Development of Vibrio spp. infection resistance related SNP markers using multiplex SNaPshot genotyping method in the clam Meretrix meretrix

Fish & Shellfish Immunology 43 (2015) 469e476

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Development of Vibrio spp. infection resistance related SNP markers using multiplex SNaPshot genotyping method in the clam Meretrix meretrix Qing Nie a, b, Xin Yue a, Baozhong Liu a, * a b

Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China University of Chinese Academy of Sciences, Beijing 100039, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 26 September 2014 Received in revised form 22 January 2015 Accepted 26 January 2015 Available online 2 February 2015

The clam Meretrix meretrix is a commercially important mollusc species in the coastal areas of South and Southeast Asia. In the present study, large-scale SNPs were genotyped by the Multiplex SNaPshot genotyping method among the stocks of M. meretrix with different Vibrio spp. infection resistance profile. Firstly, the AUTOSNP software was applied to mine SNPs from M. meretrix transcriptome, and 323 SNP loci (including 120 indels) located on 64 contigs were selected based on Uniprot-GO associations. Then, 38 polymorphic SNP loci located on 15 contigs were genotyped successfully in the clam stocks with different resistance to Vibrio parahaemolyticus infection (11-R and 11-S groups). Pearson's Chi-square test was applied to compare the allele and genotype frequency distributions of the SNPs between the different stocks, and seven SNP markers located on three contigs were found to be associated with V. parahaemolyticus infection resistance trait. Haplotype-association analysis showed that six haplotypes had significantly different frequency distributions in 11-S and 11-R (P < 0.05). With selective genotyping between 09-R and 09-C populations, which had different resistance to Vibrio harveyi infection, four out of the seven selected SNPs had significantly different distributions (P < 0.05) and therefore they were considered to be associated with Vibrio spp. infection resistance. Sequence alignments and annotations indicated that the contigs containing the associated SNPs had high similarity to the immune related genes. All these results would be useful for the future marker-assisted selection of M. meretrix strains with high Vibrio spp. infection resistance. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Meretrix meretrix Single nucleotide polymorphism (SNP) Selective genotyping Vibrio spp. infection resistance Marker-trait association analysis

1. Introduction In general, the main goals of breeding programs for fish and shellfish are to increase the profitability and sustainability, while maintaining genetic variability in the cultured stocks [1]. Recently, genetic breeding for growth traits using mass selection has obtained achievement of growth performance in the clam Meretrix meretrix [2,3]. Though resistance to disease has major effect on the production efficiency and profitability for marine animals, few studies focus on the resistance selection because of the complex immune mechanisms and the difficulties of resistance evaluation for the candidates [4]. Currently, one of the feasible approaches is to develop reliable molecular makers and integrate them into genetic

* Corresponding author. Tel.: þ86 532 82898696; fax: þ86 532 82898578. E-mail address: [email protected] (B. Liu). http://dx.doi.org/10.1016/j.fsi.2015.01.030 1050-4648/© 2015 Elsevier Ltd. All rights reserved.

breeding in M. meretrix, especially for resistance traits [5]. Markerassisted selection (MAS) is a very promising method in diseaseresistance breeding program, for it enables selection to depend on genotypes and improves breeding efficiency [6]. Up to now, MAS has been applied in the selection for resistance traits in several aquatic animals, such as Japanese flounder (Paralichthys olivaceus) [7] and Atlantic salmon (Salmo salar) [8]. Molecular markers can be used to detect quantitative trait loci (QTL) which control complex traits of interest and to select individuals for MAS programs [7]. Among the molecular markers, single nucleotide polymorphism (SNP) is the preferred one for molecular genetic analysis currently [9]. SNPs are co-dominant and highly abundant throughout the genome, which can be obtained by high-throughput analysis and used for both neutral and adaptive variation analysis [10e13]. Being highly polymorphic and having relatively high level of linkage disequilibrium, SNPs could be applied to whole genome scans for QTL discovery and fine-mapping

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of candidate regions associated with phenotypes of interest [14,15]. Recently, with the rapid increase of expressed sequence tags (ESTs) in databases, developing SNP markers from ESTs has been used successfully in many species [16e18]. In M. meretrix, the transcriptome obtained from the samples of four larval stages through 454 shotgun sequencing had been conducted and submitted in the NCBI Sequence Read Archive [19], which would provide a valuable opportunity for high-throughput SNPs discovery. The clam M. meretrix is an important commercial marine bivalve along the coastal areas of South and Southeast Asia and has been widely cultured in China [20,21]. However, with the applications of intensive farming and environmental degradation, mass mortalities have been reported in cultured and wild M. meretrix populations [4,5,22]. Among the causative pathogens, Vibrio is a major one leading to the mortalities of marine mollusc [23e26]. Therefore, breeding improved domesticated M. meretrix strains with high Vibrio spp. infection resistance is by far considered as one of the most important subjects in our clam breeding program [5]. Several researches on identifying SNPs involved in Vibrio spp. infection resistance in M. meretrix have been reported before [5,27], but there are few studies on genome scale to develop SNPs for this species until now. In the present study, after clustering and assembling, a total of 35,205 contigs generated from the M. meretrix transcriptome were used to identify SNP markers. The SNP-containing contigs involved in immune and defense functions were selected according to Gene Ontology (GO) categories. Then the Pearson's Chi-square test was selected to perform marker-trait association analysis based on the stock materials of M. meretrix with different Vibrio spp. infection resistance profile. The SNPs identified to be associated with Vibrio spp. infection resistance would be helpful in the resistance selective breeding of M. meretrix in future. 2. Materials and methods 2.1. Experimental clams In our previous study, the stock materials of M. meretrix, 11-R and 11-S groups, were developed with different Vibrio parahaemolyticus infection resistance [4,5]. In brief, about 1000 healthy clams were collected from Shandong natural population of M. meretrix. After challenged with V. parahaemolyticus, the clams were divided into the V. parahaemolyticus resistant group (11-R) and the susceptive group (11-S) according to the survived time. The two selected groups were applied to identify SNPs associated with V. parahaemolyticus infection resistance in the present research. At the same time, 50 clams were randomly collected from Shandong natural population, which formed the control group (11-C) to evaluate the candidate SNP markers. The Vibrio harveyi infection resistant population (09-R) and a control population (09-C) were applied as validation populations. The 09-R population was the F1 generation of survival clams challenged by V. harveyi, and the 09-C was the F1 generation of control clams. The detailed selective processes were described in Yue et al. [5] and Wang et al. [27]. 2.2. Sampling and DNA extraction For all the sampled individuals, foot tissues were dissected separately from fresh specimens and stored immediately in 95% ethanol until the DNA samples were isolated. The extractions of total genomic DNA (gDNA) and evaluations were described in detail in our previous report [4]. All the DNA stocks were diluted to 20 ng ml1 with Tris-EDTA buffer (pH 8.0) and stored at 20  C.

2.3. Development and validation of SNPs involved in resistance In previous study [19], we have analyzed the sequences of M. meretrix transcriptome obtained from the samples of four larval stages through 454 shotgun sequencing. All the original reads had been submitted in the NCBI Sequence Read Archive (Accession No. SRX023927) and the assembly data had been deposited to the NCBI Transcriptome Shotgun Assembly sequence database (accession no.: JI257612eJI292615) [19]. A total of 35,205 contigs generated from the M. meretrix transcriptome were used to identify polymorphic SNP markers. The process of developing SNPs was referred to Liu et al. [18]. In brief, the software AUTOSNP [28] was used to distinguish the candidate SNPs from sequencing errors according to sequence redundancy [18]. The EST contig assembly file in ACE formats was parsed to extract SNP information. Contigs with more than three reads were selected for SNP detection. The minor allelic redundancy (MAR) was set depending on the total sequencing depth at each nucleotide to reduce false SNP prediction. Additionally, at least two reads with the alternative allele were required to extend 20 bases upstream and downstream of SNP site to avoid possible sequencing errors near both ends of sequenced reads. The sequences containing SNPs were functionally annotated based on sequence similarity to the Uniprot protein database with BLASTx (E value ¼ 105), and classified into Gene Ontology (GO) categories according to Uniprot-GO associations. Then the SNP-containing contigs, which were annotated to be involved in immune and defense functions, were chosen for validation and genotyping. To verify the deduced SNPs, one pool of DNA from 20 clams chose randomly from 11-C group was applied to amplify the SNPs and the amplification and extension primers were designed by the primer premier 5.0 software (Premier Biosoft International). Then the amplified fragments were sequenced to detect the polymorphs of SNPs. The polymerase chain reaction (PCR) was in a total 15 ml reaction volume containing 1  PCR buffer (Promega, USA), 1.5 mmol/L MgCl2, 0.2 mmol/L dNTP mix, 0.2 mmol/L of each primer, 0.75 U Taq DNA polymerase (Promega, USA), and 1 ml template DNA (z50 ng). The PCR reactions were conducted on a PCR Thermal Cycler Dice tp 650 (TakaRa) with the following temperature profile: initial denaturation at 95  C for 3 min, 22 cycles of denaturation at 94  C for 15 s, annealing at 60e55  C for 15 s with the annealing temperature decreased by 0.5  C per cycle, and extension at 72  C for 15 s, then a final extension step at 72  C for 3 min. PCR products were run on a 1.2% agarose gel to verify the success of amplifications and then sequenced with an ABI 3730xl automated DNA sequencer, where SNPs were identified as overlapping nucleotide peaks. 2.4. Single nucleotide polymorphism (SNP) genotyping All the available polymorphic SNPs obtained via the methods above were genotyped in 183 clams, which contained 50 individuals from 11-C group, 53 ones from 11-R and 80 ones from 11-S, using Multiplex SNaPshot system (Applied Biosystems, Foster City, CA) [29]. In addition, the identified SNPs whose frequency distributions were significantly different between 11-R and 11-S were further genotyped used the same method in 64 clams selected respectively from 09-R and 09-C populations. The multiplex PCR was used to amplify the 15 selected fragments for the 38 polymorphic SNPs with sizes ranging from 99 to 358 bp. The primers and conditions used for multiplex PCR were the same as the PCR described above. PCR products were purified with the reaction volume: 3 ml of PCR products, 0.2 ml Exonuclease I (ExoI, Promega), 0.2 ml Shrimp Alkaline Phosphatase (SAP, Promega), Exol buffer 0.7 ml and 2.9 ml ddH2O. PCR Thermal Cycler Dice tp

Q. Nie et al. / Fish & Shellfish Immunology 43 (2015) 469e476

650 (TakaRa) was applied and the reactions were conducted with the following temperature profile: 37  C for 75 min and 80  C for 15 min to remove the remaining primers and unincorporated deoxynucleotide triphosphates. The purified PCR products were subjected to primer extension analysis. SNaPshot extension reactions were performed as described by the manufacturer of ABI SNaPshot Multiplex PCR Kit (Applied Biosystems, Foster City, CA) with slight changes. Briefly, the 6 ml reaction volume included 1 ml SNaPshot Multiplex Ready Reaction Mix, 2 ml of pooled extension primer, 2 ml of purified PCR products and 1 ml ddH2O. Extension used pre-denaturation at 96  C for 1 min followed by 30 cycles of denaturation at 96  C for 10 s, annealing at 52  C for 5 s and extension at 60  C for 30 s. To prevent unincorporated terminators from comigrating with the extended primers and producing high background signal, extension products were treated with 1 U of SAP (Promega) at 37  C for 60 min, followed by 15 min incubation at 80  C to inactivate the enzyme. The treated products (0.5 ml) were mixed with 9 ml of formamide and 0.5 ml of GeneScan-120 LIZ Size Standard (Applied Biosystems) and denatured at 95  C for 5 min. The fluorescently labeled fragments were separated by capillary electrophoresis on an ABI PRISM 3730 XI Genetic Analyzer (Applied Biosystems). Genemapper v4.1 (Applied Biosystems) software [30] was used to determine the genotypes of the SNPs. The primers for SNPs amplification and extension primers for Multiplex SNaPshot genotyping were showed in Supplemental Tables 1 and 2 respectively. To confirm the accuracy of the genetic analysis method with Multiplex SNaPshot, 20 individuals selected randomly from 183 samples (about 11%) were analyzed by direct sequencing at Shanghai Generay Biotech Co., Ltd (China).

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2.7. Verifying the candidate SNPs The 09-R and 09-C populations with different V. harveyi infection resistance were applied to verify the candidate SNPs whose genotype or allele frequency distributions were significantly different between 11-R and 11-S groups. The genotype and allele frequencies were added up and the Pearson's Chi-square test was employed to assess the distribution differences as above. 3. Results 3.1. Development and validation of immune related SNPs Depending on the analyses and GO annotations, 323 SNP loci (including 120 indels) located on 64 contigs, which were considered to be involved in immunity, were developed based on M. meretrix transcriptome database. Then for each contig, at least one pair of primers were designed to validate the predicted SNPs by sequencing the PCR products. In total, 225 SNPs were included for the primer design, among which 89 SNPs could be amplified successfully and 66 SNPs were verified to be polymorphic, including 45 predicted SNPs and 21 new identified SNPs. As for Multiplex SNaPshot genotyping, only 38 SNPs located on 15 contigs out of the 66 polymorphic SNPs could be genotyped successfully in experimental clams at last. The applied SNPs were numbered from R1 to R38 and the characteristics of these SNP loci were showed in Table 1. These 38 SNPs could be amplified successfully with 16 pairs of primers. At last, 44 out of 53 clams from 11-R group and 60 of 80 clams from 11-S group could be genotyped successfully for all these 38 SNPs. The results of direct sequencing confirmed the genotypes of the replicate samples with a consensus rate of 100%.

2.5. Evaluation of the SNP markers in Shandong population of M. meretrix

3.2. Evaluation of the SNP markers in Shandong population

The control group (11-C) was applied to evaluate the selected SNP markers. The HardyeWeinberg equilibrium was estimated with the software Haploview v4.2 [31], and only the SNPs that were in accordance with HardyeWeinberg equilibrium (P > 0.01) were applied for subsequent analyses.

The 11-C group was applied to evaluate the 38 polymorphic SNPs in Shandong population. A total of 78 alleles were identified according to the results of genotyping, among which two SNPs (R5 & R18) had three alleles while the others had two alleles. The SNPs R10 and R34 were deviated from HardyeWeinberg equilibrium at P < 0.01 (Table 2) and these two SNPs were excluded in subsequent assay.

2.6. Association analysis

3.3. V. parahaemolyticus infection resistance association analysis

To search for the SNP markers associated with V. parahaemolyticus infection resistance, marker-trait association analysis was utilized in the stock materials of M. meretrix with different V. parahaemolyticus infection resistance phenotype. Only the SNP loci with the minor allele frequency (MAF) greater than 1% would be applied to the association analysis [9]. Differences in the distributions of genotype and allele frequency of each SNP locus in 11-R and 11-S were assessed using the Pearson's Chi-square test, which was performed with statistical software SPSS 16.0 program. The odds ratios (OR) and 95% confidence intervals (95% CI) of alleles were also calculated. For the allele whose frequency distributions was significantly different between 11-R and 11-S, “OR <1” and “the upper limit of 95% CI <1” implied that the allele occurred more frequently in 11-R than that in 11-S, and therefore the allele was considered as V. parahaemolyticus resistance-positive allele, which contributed to the V. parahaemolyticus infection resistance; “OR >1” and “the lower limit of 95% CI >1” implied that the allele occurred more frequently in 11-S than that in 11-R, so the allele was considered as V. parahaemolyticus resistance-negative allele. Linkage disequilibrium patterns and haplotype-trait association analysis were analyzed using the Haploview software package v4.2 [31]. A significance level of 0.05 was applied for all the above tests.

After association analysis, a total of 7 SNP loci located on three contigs were identified to be associated with V. parahaemolyticus infection resistance. In detail, the genotype frequency distributions of R3, R4, R18 and R20 were significantly different between 11-R and 11-S (P < 0.05). And the allele frequency distributions of six SNPs (i.e., R1, R2, R4, R16, R18 and R20) were significantly different between 11-R and 11-S (P < 0.05). The genotype and allele frequency distributions of these 36 SNPs in 11-R and 11-S group were showed respectively in Tables 3 and 4. In addition, six V. parahaemolyticus resistance-positive alleles (OR < 1) (i.e., R1-T, R2-G, R4-C, R16-A, R18-C and R20-T) were found and showed in Table 5. Clams containing these alleles would be more resistant to V. parahaemolyticus infection. 3.4. Linkage disequilibrium patterns and haplotype association analysis Three contigs (congtig16077, contig24215 and contig34222) containing seven SNPs associated with V. parahaemolyticus infection resistance were further enrolled to analyze the Linkage disequilibrium of SNPs, and the results showed that the SNPs located on the same contig were in Linkage disequilibrium. Haplotype association

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Table 1 Characteristics of the 38 polymorphic SNP loci enrolled in this study. Contig (TSA accession)

Locus

Position (bp)

Variation

Contig (TSA accession)

Locus

Contig16077 (JI273590)

R1 R2 R3 R4 R5 R6

187 188 243 360 894 526

G/T G/T A/G C/T A/C/G C/G

Contig8762 (JI258762)

R7 R8 R9 R10 R11 R12

297 306 333 165 182 193

A/C A/T C/G A/T C/T C/G

R21 R22 R23 R24 R25 R26 R27 R28 R29

135 183 198 261 264 318 232 254 95

C/T A/G CT AG A/G A/G C/T A/G A/G

R13 R14 R15 R16 R17 R18 R19 R20

202 208 125 170 172 264 186 160

A/T A/T G/T A/G A/T A/C/T C/A T/C

R30 R31 R32 R33 R34 R35

182 223 268 322 324 212

C/T A/T A/G C/T C/T C/G

R36 R37 R38

872 944 1047

A/G A/G C/T

Contig9267 (JI276767)

Contig11812 (JI269347)

Contig32766 (JI290192) Contig31287 (JI288725)

Contig11349 (JI268887) Contig33978 (JI291399) Contig2866 (JI260461) Contig24215 (JI281693)

Contig34222 (JI291643)

analysis showed that there were six haplotypes whose frequency distributions were significantly different between 11-R and 11-S groups (P < 0.05) (Table 6). The haplotype frequencies of 187T/ 188G/243A/360C (contig16077) and 186A/160T (contig34222) in 11-R group were significantly higher than those in 11-S (P < 0.05), while the frequencies of 187G/188T/243A/360T (contig16077), 125T/170G/172A (contig24215), 125G/170G/172A (contig24215) and 186A/160C (contig34222) in 11-R were significantly lower than those in 11-S (P < 0.05). 3.5. Verifying of the candidate SNPs Marker-trait association analysis was executed in 09-R and 09-C populations with different V. harveyi infection resistance to further analyze the candidate SNPs, and four of the seven SNPs were further identified to be associated with V. harveyi infection resistance (Table 7). In detail, the allele frequency distributions of R1, R2, R16, and R18 were significantly different between 09-R and 09-C (P < 0.05), among which the genotype frequency distribution of R18 was significantly different between 09-R and 09-C (P < 0.05). In addition, three alleles (i.e., R1-T, R2-G, and R16-A) were also be identified to be V. harveyi resistance-positive alleles with OR < 1 and the upper limit of 95% CI < 1.

Table 2 HardyeWeinberg equilibrium (HWE) of the 38 SNP markers in 11-C group. Locus c2

df P value Locus c2

R1 0.346 1 R2 1.486 1 R3 0.316 1 R4 0.736 1 R5 3.664 3 R6 4.438 1 R7 4.816 1 R8 0.137 1 R9 1.378 1 R10** 29.526 1 R11 4.636 1 R12 6.487 1 R13 0.165 1

0.556 0.223 0.575 0.391 0.3 0.035 0.028 0.711 0.24 0 0.03 0.011 0.685

R14 R15 R16 R17 R18 R19 R20 R21 R22 R23 R24 R25 R26

0.505 1.393 3.517 5.411 9.359 5.657 6.093 0.275 0.385 0.001 0.067 0.002 0.069

df P value Locus c2

df P value

1 1 1 1 3 1 1 1 1 1 1 1 1

1 1 1 1 1 1 1 1 1 1 1 1

0.477 0.238 0.06 0.02 0.025 0.017 0.014 0.599 0.535 0.975 0.794 0.965 0.792

R27 R28 R29 R30 R31 R32 R33 R34** R35 R36 R37 R38

1.053 4.898 0.551 0.016 0.111 0.868 0.097 9.77 3.888 0.002 1.375 1.28

**P < 0.01, significantly deviated from HardyeWeinberg equilibrium.

0.305 0.027 0.458 0.899 0.739 0.352 0.755 0.002 0.048 0.961 0.241 0.258

Contig33286 (JI290709) Contig13187 (JI270713)

Contig3659 (JI271182) Contig26509 (JI283975)

Position (bp)

Variation

3.6. Sequence alignments and annotations of the contigs containing the associated SNPs BlastX searches in NCBI database revealed that the deduced proteins coded by these three contigs had high similarity with known proteins (Table 8). The contig16077 had high similarity to transcription factor HES-4 in Xenopus (Silurana) tropicalis (E value ¼ 4e47), which belongs to basic helix-loop-helix (bHLH) transcriptome factor family. The contig24215 was similar to SOSS complex subunit C-like protein in Cerapachys biroi with E value as 2e11 [32]. The contig34222 had high similarity with Rho-related GTP-binding protein RhoQ in Crassostrea gigas (E value ¼ 2e44). Among these four SNPs associated with Vibrio spp. infection resistance, SNPs R1, R2 and R16 were located within 30 -untranslated regions (30 -UTR) of corresponding genes and only the R18 locus was located on open reading frame (ORF) which was a synonymous mutation.

Table 3 The genotype distributions in 11-R and 11-S groups. Locus

c2

df

P value

Locus

c2

df

P value

R1 R2 R3* R4* R5 R6 R7 R8 R9 R11 R12 R13 R14 R15 R16 R17 R18* R19

3.618 4.142 6.843 5.514 6.599 2.395 1.217 3.922 3.602 2.596 1.269 0.132 0.887 1.096 3.81 3.109 12.29 1.057

2 2 2 2 5 2 2 2 2 2 2 2 2 2 2 2 5 2

0.178 0.103 0.033 0.045 0.214 0.495 0.777 0.232 0.306 0.273 0.568 0.936 0.642 0.578 0.149 0.25 0.02 0.636

R20* R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R35 R36 R37 R38

7.936 1.732 0.866 0.805 2.104 1.578 1.219 0.942 0.347 2.957 0.239 0.655 0.976 1.165 2.504 0.945 2.589 3.326

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

0.019 0.421 0.649 0.823 0.349 0.497 0.593 0.624 0.841 0.228 0.941 0.85 0.726 0.559 0.286 0.623 0.287 0.189

*P < 0.05, significant differences of the genotype distributions between these two groups.

Q. Nie et al. / Fish & Shellfish Immunology 43 (2015) 469e476 Table 4 The allele distributions in 11-R and 11-S groups. Locus

c2

df

P value

Locus

c2

df

P value

R1* R2* R3 R4* R5 R6 R7 R8 R9 R11 R12 R13 R14 R15 R16* R17 R18* R19

4.064 4.641 0.72 6.452 0.784 2.139 0.163 3.235 1.717 3.382 0.85 0.03 0.18 0.825 3.893 2.961 7.382 0.985

1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1

0.044 0.031 0.396 0.011 0.676 0.144 0.687 0.072 0.19 0.066 0.356 0.862 0.671 0.364 0.048 0.085 0.025 0.321

R20** R21 R22 R23 R24 R25 R26 R27 R28 R29 R30 R31 R32 R33 R35 R36 R37 R38

9.178 0.586 0.809 0.492 1.235 0.874 0.184 1.011 0.122 0.392 0.125 0.568 0.356 0.98 2.562 0.315 0.656 1.494

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0.002 0.444 0.368 0.483 0.266 0.35 0.668 0.315 0.727 0.531 0.723 0.451 0.551 0.322 0.109 0.575 0.418 0.222

*P < 0.05, **P < 0.01, significant differences of the allele distributions between these two groups.

4. Discussion Single Nucleotide Polymorphisms (SNPs) are the most widespread DNA sequence variations in organisms which have been widely applied in functional genomics and genetic improvement in animals and plants [33e36]. Developing reliable and efficient SNP genotyping method is the first step to make full use of these molecular markers. Up to now, there have been several novel genotyping methods amenable to high-throughput analyses, such as Oligonucleotide Ligation Assay (OLA) [37], Melting Curve Analysis (Mc) [38], SNaPshot [39], etc. [40e43]. Because of limitations such as cost, complexity and accuracy, not all these SNP genotyping technologies are widely accepted. In the present study, the Multiplex SNaPshot genotyping method was applied for its relatively high throughput, consistence and convenience [39,44]. In our study, about 57.6 percent (38/66) of SNPs could be genotyped successfully by the Multiplex SNaPshot genotyping method, which indicated that Multiplex SNaPshot system was suitable for the genotyping of SNPs in M. meretrix. Based on the principle that loci associated with trait of interest would show allelic heterogeneity between the populations with different phenotypes on the target trait [45], marker-trait

Table 5 The allelic differences of SNPs in 11-R and 11-S groups. Locus

Allele

Frequency 11-R

11-S

R1

T* G* G* T* C* T* G* A* A** T C* C** T**

80 8 80 8 82 6 46 42 49 18 21 45 43

97 23 96 24 97 23 79 41 88 17 15 86 34

R2 R4 R16 R18

R20

(0.91) (0.09) (0.91) (0.09) (0.93) (0.07) (0.52) (0.48) (0.56) (0.20) (0.24) (0.51) (0.49)

(0.81) (0.19) (0.80) (0.20) (0.81) (0.19) (0.66) (0.34) (0.73) (0.14) (0.13) (0.72) (0.28)

c2

df

P value

OR (95% CI)

4.064 4.064 4.641 4.641 6.452 6.452 3.893 3.893 7.036 1.434 4.581 9.178 9.178

1 1 1 1 1 1 1 1 1 1 1 1 1

0.044 0.044 0.031 0.031 0.011 0.011 0.048 0.048 0.008 0.231 0.032 0.002 0.002

0.422 2.371 0.400 2.500 0.309 3.241 1.759 0.568 2.189 0.642 0.456 2.417 0.414

(0.179, (1.006, (0.170, (1.065, (0.120, (1.259, (1.002, (0.324, (1.221, (0.310, (0.220, (1.358, (0.232,

0.994) 5.588) 0.939) 5.882) 0.794) 8.340) 3.090) 0.998) 3.923) 1.331) 0.946) 4.302) 0.736)

*P < 0.05, **P < 0.01, significantly different distributions of the allele frequency between these two groups.

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Table 6 The haplotype distributions of contigs involved in V. parahaemolyticus infection resistance in 11-R and 11-S groups. Contig

Contig16077

Contig24215

Contig34222

Haplotype

187T/188G/243A/360C* 187T/188G/243G/360C 187G/188T/243A/360T* 125T/170G/172A* 125G/170A/172A 125T/170G/172T 125G/170G/172A* 125T/170A/172A 186A/160C** 186A/160T* 186C/160T

Frequency 11-R

11-S

0.591 0.318 0.068 0.317 0.442 0.193 0.011 0.034 0.511 0.296 0.193

0.425 0.375 0.192 0.474 0.316 0.108 0.075 0.025 0.708 0.151 0.133

c2

df

P value

5.59 0.72 6.45 5.192 3.472 2.961 4.359 0.155 8.367 6.393 1.384

1 1 1 1 1 1 1 1 1 1 1

0.018 0.396 0.011 0.023 0.062 0.085 0.037 0.694 0.004 0.012 0.24

*P < 0.05, **P < 0.01, significantly different distributions of the haplotype frequency between these two groups.

association analyses have been applied to identify resistance related markers in fish and shellfish recently [5,46e51]. In the present study, after marker-trait association analysis using Pearson's Chi-square test, total seven SNP markers were found to be associated with V. parahaemolyticus infection resistance trait. The genotype frequency distributions of four SNPs were significantly different in the resistant group (11-R) compared to the susceptible group (11-S) (P < 0.05). The allele frequency distributions of six SNP loci showed significant difference between 11-R and 11-S groups (P < 0.05). Among these candidate SNPs, the distributions of R4, R18 and R20 showed both significant difference with the genotype and allele analysis between the two stocks (P < 0.05). In addition, six alleles were considered as V. parahaemolyticus resistance-positive allele (OR < 1), and six alleles were V. parahaemolyticus resistance-negative allele (OR > 1). The carriage of the V. parahaemolyticus resistance-positive alleles would significantly decrease mortality risk of infection by V. parahaemolyticus [5]. All these seven SNPs associated with V. parahaemolyticus infection resistance were located on three contigs. Haplotype association analysis showed that nine haplotypes were identified in these three contigs, among which six haplotypes exhibited significantly different distributions between 11-R and 11-S groups (P < 0.05). It is noted that though the distributions of certain SNPs showed no statistical significance between 11-R and 11-S groups, these SNPs may interact with other candidate SNPs to affect phenotype (i.e., epistatic effects) [52]. For example, the distributions of R15, R17 showed no significantly different in 11-R and 11-S, they could interact with R16 and composed the haplotypes 125T/ 170G/172A (contig24215), 125G/170G/172A (contig24215) whose frequency distributions were significantly different in 11-R and 11S (P < 0.05). These SNP-SNP interactions at two or more loci should not be ignored. The independent populations 09-R and 09-C with different V. harveyi infection resistance were applied to verify the seven selected V. parahaemolyticus resistance SNPs. Four out of the seven SNPs which were further identified to be associated with V. harveyi infection resistance were deduced to be associated with Vibrio spp. infection resistance. In addition, the alleles R1-T, R2-G and R16-A were considered as Vibrio spp.-positive alleles, and the allele R1-G, R2-T, R16-G and R18-A were considered as Vibrio spp.-negative alleles. However, the distributions of R3, R4 and R20 failed to show significant difference between 09-R and 09-C populations. Different genetic background between the different experimental populations together with different Vibrio spp. resistance profiles (11-R to V. parahaemolyticus, 09-R to V. harveyi) [4] were the two possible explanations to this phenomenon. Whatever, over 57% of candidate SNPs (4/7) were further confirmed suggested that the marker-trait

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Table 7 Distributions of seven SNPs associated with V. harveyi infection resistance in 09-R and 09-C populations. Locus

R1

R2

R3

R4

R16

R18

R20

Genotype

Frequency

GT GG TT GT GG TT AG AA GG CT CC TT AG AA GG AC AT CT AA CC TT TC TT CC

P value

09-R

09-C

25 3 36 24 36 4 39 19 6 25 36 3 16 31 17 5 12 15 1 2 29 26 9 29

35 6 23 35 23 6 34 26 4 34 25 5 20 21 23 1 20 9 9 2 23 28 13 23

(0.39) (0.05) (0.56) (0.38) (0.56) (0.06) (0.61) (0.30) (0.09) (0.39) (0.56) (0.05) (0.25) (0.48) (0.27) (0.08) (0.19) (0.23) (0.02) (0.03) (0.45) (0.41) (0.14) (0.45)

(0.55) (0.09) (0.36) (0.55) (0.36) (0.09) (0.53) (0.41) (0.06) (0.53) (0.39) (0.08) (0.31) (0.33) (0.36) (0.02) (0.31) (0.14) (0.14) (0.03) (0.36) (0.44) (0.20) (0.36)

Allele

Frequency 09-R

09-C

OR (95%CI)

P value

0.069

T G

97 (0.67) 31 (0.22)

81 (0.56) 47 (0.33)

0.551 (0.321, 0.946) 1.816 (1.057, 3.119)

0.030*

0.070

G T

96 (0.67) 32 (0.22)

81 (0.56) 47 (0.33)

0.574 (0.336, 0.984) 1.741 (1.017, 2.981)

0.042*

0.400

A G

77 (0.53) 51 (0.35)

86 (0.60) 42 (0.29)

1.356 (0.813, 2.261) 0.737 (0.442, 1.229)

0.242

0.156

C T

97 (0.67) 31 (0.22)

84 (0.58) 44 (0.31)

0.610 (0.354, 1.052) 1.639 (0.951, 2.825)

0.074

0.195

G A

50 (0.35) 78 (0.54)

66 (0.46) 62 (0.43)

1.661 (1.011, 2.728) 0.602 (0.367, 0.989)

0.045*

0.015*

A T C

19 (0.13) 85 (0.59) 24 (0.17)

39 (0.27) 75 (0.52) 14 (0.10)

2.514 (1.358, 4.653) 0.716 (0.431, 1.190) 0.532 (0.261, 1.083)

0.006**

0.474

C T

84 (0.58) 44 (0.31)

74 (0.51) 54 (0.38)

0.718 (0.433, 1.191) 1.393 (0.840, 2.311)

0.199

*P < 0.05, **P < 0.01, significant differences of the frequency distributions of corresponding SNPs between 09-R and 09-C populations.

Table 8 Characterization of SNP-containing contigs associated with Vibrio spp. infection resistance in M. meretrix. Associated sequence

Locus

Location

Homology with genes

GenBank accession no.

E value

Organism with similar sequence

Contig16077

R1 R2 R16 R18

30 UTR 30 UTR 30 UTR ORF (synonymous)

Transcription factor HES-4

NP_988870.1

4e47

Xenopus (Silurana) tropicalis

SOSS complex subunit C-like protein Rho-related GTP-binding protein RhoQ

EZA61557.1 EKC40913.1

2e11 2e44

Cerapachys biroi Crassostrea gigas

Contig24215 Contig34222

associations in 11-R and 11-S were a practicable way to identify SNP markers associated with Vibrio spp. infection resistance. The SNPs located in the coding regions could be divided into synonymous SNPs and non-synonymous SNPs, among which the latter are of most practical interest for causing alteration of protein structures and functions [53]. In this study, four SNPs associated with Vibrio spp. infection resistance were developed, among which only one was synonymous SNP which was located on coding region, and other three were located in 30 -UTR. In previous reports, numerous SNPs that affect biological functions are located outside the coding regions, i.e., in regulatory gene region [54]. There are evidences that SNPs located in 30 -UTR might participate in the post-transcriptional gene regulation and the stability of mRNA [55e57]. According to the results of BlastX, the three contigs (congtig16077, contig24215, contig34222) had high similarity with genes of transcription factor HES-4, SOSS complex subunit C-like protein and Rho-related GTP-binding protein RhoQ, respectively. The HES-4 in Xenopus belongs to the hairy-related proteins, which are a distinct subfamily of basic helix-loop-helix (bHLH) protein [58]. The hairy-related proteins generally function as DNA-binding transcriptional repressors that often act either independently as pattern regulators or as nuclear effectors for the Notch signaling pathway and play important roles in cell differentiation, proliferation and apoptosis [59]. The SOSS complex subunit C together with SOSS-A and SOSS-B make up the ssDNA-binding proteins (SSBs) complex, which could bind single-stranded DNA (ssDNA) generated during the initial steps of homologous recombination (HR), and play essential roles in DNA replication, recombination, and repair in

bacteria, archaea and eukarya [32,60]. As a member of Ras superfamily of small GTP-binding proteins, RhoQ (i.e., TC10) shares many properties with cdc42 and acts as molecular switch with a GTPbound “on” state and a GDP “off” state and has intrinsic GTPase activity [61]. Through interacting with the other Rho GTPases and downstream effector proteins, RhoQ plays key roles in the regulation of diverse cellular processes such as actin cytoskeletal rearrangements, proliferation, mitogen-activated protein kinase (MAPK) cascades, cell cycle progression and gene expression [62,63]. The results of sequence alignments and annotations suggested that these three contigs might have potential roles in immunity of M. metrtrix. However, the functions of these identified SNPs and contigs need to be analyzed through experiments in future. In summary, seven SNPs located on three contigs which were deduced to be associated with V. parahaemolyticus infection resistance and four of them were further identified to be associated with V. harveyi infection resistance by marker-trait association analysis in M. meretrix. These markers would be useful for the future marker-assisted selection of clam M. meretrix with high resistance to Vibrio spp. The functions of SNPs and contigs associated with Vibrio spp. resistance need for further studies in the future. Acknowledgments This work was financially supported by the Chinese National High-Tech R & D Program (2012AA10A410) and the NSFC Project (31202018).

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