Identification of Quantitative Trait Loci Associated with Resistance to Viral Haemorrhagic Septicaemia (VHS) in Turbot (Scophthalmus maximus): A Comparison Between Bacterium, Parasite and Virus Diseases

Abstract

One of the main objectives of genetic breeding programs in turbot industry is to reduce disease-related mortality. In the present study, a genome scan to detect quantitative trait loci (QTL) affecting resistance and survival to viral haemorrhagic septicaemia (VHS) was carried out. Three full-sib families with approximately 90 individuals each were genotyped and evaluated by linear regression and maximum likelihood approaches. In addition, a comparison between QTL detected for resistance and survival time to other important bacterial and parasite diseases affecting turbot (furunculosis and scuticociliatosis) was also carried out. Finally, the relationship between QTL affecting resistance/survival time to the virus and growth-related QTL was also evaluated. Several genomic regions controlling resistance and survival time to VHS were detected. Also significant associations between the evaluated traits and genotypes at particular markers were identified, explaining up to 14 % of the phenotypic variance. Several genomic regions controlling general and specific resistance to different diseases in turbot were detected. A preliminary gene mining approach identified candidate genes related to general or specific immunity. This information will be valuable to develop marker-assisted selection programs and to discover candidate genes related to disease resistance to improve turbot production.

Parallel evolution and adaptation to environmental factors in a marine flatfish: Implications for fisheries and aquaculture management of the turbot (Scophthalmus maximus)

“…LG, linkage group; Selection: type of selection and geographical region; Gene/Function: official gene symbol and function (IG: immunoglobulin); data mining: relevant genes identified related to growth (G), VHSV resistance (VH), Aeromonas salmonicida resistance (AS), reproduction (R) and osmoregulation (OS); QTLs and previous outliers from 1 Sánchez‐Molano et al., 2011; 2 Rodríguez‐Ramilo et al., 2014; 3 Rodríguez‐Ramilo et al., 2011; V2015: outliers reported by Vilas et al., 2015.…”

“…This latter outlier showed a gradual cline in allele frequency from the northernmost samples, through the Mediterranean, and up to the Black Sea: the most common allele in the Baltic (0.879) showed a range between 0.938 (ICE; North Atlantic) and 0.650 (BB‐FR; Biscay Bay) in ATL populations, a frequency of 0.382 in the single Mediterranean population (AD), and the lowest frequency in BLS (0.019‐ 0.029). The two aforementioned outlier loci were located within the confidence intervals of two overlapping QTLs for resistance to the hemorrhagic septicemia virus (VHSV) and growth (Rodríguez‐Ramilo et al., 2014) and closely linked to the important growth‐related gene IGFBP3 (insulin like growth factor binding protein 3; Robledo et al., 2016). Data mining around 5397_68 identified a candidate gene related to Aeromonas salmonicida resistance, DHX30 (DExH‐box helicase 30; Figueras et al., 2016), while mining around 7574_88 identified AQP1 (aquaporin 1), a gene related to fertilization (Figueras et al., 2016), and osmoregulation (Grady, Knepper, Burg, & Ferraris, 2014).…”

“…As expected, F ST between BAS and BLS increased when these outliers were excluded (using only neutral markers). As mentioned above, two of these markers (7033_88 and 5397_68) are linked to several growth‐related loci (Robledo et al., 2016; Rodríguez‐Ramilo et al., 2014; Sánchez‐Molano et al., 2011), and also to some candidate genes associated with osmotic stress response (Norman et al., 2014; Ortells et al., 2012). Two other outlier loci reside close to genes related to osmoregulation: 1587_12 on LG13 near RAC1 and PRKCA (Di Ciano et al., 2002; Zhuang, Hirai, & Ohno, 2000), and 2921_40 on LG16 near NAP1L1 (Wang et al., 2014).…”

Current Advances in Functional Genomics in Aquaculture

“…And as such, diferent approaches such as SNP, MTLS, AFLP, RAPD, RFLP and QTL analyses have been used for the identiication of disease resistance and susceptibility traits in diferent aquatic organisms. In the case of ish viral diseases, QTL resistance traits have been generated for grass cap reovirus (GCRV) infection in grass carp [179], nervous necrosis virus (NNV) in seabass [180], viral hemorrhagic septicemia (VHS) in turbot [181] and rainbow trout [182], infectious salmon anaemia (ISAV) virus in Atlantic salmon, lymphocytic disease virus in Japanese lounder [183] and infectious pancreatic necrosis virus (IPNV) in Atlantic salmon [184,185]. Among these, the QTL for resistance against IPNV has contributed to signiicantly reducing the IPNV incidence by >80% from 2008 when IPNV resistance ish were introduced in the Norwegian Atlantic salmon industry to 2015 [186].…”

Detection of QTL associated with three skeletal deformities in gilthead seabream (Sparus aurata L.): Lordosis, vertebral fusion and jaw abnormality

“…The advantages of considering MAS are notable as compared with the traditional selective breeding. As having an accurate linkage map is crucial to search for QTL, since the density of genetic markers is an important factor to detect them (Rodríguez-Ramilo et al, 2014). In gilthead seabream, a linkage map based on 204 microsatellite markers and 26 linkage groups (LG) is available .…”