Gene set based association analyses for the wssv resistance of pacific white shrimp litopenaeus vannamei

ABSTRACT White Spot Syndrome Virus (WSSV) is regarded as a virus with the strongest pathogenicity to shrimp. For the threshold trait such as disease resistance, marker assisted selection

(MAS) was considered to be a more effective approach. In the present study, association analyses of single nucleotide polymorphisms (SNPs) located in a set of immune related genes were

conducted to identify markers associated with WSSV resistance. SNPs were detected by bioinformatics analysis on RNA sequencing data generated by Illimina sequencing platform and Roche 454

sequencing technology. A total of 681 SNPs located in the exons of immune related genes were selected as candidate SNPs. Among these SNPs, 77 loci were genotyped in WSSV susceptible group

and resistant group. Association analysis was performed based on logistic regression method under an additive and dominance model in GenABEL package. As a result, five SNPs showed

associations with WSSV resistance at a significant level of 0.05. Besides, SNP-SNP interaction analysis was conducted. The combination of SNP loci in _TRAF6, Cu/Zn SOD_ and _nLvALF2_

exhibited a significant effect on the WSSV resistance of shrimp. Gene expression analysis revealed that these SNPs might influence the expression of these immune-related genes. This study

provides a useful method for performing MAS in shrimp. SIMILAR CONTENT BEING VIEWED BY OTHERS WHOLE-GENOME ASSOCIATION STUDY SEARCHING FOR QTL FOR _AEROMONAS SALMONICIDA_ RESISTANCE IN

RAINBOW TROUT Article Open access 08 September 2021 A MAJOR QUANTITATIVE TRAIT LOCUS AFFECTING RESISTANCE TO TILAPIA LAKE VIRUS IN FARMED NILE TILAPIA (_OREOCHROMIS NILOTICUS_) Article Open

access 14 July 2021 GENOMIC SELECTION FOR WHITE SPOT SYNDROME VIRUS RESISTANCE IN WHITELEG SHRIMP BOOSTS SURVIVAL UNDER AN EXPERIMENTAL CHALLENGE TEST Article Open access 25 November 2020

INTRODUCTION The Pacific white shrimp _Litopenaeus vannamei (L. vannamei_) is a world-wide aquaculture species. After it was firstly introduced to China in 1988, its production has increased

rapidly, and it has become the major cultured shrimp species with an annual output value of 7.4 billion US dollars. However, the huge profit is accompanied by multiple challenges like

environmental degradation and various shrimp diseases. White spot syndrome virus (WSSV) is regarded as the most severe virus for its wide spread ability and high lethality1. Up to now, there

is no effective method to prevent the shrimp from WSSV infection. Breeding of WSSV-resistant varieties should be the most effective method to solve the virus disease problem. As a threshold

trait, disease-resistance appears to have low heritability, which is easily influenced by external environments. Marker assisted selection (MAS) is considered to be an effective method in

breeding for a complex trait, especially for disease resistance. In order to conduct MAS, markers related to disease-resistance is needed. Association analysis is regarded as a powerful tool

for trait-related marker screening2,3. Through this approach, multiple quantitative trait loci (QTLs) have been successfully identified in some species4,5,6. Genome-wide association study

(GWAS) and candidate gene association study are two powerful strategies for association analyses. GWAS has been applied for mapping complex diseases traits in human beings, economic crops

and animals7,8. However, the genome reference sequence is necessary for GWAS. For species without whole genome sequences, the candidate gene strategy was more widely used due to its

advantages in specificity and accuracy for association analysis9. With the development of Single Nucleotide polymorphisms (SNPs) genotyping method, a large number of SNPs can be genotyped

simultaneously at a lower cost. Recently, SNPs in gene sets based on regulatory networks and gene pathways began to be used for association analyses10,11,12. Gene set based association

analyses can eliminate redundant information, improve the efficiency at a lower cost and detect associations with weak effects13,14. This method has been widely used in human diseases

analysis and economic traits studies of animals11,15,16. In shrimp, innate immune signaling pathways, including Toll pathway, IMD pathway and JAK/STAT pathway were involved in the immune

response of shrimp to bacterial or WSSV infection. The immune response of innate immunity includes pathogen recognition, signal transduction and production of effectors. A large number of

effectors including antimicrobial peptides (AMPs), antioxidant enzymes, hemocyanin _etc._ are produced to kill the pathogens directly. Both Toll pathway and IMD pathway were responsive to

bacteria and virus, and JAK/STAT pathway was involved in WSSV infection17. Therefore the key genes involved in the innate immunity were regarded as the basis for disease resistance of

shrimp. The generalized multifactor dimensionality reduction (GMDR) method was developed based on the multifactor dimensionality reduction (MDR) method to be applied in detecting the

interactions for gene-by-gene or gene-by-environment18,19,20,21. The genes used for GMDR analysis were usually from one pathway which showed similar functions11,15. It has been used for risk

assessment on cancer and other diseases in humans14,22,23. This method was recently introduced for genetic breeding of livestock and poultry11. In the present study, the SNPs in the gene

set of the signaling pathways regulating the innate immune response to WSSV infection were selected for association analysis. The GMDR method was used to further explore the influence of

gene-by-gene interactions on the WSSV resistance/susceptibility of shrimp. RESULTS WSSV COPY NUMBERS IN WSSV-SUSCEPTIBLE AND WSSV-RESISTANT GROUPS Before WSSV infection, the average viral

load of WSSV per ng DNA of shrimp from the experimental population was 7.46 copies/ng, so the experimental shrimp could be regarded as WSSV free. At 3 days after WSSV infection, the average

viral load in dead shrimp in shrimp reached 1.05 × 105 copies/ng DNA. After checking the virus load in shrimp from WSSV-susceptible (sus.) group or WSSV-resistant (res.) group, we found that

the viral load in shrimp from sus group was (1.286 ± 0.355) × 105 copies/ng DNA, while that in res. group was (2.793 ± 0.982) × 103 copies/ng DNA. The _P_ value from the independent t-test

between two groups was 0.001, which means that a very significant difference existed for the viral load between sus. and res. group. SNPS VALIDATION, SELECTION AND GENOTYPING Validation on

the SNPs predicted by bioinformatics analysis was shown in Table S1. The prediction accuracy on the SNPs from Illumina data was 86.5% when the quality score was set at more than 20. The

prediction accuracy for SNPs with high quality score (Q > 100) and relative low score (20 ≤ Q ≤ 100) showed no obvious difference. Meanwhile, the parameter of _duel min_ was a determinant

factor for SNPs prediction with 454 data. The prediction accuracy of SNP loci with parameter _duel min_ ≥9 reached 88.2%, while it was not accurate for loci with duel min <9. Thus SNPs

with quality score higher than 20 and duel min more than 8 were used as basic data for later SNP screening. Through bioinformatics analysis, a total of 868 loci from Illumina sequencing data

and 776 loci from 454 sequencing data, which were located in the immune related genes, were screened with the Q value higher than 20 for Illumina data and _duel min_ higher than 9 for 454

sequencing data. After blasting the related cDNA sequence of these unigenes to the assembled genome sequences established in our lab, 681 loci (593 from Illumina data, 88 from 454 data) with

high quality were obtained for further SNPs genotyping analyses in sus. and res. group. Due to the limitations of primer design for the genotyping analysis, a total of 77 SNPs from the 681

loci were chosen for the genotyping based on the SNP annotation and function. POPULATION STRATIFICATION AND ASSOCIATIONS ANALYSIS After quality control, a total of 51 SNPs were qualified for

genotyping in 93 individuals of shrimp. The multidimensional scaling analysis (MDS) on these shrimp based on 51 SNPs showed that they were evenly distributed, which indicated that no

genetic stratification existed in the studied population (Fig. 1). The genome-wide degree of inflation (λ) was 1.11 and 1.18 for additive and dominance model respectively, which showed that

the structure of the studied population had a minor impact on the association analyses. Under the additive model, three SNPs located in unigene 15411 (_TLR_, sSNP), unigene 16729 (_TRAF6_,

nsSNP) and unigene 34129 (_BRAFLDRAFT_123571,_ sSNP) were associated with WSSV resistance at a significant level (P < 0.05). Under the dominance model, five SNPs located in unigene 15411

(_TLR_, sSNP), unigene 34129 (_BRAFLDRAFT_123571,_ sSNP), unigene 16729 (_TRAF6_, nsSNP), unigene 34569 (_Cu/Zn SOD_, sSNP) and unigene 30237 (_STAM_, 3′ UTR) were significantly associated

with the WSSV resistance of shrimp (P < 0.05) (Table 1, Table S3). GMDR ANALYSIS FOR SNP-SNP INTERACTION ASSOCIATED WITH WSSV RESISTANCE All loci showing associations with WSSV resistance

identified in this study and those in our previous study24,25 were used for GMDR analyses to detect the combined effects. The best interaction models from two-loci to four-loci were shown

in Table 2. Combination of three SNPs (Unigene34569, Unigene16729 and _nLvALF2_ g.2422) exhibited significant association with resistance/susceptibility of shrimp to WSSV. The interaction

among above three SNPs were shown in Fig. 2, in which the risks of shrimp to WSSV infection caused by different combinations of SNPs were displayed. Different combinations of genotypes were

classified as ‘high risk’ (hr) group and ‘low risk’ (lr) group. Individuals with low risk combinations are supposed to be more difficult to be infected than those with high risk

combinations. Therefore, shrimp with the combination of AA-TT-AG, AA-CC-AA, AG-TT-AA, AG-TT-AG, AG-CT-AG, AG-CC-AA, AG-CC-AG in _nLvALF2_ g.2422, Unigene34569, Unigene16729 were supposed to

be susceptible to WSSV, while shrimp with the combination of AA-TT-AA, AA-CT-AA, AA-CT-AG, AG-CT-AA were resistance to WSSV. VERIFICATION OF GMDR ANALYSIS Before WSSV injection, the viral

load of WSSV in shrimp were around zero, which indicated that the shrimp was WSSV free. The viral load in shrimp from the ‘high risk’ (hr) group was much higher than that in shrimp from the

‘low risk’ (lr) group at 48 hpi, However, no obvious difference was observed between these two groups at 72 hpi (Fig. 3). The expression levels of Unigene 34569 (_Cu/Zn SOD_), Unigene 16729

(_TRAF6_) and _nLvALF2_ in hr group and lr group showed significant differences (_P_ < 0.01) (Fig. 4) in shrimp without WSSV infection. During WSSV challenge, the gene expression levels

of Unigene34569 (_Cu/Zn SOD_), Unigene16729 (_TRAF6_) in lr group were higher than those in hr group at 48 h after infection, while no difference was detected for _nLvALF2_ at this time. At

72 h after WSSV infection, the expression levels of _TRAF6_ and _nLvALF2_ in lr group were higher than those in hr group, while no difference was detected for the expression level of _Cu/Zn

SOD_ between these two groups. DISCUSSION SNPs are the most abundant and widely-distributed mutations in genomes. They can be divided into coding-region SNPs (cSNPs), perigenic SNPs (pSNPs)

and intergenic SNPs (iSNPs) based on their positions in the genomes. As the SNPs may directly influence the gene translation or gene transcription, they were widely used as molecular markers

for disease genetics and pharmacogenomics studies26. The application of SNPs in association study for quantitative trait locus (QTL) mapping also have been attracting more

interests27,28,29. As enormous transcriptome data were produced by next generation sequencing, high throughput SNPs can be easily obtained by bioinformatics prediction. In our previous

study, a total of 96,040 SNPs were predicted from two transcriptomes of _L. vannamei_. In order to reduce the false-positive rate of SNPs prediction and obtain the most accurate SNPs, we

firstly analyzed the filtering parameters for different sequencing methods in the present study. The SNPs prediction accuracy was up to more than 86% when a high Q score (for Illumina

sequencing data) and _duel min_ score (for 454 sequencing data) were set30. Shrimp immune related genes were involved in the defense of shrimp against WSSV infection31. In the present study,

77 loci located in the immune related genes were chosen as candidate SNPs for WSSV resistance analyses. The JAK/STAT signaling pathway plays key roles in the antiviral immunity17. In the

present study, one locus on unigene 30237 (_STAM_, 3′ UTR SNP) was associated with the WSSV resistance of shrimp both in additive and dominance model (P < 0.05). STAM (Signal transduction

adaptor molecule), as a key gene in the JAK/STAT signaling pathway, was directly interacted with JAK to mediate the signal transduction and promote the endocytosis of WSSV32. It was

reported that SNPs located at the 3′ UTR of the gene could alter the gene transcriptional efficiency through affecting the combination between mRNA and the regulatory elements33,34. As the

identified locus located at 3′ UTR of _STAM_, the SNP in unigene 30237 (_STAM_) which showed association with WSSV resistance might affect the transcriptional efficiency of _STAM_. The SNPs

located in unigene 16729 (_TRAF6_, nsSNP) and unigene 15411 (_TLR_, sSNP) showed significant association with WSSV-resistance (P < 0.05). TLR are the upstream factor in Toll pathway, and

_TRAF6_ can regulate the activation of NF-kB and various stress kinases35. _TRAF6_ showed up-regulation at the transcriptional level after WSSV infection, and could activate the expressions

of AMPs in _L. vannamei_36. The detected SNP in _TRAF6_ was a nonsynonymous mutation which changed the amino acid sequence. Considering the crucial function of _TRAF6_ in Toll pathway, this

nonsynonymous SNP may play important roles in shrimp to defend WSSV infection. One SNPs located in genes encoding immune effectors also showed association with WSSV-resistance. Shrimp with

allele C at locus 389 of Unigene34569 (_Cu/Zn SOD_, sSNP) showed resistance to WSSV. This SNP was a synonymous SNPs. Although the synonymous SNPs can not change the protein structure

directly, it has been proved that sSNP can affect the biological process by the following three mechanisms. One was that sSNPs and some functional nsSNPs might be in strong linkage

disequilibrium, and the second was that allele-specific differences in mRNA folding might influence the splicing, transcription and regulation. The third explanation was that the codon usage

bias can affect the protein translation and folding. As mentioned previously, multiple molecules in the humoral and cellular immune system take part in defending against WSSV. Compared to

one gene, mutations in gene pathways could explain most phenotypic variance for certain traits16,37. That’s why we chose lots of SNPs from immune gene pathways in this research. Through this

strategy, more potential genes or SNPs which might affect the WSSV-resistance could be found. In some ways, development in human researches can greatly facilitate the study in animals or

non-model species. Although thousands of SNPs were detected to be associated with various human diseases, most of them can only explain part epigenetic changes of complex disease38. As we

know, the biological processes are usually accomplished by the interactions among lots of molecules or pathways. Investigation on the contribution of functionally relevant gene-gene

(SNP-SNP) interaction and gene-environment interaction to complex diseases has been proved to be useful in many cases11,13,21,22. Statistical method including logistic regression models,

multi-loci linkage disequilibrium (LD) tests were developed to analyze a large sample size data39. Using unconditional multiplicative logistic regression method, researchers have found the

interaction of SNP genotypes in several mismatch repair genes was associated with risk for breast cancer15. MDR method was the primary tool for studying genes or SNPs interaction network for

moderate sample size data18. Different versions like OR-MDR, GMDR, MB-MDR were created for different purposes19,40,41. Based on the GMDR analysis, several SNPs as well as SNP-SNP

combination from the IGF1/FoxO signal transduction pathway were reported to be associated with economical traits in pigs12. The present study applied the GMDR method in shrimp WSSV

resistance association analysis for the first time. The loci in _LvALFs_ related to WSSV resistance of shrimp found in our previous studies were also involved24,25. In the present study, a

three-factor model consisted of loci from unigene34569, unigene16729 and _nLvALF2_ showed significant association with viral resistance. According to GMDR analysis results, lr group and hr

group were divided based on the genotyping result of above three loci. The average WSSV copy numbers in hr group was apparently higher than that in lr group at 48 h after WSSV infection,

which suggested that lr group might have higher WSSV resistance than hr group. When we compare the expression level of unigene34569, unigene16729 and _nLvALF2_ in lr group and hr group, we

found that the three genes in lr group showed higher expression levels than those in the hr group. These data indicated that the high expression of these genes might affect the replication

of WSSV. Further work needs to be done to validate the combination of SNPs related to WSSV resistance of shrimp in a larger population and investigate the feasibility of these markers in

shrimp breeding. CONCLUSION This study presents the first gene set based association analyses for WSSV resistance in _L. vannamei._ A total of 5 SNPs in these immune related genes were

detected to be associated with WSSV resistance. The marker combinations associated with WSSV resistance were also identified through SNP-SNP interaction analysis. These results were useful

for understanding the genetic basis for disease resistance in shrimp and will assist the selective breeding of shrimp with WSSV-resistance. MATERIALS AND METHODS EXPERIMENTAL ANIMALS FOR

ASSOCIATION STUDY The shrimp individuals used for association analysis were generated from multiple families of Kehai No. 1 variety, which was a new selective breeding variety in China42.

Five hundred healthy shrimp, with an average body length of 6.18 ± 1.11 cm and body weight of 3.59 ± 1.57 g, were cultured in filtered sea water at 24 °C with continuous aeration in plastic

tanks. The shrimp were reared without feeding for 2 days. Then the shrimp were fed with WSSV infected shrimp tissue (average 105–106copies/ng DNA) four times (2 times per day) to simulate

the normal infection process during shrimp culture. Every shrimp was checked to ensure that they ate shrimp tissues infected with WSSV. The shrimp which did not eat would be fed with shrimp

tissues in the second day. After the infection, the shrimp were fed with artificial food pellets twice a day. All tanks were checked daily and dead shrimps were collected and marked. On the

16th day after artificial infection, the number of live shrimp was stable. Forty eight survived individuals on 19th day were regarded as WSSV-resistant samples (res group), and forty eight

individuals died at the beginning after inoculation were regarded as WSSV-susceptible samples (sus group). All experimental materials were stored at −20 °C for DNA extraction and virus

detection. SNP VALIDATION AND SELECTION SNPs were predicted based on several transcriptomic data sets through Illumina Hiseq 2500 and Roche 454 sequencing30. According to annotations, all

SNPs in 49 unigenes predicted from Illumina data and in 7 unigenes predicted from 454 data related with JAK/STAT, TOLL and IMD pathways were picked out. SNPs with quality score higher than

20 were chosen for further analysis. In order to analyze the accuracy of predicted SNPs, we randomly selected 43 loci from Illumina data and 32 loci from 454 data for validation by PCR

method (Table S2). After validation, SNPs with high quality were selected and the genomic sequence of selected SNPs were obtained by blasting the unigenes’ cDNA sequences to the genomic

database of our lab. SNP GENOTYPING Selected SNPs located in the immune related pathways were genotyped in WSSV susceptible/resistant groups using ABI Prism SNaPshot™ Multiplex System. For

SNaPshot genotyping, the SNP must satisfy the condition that no other SNPs existed in the upstream and downstream sequence of the SNP within 150 bp. Therefore, 12 SNPs located in the genes

involved in JAK/STAT pathway, 19 SNPs in TOLL pathway related genes, 12 SNPs in IMD pathway related genes, and 34 SNPs located in other immune genes were genotyped (Table 3). The SNP

information were submitted to NCBI dbSNP with submitted SNP (ss) number from 2137123241 to 2137123317. The SNP genotyping was conducted by Shanghai MapBiotech Company Limited. For easy to

describe, the name of the unigenes used in later part represented the testing SNP locus in it. DATA ANALYSIS Quality control of genotyped SNPs was performed using “_check.marker_” function

in GenABEL package43. Those SNPs with low Minor Allele Frequency (MAF), low call rate and deviation to Hardy-Weinberg Equilibrium were dropped prior to main analysis. Multidimensional

scaling were implemented on filtered SNPs to assess the population stratification in GenABEL package. As the phenotype is binary, we performed association analysis using logistic regression

method under an additive and dominance model, the body weight of each shrimp was used as covariate. This analysis was accomplished by “_mlreg_” function in GenABEL package. The genome-wide

degree of inflation (λ) was calculated to test for any hidden substructure, and the final P-value was corrected by inflation factor. Associations were deemed statistically significant at the

5% empirical significance cutoff. In order to know the potential interactions among SNPs located in different genes, SNPs associated with WSSV resistance with statistical significance (P ≤ 

0.05) were chosen for further gene-gene interaction analysis. Meanwhile, SNPs located in _nLvALF1_ and _nLvALF2_ (loci _nLvALF1_ g.1370, g.1419 and _nLvALF2_ g.2422, g.2466, g.2529) which

was proved to be potentially associated with WSSV resistance in previous reports24,25 were also chosen for the interaction analysis. Through the cross-validation strategy, GMDR divided

various combinations of genotypes into ‘high’ or ‘low’ risk genotypes19. In the present case, the high risk means more susceptible to WSSV, and the best two-, three-, and four-factor (SNP)

models were given. Shrimps with ‘low’ risk genotypes were considered to be high resistant to WSSV than those with ‘high’ risk genotypes. Models were considered to be significant at _P_ < 

0.05. VALIDATION OF SNPS INTERACTION MODEL Two hundred health shrimp carrying no specific pathogens were chosen to verify the effect of gene-gene interaction based on the best three-factor

model given by GMDR analysis. Firstly, we separated shrimp into two groups (100 per group), then each shrimp was labeled with different combinations of four fluorescent dyes (green, red,

blue, orange) (Fig. 5) in different part of the body. After marking, two pleopods from each shrimp were taken to extract DNA for SNP examinations. Shrimp were then reared in seawater with

continuous aeration. Genomic DNA extracted from the pleopods of each shrimp were used for SNPs genotyping by Sanger sequencing. The primers used for genotyping were shown in Table 4. All PCR

products were sent to Beijing Genomics Institute for sequencing. Shrimps with predicted best combinations were grouped as ‘low’ risk (lr) group and vice versa. There were forty individuals

in lr group and hr group, respectively. Each shrimp was injected with 104 copies of WSSV (dissolved in PBS) at the last abdominal segment (set as 0 h) following the method described

previously24. Five or six shrimp were collected at 0, 48 and 72 h after WSSV injection (hpi) from lr group and hr group, separately, and these shrimps were preserved in liquid nitrogen for

further gene expression analysis. Shrimp died during the experiment were also collected for virus check. The swimming legs were picked to extract DNA for quantification of viral load. Total

RNA was extracted from cephalothoraxes. The procedures for RNA and genomic DNA extraction were the same as those described previously24. The cDNA synthesis was performed using PrimeScript RT

Reagent kit with gDNA Eraser (TaKaRa, Japan). The virus load in each shrimp was checked according to the method described by Sun _et al_.44. Transcriptional levels of the three genes

referred in the best three-factor models, including unigene34569, Unigene16729 and _nLvALF2_ were analyzed by real-time qPCR (RT-qPCR). 18 S rRNA was used as a reference gene. Relative

expression levels were calculated by 2−ΔΔCt method. The average transcriptional level of the genes in hr group and lr group at 0 h were used as controls. All these data were analyzed by

independent samples _t-test_ with a significance level of _P_ = 0.05 using SPSS 16.0. ADDITIONAL INFORMATION HOW TO CITE THIS ARTICLE: Yu, Y. _et al_. Gene set based association analyses for

the WSSV resistance of Pacific white shrimp _Litopenaeus vannamei. Sci. Rep._ 7, 40549; doi: 10.1038/srep40549 (2017). PUBLISHER'S NOTE: Springer Nature remains neutral with regard to

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Science Foundation of China (Grant No. 31502161 and 31272683), China Agriculture Research System-47 (CARS-47), and the Scientific and Technological Innovation Project Financially Supported

by Qingdao National Laboratory for Marine Science and Technology (No. 2015ASKJ02). AUTHOR INFORMATION Author notes * Yang Yu and Jingwen Liu: These authors contributed equally to this work.

AUTHORS AND AFFILIATIONS * Key Laboratory of Experimental Marine Biology, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, 266071, China Yang Yu, Jingwen Liu, Fuhua Li, Xiaojun

Zhang, Chengsong Zhang & Jianhai Xiang * University of Chinese Academy of Sciences, Beijing, 100049, China Jingwen Liu * Laboratory for Marine Biology and Biotechnology, Qingdao

National Laboratory for Marine Science and Technology, Qingdao 266237, China. , Fuhua Li & Jianhai Xiang Authors * Yang Yu View author publications You can also search for this author

inPubMed Google Scholar * Jingwen Liu View author publications You can also search for this author inPubMed Google Scholar * Fuhua Li View author publications You can also search for this

author inPubMed Google Scholar * Xiaojun Zhang View author publications You can also search for this author inPubMed Google Scholar * Chengsong Zhang View author publications You can also

search for this author inPubMed Google Scholar * Jianhai Xiang View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Y.Y. and J.L. conducted the

experiment and data processing. J.X. and F.L. conceived and supervised the project. X.Z. contributed to prepare the genomic DNA for SNP genotyping. C.Z. prepare and cultured the experimental

animals. Y.Y., J.L. and F.L. wrote the manuscript. All authors have read and approved the manuscript. CORRESPONDING AUTHOR Correspondence to Fuhua Li. ETHICS DECLARATIONS COMPETING

INTERESTS The authors declare no competing financial interests. SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION (PDF 177 KB) RIGHTS AND PERMISSIONS This work is licensed under a Creative

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of this license, visit http://creativecommons.org/licenses/by/4.0/ Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Yu, Y., Liu, J., Li, F. _et al._ Gene set based association

analyses for the WSSV resistance of Pacific white shrimp _Litopenaeus vannamei_. _Sci Rep_ 7, 40549 (2017). https://doi.org/10.1038/srep40549 Download citation * Received: 04 July 2016 *

Accepted: 07 December 2016 * Published: 17 January 2017 * DOI: https://doi.org/10.1038/srep40549 SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

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