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      Detection of growth-related QTL in turbot ( Scophthalmus maximus)

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          Abstract

          Background

          The turbot ( Scophthalmus maximus) is a highly appreciated European aquaculture species. Growth related traits constitute the main goal of the ongoing genetic breeding programs of this species. The recent construction of a consensus linkage map in this species has allowed the selection of a panel of 100 homogeneously distributed markers covering the 26 linkage groups (LG) suitable for QTL search. In this study we addressed the detection of QTL with effect on body weight, length and Fulton's condition factor.

          Results

          Eight families from two genetic breeding programs comprising 814 individuals were used to search for growth related QTL using the panel of microsatellites available for QTL screening. Two different approaches, maximum likelihood and regression interval mapping, were used in order to search for QTL. Up to eleven significant QTL were detected with both methods in at least one family: four for weight on LGs 5, 14, 15 and 16; five for length on LGs 5, 6, 12, 14 and 15; and two for Fulton's condition factor on LGs 3 and 16. In these LGs an association analysis was performed to ascertain the microsatellite marker with the highest apparent effect on the trait, in order to test the possibility of using them for marker assisted selection.

          Conclusions

          The use of regression interval mapping and maximum likelihood methods for QTL detection provided consistent results in many cases, although the high variation observed for traits mean among families made it difficult to evaluate QTL effects. Finer mapping of detected QTL, looking for tightly linked markers to the causative mutation, and comparative genomics are suggested to deepen in the analysis of QTL in turbot so they can be applied in marker assisted selection programs.

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          Most cited references 32

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          Empirical threshold values for quantitative trait mapping.

          The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.
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            The genetic architecture of quantitative traits.

             Mark Mackay (2000)
            Phenotypic variation for quantitative traits results from the segregation of alleles at multiple quantitative trait loci (QTL) with effects that are sensitive to the genetic, sexual, and external environments. Major challenges for biology in the post-genome era are to map the molecular polymorphisms responsible for variation in medically, agriculturally, and evolutionarily important complex traits; and to determine their gene frequencies and their homozygous, heterozygous, epistatic, and pleiotropic effects in multiple environments. The ease with which QTL can be mapped to genomic intervals bounded by molecular markers belies the difficulty in matching the QTL to a genetic locus. The latter requires high-resolution recombination or linkage disequilibrium mapping to nominate putative candidate genes, followed by genetic and/or functional complementation and gene expression analyses. Complete genome sequences and improved technologies for polymorphism detection will greatly advance the genetic dissection of quantitative traits in model organisms, which will open avenues for exploration of homologous QTL in related taxa.
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              The use of molecular genetics in the improvement of agricultural populations.

              Substantial advances have been made in the genetic improvement of agriculturally important animal and plant populations through artificial selection on quantitative traits. Most of this selection has been on the basis of observable phenotype, without knowledge of the genetic architecture of the selected characteristics. However, continuing molecular genetic analysis of traits in animal and plant populations is leading to a better understanding of quantitative trait genetics. The genes and genetic markers that are being discovered can be used to enhance the genetic improvement of breeding stock through marker-assisted selection.
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                Author and article information

                Journal
                BMC Genomics
                BMC Genomics
                BioMed Central
                1471-2164
                2011
                29 September 2011
                : 12
                : 473
                Affiliations
                [1 ]Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Ctra. Coruña Km. 7.5. 28040 Madrid, Spain
                [2 ]Departamento de Producción Animal, ETS Ingenieros Agrónomos, Universidad Politécnica de Madrid, Ciudad Universitaria, 28040 Madrid, Spain
                [3 ]Departamento de Xenética, Universidade de Santiago de Compostela, Facultade de Veterinaria, 27002, Lugo, Spain
                [4 ]Cluster de la Acuicultura de Galicia (CETGA), Punta de Couso s/n, 15965, Aguiño, Ribeira, Spain
                Article
                1471-2164-12-473
                10.1186/1471-2164-12-473
                3195100
                21958071
                Copyright ©2011 Sánchez-Molano et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Categories
                Research Article

                Genetics

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