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Uncovering QTL for resistance and survival time to P hilasterides dicentrarchi in turbot ( S cophthalmus maximus )
Author(s) -
RodríguezRamilo S. T.,
Fernández J.,
Toro M. A.,
Bouza C.,
Hermida M.,
Fernández C.,
Pardo B. G.,
Cabaleiro S.,
Martínez P.
Publication year - 2013
Publication title -
animal genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.756
H-Index - 81
eISSN - 1365-2052
pISSN - 0268-9146
DOI - 10.1111/j.1365-2052.2012.02385.x
Subject(s) - quantitative trait locus , biology , turbot , plant disease resistance , genetics , marker assisted selection , allele , gene , fishery , fish <actinopterygii>
Summary Disease resistance‐related traits have received increasing importance in aquaculture breeding programs worldwide. Currently, genomic information offers new possibilities in breeding to address the improvement of this kind of traits. The turbot is one of the most promising European aquaculture species, and P hilasterides dicentrarchi is a scuticociliate parasite causing fatal disease in farmed turbot. An appealing approach to fight against disease is to achieve a more robust broodstock, which could prevent or diminish the devastating effects of scuticociliatosis on farmed individuals. In the present study, a genome scan for quantitative trait loci ( QTL ) affecting resistance and survival time to P . dicentrarchi in four turbot families was carried out. The objectives were to identify QTL using different statistical approaches [linear regression ( LR ) and maximum likelihood ( ML )] and to locate significantly associated markers for their application in genetic breeding strategies. Several genomic regions controlling resistance and survival time to P . dicentrarchi were detected. When analyzing each family separately, significant QTL for resistance were identified by the LR method in two linkage groups ( LG 1 and LG 9) and for survival time in LG 1, while the ML methodology identified QTL for resistance in LG 9 and LG 23 and for survival time in LG 6 and LG 23. The analysis of the total data set identified an additional significant QTL for resistance and survival time in LG 3 with the LR method. Significant association between disease resistance‐related traits and genotypes was detected for several markers, a single one explaining up to 22% of the phenotypic variance. Obtained results will be essential to identify candidate genes for resistance and to apply them in marker‐assisted selection programs to improve turbot production.