genetic factors that influence infectious disease resistance in salmonids and their application in breeding programs EDUARDO FUENZALIDA Genetic factors that influence infectious disease resistance in salmonids and their application in breeding programs
Arch Med Vet 42, 1-13 (2010)
JM Yáñez, V Martínez *
Genomics Unit and Animal Breeding, Research Laboratory of Animal Biotechnology and Genomics (FAVET-INBIOGEN), Faculty of Science Veterinarias y Pecuarias, Universidad de Chile, Santiago, Chile.
# Programa de Becas de Postgrado de CONICYT (21060530)
* Casilla 2 correo 15, La Granja, Santiago, Chile; vmartine@uchile.cl.
SUMMARY
The control of infectious diseases is essential in the success of salmon aquaculture. Genetic improvement of disease resistance may provide a feasible and sustainable option for the management of these diseases. Marker-assisted selection (MAS) or Gene-assisted selection (GAS), represent a valuable alternative to the conventional schemes for improving disease resistance using pedigree. Nevertheless a previous knowledge of the genetic factors involved in the trait is necessary to implement this methodology. In this review, the most important aspects of genetic resistance to infectious diseases in salmonids and their suitability for breeding programmes are both reviewed and discussed. Firstly, we briefly mention the most important infectious diseases in Chile. Furthermore, we include aspects related with conventional breeding for this quantitative trait, such as selection criteria, genetic variation of resistance and genetic correlations with other traits. We also review three approaches used in molecular identification of genetic factors involved in resistance: candidate genes, with particular emphasis on the major histocompatibility complex (MHC) or MH genes, detection of quantitative trait loci (QTL) and gene expression studies. Finally, we discuss the use of this information in the Implementation of Schemes That include breeding disease resistance in Their breeding goal.
Key words: salmon, disease resistance, breeding Programmes, QTL. SUMMARY
The control of infectious diseases is critical to the success of salmon farming. Genetic improvement of disease resistance may provide a feasible and sustainable option for controlling them. The Molecular Marker Assisted Selection (MAS) or Genes (GAS) is projected as a valuable alternative to conventional breeding for resistance. However, to implement this methodology is required prior knowledge of the genetic factors involved in character. In this paper we review and discuss the most important aspects of genetic resistance to infectious diseases in salmonids and their applicability to breeding programs. First, it briefly presents the most important infectious diseases nationwide. In addition, aspects related to conventional breeding for this quantitative trait, such as selection criteria, genetic variation of resistance and genetic correlations with other production traits of interest. Moreover, we review three molecular approaches used in identifying genetic factors involved in resistance: genes, candidate emphasis on the major histocompatibility complex (MHC) or MH genes, detection of quantitative effect loci (QTL) and gene expression studies. Finally, we reviewed and discussed in relation to the use of this information in implementing molecular breeding programs that include resistance to infectious diseases within its target selection.
Keywords: salmon, disease resistance, breeding programs, QTL. INTRODUCTION
The salmon farming is a major aquaculture activities at national level and one that generates products aquaculture more economic value worldwide (FAO 2006). As with other intensive animal production systems success of this activity depends largely on the control of infectious diseases. A clear example of the impact that can cause certain diseases in salmon farming is the crisis that has hit in recent years the domestic salmon industry as a result of outbreaks of infectious salmon anemia (ISA).
breeding programs have increased the economic return to the farms. The breeding objective should be defined for each species and each population. In general, all those traits of economic importance should be included in the breeding objective. Therefore, in salmon include traits related to body growth, color and texture of meat, as well as genetic resistance to viral diseases and bacterial type (Gjedrem 2000). So far, breeding programs have included only disease resistance based on information from relatives, which affects the degree of genetic progress achievable in each generation. This is because the accuracy with which breeding values \u200b\u200bare predicted using family information is lower compared with that obtained when using information from the candidates themselves breeders (Falconer and Mackay 1996). Furthermore, kin selection can not use Mendelian sampling variation and at the same time, it is difficult to manage inbreeding rates to acceptable levels when the number of families is relatively low (Martinez 2007).
DNA markers are variations in the sequence of this molecule, which allow genotype identification by laboratory tests. These markers have been used in identifying quantitative effect loci (QTL) in different species, ie the search for genomic regions that explain a relatively high proportion of variation for a particular genetic characteristic. The information of molecular markers linked to these QTL and genes of major effect can be included in breeding programs, in what has been called marker-assisted selection (MAS) or genes (GAS). These methodologies can be useful in programs that include characters complicated to measure in the candidate selection, as is the case of infectious disease resistance (Meuwissen 2003). In order to implement programs involving MAS or GAS disease resistance within the breeding objective, we first must understand the genetic factors that explain the quantitative variation for this feature. In recent years there has been progress in dissecting the genetic component involved in the host response against certain pathogens, mainly using animal models such as chicken and mouse (Qureshi et al 1999). In salmonids there is scant information about the genetic architecture of disease resistance, but it is hoped that with the current development and availability of genomic resources in these species and the most knowledge about the biology of the immune response in teleosts, is available in future more background on QTL or genes involved in the change of character.
This review briefly discusses the health aspects more relevant to national salmon farming today. In addition, we review aspects of conventional breeding to increase resistance to disease and the molecular tools used in identifying genetic factors involved in these characters. Finally, we discuss the utility of molecular information on the implementation of programs for genetic improvement in salmon, including resistance within the breeding objective. INFECTIOUS DISEASES
salmon
CHILE In Chile, salmon farming has produced a remarkable and sustained development over the past twenty years, reaching a volume Approximate total harvest of 628 thousand tons during the year 2006.1 The health status of farmed fish is one of the main factors affecting the economic return of the salmon industry. Despite the scientific, professional and technical expertise aimed at improving aspects of health management, new pathological conditions have emerged gradually in the different species of salmonid fish. From the economic point of view, infectious diseases (viral and bacterial) are the most relevant diseases (Smith et al 2001).
Among the main bacterial disease affecting salmonid fish farming in Chile can be Piscirickettsiosis mention or salmon rickettsial syndrome (SRS), bacterial kidney disease (BKD), the syndrome of juvenile rainbow trout (sTfR) and enteric disease of red mouth (ERM). Among the most important viral diseases are infectious pancreatic necrosis (IPN), viral erythrocytic necrosis (VEN) (Smith et al 2001) and recently the ISA virus. A detailed description of each of these diseases largely outweigh the purpose of this review. However, three of them deserve to be highlighted because of its current impact on the national salmon. First, it is noteworthy that he has been responsible Piscirickettsiosis of the biggest losses in the growing stage in the Chilean salmon industry. For example, according to the Technological Institute of Salmon (INTESAL), an institution that monitors illness among producers, in January 2007 caused piscirickettsiosis more than half (55.6%) of total monthly mortality in terms of biomass, taking into account information obtained for Atlantic salmon (Salmo salar), coho salmon (Oncorhynchus kisutch) and rainbow trout (Oncorhynchus mykiss) (Leal and Woywood 2007). Moreover, despite the fact that in Chile the IPN virus caused smaller losses in terms of tonnes of dead fish, because it is a disease that primarily affects juveniles, according to the Fisheries Research Fund (FIP) IPN virus outbreaks in the country affected 48% of total farms, 61% of the centers of smoltification and 49% of centers finished in 1999 (FIP 2003), causing huge losses to the industry. Finally, in mid-2007 there was an outbreak of ISA causing high mortalities in Atlantic salmon in a fish farm located in Chiloé. Since that date there have been consecutively numerous outbreaks, which has been considered the ISA as a disease prevalent in areas X and XI of the southern regions of the country, causing major economic losses to the industry (SERNAPESCA 2008).
whereas these diseases are of great importance to the salmon industry in Chile and that the measures used for the prevention and treatment of them (vaccinations, antibiotics, etc..) Showed no significant results to date, it is imperative to develop new strategies to establish an effective and sustainable control of these diseases. Genetic improvement of disease resistance is a feasible solution to the problem (Stear et al 2001). However, in the national research so far has been low and there are no published studies aimed at identifying genetic factors that influence resistance. Nonetheless, at present carried out several research projects using quantitative genetic tools, molecular and functional genomics to identify genetic factors associated with resistance to the three most economically important diseases for the national industry Piscirickettsiosis , IPN and ISA.2
CONVENTIONAL BREEDING RESISTANCE TO INFECTIOUS DISEASES
resistance to infectious diseases can be defined as the ability to initiate and maintain host responses aimed at preventing the establishment of an infectious agent and / or remove the body. Select animals to increase disease resistance is a feasible method to improve productivity and animal welfare, which also offers certain advantages over other methods of control against infections (Stear et al 2001). In Norway, since 1993 has included the resistance to viral and bacterial diseases in conventional breeding programs in salmon (Gjøen and Bentsen, 1996). However, this methodology presents some difficulties mainly related to the selection criteria used to measure the resistance. Here, we review the main aspects of conventional breeding for resistance to infectious diseases in salmon.
CRITERIA SELECTION IN THE IMPROVEMENT OF RESISTANCE
CONVENTIONAL
Different selection criteria that could potentially be used to measure disease resistance in fish breeding programs. While in some schemes and the selection is used by experimental challenges against pathogens, remains an open question what the most appropriate selection criteria, ie which measurement should be performed in candidates for measuring resistance. Here we review some aspects studied so far in this field.
Challenge against pathogens. Disease resistance can be measured in terms practical and survival (or mortality) of individuals against infection. You can use epidemiological data from field outbreaks to make inferences about the genetic resistance to infectious diseases. For this purpose it is necessary that the pedigree of the population can be determined and that the records are structured survival as family information (Guy et al 2006). However, using information from field outbreaks has some disadvantages such as the difficulty to recognize the exact cause of death, because the factors that influence survival under these conditions are different. Furthermore, the availability of information depends on the occurrence of outbreaks, which are usually controlled to reduce economic losses. Moreover, the inference of the pedigree by using molecular markers can increase costs. However, survival data can be obtained from experimental challenges, which can be standardized, facilitating evaluation of the results. Also, the challenges are more advantageous presmolts in terms of costs, compared with postsmolts used in field studies. Notwithstanding the foregoing, it is required that there is a high correlation between the results obtained from pre and postsmolts challenges. To date, we have reported a high correlation genetics (up to 0.95) between field trials and experimental challenges to furunculosis in Atlantic salmon (Gjøen et al 1997, Ødegård et al 2006). Therefore, for selection schemes that include resistance to a particular disease within its goal of improvement, challenge tests are more accurate and appropriate field outbreaks, since they reduce the variability caused by environmental factors and are more feasible to implement in practice. Indeed, in this way is as determined resistance to viral and bacterial diseases in the conventional breeding program for Atlantic salmon, done in by Aquagen Norway (Gjøen and Bentsen 1996, Aquagen 2005).
immunological and physiological variables. Direct selection to improve the genetic resistance to diseases based on evidence challenges can be costly and time consuming. Furthermore, in conventional breeding programs can only be compiled using information from relatives. Indirect selection based on the measurement of other characteristics that are genetically correlated with disease resistance, could simplify data collection and at the same time could allow the incorporation of individual information. Some studies have aimed to determine the variation genetic physiological and immunological variables, and the correlation between them and the survival of the salmon challenge tests. Examples of variables that have been studied to date are: hemolytic activity of serum and serum lysozyme activity (Røed et al 1993, Lund et al 1995), plasma cortisol (Fevolden et al 1993), and IgM antibody titers (Lund et al 1995), α2-antiplasmin serum (Jump et al 1993), bactericidal activity and complement activity (Hollebecq et al 1995). However, even though some studies show significant correlations between resistance and immune parameters, the proportion of variation overall survival can be explained by the immunological variables has been considered too low to be useful as selection criteria. Thus the prediction of breeding values \u200b\u200bfor survival based on these variables may not be adequate (and Bentsen Gjøen 1996). The latter is reasonable considering the complexity of the mechanisms involved in immune response and the large number of factors may be involved in disease resistance, which causes a great difficulty when attempting to use the information in a single parameter in the determination of the resistance. On the other hand, the use of all related physiological variables survival with a methodology would be costly and less feasible to implement in practice.
GENETIC VARIATION OF RESISTANCE TO INFECTIOUS DISEASES
A prerequisite for improving a property by artificial selection it is this genetic variation. Heritability (h2) is the proportion of total phenotypic variance that is attributable to additive genetic variance (Falconer and Mackay 1996). It is important to note that this property is not only a character but also of the population, environmental conditions and how they evaluated the phenotype (Falconer and Mackay 1996). Depending on how you measure disease resistance, heritability may have different values \u200b\u200band incomparable, due to differences in defining the character and the model used in the analysis (Ødegård et al 2006, Ødegård et al 2007). To date, there are a large number of papers which described additive genetic variation and to estimate the heritability values \u200b\u200bfor resistance to various viral and bacterial diseases in salmonids (Table 1). This shows that the genetic improvement of these characteristics will be satisfactory and is a viable alternative for the control of these diseases in salmonids. However, given the specific case of the absence of such studies for resistance to Piscirickettsiosis, one of the most important diseases for the national salmon.
Table 1. Values \u200b\u200bof heritability (h2) and standard error (±), estimated for resistance to various infectious diseases in salmon.
heritability values \u200b\u200b(h2) and Their standard error (±) for resistance to infectious diseases in salmonids Different.
genetic correlation between resistance to infectious diseases INTEREST AND OTHER CHARACTERISTICS OF PRODUCTIVE
The potential to simultaneously improve the genetic resistance to different diseases and other economically important characteristics depend on the genetic correlations among these traits. Few studies have aimed to identify genetic correlations for disease resistance in salmon. Some work on Atlantic salmon have shown a positive genetic correlation between resistance to various bacterial diseases such as furunculosis, BKD, vibriosis and cold water vibriosis (Gjedrem and Gjøen 1995, Gjøen et al 1997). However, there is a weak negative genetic correlation between resistance to viral disease ISA and other bacterial diseases such as furunculosis, vibriosis and cold water vibriosis (Gjøen et al 1997). In rainbow trout analyzed the genetic correlation between the disease viral hemorrhagic septicemia (VHS) and bacterial diseases and sTfR ERM. Contrary to what was observed in Atlantic salmon, the genetic correlation between resistance to bacterial diseases was weakly negative, whereas resistance to viral disease was positively correlated with sTfR and negatively correlated with ERM (Henryon et al 2005). However, all these correlations were estimated with some degree of uncertainty due to the low number of families used in the study (Henryon et al 2005).
Moreover, it is important to understand the genetic correlation between disease resistance and other characteristics in salmon production. To date, we have determined genetic correlations between disease resistance and traits related to growth (weight and body length, growth rate and food conversion efficiency). The results range from negative genetic correlations moderate to low (Henryon et al 2002), through inconsistent correlations (Beacham and Evelyn 1992, Henryon et al 2002), to moderate and low positive correlations (Standal and Gjerde 1987, Gjedrem et al 1991, Perry et al 2004). INTERFACE
-MOLECULAR AND GENETIC IMPROVEMENT OF RESISTANCE TO DISEASES
Large advances in the generation and use of molecular markers, automated sequencing methods and new techniques available to the transcriptomics data analysis, have helped to identify QTL and genes associated with complex traits in various species of vertebrates. Salmonids, are not out of this boom in the development of new techniques for genomic analysis. This fact is reflected in the considerable increase of genomic resources for salmonid species during the past five years and the consolidation of an international collaborative group aimed at establishing priorities and maintenance of these resources (Consortium for Genomic Research on All salmonids Program, cGRASP, Canada). Currently, these tools are starting to be used in identifying genetic factors involved in resistance to infectious diseases in salmonids. The strategies used in the study of the genetic architecture of resistance are based mainly on the analysis of candidate genes, QTL mapping and gene expression studies. CANDIDATE GENES
candidate gene theory says that a significant proportion of the phenotypic variation of a feature in a population is determined by the presence of polymorphisms in major effect genes involved in the expression of that character, allowing the identification of these genes (Rothschild and Soller 1997). This approach requires knowledge of the biology of the species, biochemical pathways and, especially, gene sequences, to study the variation of specific candidates. In aquaculture, the availability of gene sequences is limited, but it is expected that a greater number of genes to be incorporated into public databases in the short term.
In vertebrates, the major histocompatibility complex or MHC has attracted much attention in studies of association between genetic variants and disease resistance. However, have been proposed and studied other genes may also play an important role in the mechanism of resistance in productive species, model organisms and humans (Hill 1999, Qureshi et al 1999). To date, no studies that aim to define the association between other candidate genes, other than the MHC, and resistance to infectious diseases in salmon.
major histocompatibility complex (MHC). The MHC is a multigene family that acts at the interface between the immune system and infectious diseases. The MHC gene family comprises two subfamilies: class I genes and class II (Bernatchez and Landry 2003). Both classes are for membrane glycoproteins involved processing and elimination of pathogens (Thorgaard et al 2002). MHC class I genes are expressed on the surface of all nucleated somatic cells. Play an important role in immune defense against intracellular pathogens by binding peptides, mainly viral, in the cytoplasm and presenting them to lymphocytes TCD8 + (Bernatchez and Landry 2003, Grimholt et al 2003).
In vertebrates, two types of MHC class I: classical (Ia) and non-classical (Ib) (Klein and O'Huigin 1994). MHC class Ia genes are highly polymorphic, expressed in most tissues during infection and its transcription is modulated by specific promoter elements (Interferon-mediated response). By contrast, MHC class Ib genes show less polymorphism, restricted expression and are not modulated at the transcriptional level during infection (Thorgaard et al 2002).
On the other hand, class II MHC genes have a more restricted expression pattern, as expressed in antigen presenting cells (B lymphocytes and macrophages). Basically, they are involved in maintaining the extracellular environment by presenting antigens, particularly bacterial cells to CD4 + (Bernatchez and Landry 2003, Grimholt et al 2003).
MHC genes have been identified, cloned and characterized in salmon Atlantic rainbow trout and other salmonids (Grimholt et al 1993, Hordvik et al 1993, Hansen et al 1996, Shum et al 1999, Shum et al 2002). In addition, it has been shown that these genes are highly polymorphic in these species (Grimholt et al 1994, Miller and Withler 1996, Hansen et al 1999, Garrigan and Hedrick 2001, Aoyagi et al 2002, Grimholt et al 2002). As in other vertebrates, two types of class I genes, the UAA that are highly divergent, non-polymorphic and expressed at low levels, and the UBA are polymorphic, expressed at high levels in spleen and structural features similar to those of class Ia molecules (classical), which present antigens to T lymphocytes (Shum et al 1999). The class II genes are divided into class II A (SAA) and II B or (DAB), depending on whether coding for α or β chain of the molecule, respectively (Grimholt et al 2000, Stet et al 2002). Both loci (DAA and DAB) cosegregated as haplotypes, suggesting a close physical linkage between them in Atlantic salmon (Stet et al 2002). This, coupled with the expression of a single locus of MH class I genes (Grimholt et al 2002), could allow more easily associate MH alleles and disease resistance in this species (Stet et al 2002).
In teleosts, unlike what happens in other vertebrates, genes class I and II MH segregate independently (Hansen et al 1996, Bingulac-Popovic et al 1997, Sato et al 2000), therefore, instead of speaking of a 'complex' major histocompatibility complex (MHC) is more appropriate to speak of 'genes MH 'in these species (Grimholt et al 2003). The absence of linkage between genes class I and class II has potential implications in the natural selection of polymorphisms in both classes. If these two types of genes were linked, the selection of a particular class II allele inevitably change the frequency of an allele linked class I and vice versa (Shum et al 2001). Furthermore, the absence of linkage allows independent segregation of immunological traits in fish genes associated with class I (cytotoxicity in response to viral infection) and class II genes (humoral response against bacteria).
have investigated the association between a polymorphism linked to MH class II genes and resistance against the virus of infectious hematopoietic necrosis (IHN) in backcrosses of rainbow trout and cutthroat trout (Oncorhynchus clarki). The effect of polymorphism on survival was small and significant in one of the two families studied, suggesting that this mutation may be linked to a gene that influences resistance to IHN. However, environmental effects and the involvement of other genes involved in the property may mitigate the effect of this locus (Palti et al 2001). In Atlantic salmon have demonstrated the association between MH class IIB alleles and resistance specific Aeromonas salmonicida (Langefors et al 2001, Lohman et al 2002). In this same species have been associated with gene variants MH class I and class II in susceptibility to IHN (Miller et al 2004). Moreover, we have determined the association between specific alleles of genes MH class I and II and the resistance to ISA and furunculosis independently (Grimholt et al 2003). Based on the results of this study, we selected resistant and susceptible genotypes, which were subsequently crossed to generate progeny with genotypes for high and low mortality expected. The study partially confirmed the expectations in terms of resistance and susceptibility of individual alleles MH class I and IIA against ISA, previously set by Grimholt et al (2003). This is because some variants that showed to be associated with resistance in the previous study did not establish a better survival in this second trial, however, variants previously associated with susceptibility repeated their performance (Kjøglum et al 2006).
Although associations have been established between MH gene variants and disease resistance has been shown that it is not the only factor influencing genetic variation of this feature in salmon. In Salmon Atlantic has been found that there is an effect that is not related to the MH genes in terms of resistance to IPN, furunculosis and ISA (Kjøglum et al 2005). This effect, nearly 10% on average for the three diseases produce significant genetic variation in the survival of challenged families (alleles identical in MH), which can be explained on the basis of a difference in polygenic effects associated with feature. Additionally, this study found an effect associated with the tank, which significantly influence the observed variation for resistance to IPN. Therefore, this technical factor should be considered when analyzing genetic resistance to infectious diseases by challenge tests (Kjøglum et al 2005). Due to the polygenic nature of disease resistance is important to consider the background genome and the interactions that can occur between genes (epistasis), which makes further analysis of individual candidate genes involved in resistance variation. DETECTION OF QTL
QTL mapping is a strategy that can provide information about the location and effect of genes that are influencing a complex quantitative trait, such as resistance to infectious diseases. Detection methodologies QTL is based on the use of DNA markers to identify genomic regions involved in genetic variation of a given character of productive interest.
DNA markers. The development of markers based on DNA variations have generated a great impact on studies of genetic variation in animals and fish. Within the DNA markers are widely used RFLPs (polymorphism restriction fragment length), RAPDs (random amplified polymorphic DNA), AFLP (amplified fragment length polymorphism), microsatellites and SNPs (single nucleotide polymorphisms .) These vary each other in their mode of inheritance, identification and detection methods, number of locus ranging and polymorphic information content (Liu and Cordes 2004).
The AFLPs have the advantage that they can be generated more easily and more cheaply than SNPs and microsatellites, because it is not needed for generation of genomic information. However, its mode of inheritance, as is codominant RAPDs, ie can not distinguish heterozygous from homozygous dominant genotypes without using special equipment and software (Piepho and Koch 2000). This reduces the amount of information provided by these markers.
Microsatellites are nucleotide sequences, between 1 to 6 base pairs repeated in tandem. The different alleles are generated due to variation in the number of repetitions. Key features found to be highly variable, codominant inheritance file and found to be abundant and widely distributed throughout the genome. The great advantage of microsatellites is their high degree of polymorphism. Moreover, his analysis is based on the amplification of DNA using chain reaction (PCR) is performed so quickly, at low cost and the amount of DNA required is minimal (in the order of nanograms). In salmonids there are many microsatellites available for use in genetic studies (Phillips et al 2009).
Furthermore, the SNPs may be present in both coding and noncoding regions. They are usually biallelic, are evenly distributed on the genome and are more abundant than microsatellites. Therefore, are ideal for the construction of dense genetic maps, which can be used in fine mapping of QTL and genes help identify causal genetic variation for specific characters. Recently, based on the alignment of sequences over 100,000 ESTs (expressed sequence tags) have been detected more than 2,500 Putative SNPs in Salmo salar, with a higher validation rate to 70% (Hayes et al 2007).
QTL detection for disease resistance in salmonids. The development of a genetic map is the first step toward identifying QTL. To date, we have built linkage maps for rainbow trout (Young et al 1998, Sakamoto et al 2000, Nichols et al 2003, Guyomard et al 2006), Atlantic salmon (Moen et al 2004b, Gilbey et al 2004 ), brown trout (Salmo trutta) (Gharbi et al 2006) and arctic char (Salvelinus alpinus) (Woram et al 2004). Studies with microsatellites
aimed to detect QTL for disease resistance in salmonids have QTL identified two medium and large effect for resistance to IPN in rainbow trout. These loci explained a large proportion (27% and 34%) of the phenotypic variation in a family from a backcross of a strain susceptible to IPN (YK-RT101) and one resistant (YN-RT201) (Ozaki et al 2001).
In this same species using AFLP and microsatellite markers were found associated with resistance to IHN in three different linkage groups (Rodriguez et al 2004). For the same disease have been detected RFLP markers associated with resistant and susceptible families in backcrosses of rainbow trout and cutthroat trout (Palti et al 1999).
In Atlantic salmon, with a multifaceted strategy that combines the Transmission Disequilibrium Test (TDT), the identification of Mendelian segregation of markers and survival analysis, we were able to detect two QTL associated with resistance to ISA, using AFLP markers in two families own brothers (Moen et al 2004a). In a more recent study, it has managed to validate the presence of one of the QTL detected by Moen et al (2004a), this time using microsatellite markers in a larger number of fish. This QTL explained 6% of the phenotypic variation for resistance to ISA and has been located in linkage group 8 of Atlantic salmon (following the notation SALMAP) (Moen et al 2007).
In this same species using differential recombination rates between sexes have detected three QTL for resistance to IPN through a methodology based on two stages. First, analysis was performed using only the recombination of the males to determine linkage groups with significant effects, using two to three microsatellite loci per chromosome. Next, using the recombination of the females and a greater number of markers per linkage group were confirmed and positioned the QTL detected previously (Houston et al 2008b). The most significant QTL, mapped on linkage group 21, was subsequently confirmed through analysis of nine additional families and a greater saturation of markers (Houston et al 2008a).
Overall confidence intervals for QTL are extensive. This has two consequences. First, wide intervals may contain a large number of genes (thousands) and, therefore, identify the causal polymorphism of the variation is very complex. Secondly, the use of these QTL in MAS programs is complicated because the linkage phase between marker and QTL throughout the population may be different from that given in families, subject to study. An alternative to reduce the confidence intervals in QTL mapping is to use information from linkage analysis in conjunction with information from the LD population (Meuwissen et al 2002). Through simulation studies, methodologies that use this information together have been tested successfully in the fine mapping of QTL in commercial populations of salmon (Hayes et al 2006). This, together with the availability of dense genetic maps, help to reduce the confidence intervals on identification of QTL in these species. The information in these QTL, together with the sequencing and physical mapping of the genome, will facilitate the identification of mutations causing resistance through positional studies. These mutations may be used directly in breeding programs. FUNCTIONAL GENOMICS
Functional genomics, defined as the application of experimental methods of genomic coverage or system for assessing gene function from data and background from structural genomics (mapping and sequencing), is becoming a primary area of \u200b\u200binterest (Hiendleder et al 2005). The basic idea of \u200b\u200bthe methodologies used in this area is to broaden the spectrum of biological research at a holistic level where they study, simultaneously, a large amount of transcribed genes and proteins. Currently, resources Available in salmon genomics have allowed some approaches to the study of infectious diseases response using functional genomics tools.
gene expression. Genes whose expression levels are changed in response to infection can be identified by high throughput techniques (High-throughput). For example, using a technique called subtractive hybridization under conditions of suppression (SSH), using liver samples from individuals injected with a bacterin of V. anguillarum and normal individuals, we detected more than 25 genes involved in immune response in rainbow trout, including protein sequences acute phase of inflammation, coagulation and complement system (Bayne et al 2001). By the same technique, genes involved in signal transduction, innate immunity and other processes have been identified as relevant to the challenge with A. salmonicida in Atlantic salmon (Tsoi et al 2004).
On the other hand, the availability of libraries of ESTs and cDNAs has allowed the development of DNA microarrays that can be used to study the differential expression of a large number of genes simultaneously in salmonids (Rise et al 2004b, Ewart et al 2005, von Schalburg et al 2005). Using a microarray of human cDNAs were identified transcripts Atlantic salmon differentially expressed against challenge with A. salmonicida. However, due to the divergence of species, only 6% of the sequences of DNA microarray hybridization showed detectable anti-liver cDNA salmon (Tsoi et al 2003). Rise et al (2004a) conducted the first study based on a microarray constructed from cDNAs libraries of salmon. Thus, in Atlantic salmon genes have been identified that show differential expression, depending on whether macrophages from infected and uninfected Piscirickettsia salmonis and hematopoietic kidney challenged or challenged individuals from the same pathogen. These genes are characterized by immune relevant functional annotations could be used as molecular biomarkers of infection with P. salmonis (Rise et al 2004a). Furthermore, using a cDNA microarray of Atlantic salmon with more than 4,000 genes extracted from liver, spleen and anterior kidney, have discovered several genes differentially expressed in response to infection by A. salmonicida (Ewart et al 2005). Moreover, we analyzed the transcriptomic response compared to vaccination with a bacterin of A. salmonicida in Atlantic salmon, revealing temporal and tissue differences in expression levels, which can have relevance in the establishment of protection (Martin et al 2006). Moreover, we have studied the gene expression profile in response to a DNA vaccine IHN virus in rainbow trout, detected 910 genes modulated in the injection site and also identified the overexpression of genes related to type I interferon system (IFN-1) in other tissues, suggesting that this system is the basis of early antiviral immunity (Purcell et al 2006). In the above studies, some transcripts that showed variation in expression levels compared to infection had no homologs in GenBank, so their functions remain still unknown (Rise et al 2004a, Ewart et al 2005, Martin et al 2006, Purcell et al 2006). Therefore, it should work based on the encoded protein sequences and their role in the immune response to decipher its role against infection.
To date no studies designed to analyze the differential expression between resistant and susceptible fish for a particular infection. The information provided by this type of analysis can be used in the discovery of new groups of genes, with or without an assigned function, which could be related to disease resistance (Walsh and Henderson 2004). However, it should be noted that differentially expressed genes may be, probably acting in trans, ie, whose expression is regulated by other genes (Martinez 2007), which will have implications when these polymorphisms associated with resistance. Another possibility is to consider the differential expression levels as a quantitative trait. This may allow the identification of QTL associated with differences in gene expression patterns between resistant and susceptible individuals (eQTL) (Pomp et al 2004, Koning et al 2005). However, it is still unclear how the information provided by analysis of expression can be used in breeding programs (Walsh and Henderson 2004).
MOLECULAR MARKER ASSISTED SELECTION (MAS) AND GENE-ASSISTED SELECTION (GAS)
A crucial parameter in the detection of QTL and the subsequent application of MAS programs is the level of linkage disequilibrium (LD) that exists between markers and causal mutations of the variation in population-level character. Linkage Disequilibrium
whereas 2 loci, A and B, each with two alleles, A / and B / b, the LD is defined as: P (A Otherwise, the probability of A given B is different from the probability of A given b. The linkage equilibrium (LE), therefore, is the opposite situation: P (A marker exists only within families and not throughout the population, rates of recombination can break the association between marker alleles and QTL between families. Therefore, the linkage phase between marker and QTL should be determined in each generation and each family separately. To determine if the marker and QTL are in LD within each family is required phenotypic records and genotypes in each generation. This makes the implementation of MAS using markers in LD with the QTL only within families is very attractive (Dekkers and van der Werf 2007). In the case of disease resistance, all available within the program families must be challenged in each generation, as in conventional schemes.
MORE IN STOCKS IN LD
Using dense map information can make use of LD between markers and the causative mutation of character variation across the population (LD between families). There are two possibilities to exploit the LD population in MAS schemes. You can use the information on the effect of a particular haplotype in LD with the polymorphism beneficial across the population or can predict the genetic value of individual maps from dense genomic coverage (Selection genomic) (Lande and Thompson 1990 , Meuwissen et al 2001). The effectiveness of these strategies depends on the magnitude of the effects associated with or polymorphisms. When estimating the effects of haplotypes across the entire genome, it is possible to use these effects to select from generations after the initial estimate, without having the phenotypes (Meuwissen et al 2001). Recombination LD will decay in each generation and the magnitude of this decrease will depend on various population parameters (Meuwissen et al 2001). In practice, it is necessary to verify the response to selection in each generation and this can re-estimate the effects using a random sample of individuals in the population. GAS
have been identified as GAS using populations in LE you get a higher genetic progress in relation to that achievable by using MAS. This is because in MAS schemes are selected markers associated with QTL and in the case of GAS, is selected directly favorable polymorphism (Villanueva et al 2002). However, in practice it is likely that MAS programs are carried out using information from a large number of markers to predict the allelic effects of QTL over the same time while GAS programs, probably only a limited number of polymorphisms available. Therefore, MAS schemes can generate greater genetic progress because they use a higher proportion of genetic variance. However, it is necessary to take into account the cost associated with genotype a larger number of markers (Martinez 2007).
factors influencing the profitability of GAS include: the amount of variation explained by the available genes, the frequency of favorable alleles and the availability of a test for the study population, potential pleiotropic effects and costs related to the implementation of genotyping. Therefore, it is necessary to assess these factors from a financial standpoint and determine its benefit compared with other methods described.
USE OF MAS IN IMPROVING THE RESISTANCE
Best Linear Unbiased Predictor (BLUP) uses aggregate information from the pedigree and phenotypes to predict the genetic values \u200b\u200bof individuals. Molecular markers give a new source of information whose impact will be given greater precision when predicting breeding values. Thus, the response to selection will be substantially better in character in which the accuracy is low, ie, characters with low heritability or characters that can not be measured in their own candidates for selection, as is the case of disease resistance (Meuwissen 2003). The relative increase in accuracy depends on the amount of variation explained by markers, which depends on the number of QTL identified and used in breeding schemes (Lande and Thompson 1990). In
productive species has shown that the effects of QTL have a leptokurtic distribution, with a small number of loci of large effect and a larger number of loci of small effect (Hayes and Goddard 2001), which is probably the case aquaculture species (Martínez et al 2005). Therefore, it is expected that more than one marker is necessary for MAS programs are efficient (Martinez 2007). Also, consider the pleiotropic effects of the different polymorphisms and possible negative effects on other features. For example, negative genetic correlations detected between resistance to viral and bacterial diseases can be a problem if the goal is to select for resistance against a wide range of pathogens. On the other hand, failure to consider non-additive genetic effects in the analysis model could decrease the precision with which estimates the effects-additive genetic.
is likely that future SNP genotyping on a large scale allows the use of these markers in the selection of players in a cost-effective. Therefore, we can expect MAS programs using LD markers through of the population are implemented at the whole genome. However, it is necessary to determine the economic benefits of the use of a single haplotype versus the use of multiple haplotypes and determine which of the two methods is best for the population under selection.
also practice other factors should be considered when using QTL information to improve disease resistance in salmonids, for example, the epidemiology of various diseases and economic considerations relating to program implementation. CONCLUSIONS
There is limited and scattered information regarding the genetic factors involved in resistance to infectious diseases in salmon. However, the early development of genomic resources in these species will provide new tools for genetic dissection of these characteristics. The use of these tools is very helpful to identify loci or genes that significantly influence the variation of these characters. This information will be essential to implement programs involving MAS or GAS resistance within the breeding objective. These methods increase the accuracy in the selection of candidates for breeding, thereby improving the response to selection. However, each case must study the economic feasibility of implementing these new strategies and the benefit compared to conventional breeding schemes. NOTES
1 Salmonchile 2007, http://www.salmonchile.cl/files/T4-Mundial% 201996-2006.pdf [Consultation: 12/02/2007].
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Last year, the idea was adopted by many Arab countries and most African countries. The only opponents were the Republic of South Africa and head of the League of Arab States. The initiative was viewed negatively by the U.S. and the European Union, French President Nicolas Sarkozy , calling Libya "a threat to the financial security of mankind" , but Gadhafi did not swayed and continued to push for the creation of a united Africa. 
If Gadhafi's government falls, it will be interesting to see if the new central bank joined the BIS, the national oil industry if it is sold to investors, and if education and health care are still free. 
