English   español  
Please use this identifier to cite or link to this item: http://hdl.handle.net/10261/191426
logo share SHARE   Add this article to your Mendeley library MendeleyBASE
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:


Prediction of fish growth potential from gene expression measurements: insights from gilthead seabream

AuthorsSilva, T. S.; Ramos-Pinto, L.; Martos-Sitcha, Juan Antonio ; Calduch-Giner, Josep A. ; Pérez-Sánchez, Jaume ; Fernandes, J.M.O.; Costas, Benjamín; Conceição, Luis E. C.
Issue Date21-Mar-2018
Citation5th International Symposium on Genomics in Aquaculture (2018)
AbstractPrediction of growth potential and the effect of genetic, environmental and nutritional effects on growth is a central concern in aquaculture research. On the other hand, objective assessment of growth potential usually relies on mid- to long-term growth trials, which carry very high costs. In this presentation, we will discuss the estimation of long-term growth (t = 3 months) in gilthead seabream (Sparus aurata) based on gene expression measurements performed at an early stage (t = 2 weeks), comparing it with results from a similar previous trial in Atlantic salmon (Salmo salar). For this trial, gilthead seabream juveniles were reared in 21 tanks (1000 L) over the course of 3 months, being fed with a wide range of diets that differed in terms of nutritional composition, type of ingredients and performance/quality. At 2 weeks after the start of the trial, some fish were sampled to obtain liver, muscle and head kidney samples for gene expression analysis. At the end of the 3 month period, all remaining fish were bulk weighed and per-tank performance indicators estimated. Gene expression measurements were performed using reverse transcription and real-time qPCR protocols, with over 30 genes being measured per tissue. These datasets were used to calibrate linear models of growth performance measures using L1-regularized linear regression, which were then further refined by AIC-guided stepwise variable removal. Results show that this approach seems feasible in general, as previously seen with Atlantic salmon, though the prediction quality was not as high as for salmon (R2 = 0.6, rather than R2 = 0.9). The reasons identified for this difference in prediction are threefold: first, the fact that individual measurements of gene expression were performed (as opposed to pooled measurements); second, the wider range of tested dietary formulations (which increases model robustness but decreases its apparent predictive power); third, the relative diversity of ontological categories of genes, which was reduced in this trial. In conclusion, while this study confirms the feasibility of this type of approach, it underlines the challenges of obtaining a robust prediction model for fish growth potential.
DescriptionTrabajo presentado en el 5th International Symposium on Genomics in Aquaculture, celebrado en Albufeira (Portugal) del 21 al 23 de marzo de 2018
Appears in Collections:(IATS) Comunicaciones congresos
Files in This Item:
File Description SizeFormat 
accesoRestringido.pdf15,35 kBAdobe PDFThumbnail
Show full item record
Review this work

WARNING: Items in Digital.CSIC are protected by copyright, with all rights reserved, unless otherwise indicated.