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United States Department of Agriculture

Agricultural Research Service

Research Project: UTILIZATION OF GENOMICS FOR IMPROVING PRODUCTION TRAITS IN COOL AND COLD WATER AQUACULTURE

Location: Cool and Cold Water Aquaculture Research

Title: Detection of quantitative trait loci affecting response to crowding stress in rainbow trout

Authors
item Vallejo, Roger
item Rexroad, Caird
item Liu, Sixin
item Palti, Yniv
item Weber, Gregory

Submitted to: Gordon Research Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: February 1, 2011
Publication Date: February 20, 2011
Citation: Vallejo, R.L., Rexroad III, C.E., Liu, S., Palti, Y., Weber, G.M. 2011. Detection of quantitative trait loci affecting response to crowding stress in rainbow trout. Gordon Research Conference Proceedings: Quantitative genetics & Genomics: From Genome to Phenotype. p. 151.

Technical Abstract: Aquaculture environmental stressors such as handling, overcrowding, sub-optimal water quality parameters and social interactions negatively impact growth, feed intake, feed efficiency, disease resistance, flesh quality and reproductive performance in rainbow trout. To identify QTL affecting response to crowding stress, a genome scan was performed with over 400 microsatellite loci using seven full-sib families (n = 234 fish). Plasma cortisol concentration following a ~3 hour confinement was used as a measure for stress response. The parents and offspring had four repeated measurements of plasma cortisol which were used to develop two traits: (1) estimated breeding value for plasma cortisol measurements (EBV); and (2) index BLUP of plasma cortisol repeated measurements (BLUP3). Genetic maps were developed with MULTIMAP (Matise et al. 1994). In QTL analysis: First, a half-sib (HS) regression analysis was performed with QTL Express (Seaton et al. 2002) using combined HS family analysis. Second, the chromosomes with significant QTL were analyzed by sire- or dam-HS family to identify QTL allele segregating parents. Third, chromosomes with suggestive-significant QTL were analyzed using a combined linkage disequilibrium and linkage analysis method (LDLA) with GridQTL (Hernandez-Sanchez et al. 2009). HS regression analysis of combined HS families detected 10 suggestive and two significant QTL. The significant QTL explained 56% of the phenotypic variance. By sire- and dam-HS family analysis indicates that dams 2 and 5, and sires 1 and 3 are segregating alleles for significant QTL of chromosomes 10 and 19, respectively. The LDLA method validated most of the QTL detected with HS regression analysis. The characterization of stress response QTL will enable understanding the genetic and physiological basis of stress response and its impact on other production traits.

Last Modified: 10/1/2014
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