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Open Access Research article

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

Enrique Sánchez-Molano1, Alex Cerna2, Miguel A Toro2, Carmen Bouza3, Miguel Hermida3, Belén G Pardo3, Santiago Cabaleiro4, Jesús Fernández1* and Paulino Martínez3

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

Author 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

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BMC Genomics 2011, 12:473  doi:10.1186/1471-2164-12-473

Published: 29 September 2011

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.