Study of the third generation of Gilthead Sea Bream (Sparus aurata, L.) from genetic selection program progensa®. New insights on selection traits and industrial applications

Abstract

Estimating genetic parameters is necessary both for determining the best traits to select for (higher heritability would result in more genetic gain in the next generation) and for determining the best candidates to breed among population (those with higher Estimated Breeding Value for the desired trait). One widespread trend in the last years in breeding programs has been searching for alternative traits to perform indirect selection. Traits with higher heritability estimates and good genetic correlations with classic traits of interest for the industry. In industries where production stocks are comprised by a relatively high number of individuals, it becomes very relevant to use systems and methods that allow to record data in a fast and automated way. The present study and analysis have been conducted within the framework of PROGENSA-III, a Spanish national project which aims to optimise gilthead seabream genetic selection programs from multiple scopes, including development and application of Key Enabling Technologies (KETs). Breeder contribution and family number were calculated in independent spawning batches (4DL model) and the mix of two different batches (2x4DL model), in order to evaluate the potential on genetic variability of mixing eggs from different spawning events during the spawning season, when obtaining offspring by mass spawning. Results showed that 2x4DL model was more effective in terms of increasing breeder contribution in the descendance. Morphometric traits have hold interest as candidates for indirect selection due to their non-invasive assessment nature and their potential adaptability to automatization. In this study, 18 Non-invasive Technological traits (NiT traits) related with morphometry and carcass measured from images by using image analysis software IMAFISH_ML were evaluated. Height mNiT traits (morphometric Non-invasive Technological traits) around head (Fish Maximum Height, Head Height) showed high heritability estimates and genetic correlations between them and with growth traits, reflecting their potential as selection traits in gilthead seabream, and concluding that using image analysis and fast data recording systems such as IMAFISH_ML is highly recommendable in the selection processes of this species. Heritability estimates for processed weights were higher than for entire body weight. Classically measured carcass traits and cNiT traits (carcass Non-invasibe Technological traits) are strongly correlated, excluding dressing %. NiT traits related to filleting measured through image analysis software, such as Fillet Maximum Length, Fillet Area and Total Lateral Area showed high and positive genetic correlation with growth, carcass weight, fillet weight and filleting %. Thus, indicating that, not only selecting for these traits would improve BW and carcass traits, but also that it could be achieved using non-invasive technologies.