Location: Cool and Cold Water Aquaculture ResearchTitle: QTL affecting stress response to crowding in a rainbow trout broodstock population Author
Submitted to: BioMed Central (BMC) Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/31/2012
Publication Date: 11/7/2012
Publication URL: http://handle.nal.usda.gov/10113/56830
Citation: Rexroad III, C.E., Vallejo, R.L., Liu, S., Palti, Y., Weber, G.M. 2012. QTL affecting stress response to crowding in a rainbow trout broodstock population . BioMed Central (BMC) Genetics. 13:97. DOI:10.1186/1471-2156-13-97 Interpretive Summary: Identifying genetic variation in complex traits such as response to stress will enhance our ability to mitigate the negative effects of stressors on aquaculture production efficiency through selective breeding or management practices. We used DNA markers to identify genetic variation in stress response to crowding at the chromosome level in seven families from a broodstock population under selection for increased growth rate. In all, eight chromosomes are responsible for at least 53% of the total variation. These chromosomes are largely different from others previously identified for a similar trait, documenting that unique genetic factors independently affect cortisol response in ways that may result in different impacts on production traits such as growth.
Technical Abstract: Background Genomic analyses have the potential to impact selective breeding programs by identifying markers that serve as proxies for traits which are expensive or difficult to measure. Also, identifying genes affecting traits of interest enhances our understanding of their underlying biochemical pathways. To this end we conducted genome scans of seven rainbow trout families from a single broodstock population to identify quantitative trait loci (QTL) having an effect on stress response to crowding as measured by plasma cortisol concentration. Our goal was to estimate the number of major genes having large effects on this trait in our broodstock population through the identification of QTL. Results A genome scan including 380 microsatellite markers representing 29 chromosomes resulted in the de novo construction of genetic maps which were in good agreement with the NCCCWA genetic map. Unique sets of QTL were detected for two traits which were defined after observing a low correlation between repeated measurements of plasma cortisol concentration in response to stress. A highly significant QTL was detected in three independent analyses on Omy16, many additional suggestive and significant QTL were also identified. With linkage-based methods of QTL analysis such as half-sib regression interval mapping and a variance component method, we determined that the significant and suggestive QTL explain about 40-43% and 13-27% of the phenotypic trait variation, respectively. Conclusions The cortisol response to crowding stress is a complex trait controlled in a sub-sample of our broodstock population by multiple QTL on at least 8 chromosomes. These QTL are largely different from others previously identified for a similar trait, documenting that population specific genetic variants independently affect cortisol response in ways that may result in different impacts on growth. Also, mapping QTL for multiple traits associated with stress response detected trait specific QTL which indicate the significance of the first plasma cortisol measurement in defining the trait. Fine mapping these QTL can lead towards the identification of genes affecting stress response and may influence approaches to selection for this economically important stress response trait.