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Open Access Research article

QTL detection for Aeromonas salmonicida resistance related traits in turbot (Scophthalmus maximus)

Silvia T Rodríguez-Ramilo1, Miguel A Toro2, Carmen Bouza3, Miguel Hermida3, Belén G Pardo3, Santiago Cabaleiro4, Paulino Martínez3 and Jesús Fernández5*

  • * Corresponding author: Jesús Fernández jmj@inia.es

Author affiliations

1 Departamento de Bioquímica, Genética e Inmunología, Facultad de Biología, Universidad de Vigo, 36310 Vigo, Spain

2 Departamento de Producción Animal, ETS Ingenieros Agrónomos, Universidad Politécnica de Madrid, Ciudad Universitaria, 28040 Madrid, Spain

3 Departamento de Genética, Facultad de Veterinaria, Universidad de Santiago de Compostela, 27002, Lugo, Spain

4 Cluster de la Acuicultura de Galicia (CETGA), A Coruña, 15965, Spain

5 Departamento de Mejora Genética Animal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria, Ctra. A Coruña Km. 7.5, 28040 Madrid, Spain

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Citation and License

BMC Genomics 2011, 12:541  doi:10.1186/1471-2164-12-541

Published: 2 November 2011

Abstract

Background

Interactions between fish and pathogens, that may be harmless under natural conditions, often result in serious diseases in aquaculture systems. This is especially important due to the fact that the strains used in aquaculture are derived from wild strains that may not have had enough time to adapt to new disease pressures. The turbot is one of the most promising European aquaculture species. Furunculosis, caused by the bacterium Aeromonas salmonicida, produces important losses to turbot industry. An appealing solution is to achieve more robust broodstock, which can prevent or diminish the devastating effects of epizooties. Genomics strategies have been developed in turbot to look for candidate genes for resistance to furunculosis and a genetic map with appropriate density to screen for genomic associations has been also constructed. In the present study, a genome scan for QTL affecting resistance and survival to A. salmonicida in four turbot families was carried out. The objectives were to identify consistent QTL using different statistical approaches (linear regression and maximum likelihood) and to locate the tightest associated markers for their application in genetic breeding strategies.

Results

Significant QTL for resistance were identified by the linear regression method in three linkage groups (LGs 4, 6 and 9) and for survival in two LGs (6 and 9). The maximum likelihood methodology identified QTL in three LGs (5, 6 and 9) for both traits. Significant association between disease traits and genotypes was detected for several markers, some of them explaining up to 17% of the phenotypic variance. We also identified candidate genes located in the detected QTL using data from previously mapped markers.

Conclusions

Several regions controlling resistance to A. salmonicida in turbot have been detected. The observed concordance between different statistical methods at particular linkage groups gives consistency to our results. The detected associated markers could be useful for genetic breeding strategies. A finer mapping will be necessary at the detected QTL intervals to narrow associations and around the closely associated markers to look for candidate genes through comparative genomics or positional cloning strategies. The identification of associated variants at specific genes will be essential, together with the QTL associations detected in this study, for future marker assisted selection programs.