDNA using two primers that annealed to the ends of the adapters. The libraries were then sequenced by the paired-end sequencing method with read length of 90 bp using Illumina Genome Analyzer (HiSeqTM2000 Sequencing System). Read alignment and SNP calling The Pacific oyster genome sequences were first “indexed” using Bowtie software (Langmead et al. 2009) so as to accelerate subsequent reads mapping. After filtering low-quality reads with more than one 'N' or mean base-calling quality 60% of the predicted gene set. In these regions, about 4.1, 4.2 and 4.3 × 105 putative SNPs were identified in the QD, QH and DL populations, respectively, corresponding to a SNP every 56 nucleotides on average (Table 3). In the combined population, the number of putative SNPs increased to 5.8 × 105 (File S2), and the SNP density reached 2.1%. Approximately 89% of the putative SNPs were heterozygous, implying high polymorphism of the expressed genes. The 5.8 × 105 putative SNPs included 4.1 × 105 synonymous SNPs and 1.7 × 105 non-synonymous SNPs. Transition SNPs comprised most of the SNPs, including 2.0 × 105 A/G and 2.0 × 105 C/T, and 1.9 × 105 transversion SNPs involved 4.9 × 104 A/C, 4.9 × 104 G/T, 5.9 × 104 A/T and 3.1 × 104 C/G (Fig. 1). Investigation of the putative SNP density in genes derived from the combined population revealed a right-skewed curve (Fig. 2) with a large proportion of genes containing extremely high densities of SNPs, indicating a non-uniform distribution of SNPs in different genes. There were 668 genes that contained SNPs in over 5% of their coding sequence, and GO annotation demonstrated that these genes were enriched for the biological processes of translation, protein dephosphorylation, protein polymerization, cellular protein metabolism, protein metabolism, translational elongation and superoxide metabolism (Table 4). No putative SNPs were detected in 561 genes, and GO analysis indicated that genes with functions in nucleic acid binding and acid-amino acid ligase activity were significantly overrepresented. There were 1395 genes where the density of non-synonymous coding SNPs exceeded 2% of the coding sequence, which represented a >3-fold increase relative to the average SNP density of 0.6%. GO annotation analysis indicated a significant enrichment of genes involved in protein dephosphorylation, apoptosis and the response to biotic stimuli (Table 5). SNP validation by HRM analysis PCR-amplicon melting curves distinguish between heterozygotes and homozygotes according to different melting temperatures, and specific genotyping can be obtained by melting the amplicon duplexes. Homozygotes present a single peak, whereas heterozygotes present double peaks, which are usually lower than the homozygote peaks (Fig. 3). Different alleles result in different product-melting transitions based on the stability of the nucleotide base pair mismatches, allowing even single nucleotide variants to be easily recognized and genotyped. In total, 2,962 pairs of primers were designed and screened in our study, among which 2,582 pairs passed the PAGE screening process. After HRM analysis, 1,671 loci were successfully genotyped in eight samples of C. gigas, accounting for 64.7% of the total selected candidate SNPs screened by HRM. Among the 1,671 candidate loci, 1,301 (77.9%) loci were polymorphic (Fig. 4, File S3). Approximately 76.0% of the genotyped SNPs were transitions and comprised 41.0% C/T and 35.0% A/G. Transversions were much less frequent than transitions, including 11.5% A/C, 8.3% G/T, 2.9% A/T and 1.6% C/G. The 1,301 polymorphic SNPs covered 672 scaffolds over the whole genome, and these SNPs were distributed in 897 genes of the 206 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The pathways involved metabolism, stress, immunity, resistance adjustment and body composition of C. gigas. Among the 897 genes, 275 were stress-responsive genes (Zhang et al. 2012) (File S4) under the challenges of high/low salinity, high/low temperature and exposure to air and heavy metals. Notably, 70 polymorphic SNPs were located in 50 of the 88 expanded heat shock

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protein (HSP) 70 genes (Zhang et al. 2012) (File S3). PCR amplicons of ten randomly selected candidate SNPs were sequenced using the Sanger method. The genotypes of the selected SNPs obtained by HRM analysis were confirmed by Sanger sequencing, which validated the accuracy of the HRM analysis (File S5). For five of the six SNPs that could not be genotyped by HRM, unpredicted variation (including SNPs and In/Del) was found in the same amplicon. Sanger sequencing failed for the sixth locus because of poor amplification. Characterization of single nucleotide markers Among the 20 monomorphic loci, only one proved variable. Among the 20 polymorphic SNPs, two showed different melting curves in two and five of the 48 individuals because of the extra unpredicted SNPs in the same amplicon (Sanger sequencing certified, File S6). The rest of the SNPs (18) presented the same regular melting curves observed in the eight original samples (File S7). All 18 of the polymorphic loci had two alleles, and the minor allele frequency ranged from 0.022 to 0.490. The observed heterozygosities ranged from 0.044 to 0.521 and expected heterozygosities ranged from 0.043 to 0.505. No LD between loci was observed, but deviation from HWE was observed at three loci in the population after Bonferroni correction (P < 0.05) (Table 6). Discussion Using the Pacific oyster genome as a reference, we comprehensively investigated the genic SNPs in oysters by transcriptome analysis, summarized the putative SNP density and distribution of the coding region and further developed 1,301 polymorphic SNP markers based on an improved small-amplicon HRM assay. Because all of the SNPs were genic, and many of the related genes have been identified as involved in various commercially and ecologically important traits, these coding SNPs may be of great interest for their possible effects on phenotype. This study represents the first large-scale primer-based genic SNP marker development in C. gigas, which will provide valuable molecular resources for high-resolution linkage mapping, association analysis, gene verification and other genomic, genetic and ecological studies of the oyster. SNPs in the coding region of the oyster Oysters possess high sequence variation (Hedgecock et al. 2005). According to our previous estimate, 1.7% of the SNPs in the whole genome and 1.5% of the SNPs in exons were assessed in two oysters, suggesting a high density of SNPs in the oyster genome (Zhang et al. 2012). In our study, SNPs in coding regions occur on average every 47 bp in the oyster transcriptome. The estimated cSNP density was higher than the one SNP every 60 bp in coding regions reported by Sauvage (Sauvage et al. 2007a) and was also higher than the one SNP every 133 bp in ESTs reported by Zhong (Zhong et al, 2013), which may have resulted from different sample sizes of the sequence resources where SNPs were mined. The putative SNPs were distributed unevenly among different genes, with the SNP density ranging from 0 to >10%, suggesting that different constraints are placed on these genes by natural selection (Hu et al. 2002). SNP validation by improved HRM assay

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Among the numerous SNP genotyping methods, methods based on gel separation are easily affected by subtle differences in electrophoretic conditions and instrument output results. These differences make it difficult to integrate the data from different laboratories (Seeb et al. 2007). Additionally, PCR amplicon sequencing for SNP genotyping in a large-scale sample remains costly, and microarrays for SNP genotyping are not available for oysters (Fan et al. 2003; Gunderson et al. 2005). Fluorescent probe methods (Yan et al. 2010; Helyar et al. 2012) are accurate, but the labeled probes used in these methods are expensive. Methods based on PCR product melting with DNA dyes such as HRM avoid these drawbacks. DNA dyes are non-specific and can be used for any target, and PCR-product melting analysis can identify the genotypes of the amplified products if the resolution of the melting instrument is sufficiently accurate. Sanger sequencing confirmed that the failure of genotyping by HRM for some primers in some individuals mostly likely resulted from the existence of unanticipated variation not predicted from the transcriptome sequences (File S6). The amplicons in small-amplicon HRM are much shorter than those in unlabeled-probe HRM, and we further shortened the amplicon size to 40-100 bp, which effectively minimized the presence of extra SNP loci in the same amplicon. HRM analysis of small amplicons is thus more efficient than unlabeled-probe HRM analysis because of the high density of SNPs in C. gigas, although unlabeled-probe HRM has been proven to be more sensitive in other species (Liew et al. 2007). Additionally, we adopted a novel two-step HRM rather than the original close-tube assay. First, PCR amplification was employed without addition of internal controls and LC-green, and then the unblocked internal controls and LC-green were added to the PCR products, and HRM was performed. During the two-step HRM, cost-effective, unblocked controls were used after PCR rather than adding the blocked controls before PCR (Gundry et al. 2008) (File S8). Furthermore, polyacrylamide gel electrophoresis was adopted for the primer screen to reject primers without reliable amplification before HRM analysis. All of the above modifications make HRM an economic, relatively moderate-throughput SNP genotyping method. Ratio of SNP validation In our study, the overall ratio of SNP validation reached 43.9% (1,301/2,962), which was higher than the 24.0% (56/233) found in the Pacific oyster (Zhong et al. 2013) and the 11.6% (44/378) (Jiang et al. 2011) and 33.7% (101/300) (Li et al. 2013) found in the Zhikong scallop, where unlabeled-probe HRM was used. Of note, the failure rate of primer selection (12.8%, 380/2,962) decreased dramatically compared to the values of 33.9% (79/233) previously observed in the Pacific oyster (Zhong et al. 2013) and 50.5% (191/378) (Jiang et al. 2011) and 41.3% (124/300) (Li et al. 2013) previously observed in the Zhikong scallop, indicating that the higher validation rate in our study partially resulted from the great enhancement of the success rate of primer screening. One important reason for the amplification failure was the possible presence of intron-exon junctions in the expressed sequences used for primer design (Qi et al. 2010; Li et al. 2013). In addition to their use as a reference during transcriptome mapping, the oyster genome and predicted gene sets (Zhang et al. 2012) make the intron-exon junctions visible in coding sequences and help to avoid spanning or overlapping splice junctions in primer design. Additionally, the genotyping ratio of the novel two-step HRM was 50.4% (1,301/2,582), which was higher than the 36.4% (56/154) found in the Pacific oyster by unlabeled-probe HRM (Zhong et al. 2013). This ratio was similar to that in the Zhikong scallop, which ranged between 23.5% (44/187) (Jiang et al. 2011) and 57.4% (101/176) (Li et

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al. 2013) when unlabeled-probe HRM was used. These findings indicate that the two-step HRM assay used in our study was efficient for SNP validation of C. gigas. Annotation of SNP markers We performed HRM analysis for 1,671 putative genic SNPs of C. gigas, and all sequence and annotation information of these loci are summarized in Supplementary File S3. The 1,301 polymorphic SNPs covered 672 scaffolds over the whole genome and were located in 897 genes of 206 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and 616 had GO categories assigned. According to the 61 stress responsive transcriptome provided by Zhang (Zhang et al. 2012), 275 of the 897 genes were stress-responsive genes. These genes would be useful in candidate gene association studies of stress adaptation, including adaptation to salinity, temperature and exposure to air and heavy metal. Among the 275 stress-responsive genes, 189 had GO categories assigned, including oxidoreductase activity (GO: 0016491), superoxide metabolism (GO: 0006801), transporter activity (GO: 0005215), monooxygenase activity (GO: 0004497) and protein modification (GO: 0006464). HSP 70 may play an important role in the oyster’s adaptation to the stressful intertidal environment, especially in the adaptation to heat (Zhang et al. 2012). In our study, 70 polymorphic SNPs obtained from 50 HSP 70 genes were validated, and their functional diversity was determined. In addition, the SNPs of more than 60 genes that belong to the solute carrier (SLC) families were also validated. SLC superfamily genes encode membrane-bound transporters and play important roles in the balance of ions, amino acids and organic anions under stress conditions (Schlessinger et al. 2010; Marra et al. 2012; Kenkel et al. 2013). For example, sodium- and chloride-dependent transporter genes may be important for oyster osmotic adaptation. SNPs in the oyster osmotic stress-responsive genes, pyrroline-5-carboxylate reductase (P5CR) and aquaporin (Meng et al. 2013), were also found in our results. SNPs were also validated in some other stress responsive genes, which include oxidation-reduction genes, immune response genes and chemical defense genes, such as Cu-Zn SOD, CYP450, TLR and GST. Acknowledgements We thank Dr. Ximing Guo from Rutgers University for his discussions about the work. We are also grateful to Dr. Jie Meng who provided help in stress-responsive gene analysis. This research was supported by the National Basic Research Program of China (973 Program, No. 2010CB126402), the National Natural Science Foundation of China (No. 41206149), the National High Technology Research and Development Program (863 program, No. 2012AA10A405), the earmarked fund for Modern Agro-industry Technology Research System (CARS-48), and the Taishan Scholars Climb Program of Shandong.

References Albrechtsen A, Nielsen F, Nielsen R (2010) Ascertainment biases in SNP chips affect measures of population divergence. Molecular Biology and Evolution, 27(11), 2534-2547. Alunno-Bruscia M, Bourlès Y, Maurer D et al. (2011) A single bio-energetics growth and reproduction model for the oyster Crassostrea gigas in six Atlantic ecosystems. Journal of Sea Research, 66(4), 340-348.

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Bai J, Li Q, Kong LF, Li RH (2009) Characterization of 20 single nucleotide polymorphism markers in the Pacific oyster (Crassostrea gigas). Animal Genetics, 40(6), 1004-1004. Beuzen ND, Stear MJ, Chang KC (2000) Molecular markers and their use in animal breeding. Vet J, 160(1), 42-52. Brandon Gaut (2012) Arabidopsis thaliana as a model for the genetics of local adaptation. Nature genetics, 44(2), 115-116. Brookes AJ (1999) The essence of SNPs. Gene, 234(2), 177-186. Chen S, Yang P, Jiang F et al. (2010) De Novo Analysis of Transcriptome Dynamics in the Migratory Locust during the Development of Phase Traits. PLoS one, 5(12), e15633. Clark AG, Hubisz MJ, Bustamante CD et al. (2005) Ascertainment bias in studies of human genome-wide polymorphism.Genome research, 15(11), 1496-1502. Cotter E, Malham SK, O'Keeffe S et al. (2010) Summer mortality of the Pacific oyster, Crassostrea gigas, in the Irish Sea: The influence of growth, biochemistry and gametogenesis. Aquaculture, 303(1), 8-21. Curole JP, Hedgecock D (2005) High frequency of SNPs in the Pacific oyster genome. Plant & Animal Genomes XIII Conference, W026: http://www.intl-pag.org/13/abstracts/PAG13_W026.html. Davey JW, Hohenlohe PA, Etter PD et al. (2011) Genome-wide genetic marker discovery and genotyping using next-generation sequencing. Nature Reviews Genetics, 12(7), 499-510. Dégremont L, Bédier E, Boudry P (2010) Summer mortality of hatchery-produced Pacific oyster spat (Crassostrea gigas). II. Response to selection for survival and its influence on growth and yield. Aquaculture, 299(1), 21-29. Dégremont L, Guyader T, Tourbiez D et al. (2013) Is horizontal transmission of the Ostreidherpervirus OsHV-1 in Crassostrea gigas affected by unselected or selected survival status in adults to juveniles? Aquaculture, 408-409: 51-57. Dundon WG, Arzul I, Omnes E et al. (2011) Detection of Type 1 Ostreid Herpes variant (OsHV-1 μvar) with no associated mortality in French-origin Pacific cupped oyster Crassostrea gigas farmed in Italy. Aquaculture, 314(1), 49-52. Erali M, Palais R, Wittwer C (2008) SNP genotyping by unlabeled probe melting analysis. Methods MolBiol, 429, 199-206. Fan JB, Oliphant A, Shen R et al. (2003) Highly parallel SNP genotyping. Cold Spring HarbSymp Quant Biol, 68, 69-78. Feldmeyer B, Wheat CW, Krezdorn N et al. (2011) Short read Illumina data for the de novo assembly of a non-model snail species transcriptome (Radix balthica, Basommatophora, Pulmonata), and a comparison of assembler performance. BMC Genomics, 12(1), 317.

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Fleury E, Huvet A (2012) Microarray analysis highlights immune response of Pacific oysters as a determinant of resistance to summer mortality. Marine Biotechnology, 14(2), 203-217. Fleury E, Moal J, Boulo V et al. (2010) Microarray-based identification of gonad transcripts differentially expressed between lines of Pacific oyster selected to be resistant or susceptible to summer mortality. Marine Biotechnology, 12(3), 326-339. Gavery MR, Roberts SB (2012) Characterizing short read sequencing for gene discovery and RNA-Seq analysis in Crassostrea gigas. Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, 7(2), 94-99. Gunderson KL, Steemers FJ, Lee G et al. (2005) A genome-wide scalable SNP genotyping assay using microarray technology. Nat Genet, 37(5), 549-554. Gundry CN, Dobrowolski SF, Martin YR et al. (2008) Base-pair neutral homozygotes can be discriminated by calibrated high-resolution melting of small amplicons. Nucleic Acids Res, 36(10), 3401-3408. Guo X, Li Q, Wang QZ et al. (2012) Genetic mapping and QTL analysis of growth-related traits in the Pacific oyster. Marine Biotechnology, 14(2), 218-226. Hedgecock D, Gaffney PM, Goulletquer P et al. (2005) The case for sequencing the Pacific oyster genome. J Shellfish Res, 24, 429-441. Helyar SJ, Limborg MT, Bekkevold D et al. (2012) SNP discovery using next generation transcriptomic sequencing in Atlantic herring (Clupeaharengus). PloS one, 7(8), e42089. Hillman, RE (1965) The American oyster, Crassostrea virginica gmelin. Chesapeake Science, 6(3), 199-199. Hu G, Modreck B, Stensland HR et al. (2002) Efficient discovery of single-nucleotide polymorphisms in coding regions of human genes. The Pharmacogenomics Journal, 2(4), 236-242. Hubert S, Hedgecock D (2004) Linkage maps of microsatellite DNA markers for the Pacific oyster Crassostrea gigas. Genetics, 168(1), 351-362. Imai T, Mori K, Sugawara Y et al. (1969) Studies on the mass mortality of oysters in Matsushima Bay VII. Pathogenetic investigation. Jiang G, Li J, Li L et al. (2011) Development of 44 gene-based SNP markers in Zhikong scallop, Chlamys farreri. Conservation Genetics Resources, 3(4), 659-663. Kenkel CD, Meyer E, Matz MV (2013) Gene expression under chronic heat stress in populations of the mustard hill coral (Porites astreoides) from different thermal environments. Molecular ecology, 22(16), 4322-4334. Kong N, Li Q, Yu H et al. (2013) Heritability estimates for growth-related traits in the Pacific oyster (Crassostrea gigas) using a molecular pedigree. Aquaculture Research. doi: 10.1111/are.12205

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Kim WJ, Jung H, Gaffney PM (2011) Development of type I genetic markers from expressed sequence tags in highly polymorphic species. Marine Biotechnology, 13(2), 127-132. Langmead B, Trapnell C, Pop M et al. (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol, 10(3), R25. Level AF, Korte A, Cooper MD et al. (2011) A map of local adaptation in Arabidopsis thaliana. Science, 334(6052), 86-89. Liew M, Seipp M, Durtschi J (2007) Closed-Tube SNP Genotyping Without Labeled Probes - A Comparison Between Unlabeled Probe and Amplicon Melting. Am J ClinPathol, 127, 341-348. Li F, Niu B, Huang Y et al. (2012) Application of High-Resolution DNA Melting for Genotyping in Lepidopteran Non-Model Species: Ostrinia furnacalis (Crambidae). PLoS one, 7(1), e29664. Li G, Hubert S, Bucklin K et al. (2003) Characterization of 79 microsatellite DNA markers in the Pacific oyster Crassostrea gigas. Mol Ecol Notes, 3(2), 228-232. Li H, Handsaker B, Wysoker A et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078-2079. Li J, Bao Z, Li L et al. (2012) Development of 101 novel EST-derived single nucleotide polymorphism markers for Zhikong scallop (Chlamys farreri). Journal of Ocean University of China, 12(3), 403-412. Li L, Guo X (2004) AFLP-based genetic linkage maps of the pacific oyster Crassostrea gigas Thunberg. Mar Biotechnol (NY), 6(1), 26-36. Liu ZJ, Cordes JF (2004) DNA marker technologies and their applications in aquaculture genetics. Aquaculture, 238(1), 1-37. Li Q, Wang Q, Qi M et al. (2011) Development, characterization, and inheritance of 113 novel EST-SSR markers in the Pacific oyster (Crassostrea gigas). Genes & Genomics, 33(3), 313-316. Marra NJ, Eo SH, Hale MC et al. (2012) A priori and a posteriori approaches for finding genes of evolutionary interest in non-model species: Osmoregulatory genes in the kidney transcriptome of the desert rodent Dipodomys spectabilis (banner-tailed kangaroo rat). Comparative Biochemistry and Physiology Part D, 7(4), 328-339. Maurer D, Comps M, His E (1986) Caractéristiques des mortalités estivales de l'huître Crassostrea gigas dans le bassin d'Arcachon. Haliotis, (15), 309-317. Meng J, Zhu Q, Zhang L et al. (2013) Genome and Transcriptome Analyses Provide Insight into the Euryhaline Adaptation Mechanism of Crassostrea gigas. PloS one, 8(3), e58563. Meyer E, Manahan DT (2010) Gene expression profiling of genetically determined growth variation in bivalve larvae (Crassostrea gigas). The Journal of Experimental Biology, 213(5), 749-758. Meyers TR, Short S, Eaton W (1990) Summer mortalities and incidental parasitisms of cultured Pacific oysters in Alaska. Journal of Aquatic Animal Health, 2(3), 172-176.

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Miller PA, Elliott NG, Koutoulis A et al. (2012) Genetic diversity of cultured, naturalized, and native Pacific oysters, Crassostrea gigas, determined from multiplexed microsatellite markers. Journal of Shellfish Research, 31(3), 611-617. Nielsen R, Paul JS, Albrechtsen A et al. (2011) Genotype and SNP calling from next-generation sequencing data. Nature Reviews Genetics, 12(6), 443-451. Novaes E, Drost DR, Farmerie WG et al. (2008) High-throughput gene and SNP discovery in Eucalyptus grandis, an uncharacterized genome. BMC Genomics, 9, 312. Olivier D, Heinecken L, Jackson S (2013) Mussel and oyster culture in Saldanha Bay, South Africa: potential for sustainable growth, development and employment creation. Food Security, 1-17. Park YJ, Lee JK, Kim NS (2009) Simple sequence repeat polymorphisms (SSRPs) for evaluation of molecular diversity and germplasm classification of minor crops. Molecules, 14, 4546-4569. Pepin JF, Soletchnik P, Lupo C et al. (2011, September) Situation of wild beds of Crassostrea gigas regarding OsHV-1 µvar: spatiotemporal description of contamination in Pacific oyster wild beds in Marennes Oleron Bay. In 15th International European Association of Fish Pathologists conference, Split, Croatia, 12-16th September 2011. Plough LV, Hedgecock D (2011) Quantitative trait locus analysis of stage-specific inbreeding depression in the Pacific oyster Crassostrea gigas. Genetics, 189(4), 1473-1486. Qi H, Liu X, Wu F et al. (2010) Development of gene-targeted SNP markers for genomic mapping in Pacific abalone Haliotis discus hannai Ino. Molecular biology reports, 37(8), 3779-3784. Qi H, Wu Q, Li L et al. (2009) Development and characterization of microsatellite markers for the Pacific Oyster Crassostrea gigas Conservation Genet Resour, 1, 451-453. Rico-Villa B, Pouvreau S, Robert R (2009) Influence of food density and temperature on ingestion, growth and settlement of Pacific oyster larvae, Crassostrea gigas. Aquaculture, 287(3), 395-401. Roberts SB, Hauser L, Seeb LW et al. (2012) Development of genomic resources for Pacific Herring through targeted transcriptome pyrosequencing. PloS one, 7(2), e30908. Roque A, Carrasco N, Andree KB et al. (2012) First report of OsHV-1 microvar in Pacific oyster (Crassostrea gigas) cultured in Spain. Aquaculture, 324, 303-306. Sauvage C, Bierne N, Lapegue S et al. (2007a) Single Nucleotide polymorphisms and their relationship to codon usage bias in the Pacific oyster Crassostrea gigas. Gene, 406(1), 13-22. Sauvage C, Boudry P, Lapegue S (2009) Identification and characterization of 18 novel polymorphic microsatellite makers derived from expressed sequence tags in the Pacific oyster Crassostrea gigas. Molecular Ecology Resources, 9(3), 853-855. Sauvage C, Boudry P, De Koning DJ et al. (2010) QTL for resistance to summer mortality and OsHV-1 load in the Pacific oyster (Crassostrea gigas). Animal genetics, 41(4), 390-399.

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Sauvage C, Lapègue S, Boudry P (2007b) Identification of SNPs and their mapping for the identification of QTLs of resistance to summer mortality in the Pacific oyster Crassostrea gigas. Abstracts / Aquaculture, 272(S1): S238-S321 Schlessinger A, Matsson P, Shima JE et al. (2010) Comparison of human solute carriers. Protein Science, 19(3), 412-428. Seeb LW, Antonovich A, Banks MA et al. (2007) Development of a standardized DNA database for Chinook salmon. Fisheries, 32(11), 540-552. Seipp MT, Durtschi JD, Liew MA et al. (2007) Unlabeled Oligonucleotides as Internal Temperature Controls for Genotyping by Amplicon Melting. J MolDiagn, 9(3), 284-289. Sekino M, Hamaguchi M, Aranishi F et al. (2003) Development of novel microsatellite DNA markers from the Pacific oyster Crassostrea gigas. Mar Biotechnol (NY), 5(3), 227-233. Smith BL, Lu CP, Bremer JRA (2010) High-resolution melting analysis (HRMA) a highly sensitive inexpensive genotyping alternative for population studies. Molecular Ecology Resources, 10(1), 193-196. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics, 25(9), 1105-1111. Trick M, Long Y, Meng JL et al. (2009) Single nucleotide polymorphism (SNP) discovery in the polyploid Brassica napus using Solexa transcriptome sequencing. Plant Biotechnol J, 7(4), 334-346. Ujino-Ihara T, Taguchi Y, Moriguchi Y et al. (2010) An efficient method for developing SNP markers based on EST data combined with high resolution melting (HRM) analysis. BMC research notes, 3(1), 51. Weir BS (1979) Inferences about linkage disequilibrium. Biometrics 35, 235-254. Yan J, Yang X, Shah T et al. (2010) High-throughput SNP genotyping with the Golden Gate assay in maize. Mol Breeding, 25(3), 441-451. Yang SS, Tu ZJ, Cheung F et al. (2011) Using RNA-Seq for gene identification, polymorphism detection and transcript profiling in two alfalfa genotypes with divergent cell wall composition in stems. BMC Genomics, 12, 199. Ye MH, Chen JL, Zhao GP et al. (2010) Sensitivity and specificity of high-resolution melting analysis in screening unknown SNPs and genotyping a known mutation. AnimSci Pap Rep, 28(2), 161-170. Yeh FC, Yang R, Boyle TJ et al. (2000) POPGENE 32, Microsoft Windows-Based Freeware for Population Genetic Analysis. Molecular Biology and Biotechnology Centre, University of Alberta, Edmonton, Canada. Yu H, Li Q (2007) EST-SSR markers from the Pacific oyster, Crassostrea gigas. Molecular Ecology Notes, 7(5), 860-862.

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Yu H, Li Q (2008) Exploiting EST databases for the development and characterization of EST–SSRs in the Pacific oyster (Crassostrea gigas). Journal of Heredity, 99(2), 208-214. Yu Z, Wang Y, Fu D (2010) Development of Fifty-one novel EST-SSR loci in the Pacific oyster, Crassostrea gigas by data mining from the public EST database. Conservation Genetics Resources, 2(1), 13-18. Zhang G, Fang X, Guo X et al. (2012) Oyster genome reveals stress adaptation and complexity of shell formation. Nature, 490, 49-54. Zhong X, Li Q, Yu H et al. (2013) Development and Validation of Single‐nucleotide Polymorphism Markers in the Pacific Oyster, Crassostrea gigas, Using High‐resolution Melting Analysis. Journal of the World Aquaculture Society, 44(3), 455-465. Zhou LM, Myers AN, Vandersteen JG et al. (2004) Closed-tube genotyping with unlabeled oligonucleotide probes and a saturating DNA dye. Clinical Chemistry, 50(8), 1328-1335. Authors’ contributions Jiafeng Wang exploited the genotyping platform based on the HRM technique, conducted the SNPs validation and wrote the paper. Haigang Qi performed the in silico putative SNP discovery and relevant data analysis and wrote the paper. Li Li performed the sampling of animal materials and transcriptome analysis and revised the manuscript. Huayong Que performed data analysis for the additional supporting files and revised the manuscript. Di Wang performed validation of some of the SNPs. Guofan Zhang planned the work and thoroughly revised the paper. Competing interests The authors declare that they have no competing interests. Data accessibility Transcriptome sequences: NCBI Sequence Read Archive (http://www.ncbi.nlm.nih.gov/Traces/sra/) accession number: QD: SRA062052, QH: SRA098807, DL: SRA098809. All images of the HRM curves of the 1,671 SNPs genotyped can be freely found at http://oysterdb.cn/art/supp/snp/snp.html Detailed information, including the 1,671 screened loci, the 0.58 million putative SNPs and the expression values of the 275 stress responsive genes have been provided as online supplemental data.

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Legends Figures Fig. 1 Classification of putative SNPs identified from the combination of Qingdao, Qinhuangdao and Dalian populations. The putative polymorphic SNPs are marked with grey bars, and putative monomorphic SNPs are marked with black bars. The letter before “>” indicates the base type in the oyster genome, i.e., the reference base, and the letter after “>” indicates the base type in the combined population. Y indicates C/T, R indicates A/G, M indicates A/C, K indicates G/T, W indicates A/T, and S indicates C/G. Fig. 2 Distribution of the SNP density in genes from the combined population of Qingdao, Qinhuangdao and Dalian. The bar (left y-axis) indicates the number of genes in the given SNP density range. The dotted curve (right y-axis) indicates the cumulative percentage of genes in the given SNP density range. Fig. 3 High-resolution melting curves of Cg_cSNP_1545 confirmed the presence of a polymorphic SNP. A) Original melting curves. B) Melting peaks after logarithm calculation. C) Normalized melting curves of the amplicons. The homozygote produced only one peak (a, b), whereas the heterozygote showed two peaks (c, d). One peak represented the Tm of mismatched hybrid amplicons (c), and the other represented the Tm of perfectly matched amplicons (d). The perfectly matched amplicon showed a higher characteristic melting temperature (Tm) than the hybrid mismatched amplicon. Fig. 4 Workflow of SNP development in the oyster. 1 Proportions in all of the primers screened by HRM. 2 Proportions in all of the primers successfully screened by HRM. Supplementary Data Additional supplementary information can be found in the online version of this article. File S1. The breadth of coverage of all genes in the predicted gene set of the oyster genome mapped by transcriptome sequencing reads. The file contains the breadth of coverage from Qingdao, Qinhuangdao, Dalian and the combined population, in addition to the coordinates of the coding regions with a minimum sequencing depth of 4 in the combined population. File S2. List of the 0.58 million putative SNPs found in the study. The file contains the putative SNP information including the scaffold ID, coordinate, the reference base at the corresponding position, SNP type and gene ID. File S3. Summary of the 1,671 putative SNPs genotyped by HRM analysis. The table contains putative SNP type, primer sequences, melting temperature of PCR, expected product sequence and gene annotation. File S4. Expression values of the 275 stress-responsive genes under varying degrees of different stimuli, including salinity, temperature and exposure to air and heavy metals. The expression levels were estimated by the reads per kilobase per million reads (RPKM) measure. The annotation of GO, KEGG and Swiss-Prot was provided for each gene.

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File S5. SNP confirmation by Sanger sequencing. A) Melting curves with three types of genotypes marked with c, d and g. B) PCR sequence alignment. Three types of amplicons corresponding to different melting curves were sequenced, respectively, and four clones were listed in each type of selected amplicon. Ref: reference sequence; a and c: sequence of the two homozygotes; b: sequence of the heterozygote. File S6. Existence of unpredicted SNPs in addition to the target SNP in the same amplicon.

A) High-resolution melting peaks of the amplicons that contained an unpredicted SNP in addition to the target SNP. a: Melting curves of one homozygote; b, c and d: melting curves of different heterozygotes. B) Sequence alignment of the amplicons indicates the presence of the extra SNP in the same amplicon. Light gray is used to highlight the target SNP, whereas dark gray shows the unpredicted SNP. Ref: the reference sequence. File S7. The genotyping result of Cg_cSNP_635 with different numbers of samples. A) Melting curves of Cg_cSNP_635 for eight samples. B) Melting curves of Cg_cSNP_635 for 48 samples. The same regular melting curves were acquired with different numbers of samples. File S8. High-resolution melting peaks with/without temperature calibration of Cg_cSNP_1965 (A1 and A2) and Cg_cSNP_2012 (B1 and B2). A1, B1) High-resolution melting peaks without temperature calibration. A2, B2) High-resolution melting peaks with temperature calibration. After temperature calibration, the tightly integrated melting curves made it easier to distinguish one peak from the other.

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Table 1. Statistics of samples used in the experiment

sample groups G1

G2

source

sample size

Qingdao

20

Qinhuangdao

20

Dalian

20

Qingdao

3

storage

sample use RNA,

liquid nitrogen

transcriptome sequencing

DNA, Qinhuangdao

2

Dalian

3

ethanol

HRM validation DNA,

G3

Qingdao

48

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Table 2. Summary of transcriptome sequencing and mapping to the oyster genome

Mapped bases (Gbp)

Map rate (%)

Coverage

5.35

3.94

73.6

107

77.7

4.85

3.52

72.6

96

38.5

76.4

4.53

3.23

71.2

88

127.4

77.8

14.74

10.68

72.5

291

No. of reads (M)

Mapped reads (M)

Map rate (%)

No. of bases

QD

59.5

47.0

79.1

QH

53.9

41.9

DL

50.3

Combined

163.7

Population *

(Gbp)

* QD: Qingdao; QH: Qinhuangdao DL: Dalian. The combination of the three populations is denoted as “combined”.

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Table 3. Summary of putative SNPs identified from populations QD, QH and DL and the combined population QD 1

QH

DL

Combined

≥ 50

= 100

≥ 50

= 100

≥ 50

= 100

≥ 50

= 100

Gene_no

17,096

2,434

17,126

2,847

17,575

2,939

20,140

4,905

Gene rate (%)

61.0

8.7

61.1

10.2

62.7

10.5

71.9

17.5

Gene size (Mbp)

26.3

2.7

26.3

3.2

26.9

3.3

29.8

6.1

Covered size (Mbp)

23.0

2.7

23.2

3.2

23.8

3.3

27.3

6.1

Covered rate (%)

87.4

100

88.3

100

88.5

100

91.5

100

SNP no. (K)

413.2

51.3

415.3

60.6

432.4

64.7

582.0

135.2

SNP density (%)

1.80

1.91

1.79

1.89

1.82

1.95

2.13

2.20

2

111.1

11.6

111.4

13.8

117.3

14.8

168.1

32.9

ncSNP density (%)

0.48

0.43

0.48

0.43

0.49

0.45

0.62

0.54

3

302.1

39.8

303.9

46.8

315.1

49.9

413.9

102.3

scSNP density (%)

1.31

1.48

1.31

1.46

1.33

1.51

1.52

1.66

4

Mono SNP no. (K)

66.5

3.7

68.3

4.6

69.5

5.0

63.6

7.2

5

Poly SNP no. (K)

346.7

47.6

347.1

56.0

362.9

59.7

518.4

128.0

ncSNP no. (K)

scSNP no. (K)

1 The breadth of coverage of genes used for SNP calling was equal to or greater than 50%. 2 Putative non-synonymous cSNP. 3 Putative synonymous cSNP.

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4 Putative monomorphic SNP.

5 Putative polymorphic SNP.

Table 4. GO enrichment of genes with a SNP density of ≥ 5% GO_ID

GO_Term

GO_Class*

P value

No. of genes

GO:0006412

translation

BP

0.000000

30

GO:0006470

protein dephosphorylation

BP

0.000000

20

GO:0051258

protein polymerization

BP

0.000002

8

GO:0044267

cellular protein metabolic process

BP

0.000002

58

GO:0019538

protein metabolic process

BP

0.000022

67

GO:0006414

translational elongation

BP

0.001135

4

GO:0006801

superoxide metabolic process

BP

0.001589

4

GO:0032991

macromolecular complex

CC

0.000000

43

GO:0043232

intracellular non-membrane-bounded organelle

CC

0.000000

39

GO:0030529

ribonucleoprotein complex

CC

0.000000

28

GO:0005840

ribosome

CC

0.000000

27

GO:0044444

cytoplasmic part

CC

0.000000

31

GO:0005874

microtubule

CC

0.000002

8

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GO:0005737

cytoplasm

CC

0.000057

33

GO:0044430

cytoskeletal part

CC

0.000062

12

GO:0005198

structural molecule activity

MF

0.000000

35

GO:0003735

structural constituent of ribosome

MF

0.000000

27

GO:0005102

receptor binding

MF

0.000000

26

GO:0004725

protein tyrosine phosphatase activity

MF

0.000000

20

GO:0005515

protein binding

MF

0.000496

150

GO:0005044

scavenger receptor activity

MF

0.004141

8

*BP: biological process; CC: cellular component; MF: molecular function

Table 5. GO enrichment of genes with a non-synonymous cSNP density of ≥ 2% GO_ID

GO_Term

GO_Class

P value

No. of genes

GO:0006470

protein dephosphorylation

BP

0.000000

25

GO:0006915

apoptosis

BP

0.004130

12

GO:0009607

response to biotic stimulus

BP

0.008942

6

GO:0005102

receptor binding

MF

0.000000

40

GO:0005515

protein binding

MF

0.000000

299

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GO:0004721

phosphoprotein phosphatase activity

MF

0.000000

25

GO:0004725

protein tyrosine phosphatase activity

MF

0.000000

24

GO:0005488

binding

MF

0.000000

455

GO:0030414

peptidase inhibitor activity

MF

0.000008

18

GO:0004197

cysteine-type endopeptidase activity

MF

0.000040

12

GO:0008234

cysteine-type peptidase activity

MF

0.000133

14

GO:0005044

scavenger receptor activity

MF

0.000305

12

GO:0004866

endopeptidase inhibitor activity

MF

0.000327

13

GO:0005509

calcium ion binding

MF

0.004130

37

GO:0004402

histone acetyltransferase activity

MF

0.005400

3

GO:0043169

cation binding

MF

0.006425

144

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Table 6. Characterization of 18 SNP loci in 48 oysters of the Qingdao (G3) population

Locus

Obs_Het

Exp_Het1

Nei2

HWE

MAF

Cg_cSNP_485

0.200

0.182

0.180

0.484

0.100

Cg_cSNP_496

0.286

0.281

0.278

0.913

0.167

Cg_cSNP_501

0.478

0.410

0.406

0.251

0.283

Cg_cSNP_502

0.083

0.081

0.080

0.795

0.042

Cg_cSNP_635

0.479

0.503

0.498

0.737

0.469

Cg_cSNP_636

0.125

0.221

0.219

0.002＃

0.125

Cg_cSNP_637

0.521

0.505

0.500

0.827

0.490

Cg_cSNP_638

0.370

0.305

0.301

0.138

0.185

Cg_cSNP_648

0.404

0.447

0.442

0.508

0.330

Cg_cSNP_649

0.070

0.190

0.187

0.000＃

0.105

Cg_cSNP_652

0.044

0.043

0.043

0.916

0.022

Cg_cSNP_655

0.512

0.484

0.478

0.702

0.395

Cg_cSNP_659

0.458

0.483

0.478

0.717

0.396

Cg_cSNP_660

0.417

0.449

0.444

0.612

0.333

Cg_cSNP_665

0.195

0.438

0.433

0.000＃

0.317

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Cg_cSNP_732

0.467

0.505

0.499

0.610

0.478

Cg_cSNP_753

0.396

0.468

0.463

0.278

0.365

Cg_cSNP_761

0.255

0.225

0.223

0.339

0.128

Mean

0.317

0.342

0.339

0.502

0.261

1 Expected heterozygosity was computed using Levene (1949). 2 Nei's (1973) expected heterozygosity. Exp_Het, expected heterozygosity; Obs_Het, observed heterozygosity; MAF, minor allele frequency. # Indicates a significant deviation (P < 0.05) from Hardy-Weinberg equilibrium after Bonferroni correction (k = 18).

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