INVESTIGATION Genome-Wide Association Analysis for Resistance to Infectious Pancreatic Necrosis Virus Identifies Candidate Genes Involved in Viral Replication and Immune Response in Rainbow Trout (Oncorhynchus mykiss) Francisco H. Rodríguez,*,† Raúl Flores-Mara,*,‡ Grazyella M. Yoshida,*,§ Agustín Barría,* Ana M. Jedlicki,* Jean P. Lhorente,§ Felipe Reyes-López,** and José M. Yáñez*,††,1 *Facultad de Ciencias Veterinarias y Pecuarias, Universidad de Chile, 8820808, La Pintana, Santiago, Chile, †Facultad de Medicina Veterinaria y Zootecnia, Universidad Nacional del Altiplano, Av. Floral 1153, Puno, Perú, ‡Escuela Profesional de Medicina Veterinaria y Zootecnia, Facultad de Ciencias de la Salud, Universidad Andina Néstor Cáceres Velásquez, Juliaca, Puno, Perú, §Benchmark Genetics Chile, Puerto Montt, Chile, **Department of Cell Biology, Physiology and Immunology, Universitat Autònoma de Barcelona, 08193, Barcelona, Spain, and ††Núcleo Milenio INVASAL, Concepción 4070386, Chile ORCID IDs: 0000-0002-3967-0410 (F.H.R.); 0000-0002-9722-2050 (R.F.-M.); 0000-0002-6788-7369 (G.M.Y.); 0000-0002-2813-4559 (A.B.); 0000-0003-0626-1247 (A.M.J.); 0000-0002-9157-4231 (J.P.L.); 0000-0002-5001-457X (F.R.-L.); 0000-0002-6612-4087 (J.M.Y.) ABSTRACT Infectious pancreatic necrosis (IPN) is a viral disease with considerable negative impact on the KEYWORDS rainbow trout (Oncorhynchus mykiss) aquaculture industry. The aim of the present work was to detect Bayes C genomic regions that explain resistance to infectious pancreatic necrosis virus (IPNV) in rainbow trout. A Oncorhynchus total of 2,278 fish from 58 full-sib families were challenged with IPNV and 768 individuals were genotyped mykiss (488 resistant and 280 susceptible), using a 57K SNP panel Axiom, Affymetrix. A genome-wide association Disease study (GWAS) was performed using the phenotypes time to death (TD) and binary survival (BS), along with resistance the genotypes of the challenged fish using a Bayesian model (Bayes C). Heritabilities for resistance to IPNV GWAS estimated using genomic information, were 0.53 and 0.82 for TD and BS, respectively. The Bayesian GWAS QTL detected a SNP located on chromosome 5 explaining 19% of the genetic variance for TD. The proximity of Candidate genes Sentrin-specific protease 5 (SENP5) to this SNP makes it a candidate gene for resistance against IPNV. In case of BS, a SNP located on chromosome 23 was detected explaining 9% of the genetic variance. However, the moderate-low proportion of variance explained by the detected marker leads to the conclu- sion that the incorporation of all genomic information, through genomic selection, would be the most appropriate approach to accelerate genetic progress for the improvement of resistance against IPNV in rainbow trout. Infectious pancreatic necrosis (IPN) is a highly transmissible (Salmo salar) (Guy et al. 2006) and rainbow trout (Oncorhynchus disease caused by IPN virus (IPNV) which belongs to the Birna- mykiss) families (Flores-Mara et al., 2017). viridae family, genus Aquabirnavirus (Roberts and Pearson 2005). Disease resistance represents a broad term defined as the ability of a This virus affects several wild and cultured aquatic organisms. host to exert some degree of control over the pathogen life cycle (Bishop Salmonid species are especially susceptible to IPNV, thus this and Woolliams 2014). From a quantitative genetics point of view, disease has a great impact on fish farm operations. The mortality disease resistance in fish can be measured using survival data from levels during an IPN outbreak are influenced by numerous fac- either field tests or controlled experimental challenges (Ødegård et al. tors; including species, age of the fish, environmental conditions 2011, Yáñez et al. 2014). In salmonids it has been possible to determine (Dorson and Touchy 1981), and genetic background, which the presence of significant genetic variation for resistance to bacterial has been proven to confer resistance to some Atlantic salmon (Gjedrem et al. 1991; Gjedrem and Gjøen 1995; Leeds et al. 2010; Volume 9 | September 2019 | 2897 Silverstein et al. 2009; Yáñez et al. 2013; Yáñez et al. 2016; Vallejo et al. and 20 males of rainbow trout from the 2014 year-class. For more details 2017; Yoshida et al. 2018; Barría et al. 2018), parasitic (Glover et al. on the description about rearing conditions and population manage- 2005; Kolstad et al. 2005; Lhorente et al. 2012; Yáñez et al. 2013; ment see Flores-Mara et al. (2017), Neto et al. (2019) and Rodríguez Ødegård et al. 2014), and viral diseases (Guy et al. 2006; Henryon et al. (2018). et al. 2005; Ødegård et al. 2007). This implies the possibility of improv- ing, through artificial selection, resistance to various diseases in order to Experimental challenge enhance disease control strategies in farmed fish. For a detailed step-by-step protocol for experimental challenge Prior to include IPNV resistance as part of the breeding goal it is please see Yoshida et al. (2019). Briefly, fish at an average age of necessary to determine the genetic variance and heritability for the 154 (SD = 15) days, weighing an average of 2.24g (SD = 0.71) were trait. The current dataset comprises animals that were subjected to kept in a single 0.25 m3 tank under a fresh water recirculation an IPNV experimental challenge, as previously reported by system. The experimental challenge was performed in two steps; i) Yoshida et al. (2019). However, these authors focused in estimat- intraperitoneal injection of inoculum, at a concentration of 107.82 ing genetic parameters and evaluating the accuracy of estimated TCID50/mL, using a quantity of 0.05 mL/fish; and ii) immersion breeding values using pedigree- and genomic-based information. pouring 1.1 L of the inoculum plus 5 L of water into the tank con- In contrast, the current work provides novel insights of the genetic taining 130 L of fresh water, maintained at retained flow at 17° for architecture and the identification of genomic regions associated 4 h. Mortality was individually recorded on a daily basis. At day to the resistance against IPNV in rainbowtrout. The detection of 63 the experiment was stopped and all surviving animals were killed. quantitative trait loci (QTL) is the starting point for determining Fin clip samples were taken from 768 fish and stored in 98% ethanol functional variants involved in quantitative traits. In addition, this at -80° until genomic DNA extraction. The phenotypic data for information could be used to accelerate the improvement of traits resistance to IPNV were obtained from a total of 2,278 fish. through the application of marker assisted selection (MAS) or genomic selection (GS) (Yáñez et al. 2015). The identification of Genotyping genes that underlie QTL can lead to fundamental knowledge of For the protocol used for genomic DNA extraction, genotyping and genetic regulation for disease resistance and host-virus interac- quality control (QC) please see Yoshida et al. (2019). Briefly, each tions in fish (Moen et al., 2009). fish was genotyped using a 57K Affimetrix Axiom SNP array de- QTL for resistance against various diseases have been deter- veloped by Palti et al. (2015). The QC included Hardy Weinberg mined in salmon and trout (Barría et al., 2018; Kjøglum et al., 2006; equilibrium, minor allele frequency and call rate for SNPs and sam- Ozaki et al., 2007; Correa et al., 2016; Correa et al., 2015; Moen ples. The animals and markers which passed the QC were subse- et al., 2015; Houston et al., 2008; Houston et al., 2012). In Atlantic quently used for genomic association analysis. salmon there is considerable evience for a major QTL for resis- tance against IPNV in post-smolts, based on data from both field Genome-wide association analysis (GWAS) outbreaks (Houston et al., 2008) and experimental challenges The resistance phenotypes were defined as the time to death (TD), (Moen et al., 2009). The discovery of a candidate gene that can measured in days with values ranging from 1 to 63, depending on the bind the virus and successful marker-assited selection for IPNV day of death; and the binary survival (BS), recorded as 1 if the resistance has also been reported in Atlantic salmon (Moen et al., individual died during the challenge and 0 if the individual survived 2015). However, in rainbow trout there is limited information on until the end of the trial. The Bayes C (Habier et al. 2011) analyses the molecular genetic basis of resistance to IPNV. Thus, the aim of were performed using the GS3 software (Legarra et al. 2010). A total the present study was to perform a genome-wide association of 200,000 iterations were used in the Gibbs sampling, with a bur- (GWAS) analysis to determine the genetic architecture for IPNV n-in period of 50,000 cycles where results were saved every 50 cycles, resistance and to identify potential candidate genes involved in the totaling 4,000 samples. Convergence and autocorrelation were genetic variation of this trait in rainbow trout. assessed by visual inspection of trace plots of the posterior variance components. The adjusted model can be described, in matrix nota- MATERIALS AND METHODS tion, as follows: Fish X n The population used in this study belonged to the breeding population y ¼ Xb þ Zu þ gi ai di þ e maintained at Aguas Claras S.A. Experimental tests were conducted i¼1 at the ATC Patagonia Research Center - Aquainnovo (Puerto Montt, where y is the vector of phenotypic records for TD or BS; X is an Chile).The fish belongedto 58full-sib familiesgenerated from 58females incidence matrix of fixed effects; b is the vector of fixed effect (tagging weight as covariate); Z is an incidence matrix of polygenic effects; u is Copyright © 2019 Rodriguez et al. a random vector of polygenic effects of all individuals in the pedigree; doi: https://doi.org/10.1534/g3.119.400463 gi is the vector of the genotypes for the ith SNP for each animal; ai is Manuscript received April 28, 2019; accepted for publication July 2, 2019; the random allele substitution effect of the ith SNP, di is an indicator published Early Online July 19, 2019. variable (0, 1) sampled from a binomial distribution with such de- This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/ termined parameters that 1% of the markers were included in the licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction model; and e is a vector of residual effects. in any medium, provided the original work is properly cited. The proportion of the genetic variance explained by each2 SNP was 2p q a Supplemental material available at FigShare: https://figshare.com/articles/ calculated according to the following formula: Vgi ¼ ð si 2i i Þ; where Untitled_Item/7725668. g 1 Corresponding author: Facultad de Ciencias Veterinarias y Pecuarias, Universidad pi and qi are the allele frequencies for the i-th SNP; ai is the estimated de Chile, Avenida Santa Rosa 11735, 8820808, La Pintana, Santiago, Chile, E-mail: additive effect of the i-th SNP on the phenotype; and is s2g the estimate jmayanez@uchile.cl of the polygenic variance (Lee et al. 2013). 2898 | F. H. Rodríguez et al. n Table 1 Summary statistics for the 768 genotyped fish Variable Mean Standard deviation Standard error Minimum Maximum Weight (g) 2.12 0.74 0.03 0.80 5.80 Time to death (days) 51.46 13.98 0.51 13.00 62.00 Binary survival (0 or 1) 0.36 0.48 0.02 0.00 1.00 The association of the SNPs with phenotypes were assessed using DISCUSSION Bayes factor (BF), which was calculated as follows: BF ¼ ð1 2p pÞ=ð1 2p pÞ, Experimental challenge and heritability where p is the posterior probability of a SNP to be assigned a non-zero IPN is one of the main diseases affecting salmonid aquaculture. Our effect and p = 0.99 is the a priori probability of a SNP to be included in current work was done using data obtained from an experimental the analysis (Kass & Raftery 1995; Varona et al. 2001). challenge to IPNV previously presented by Yoshida et al. (2019). Nonetheless, previous authors focused on determining the im- Candidate genes provement of genomic prediction accuracies using a single step The nucleotide sequences surrounding the top ten SNPs that approach (including genotyped and non-genotyped animals) for accounted for the largest proportion of genetic variance for each the genetic evaluations of resistance to IPNV. The current study of the IPNV resistance traits were positioned in the most recent focused on providing novel insights of the genetic architecture of version of the rainbow trout reference genome available from IPNV resistance and the genomic regions involved in this trait in a NCBI (GenBank assembly Accession GCA_002163495) (Gao farmed rainbow trout population. The animals used in this study et al., 2018; Pearse et al., 2018) using BLASTx (Basic Local Align- were genotyped using a selective genotyping approach. The alloca- ment Search Tool) (Altschul et al. 1997). The presence of anno- tion of genotyping resources was directed to the most genetically tated genes within 1Mb windows surrounding the top ten SNPs informative progeny (extremely high and low phenotypic values). was assessed. The selective genotype is a common strategy used to reduce geno- typing costs and it has been shown to be highly suitable for QTL Data availability detection in genome-wide association studies in livestock and Phenotype data and genotypes are available at the online reposi- aquaculture species. tory Figshare.Supplemental material available at FigShare: https:// The mortality rate in IPNV challenge experiments is highly variable figshare.com/articles/Untitled_Item/7725668. and depends on several factors, including the virus strain, the population evaluated, the size of the fish, environmental condi- RESULTS tions, among others. For example, mortality rates over 40% have been reported for Atlantic salmon (Roberts and Pearson 2005; Experimental challenge and samples Houston et al. 2008). Other authors have reported mortality rates The percentage of accumulated mortality was 13.77%, ranging between of 70.5% and 77.8% for the same disease and species (Moen et al. 0 to 47.6% for the most and least resistant family, respectively. A selective 2009), while in rainbow trout mortality rates range from 36 to 54% genotyping strategy was carried out and 768 fish were selected. Table 1 (Ozaki et al.,2007). shows the summary statistics of the animals selected for genotyping. Some studies have shown significant pedigree-based heritabilities for For a detailed description about experimental challenge results, please IPNV resistance in rainbow trout and Atlantic salmon (Flores- see Yoshida et al. (2019). Mara et al. 2017; Guy et al. 2006; Storset et al. 2007) using survival time as the trait definition. For instance, in Atlantic salmon, her- GWAS and candidate genes itability values ranging between 0.26 and 0.55 have been reported A total of 721 individuals and 38,296 SNP passed QC. The average for resistance to IPNV, measured as binary survival and analyzed number of SNPs per chromosome was 1,277 (SD = 384) and varied by threshold models (Wetten et al. 2007; Kjøglum et al. 2008; Guy between 594 and 1,818. The variance components and heritabilities et al. 2009; Gheyas et al. 2010; Houston et al. 2010). Previous estimated using genomic information are shown in Table 2. Heritabil- studies have reported pedigree-based heritability values of 0.39 ities were high for both traits (0.53 6 0.05 and 0.82 6 0.03 for TD and and 0.4 for time to death and 0.35 for binary survival in the same BS, respectively). The top SNP for both traits explained 18.89% and population of IPNV-infected rainbow trout (Flores-Mara et al. 9.31% of the genetic variance for TD and BS, respectively. The Figures 2017; Yoshida et al. 2019). Moreover, heritability values of 0.24 1a and 1b show the manhattan plot for both TD and BS, respectively. and 0.25 for day to death and binary survival, respectively, According to Vidal et al. (2005), a BF from 3 to 20 is suggestive of have been reported (Yoshida et al. 2019) using a combination of linkage and BF from 20 to 150 indicates linkage between the SNP and both the pedigree relationship matrix (A) and the genomic matrix the trait under investigation. In this study, we focused the results and discussion on the SNPs with a BF greater than 20. For TD, the top four n Table 2 Mean ± standard deviation of variance components estimated using genome-wide SNPs markers for time to death SNP that presented a BF greater than 20 are located on chromo- and binary survival for rainbow trout using Bayes C. somes 5 (AX-89921775), 13 (AX-89964133), 21 (AX-89928391) and 30 (AX-89972475). The sum of these SNPs explained 32.99% of all Total genetic genetic variance. For the BS trait only the top one SNP presented a Trait variance1 Residual variance Heritability BF greater than 20, it is located on chromosome 23 (AX-89938762, Time to death 191.066139.10 78.6066.78 0.5360.05 Figure 1b) and explained 9.31% of the genetic variance for this trait. The Binary Survival 4.6661.05 1.0060.00 0.8260.03 most relevant genes next to the top ten SNPs that explained the highest 1 Correspond to the genetic variance due to markers and the polygenic proportion of genetic variance for each trait are shown in Table 3. pedigree-based additive genetic variance. Volume 9 September 2019 | GWAS of IPNV Resistance in Rainbow Trout | 2899 Figure 1 Manhattan plot of variance explained by each SNP using Bayes C method. (A) Time to death. (B) Binary survival. 2900 | F. H. Rodríguez et al. n Table 3 Top 10 SNPs associated with resistance to IPNV in rainbow trout identified using Bayes C model. Marker Chr1 Position (Mb) BF2 Var3 Gene4,5(6) Time to death AX-89921775 5 86.195 93.824 0.188 Sentrin-specific protease 5 (346.42) AX-89964133 13 18.398 43.282 0.087 C-C chemokine receptor type 7 (91.96); Integrin alpha-11-like (239.47); Secretory phospholipase A2 receptor-like protein (279.15) AX-89928391 21 40.422 21.698 0.028 NI7 AX-89972475 Scaffold 3.313 22.659 0.024 Protein FAM49A ( 228.21) AX-89919030 9 59.547 15.660 0.013 Protein FAM3A (148.87) E3 ubiquitin-protein ligase HUWE1 (373.86) AX-89929860 21 33.915 14.577 0.013 Interleukin-8 (252.85) AX-89919605 8 15.655 14.905 0.012 Laminin subunit alpha-4 (403.02) AX-89964260 6 71.349 13.728 0.011 NI AX-89954884 11 23.271 14.522 0.010 Integrin beta-1 (498.18) AX-89955973 5 81.679 13.401 0.009 Dynamin 1 (83.40) Binary Survival AX-89938762 23 13.468 22.504 0.093 Apoptosis-stimulating of p53 protein 2-like ( 274.14) AX-89960378 6 69.996 9.787 0.016 NI AX-89950201 5 29.774 9.274 0.014 Myosin-IIIb-like (60.73) Protein FAM78A-like (418.01) AX-89951900 23 8.146 9.208 0.014 NI AX-89944302 5 29.773 8.910 0.013 Myosin-IIIb-like (60.41) Protein FAM78A-like (417.69) AX-89928391 21 40.422 8.618 0.013 NI AX-89952306 14 0.420 8.264 0.010 NI AX-89946704 5 86.995 7.395 0.008 Lamin-B2 ( 351.97) AX-89933583 15 13.021 7.392 0.008 E3 ubiquitin-protein ligase RNF31-like (377.72); Interferon regulatory factor 4-like (337.82); Signalosome complex subunit 5 (428.51) AX-89962297 8 12.919 7.680 0.007 Unconventional myosin-VI-like (38.04); Collagen alpha-1(XII) chain-like (195.72); Sentrin-specific protease 6 (73.33) 1 Chromosome. 2 Bayes factor. 3 Proportion of variance explained by each individual marker. 4 Suggestive candidate genes located within 1-Mb window. 5 Oncorhynchus mykiss used as reference species. 6 Proximity to the SNP (kbp) 7 Not identified (G), also called the H matrix (Aguilar et al. 2010). The heritability Some factors could influence the QTL detection, such as the values calculated in the present study using genomic information genetic architecture of the traits, the number of animals genotyped are higher than values previously reported in rainbow trout and and phenotyped, trait’s heritability, linkage disequilibrium (LD) be- indicate that selection to improve resistance to IPNV is feasible. tween markers and QTL, and models for QTL detection (Van den The proportion of genetic variance explained by the SNP with the Berg et al. 2013). The LD is dependent on the demographic history, largest effect was larger for trait TD than the SNP identified for effective population size, among others, whereas heritability is a trait BS. property of the trait, population and environmental condition in which the trait was measured (Falconer and Mackay 1996), suggest- GWAS and candidate genes ing that these parameters are intrinsic for a specific population. In Previous studies in Atlantic salmon have determined two significant our study we did not identify QTL with major effects, similar to QTL for resistance against IPNV. The most significant one, explaining those found by Houston et al. (2012) and Moen et al. (2015) in 29% and 83% of the phenotypic and genetic variance, respectively, was Atlantic salmon. Moreover, none of the QTL presented here were identified on chromosome 26 (Houston et al. 2008; 2012). This QTL nearby the known location of the epithelial cadherin gene in the was confirmed in an independent Atlantic salmon population from rainbow trout genome. A recent study compared the genomic re- Norway (Moen et al. 2009). In rainbow trout, Ozaki et al. (2001) found gions associated to the resistance against a bacterial infection in 2 QTL associated with resistance to IPNV using the linkage map elab- three salmonid species (O. kisutch, S. salar and O. mykiss), indicat- orated by Sakamoto et al. (2000) and based on microsatellite markers. ing that there is a reduced overlapping between the particular genes The first QTL was found in linkage group 3 while the second QTL was involved in the trait among species (Yáñez et al. 2019). identified in linkage group 22, each of which explains about 17% of the The SNP that explained the greatest proportion of genetic variance phenotypic variance (Ozaki et al. 2001). The same QTL were confirmed for TD in the present study (AX-89921775) is located on chromosome in a subsequent study (Ozaki et al. 2007), in which another significant 5 (Figure 1a), in a region that contains a gene that encodes Sentrin- QTL was detected in linkage group 12. Based on the linkage map de- specific protease 5. In mammals, this protein participates in the SUMO veloped by Palti et al. (2012) these significant markers were located on pathway (small ubiquitin-like modifier). Its function is mainly related to chromosome 14 and 16, respectively (Hu et al. 2016). the activity of isopeptidase (Pinto et al. 2012) and the regulation of Volume 9 September 2019 | GWAS of IPNV Resistance in Rainbow Trout | 2901 biological processes that are key to viral replication, including genetic CONCLUSIONS transcription, cell cycle, apoptosis, intracellular and intranuclear traf- This is the first work reporting the detection and position of QTL ficking, and protein stability. involved in IPNV resistance in rainbow trout using SNP markers. Resistance to infection can be the result of a more robust host Resistance to IPNV can be described as a moderately oligogenic trait immune response to the invading agent. In this study we identified since there are probably several loci involved; each with a moderate-low SNP located near several genes that encode proteins whose function is to effect. Potential candidate genes have been identified close to the control inflammation as well as other immune-related responses, in- associated SNPs, whose biological role in the fish immune response cluding the gene that encodes Secretory phospholipase A2 receptor-like suggest they could be involved in the mechanisms of resistance against protein (close to SNP AX-89964133 in chromosome 13). Phospholi- IPNV. pases A2 are enzymes released in plasma and extracellular fluids during inflammatory diseases, that induces the release of the pro-inflamma- ACKNOWLEDGMENTS tory cytokines TNF-a and IL-6 depending on the concentration of The authors would like to thank the company Aguas Claras S.A. for protein (Granata et al. 2005). providing the fish and data used in the present study. This study has Thr SNP AX-89933583 (chromosome 15) is associated to BS, been partially funded by a CORFO grant (12PIE-17669), Government which is located in a region that encodes interferon regulatory of Chile. Francisco H. Rodríguez and Raúl Flores-Mara would like to factor 4-like (irf4). This protein is important in both the regula- thank the grant Presidente de la República of the Government of Peru. tion of interferons in response to virus infection and the regulation of inducible genes by interferon. In fact, in mammals irf4 plays a LITERATURE CITED central role in TH cell regulation (Mahnke et al. 2016), a funda- Adcock, I. M., and G. Caramori, 2001 Cross-talk between pro-inflamma- mental cell-mediated process for the host antiviral response. The tory transcription factors and glucocorticoids. Immunol. Cell Biol. 79: SNP AX-89933583 is also located in a gene region that encodes 376–384. https://doi.org/10.1046/j.1440-1711.2001.01025.x for signalosome complex subunit 5-like, that is involved in Altschul, S. F., T. L. Madden, A. A. Schäffer, J. Zhang, Z. Zhang et al., the regulation of nuclear factor kappa B (NF-kB) one of the most 1997 Gapped BLAST and PSI-BLAST: A new generation of protein relevant transcription factors involved in the control of several database search programs. Nucleic Acids Res. 25: 3389–3402. https:// pro-inflammatory proteins (Adcock and Caramori 2001). doi.org/10.1093/nar/25.17.3389 Aguilar, I., I. Misztal, D. L. Johnson, A. Legarra, S. Tsuruta et al., 2010 Hot The activation of the immune response also requires the expression topic: A unified approach to utilize phenotypic, full pedigree, and geno- of genes responsible for the activation and recruitment of immune cell mic information for genetic evaluation of Holstein final score. J. Dairy Sci. populations to the site of infection. In our study, SNP AX-89929860 was 93: 743–752. http://www.sciencedirect.com/science/article/pii/ located in a gene region that codes for Interleukin-8 (IL-8), that is a S0022030210715174. https://doi.org/10.3168/jds.2009-2730 member of the CXC chemokine subfamily directly associated with the Barría, A., K. A. Christensen, G. M. Yoshida, K. Correa, A. M. Jedlicki et al., pro-inflammatory response because it attracts different immune cell 2018 Genomic Predictions and Genome-Wide Association Study of populations including neutrophils, T lymphocytes and basophils Resistance Against Piscirickettsia salmonis in Coho Salmon (Oncorhyn- (Reyes-Cerpa et al. 2012). Importantly, an increased gene expression chus kisutch) Using ddRAD Sequencing. G3: (Bethesda) 8: 1183–1194. for Interleukin-8 was observed in symptomatic IPNV-infected head Bishop, S. C., and J. A. Woolliams, 2014 Genomics and disease resistance kidney trout but not in fish with persistent asymptomatic infection studies in livestock. Livest. Sci. 166: 190–198. https://doi.org/10.1016/ j.livsci.2014.04.034 (Reyes-Cerpa et al. 2014), suggesting its potential effect on the inflam- Bowers, R. M., S. E. Lapatra, and A. K. Dhar, 2008 Detection and quanti- matory response. SNP AX-89954884 is located near integrin beta 1; tation of infectious pancreatic necrosis virus by real-time reverse tran- which is a membrane receptor involved in cell adhesion and recogni- scriptase-polymerase chain reaction using lethal and non-lethal tissue tion in a variety of processes including the immune response, indicating sampling. J. Virol. Methods 147: 226–234. https://doi.org/10.1016/ a potential role on influencing the pro-inflammatory response on j.jviromet.2007.09.003 IPNV-infected fish. Correa, K., A. Maass, J. P. Lhorente, M. E. López, L. Bassini et al., Previous reports in Atlantic salmon have shown clear differ- 2015 Genome-wide association analysis reveals loci associated with ences in the gene expression pattern between IPNV-susceptible and resistance against Piscirickettsia salmonis in two Atlantic salmon (Salmo –resistant phenotypes showing differences in ubiquitin-depen- salar L.) chromosomes. BMC Genomics 16: 854. https://doi.org/10.1186/ dent and apoptosis processes (Reyes-López et al. 2015; Robledo s12864-015-2038-7 Correa, K., J. P. Lhorente, L. Bassini, M. E. López, A. DI Genova et al., et al. 2016). In our study two SNPs (AX-89919030; AX-89938762) 2016 Genome wide association study for resistance to Caligus roger- were located in regions that contain genes that encode for E3 cresseyi in Atlantic salmon (Salmo salar L.) using a 50K SNP genotyping ubiquitin-protein ligase HUWE1 and Apoptosis-stimulating of array. Aquaculture. p53 protein 2-like, respectively. E3 ubiquitin-protein ligase Danzmann, R. G., M. Cairney, W. S. Davidson, M. M. Ferguson, K. Gharbi HUWE1 mediates the proteasomal degradation of target proteins, et al., 2005 A comparative analysis of the rainbow trout genome with and the results presented here provide more evidence that ubiq- 2 other species of fish (Arctic charr and Atlantic salmon) within the uitin-dependent processes could be important factors in IPNV tetraploid derivative Salmonidae family (subfamily: Salmoninae). Ge- disease resistance. The role of HUWE1 in proteasomal degrada- nome 48: 1037–1051. https://doi.org/10.1139/g05-067 tion of target proteins opens the possibility that other processes Dorson, M., and C. Touchy, 1981 The influence of fish age and water associated with this function, like vesicular trafficking, could also temperature on mortalities of rainbow trout, Salmo gairdneri Richardson, caused by a European strain of infectious pancreatic necrosis virus. J. Fish be involved with susceptibility to IPNV. Dis. 4: 213–221. https://doi.org/10.1111/j.1365-2761.1981.tb01128.x The results obtained from this study indicate that the QTL Falconer, D. S., and T. F. C. Mackay, 1996 Introduction to Quantitative involved in IPNV resistance contribute to a moderate-low propor- Genetics, Harlow, UK. tion of the variance of this trait. From a practical prespective, the Flores-Mara, R., F. H. Rodríguez, R. Bangera, J. P. Lhorente, R. Neira et al., implementation of this information in MAS is probably not the most 2017 Resistance against infectious pancreatic necrosis exhibits signifi- efficient approach (Correa et al. 2015). cant genetic variation and is not genetically correlated with harvest 2902 | F. H. Rodríguez et al. weight in rainbow trout (Oncorhynchus Mykiss). Aquaculture 479: 155– 01621459.1995.10476572. https://doi.org/10.1080/01621459.1995. 160. https://doi.org/10.1016/j.aquaculture.2017.05.042 10476572 Gao, G., T. Nome, D. E. Pearse, T. Moen, K. A. Naish et al., 2018 A new Kjøglum, S., S. Larsen, H. G., Bakke, and U. Grimholt, 2006 How specific single nucleotide polymorphism database for rainbow trout generated MHC class I and class II combinations affect disease resistance against through whole genome resequencing. Front. Genet. 9: 147. https:// infectious salmon anaemia in Atlantic salmon (Salmo salar). Fish doi.org/10.3389/fgene.2018.00147 Shellfish Immunol 21: 431–441. Genabel Project Developers, 2015 GenABEL Tutorial. [Accessed May 2, Kjøglum, S., M. Henryon, T. Aasmundstad, and I. Korsgaard, 2017]. https://doi.org/10.5281/zenodo.19738 2008 Selective breeding can increase resistance of Atlantic salmon to Gheyas, A. A., C. S. Haley, D. R. Guy, A. Hamilton, A. E. Tinch et al., furunculosis, infectious salmon anaemia and infectious pancreatic ne- 2010 Effect of a major QTL affecting IPN resistance on production crosis. Aquacult. Res. 39: 498–505. https://doi.org/10.1111/j.1365- traits in Atlantic salmon. Anim. Genet. 41: 666–668. https://doi.org/ 2109.2008.01904.x 10.1111/j.1365-2052.2010.02051.x Kolstad, K., P. A. Heuch, B. Gjerde, T. Gjedrem, and R. Salte, 2005 Genetic Gjedrem, T., R. Salte, and H. M. Gjøen, 1991 Genetic variation in suscep- variation in resistance of Atlantic salmon (Salmo salar) to the salmon tibility of Atlantic salmon to furunculosis. Aquaculture 97: 1–6. https:// louse Lepeophtheirus salmonis. Aquaculture 247: 145–151. https:// doi.org/10.1016/0044-8486(91)90274-B doi.org/10.1016/j.aquaculture.2005.02.009 Gjedrem, T., and H. Gjøen, 1995 Genetic variation in susceptibility of Lee, S. H., B. H. Choi, D. Lim, C. Gondro, Y. M. Cho et al., 2013 Genome- Atlantic salmon, Salmo salar L., to furunculosis, BKD and cold water Wide Association Study Identifies Major Loci for Carcass Weight on vibriosis. Aquacult. Res. 26: 129–134. https://doi.org/10.1111/j.1365- BTA14 in Hanwoo (Korean Cattle). PLoS One 8: e74677. https://doi.org/ 2109.1995.tb00892.x 10.1371/journal.pone.0074677 Glover, K., T. Aasmundstad, F. Nilsen, A. Storset, and Ø. Skaala, Leeds, T. D., J. T. Silverstein, G. M. Weber, R. L. Vallejo, Y. Palti et al., 2005 Variation of Atlantic salmon families (Salmo salar L.) in suscep- 2010 Response to selection for bacterial cold water disease resistance in tibility to the sea lice Lepeophtheirus salmonis and Caligus elongatus. rainbow trout. J. Anim. Sci. 88: 1936–1946. https://doi.org/10.2527/ Aquaculture 245: 19–30. https://doi.org/10.1016/ jas.2009-2538 j.aquaculture.2004.11.047 Legarra, A., A. Ricard, F. Olivier, and I. Flutre, 2010 GS3–Genomic Goddard, M. E., and B. J. Hayes, 2007 Genomic selection. J. Anim. Breed. selection, Gibbs sampling, Gauss Seidel and BayesCp. Available at: Genet. 124: 323–330. https://doi.org/10.1111/j.1439-0388.2007.00702.x https://github.com/alegarra/gs3 [Accessed December 12, 2016]. Granata, F., A. Petraroli, E. Boilard, S. Bezzine, J. Bollinger et al., Lhorente, J. P., J. A. Gallardo, B. Villanueva, A. M. Araya, D. A. Torrealba 2005 Activation of cytokine production by secreted phospholipase A2 et al., 2012 Quantitative genetic basis for resistance to Caligus roger- in human lung macrophages expressing the M-type receptor. J. Immunol. cresseyi sea lice in a breeding population of Atlantic salmon (Salmo 174: 464–474. https://doi.org/10.4049/jimmunol.174.1.464 salar). Aquaculture 324–325: 55–59. https://doi.org/10.1016/ Guy, D. R., S. C. Bishop, S. Brotherstone, A. Hamilton, R. J. Roberts et al., j.aquaculture.2011.10.046 2006 Analysis of the incidence of infectious pancreatic necrosis Neto, R. V. R., G. M. Yoshida, J. P. Lhorente, and J. M. Yáñez, mortality in pedigreed Atlantic salmon, Salmo salar L., populations. 2019 Genome-wide association analysis for body weight identifies J. Fish Dis. 29: 637–647. https://doi.org/10.1111/j.1365- candidate genes related to development and metabolism in rainbow trout 2761.2006.00758.x (Oncorhynchus mykiss). Mol. Genet. Genomics 294: 563–571. https:// Guy, D. R., S. C. Bishop, J. A. Woolliams, and S. Brotherstone, 2009 Genetic doi.org/10.1007/s00438-018-1518-2 parameters for resistance to infectious pancreatic necrosis in pedigreed Mahnke, J., V. Schumacher, S. Ahrens, N. Käding, L. M. Feldhoff et al., Atlantic salmon (Salmo salar) post-smolts using a Reduced Animal 2016 Interferon Regulatory Factor 4 controls TH1 cell effector Model. Aquaculture 290: 229–235. https://doi.org/10.1016/ function and metabolism. Sci. Rep. 6: 35521. https://doi.org/10.1038/ j.aquaculture.2009.02.015 srep35521 Habier, D., R. L. Fernando, K. Kizilkaya, and D. J. Garrick, 2011 Extension Moen, T., M. Baranski, A. K. Sonesson, and S. Kjøglum, 2009 Confirmation of the bayesian alphabet for genomic selection. BMC Bioinformatics 12: and fine-mapping of a major QTL for resistance to infectious pancreatic 186. http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471– necrosis in Atlantic salmon (Salmo salar): population-level associations 2105–12–186. https://doi.org/10.1186/1471-2105-12-186 between markers and trait. BMC Genomics 10: 368. https://doi.org/ Henryon, M., P. Berg, N. J. Olesen, T. E. Kjær, W. J. Slierendrecht et al., 10.1186/1471-2164-10-368 2005 Selective breeding provides an approach to increase resistance of Moen, T., J. Torgersen, N. Santi, W. S. Davidson, M. Baranski et al., rainbow trout (Oncorhynchus mykiss) to the diseases, enteric redmouth 2015 Epithelial cadherin determines resistance to infectious pancreatic disease, rainbow trout fry syndrome, and viral haemorrhagic septicaemia. pecrosis virus in atlantic salmon. Genetics 200: 1313–1326. https:// Aquaculture 250: 621–636. https://doi.org/10.1016/ doi.org/10.1534/genetics.115.175406 j.aquaculture.2004.12.022 Ozaki, A., T. Sakamoto, S. Khoo, K. Nakamura, M. R. Coimbra et al., Houston, R. D., C. S. Haley, A. Hamilton, D. R. Guy, A. E. Tinch et al., 2001 Quantitative trait loci (QTLs) associated with resistance/suscep- 2008 Major Quantitative Trait Loci Affect Resistance to Infectious tibility to infectious pancreatic necrosis virus (IPNV) in rainbow trout Pancreatic Necrosis in Atlantic Salmon (Salmo salar). Genetics 178: (Oncorhynchus mykiss). Mol. Genet. Genomics 265: 23–31. https:// 1109–1115. https://doi.org/10.1534/genetics.107.082974 doi.org/10.1007/s004380000392 Houston R. D., C. S. Haley, A. Hamilton, D. R. Guy, J. C. Mota-Velasco et al., Ozaki, A., S. K. Khoo, Y. Yoshiura, M. Ototake, T. Sakamoto et al., 2010 The susceptibility of Atlantic salmon fry to freshwater infectious 2007 Identification of Additional Quantitative Trait Loci (QTL) Re- pancreatic necrosis is largely explained by a major QTL. Heredity 105: sponsible for Susceptibility to Infectious Pancreatic Necrosis Virus in 318–327. Rainbow Trout. Mol. Genet. Genomics 265: 23–31. Houston, R. D., J. W. Davey, S. C. Bishop, N. R. Lowe, J. C. Mota-Velasco Ødegård, J., I. Olesen, B. Gjerde, and G. Klemetsdal, 2007 Positive genetic et al., 2012 Characterisation of QTL-linked and genome-wide restric- correlation between resistance to bacterial (furunculosis) and viral (in- tion site-associated DNA (RAD) markers in farmed Atlantic salmon. fectious salmon anaemia) diseases in farmed Atlantic salmon (Salmo BMC Genomics 13: 244. https://doi.org/10.1186/1471-2164-13-244 salar). Aquaculture 271: 173–177. https://doi.org/10.1016/ Hu, Z. L., C. A. Park, and J. M. Reecy, 2016 Developmental progress and j.aquaculture.2007.06.006 current status of the Animal QTLdb. Nucleic Acids Res. 44: D827–D833. Ødegård, J., M. Baranski, B. Gjerde, and T. Gjedrem, 2011 Methodology for https://doi.org/10.1093/nar/gkv1233 genetic evaluation of disease resistance in aquaculture species: Challenges Kass, R. E., and A. E. Raftery, 1995 Bayes Factors. J. Am. Stat. Assoc. 90: and future prospects. Aquacult. Res. 42: 103–114. https://doi.org/10.1111/ 773–795 http://www.tandfonline.com/doi/abs/10.1080/ j.1365-2109.2010.02669.x Volume 9 September 2019 | GWAS of IPNV Resistance in Rainbow Trout | 2903 Ødegård, J., T. Moen, N. Santi, S. A. Korsvoll, S. Kjøglum et al., moderately heritable and is not adversely correlated with growth. J. Anim. 2014 Genomic Prediction in an Admixed Population of Atlantic Sci. 87: 860–867. https://doi.org/10.2527/jas.2008-1157 Salmon (Salmo Salar). Front. Genet. 5: 1–8. https://doi.org/10.3389/ Storset, A., C. Strand, M. Wetten, S. Kjøglum, and A. Ramstad, fgene.2014.00402 2007 Response to selection for resistance against infectious pancreatic Palti, Y., C. Genet, G. Gao, Y. Hu, F. M. You et al., 2012 A Second Gen- necrosis in Atlantic salmon (Salmo salar L.). Aquaculture 272: S62–S68. eration Integrated Map of the Rainbow Trout (Oncorhynchus mykiss) https://doi.org/10.1016/j.aquaculture.2007.08.011 Genome: Analysis of Conserved Synteny with Model Fish Genomes. Mar. Varona, L., L. García-Cortés, and M. Pérez-Enciso, 2001 Bayes factors for Biotechnol. (NY) 14: 343–357. https://doi.org/10.1007/s10126-011-9418-z detection of Quantitative Trait Loci. Genet. Sel. Evol. 33: 133 http:// Palti, Y., G. Gao, S. Liu, M. P. Kent, S. Lien et al., 2015 The development www.gsejournal.org/content/33/2/133. https://doi.org/10.1186/1297- and characterization of a 57K single nucleotide polymorphism array for 9686-33-2-133 rainbow trout. Mol. Ecol. Resour. 15: 662–672. https://doi.org/10.1111/ Vallejo, R. L., S. Liu, G. Gao, B. O. Fragomeni, A. G. Hernandez et al., 1755-0998.12337 2017 Similar Genetic Architecture with Shared and Unique Quantita- Pearse, D. E., N. J. Barson, T. Nome, G. Gao, M. A. Campbell et al., tive Trait Loci for Bacterial Cold Water Disease Resistance in Two 2018 Sex-dependent dominance maintains migration supergene in Rainbow Trout Breeding Populations. Front. Genet. 8: 156. https:// rainbow trout. bioRxiv https://doi.org/10.1101/504621. doi.org/10.3389/fgene.2017.00156 Pinto, M. P., A. F. Carvalho, C. P. Grou, J. E. Rodríguez-borges, C. Sá- Vidal, O., J. L. Noguera, M. Amills, L. Varona, M. Gil et al., 2005 Identification Miranda et al., 2012 Heat shock induces a massive but differential in- of carcass and meat quality quantitative trait loci in a Landrace pig activation of SUMO-specific proteases. Biochim. Biophys. Acta 1823: population selected for growth and leanness. Journal of Animal Science, 1958–1966. https://doi.org/10.1016/j.bbamcr.2012.07.010 83:293. Available at: https://www.animalsciencepublications.org/publications/ Phillips, R. B., K. A. Keatley, M. R. Morasch, A. B. Ventura, K. P. Lubieniecki jas/abstracts/83/2/0830293 [Accessed December 6, 2016]. et al., 2009 Assignment of Atlantic salmon (Salmo salar). linkage Wetten, M., T. Aasmundstad, S. Kjøglum, and A. Storset, 2007 Genetic groups to specific chromosomes : Conservation of large syntenic blocks analysis of resistance to infectious pancreatic necrosis in Atlantic salmon corresponding to whole chromosome arms in rainbow trout (Onco- (Salmo salar L.). Aquaculture 272: 111–117. https://doi.org/10.1016/ rhynchus mykiss). BMC Genet. 11: 1–11. j.aquaculture.2007.08.046 Reyes-Cerpa, S., K. Maisey, F. Reyes-López, D. Toro-Ascuy, A. M. Sandino Yáñez, J. M., R. Bangera, J. P. Lhorente, M. Oyarzún, and R. Neira, et al., 2012 Fish cytokines and immune response, in New Advances and 2013 Quantitative genetic variation of resistance against Piscirickettsia Contributions to Fish Biology. Rijeka, Croatia: Intech. p 3–57. https:// salmonis in Atlantic salmon (Salmo salar). Aquaculture 414–415: 155– doi.org/10.5772/53504 159. https://doi.org/10.1016/j.aquaculture.2013.08.009 Reyes-Cerpa, S., F. Reyes-López, D. Toro-Ascuy, R. Montero, K. Maisey et al., Yáñez, J. M., S. Naswa, M. E. López, L. Bassini, M. E. Cabrejos et al., 2014 Induction of anti-inflammatory cytokine expression by IPNV in 2014 Development of a 200K SNP Array for Atlantic Salmon : persistent infection. Fish Shellfish Immunol. 41: 172–182. https://doi.org/ Exploiting across continents genetic variation. 10th world congress of 10.1016/j.fsi.2014.08.029 genetics applied to livestock production development, (February 2016). Reyes-López, F. E., E. Vallejos-Vidal, S. Reyes-Cerpa, A. M. Sandino, L. Tort Yáñez, J. M., S. Newman, and R. D. Houston, 2015 Genomics in Aqua- et al., 2015 Differential immune gene expression profiles in susceptible culture to Better Understand Species Biology and Accelerate Genetic and resistant full-sibling families of Atlantic salmon (Salmo salar) chal- Progress. Front. Genet. 6: 128. https://doi.org/10.3389/fgene.2015.00128 lenged with infectious pancreatic necrosis virus (IPNV). Developmental Yáñez, J. M., R. Bangera, J. P. Lhorente, A. Barría, M. Oyarzún et al., and Comparative Immunology, 53(1), 210–221. https://doi.org/10.1016/ 2016 Negative Genetic Correlation between Resistance against Piscir- j.dci.2015.06.017 ickettsia Salmonis and Harvest Weight in Coho Salmon (Oncorhynchus Roberts, R. J., and M. D. Pearson, 2005 Infectious pancreatic necrosis in Kisutch). Aquaculture 459. Elsevier B.V.: 8–13. https://doi.org/10.1016/ Atlantic salmon, Salmo salar L. J. Fish Dis. 28: 383–390. https://doi.org/ j.aquaculture.2016.03.020 10.1111/j.1365-2761.2005.00642.x Yáñez, J. M, G. M. Yoshida, A. Parra, K. Correa, A. Barria A. et al., Robledo, D., J. B. Taggart, J. H. Ireland, B. J. McAndrew, W. G. Starkey et al., 2019 Comparative genomic analysis of three salmonid species identifies 2016 Gene Expression Comparison of Resistant and Susceptible At- functional candidate genes involved in resistance to the intracellular lantic Salmon Fry Challenged with Infectious Pancreatic Necrosis Virus bacterium Piscirickettsia salmonis. https://doi.org/10.3389/ Reveals a Marked Contrast in Immune Response. BMC Genomics 17: fgene.2019.00665 279. https://doi.org/10.1186/s12864-016-2600-y Yoshida, G. M., R. Bangera, R. Carvalheiro, K. Correa, R. Figueroa et al., Rodríguez, F. H., G. Cáceres, J. P. Lhorente, S. Newman, R. Bangera et al., 2018 Genomic Prediction Accuracy for Resistance Against Piscirick- 2018 Genetic (co)variation in skin pigmentation patterns and growth in ettsia Salmonis in Farmed Rainbow Trout. Genes Genomes Genetics 8: rainbow trout. Animal 13: 675–682. https://doi.org/10.1017/ 719–726. https://doi.org/10.1534/g3.117.300499 S175173111800188X Yoshida, G. M., R. Carvalheiro, F. H. Rodríguez, J. P. Lhorente, and J. M. Sakamoto, T., R. G. Danzmann, K. Gharbi, P. Howard, A. Ozaki et al., Yáñez, 2019 Single-step genomic evaluation improves accuracy of 2000 A microsatellite linkage map of rainbow trout (oncorhynchus breeding value predictions for resistance to infectious pancreatic necrosis mykiss) characterized by large sex – specific differences in recombination virus in rainbow trout. Genomics 111: 127–132. https://doi.org/10.1016/ rates. Genetics 155: 1331–1345. j.ygeno.2018.01.008 Silverstein, J. T., R. L. Vallejo, Y. Palti, T. D. Leeds, C. E. Rexroad et al., 2009 Rainbow trout resistance to bacterial cold-water disease is Communicating editor: R. Houston 2904 | F. H. Rodríguez et al.