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                <title xml:lang="en">Coding Gene SNP Mapping Reveals QTL Linked to Growth and Stress Response in Brook Charr (Salvelinus fontinalis)</title>
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                  <date type="datePub">2012</date>
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                <term xml:lang="en">SEXUAL-MATURATION</term>
                <term xml:lang="en">ARCHITECTURE</term>
                <term xml:lang="en">GENOME</term>
                <term xml:lang="en">BODY-WEIGHT</term>
                <term xml:lang="en">RAINBOW-TROUT</term>
                <term xml:lang="en">INFECTIOUS PANCREATIC NECROSIS</term>
                <term xml:lang="en">TROUT ONCORHYNCHUS-MYKISS</term>
                <term xml:lang="en">stress response</term>
                <term xml:lang="en">growth</term>
                <term xml:lang="en">single-nucleotide polymorphism</term>
                <term xml:lang="en">QTL detection</term>
                <term xml:lang="en">linkage mapping</term>
                <term xml:lang="fr">Salvelinus fontinalis</term>
                <term xml:lang="fr">QUANTITATIVE TRAIT LOCI</term>
                <term xml:lang="fr">SALMON SALMO-SALAR</term>
                <term xml:lang="fr">POPULATIONS</term>
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              <p>Growth performance and reduced stress response are traits of major interest in fish production. Growth and stress-related quantitative trait loci (QTL) have been already identified in several salmonid species, but little effort has been devoted to charrs (genus Salvelinus). Moreover, most QTL studies to date focused on one or very few traits, and little investigation has been devoted to QTL identification for gene expression. Here, our objective was to identify QTL for 27 phenotypes related to growth and stress responses in brook charr (Salvelinus fontinalis), which is one of the most economically important freshwater aquaculture species in Canada. Phenotypes included 12 growth parameters, six blood and plasma variables , three hepatic variables, and one plasma hormone level as well as the relative expression measurements of five genes of interest linked to growth regulation. QTL analysis relied on a linkage map recently built from S. fontinalis consisting of both single-nucleotide polymorphism (SNP, n = 266) and microsatellite (n =81) markers in an F 2 interstrain hybrid population (n = 171). We identified 63 growth-related QTL and four stress-related QTL across 18 of the 40 linkage groups of the brook charr linkage map. Percent variance explained, confidence interval, and allelic QTL effects also were investigated to provide insight into the genetic architecture of growth-and stress-related QTL. QTL related to growth performance and stress response that were identified could be classified into two groups: (1) a group composed of the numerous, small-effect QTL associated with some traits related to growth (i.e., weight) that may be under the control of a large number of genes or pleiotropic genes, and (2) a group of less numerous QTL associated with growth (i.e., gene expression) and with stress-related QTL that display a larger effect, suggesting that these QTL are under the control of a limited number of genes of major effect. This study represents a first step toward the identification of genes potentially linked to phenotypic variation of growth and stress response in brook charr. The ultimate goal is to provide new tools for developing Molecular Assisted Selection for this species. KEYWORDS linkage mapping QTL detection single-nucleotide polymorphism growth stress response Salvelinus fontinalis Quantitative trait loci (QTL), the portions of a species' genome that affect the variation of heritable phenotypic traits, are revealed by the association of phenotypes with molecular markers. They provide insight into the number of loci affecting a trait and on the distribution of the QTL effects of each locus (Lynch and Walsh 1998). QTL analysis has become a popular method for studying the genetic basis of continuous variation in a variety of systems and is now an integral tool in medical genetics, livestock production, plant breeding, and population genetics of model organisms (Slate 2005). Thus, this approach is of great interest when studying experimental populations obtained from livestock species. Knowledge gained from these analyses can be used to improve traits of economic importance such as growth, resistance to pathogens, sexual maturation, or stress response through the clarification of their underlying genetic basis. Such information helps the development of selection programs aiming to improve efficiency,</p>
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