In aquaculture, disease resistance related traits are of particular importance. The reason is that interactions between fish and pathogens, that may be harmless under natural conditions, often result in disease problems in aquaculture systems because of the added stress from biological, physical and chemical factors . This is especially important due to the fact that, in contrast to farm animals, the strains used in aquaculture usually have been very recently derived from wild strains  and, therefore, have had little time to adapt to the new disease pressures within the aquaculture environment.
Improvements in the performance of any productive or physiological characteristic of the cultured species can be achieved (if the trait is genetically determined) through artificial selection. Several breeding programmes have been developed for different traits in aquaculture species usually involving growth rate, cold tolerance and disease resistance. However, implementing classical breeding programs focused on disease resistance traits could be highly problematic, since the phenotypic measurement of these traits is often complex and expensive. It is not possible to evaluate specimens to be selected and thus, evaluation has to be performed on relatives. Moreover, the required challenges may cause animal suffering and increase risks of infection at farm facilities . Despite those disadvantages, some populations of Atlantic salmon have been already selected for resistance to bacterial and viral diseases. Selection for resistance to Infectious Pancreatic Necrosis (IPN) virus, based on bath challenge tests of several hundred families of first feeding fry, showed 66.6% and 29.3% mortality for low and high resistant strains, respectively . Also, high resistance to IPN in rainbow trout was achieved by selective breeding . In that study, the commercial strain RT-201 artificially challenged with IPN virus showed a mortality of 4.3%, whereas the highly sensitive controls reached 96.1%. In carps, Schäperclaus  found resistance to the dropsy disease, selected lines suffering low mortality (11.5%) well below the unselected ones (57%).
Positive response to selection pressures is possible because resistance against particular diseases affecting aquaculture species often shows moderate to high heritabilities and, thus, there is a large potential for genetic improvement. For example recent heritability estimates for resistance to Aeromonas salmonicida ranged from 0.43 to 0.62 in Atlantic salmon [reviewed by ], and it was 0.51 ± 0.03 in Salvelinus fontinalis .
Turbot (Scophthalmus maximus) is a flatfish that has been intensively cultured during the last decade due to its great commercial value. Its production in Europe has increased from 3000 Tm in 1996 to 9246 Tm in 2009 . Increasing growth rate, controlling sex ratio (females largely outgrow males) and enhancing disease resistance currently constitute the main goals of genetic breeding programmes in this species. Pathologies constitute one of the main problems of turbot culture. Among these, furunculosis, caused by Aeromonas salmonicida, has produced important losses to turbot industry [10, 11]. Genomic resources of turbot have increased in the last years [12, 13] and an immune-enriched oligo-microarray was designed and applied to identify candidate genes of resistance to A. salmonicida . Also, a microsatellite consensus genetic map including centromere positions was reported in this species [15, 16], and recently, 31 EST-linked microsatellites, particularly useful for comparative genomics, were added to the consensus map . Combining functional genomics strategies with the detection of genomic regions associated to productive characters (using genetic maps) increases the power to identify genes involved in the phenotypic differences occurring within and between families.
Besides an infinitesimal component (due to small effects of a huge number of loci) the variation in quantitative traits may be also controlled by a few genes with larger effects. Genomic regions closely linked to those genes show association with the trait phenotype and are known as quantitative trait loci (QTL) . The effects of allele segregation at molecular markers throughout the genome can be used to determine the number and position of trait-related QTL, as well as the magnitude of their effects . If, eventually, the responsible gene of a large effect on a trait is detected and the causal mutation determined, selection could be exerted directly on the genotype for that locus (Gene Assisted Selection, GAS). Alternatively, genetic maps provide DNA markers tightly linked to genes affecting different traits. Such markers can be used in Marker-Assisted Selection (MAS), selection based partly or fully on DNA marker genotypes. Consequently, disease resistance traits are candidates for the implementation of MAS and, especially, GAS programs, which would allow the evaluation of individuals without exposing them to the pathogen or relying on relatives' information alone.
There have been a certain number of studies on disease resistance QTL in the main aquaculture species, especially focused in rainbow trout and Atlantic salmon within fish. In rainbow trout, several QTL were detected for resistance to IPN and IHN (Infectious Hematopoietic Necrosis) viruses [20–23] and to different parasites, including Ceratomyxa shasta and Myxobolus cerebralis [24, 25]. The identified QTL in response to IHN virus were detected in more than one family supporting their consistency , and in the case of resistance to Myxobolus cerebralis, a very strong association was detected which explained between 50 and 86% of the phenotypic variance across families . In Atlantic salmon, QTL for disease resistance have been reported for the ISA (Infectious Salmon Anaemia) virus , for the bacterium Aeromonas salmonicida responsible of furunculosis  and for the ectoparasite Gyrodactylus salaries . Of particular relevance in this species was a recent study on ten full-sib families for mapping QTL for resistance against the IPN virus in post-smolts of Scottish origin, based on data from a field trial . In this study, a major QTL explained up to 21% of the phenotypic variation in the data set and was found to segregate in 7 out of 20 parents investigated. Additionally, this QTL mapped to the same location of a recently detected QTL for IPN-resistance that explained 29% of the phenotypic variance using ten large full-sib families of challenge-tested Norwegian Atlantic salmon. This particular QTL was found to be segregating in 10 out of 20 parents, and a subsequent fine-mapping with additional markers narrowed the QTL peak to a 4 cM region on linkage group 21 . This QTL, detected in two different populations, is now being implemented in within-family selection in both Scotland  and Norway . QTL for disease resistance have also been reported in a few cases in non-salmonid teleosts, including those for stress/immune response in tilapia , for resistance to pasteurellosis in gilthead sea bream  and for resistance to lymphocystis viral disease in Japanese flounder . Other QTL for disease resistance traits in aquaculture species were identified in Crassostrea virginica, Ostrea edulis and Paralichthys olivaceus [see 7 for a review].
To date, only two studies identified QTL in turbot. One detected a QTL for body length highly associated with the marker YSKr51, explaining 12.4% of the phenotypic variance , and the other one identified a significant sex-determining QTL highly associated with the SmaUSC-E30 marker, which allowed for correct sex determination in up to 98.4% of the studied individuals . The construction of a genetic map with appropriate marker density is necessary to detect QTL controlling quantitative traits of economic interest in aquaculture . However, genetic linkage maps of most aquaculture species have only recently been available. Bouza et al.  reported an updated consensus including a total of 273 microsatellites clustered in 26 linkage groups, comprising 1343.2 cM length, with an average distance between markers of 6.5 ± 0.5 cM.
In the present work, a genome scan for QTL affecting resistance and survival to A. salmonicida in four turbot families was carried out using the reported microsatellite panel. The objectives were: (1) to locate QTL on the available linkage map, (2) to compare QTL obtained through the use of two different methodologies: linear regression and maximum likelihood, and (3) to determine the association between markers and traits.